DEV Community: Vektor Memory

VEKTOR 2.0.6: Wan2.2 Image & Video generation—Suite: Deck, Folio & Docs tools

Vektor Memory — Fri, 13 Nov 2026 01:12:14 +0000

Today we’re excited to release VEKTOR 2.0.6. It lets you describe a picture or a short video and have your own computer make it. We also added an LLM assistant bar at the bottom of every Studio tab, so you can type changes and improve generation quality.

Suite is an addition to the most used business tools for spreadsheets, slides, and documents.

A complete job toolkit rebuild for resumes and cover letters that gives you options to create or extract info from a website or files to make creation faster.

Our Ethos in this build
Our ideas that shaped this release, and each one shows up in a feature you can use today.

You control your workflows. The assistant proposes; you decide. Edits arrive as a list you tick, memories wait in an inbox, deleting always asks, and every result can be undone.

Your data is yours. Memory, files and projects live on your computer, in files you can open and keep. Syncing between your own machines needs no cloud.

Your information stays private. Pictures, clips, spreadsheets and documents are made and converted on your computer. Secrets are refused before they reach memory, and Google sign-in asks for a single permission.

We do not have any watermarking in generations and will never have any ads in our software; all content is yours to use anywhere.

Security is built in, tools you connect are shown before they run, and anything that changes something asks first. Downloads are pinned to fixed versions and checked against a checksum.

Text from a web page or a file is treated as data, never as instructions, and the model can only return a closed list of edits that code checks twice. That limits what a prompt injection or a tool that changes behavior after you approve it (a rug pull) can do.

Cloud and local are always your choice. Use your own computer for private or free work, or switch to a cloud model when you need more power, the system notifies you of which model is being used.

We never record, save or use any of your data in our training or telemetry; we don’t have it to use.

Over 600 models and providers work with your own keys, plus Ollama local and cloud. Your plan also includes 20 million tokens of GLM-5.3-Flash through VEKTOR Included, with no key to paste, no account to make and nothing you wrote stored. A usage bar under the context window shows what is left.

Make pictures and short videos on your own computer
Studio > Image > Generate. Describe what you want, press Make it. Pick a style from one line (Photo, Ink wash, Watercolor, Claymation, Felt craft, Plush, Paper art, Pixar, Pixel art, Illustration), start from a Quick start (Portrait, Poster, Anime, Product, Landscape), and choose Landscape, Portrait or Square.

Enhance prompt turns a short idea into a detailed one with your own chat model. Afterwards you can make the picture better, upscale it, turn it into a short clip, or try another. A Recent row keeps your last ten.

The same panel makes pictures through a free provider (Cloudflare or Hugging Face), so the button works on day one.

On our testing rig, the RTX 3060/12Gb Vram, an image takes 12 seconds to generate with Wan2.2 included, an optional upscaler is also included. A five-second clip takes about five minutes, these generations will be much faster for more advanced spec GPUs.

**Animate your own pictures
**In Animate, pick AI video (on this computer) to move a picture of yours, or start from your words alone. You choose the length in seconds, and an estimate tells you how long it will take. Animate an image works on any picture, and the Shelf can send an image straight to Generate.

Use the royalty-free online sources to make images, animations, or videos from these options and remix as much as you like.

One assistant bar in every Studio tab
Type “shorten it to 20 seconds”, “dither this photo at size 2” or “add a total row”. Studio answers with a short list of changes. You tick the ones you want, and the sliders, pickers and cells move. Undo is one click. In Motion, the same bar makes a whole video from one line.

Simpler panels
Motion, Voice, Art, Dither and Generate now start with one box and one button. The adjustments open after you have a result, and the technical options stay folded under Advanced.

Edits and memories you approve
The assistant no longer saves things about you straight away. Memories wait in an inbox, the status bar shows “2 memories to review”, and you Keep, edit or Drop each one.

More tools, fewer places to look
Suite: Deck, Folio and Document move to their own button, so Studio is for making media and Suite is for business tools.

Sheet, Document and Plan: a spreadsheet, a document editor and a floor-plan tab, all driven by the bar.

177 more ready-made skills: 20 ship with VEKTOR and 157 are one click away in the Library.

Parts tab: import your own web pages and Studio splits them into building blocks you can search and reuse.

Dubbing and pronunciations: translate a script or subtitles into eight languages, and teach every voice in the app how to say your words.
Status-bar chip: Ready, Busy or Slow, with the reason, such as the model running on the CPU.

What problem does this solve for me as a user?
“I do not want to pay per picture or upload my work or have watermarks.” Generate runs on your graphics card. Nothing leaves your computer, there is no per-image fee, and a free provider covers you until you install the add-on.

“My graphics card is also running my chat model.” When a picture or clip needs the card, the chat model steps aside and returns by itself with its usual settings, so the next message is not slowed down. If memory still runs short, Free the graphics card unloads it for you.

“I do not know if my card is big enough.” The installer reads your card first. A card that is too small is told so before anything downloads, and only cards with about 10 GB or more are offered the finer-detail pack.

“The AI rewrote my whole document when I asked for one change.” The bar proposes small edits you can tick or skip, and everything can be undone.

“Simple requests take too long.” Totals, colours, formats, slide moves, a slower animation: these now run on your computer with no model and no wait.

“I need a spreadsheet, a document or a deck, and I do not want another subscription.” Sheet, Document and Design open your own Excel, Word and PowerPoint files and save Word, PDF and editable PowerPoint, all on your computer.

“I never know what the assistant saved about me.” You see every memory before it is stored, and you can edit it.

“Why is my local model suddenly slow?” The status chip names the cause. The common one, a model that has spilled from the graphics card to the CPU, was invisible before.

“I have to hunt for the right skill.” Desk picks a matching skill, and offers one from the Library when you have none that fits.

“I am afraid it will delete something.” Deleting a file or running a destructive command always asks first, even in Full access.

“My first message after a break is slow.” Desk’s small routing questions skip a local model that is not loaded yet. The rules decide at once, about 3 seconds saved, and the model warms in the background for the next message.

Technical specs: Generate: engine, packs and hardware tiers
The add-on runs Wan 2.2 through stable-diffusion.cpp (CUDA build) with GGUF weights. The installer downloads pinned files at fixed versions, checks size and SHA-256 on each, and resumes if interrupted. Downloads use a separate curl process, which reached about 20 MB/s on our line, against about 2 MB/s in-process.

The server reads your card once (nvidia-smi) and picks a tier: small (under 8 GB), mid (8 to 14 GB), large (14 to 20 GB), and extra large. The tier caps clip length, size and whether weights are offloaded. The pack guard runs before the download and again on the server, so a copy of the page cannot install a pack your card cannot run. A graphics-card lease unloads the chat model while a job runs and warms it back with the app's own settings.

Measured on one RTX 3060, one job at a time, chat model unloaded.

Known limits: the Large pack requires a 16 GB VRAM card for higher quality generations. a new model is due for release soon, we will update once released in Hugging Face.

The Studio bar: closed edits, checked twice
The model is never allowed to change anything itself. It returns a closed list of edits (set this control to this value, replace this text with that text, add this row). The server checks each edit against the tab’s own rules, the page checks it again on Apply, and anything the tab does not recognise is dropped. A bad answer can waste a turn. It cannot write to your project.

We tested the alternative first. Asked to add a meeting room, the model returned the whole room list rewritten. In four tries it was correct none of the times: rooms dropped, seat counts changed. Showing it more of the plan (160, 800 and 2,000 characters) changed nothing, so the problem was the task, not what it could see. One find-and-replace fixed it.

One machine, one local model, one request at a time: a direction we decided. We rejected the shorter controls prompt because it was faster and worse. If the model answers in words before the JSON, the server retries once with “JSON only”.

Fast routing for small questions
Desk asks a model small closed questions (which mode, yes or no). Three measures keep them from slowing a chat:

Local only if loaded. A cold 10 GB model takes about 30 s to load, which trips the first-byte stall guard. The server checks /api/ps (a few milliseconds) and, when the model is not loaded, lets the rules and the embedding shortlist decide, then warms the model for next time.

Cache-friendly prompt. The router’s instructions are now identical on every call and the allowed modes and the message come last, so Ollama reuses its cache after the first call. On a loaded model, routing answers took 282 to 1,537 ms across seven different messages, against a 3 s limit.

A fail-fast waterfall for small calls (opt in per call): providers are tried with a deadline, a rate-limited or refused provider is benched for the time it asks, a slow leader is hedged by the next provider, and an answer that fails its check is passed over. It follows the cooldown and hedged-request patterns used in “The Tail at Scale”.

Sheet, Document and Plan
Sheet: formulas (=F10-D10, =SUM(B2:B5), AVERAGE, MIN, MAX, COUNT, ROUND, ABS, IF), colours, percent and money formats, sort, and charts (columns, bars, lines, areas, stacked, pie, donut) saved as SVG. The formula engine is our own, so nothing leaves your computer to calculate.

Document: Markdown with a live preview, saved as Word, OpenDocument, PDF, a web page or Markdown. It never adds numbers you did not give it. PDF page tools split, keep, reorder (10-1 reverses) or join.

Plan: a room list becomes an isometric floor plan with seat counts, a legend and yellow highlights for changes, in four looks. Save as SVG or PNG at HD, 4K or 8K.

Your own files: Document opens Word, OpenDocument, web pages, RTF, Markdown and text. Sheet opens Excel, OpenDocument sheets and CSV. Files are converted on your computer. Work saves as a .vkstudio file you keep.

Animate
27 motions. The model writes short lines, one per element (canvas, text, count, shape, bars), about 1.5 times faster on the model call (6.3 s against 9.5 s), and a whole run on the server dropped from 40 s to 16 s. Code repairs what it can, and the model is asked again once for what code cannot fix. The result is plain HTML and CSS keyframes, so it needs no script to play.

Save Lottie exports live text and shapes, fades, slides, pops, growing bars and drawn lines. A few effects (blur, type-on, wipe, iris) fade in instead. OWNER: add the result of playing a file in a real player.

Save PowerPoint (editable text) writes real text boxes and tables (a:tbl). Checked in LibreOffice Impress. OWNER: add the Microsoft PowerPoint check. Known limit: text sits in plain boxes, not placeholders, so Outline view shows slides as empty.

Memory inbox
The tool the assistant calls to remember now writes to a small file on your computer. Only a Keep stores it, and what you edited is what is stored. Anything shaped like a password, key or token is refused before it reaches the inbox, and it holds 50 items. A setting turns the inbox off. Known limit: this covers what the assistant chooses to remember. Your own saves and Desk’s notes after a chat work as before.

Library skills
VEKTOR ships 44 skills (our 24 and 20 everyday ones). The other 157, adapted from open-source knowledge-work plugins, live in the Library: install one (about 7 KB) or all (1.1 MB). Each download is checked against its checksum, and an existing skill is never replaced. The Desk suggestion needs a word of your request in the skill’s name plus a fixed score. On seven test requests it picked the right skill for three and offered nothing for the four with no match.

Status-bar chip
It reads four things: graphics memory and heat, CPU load, free memory, and whether the local model is on the GPU or spilled to the CPU. A state changes only after two readings in a row agree. On recorded traces (steady, a spike, a game launching and quitting) it changed once per real change. It only watches. GPU readings need an NVIDIA card; without one it checks CPU and memory and says so.

Try it today
Update VEKTOR, open Studio > Image > Generate, and press Make it. For the add-on, press Install in Generate or Config > Local models. The changelog lists every change in 2.0.6, and the guide has new chapters for the bar, spreadsheets, documents, floor plans and motion graphics.

Improvements in 2.0.6
Studio and pictures

Use in Design: colours found in a picture in Animate become your Design colours in one click.

Quick starts and Recent: five starting points, and your last ten pictures one click away.

Upscale: 4x with an upscale model, or a built-in sharpen.
Design workspace for decks: layers, an assets manager, snap guides, presenter view, live charts and Morph transitions. An AI chat makes checked edits only.

Templates by business type: live in the content library, so the install stays small.

Storyboard page for Motion: one page with a player and a card per scene. It opens offline. vektor motion makes it from the terminal.

Timeline: curve handles, path tension, up to five masks on a clip.

Looping and voice: Bring the picture to life repeats exactly every 10 seconds. Each scene grows to hold its spoken line.

Plain advice on your video plan: too many words for a scene, hard-to-read colours.
Resume looks: six layouts with an accent colour.
Sharper pictures: Art saves at HD, 4K or 8K; new contrast-checked palettes.

Tables in decks: add a table slide, paste a CSV.

Moving backgrounds in Motion: particles, grid, waves or aurora.

Instant edits in Animate and Design: slower, faster, bigger text, move or rename a slide. No model, no wait.

Safety, memory and speed

Deleting always asks, even in Full access.

Steady memory in a chat: pinned memories are read once per chat window.
Plain requests on a local model answer in one go. One code edit went from 41 s to about 5 to 11 s.

Your message goes first: a quick question after an answer came back in about 2 s instead of 5 to 8.

Less re-reading each turn: prompt reading dropped from about 1.7 s to about 0.4 s where it applies.

A cloud model that does not answer is skipped after 12 s, not 30.
Optional faster engine for edits: set localEngine to llamacpp for about twice the speed on local code edits.

Smaller download: 36.4 to 31.0 MB.

Hidden console windows: git, python and helper launches no longer flash a window.

Agent and Coder

Coder inside Desk: an Auto, Chat or Code switch.
Plan first, See what changed, Work log, Background runs, Task list and questions.

Teams of helpers: up to four helpers on separate parts, after you approve. vektor agent.

Lessons, rules (AGENTS.md), commands (/review, /bugfix, /test and more), hooks.

Find definitions and uses across JavaScript, TypeScript, Python, Go, Rust, Java and more.

Scripts and batches: --json, --stream-json, --stdin, --until "npm test", --batch list.txt.

One shared service: vektor daemon start, plus a VS Code extension where every write asks first.

Desk, Vek and memory

Ask Desk about your email (read only), research agent (8 steps, 5 pages, 30 runs a day), ask Desk for a video (after you confirm the cost).

Sync, Import and Review (Config): two of your computers in step with no cloud; import ChatGPT, Claude or markdown exports with a preview.

Vek can use your MCP tools and asks before anything that changes something.

Routines are followed step by step. Models: Claude Fable 5.2 is in the Claude and OpenRouter lists.

Privacy and installs

Google sign-in asks for one permission, to send email.
Secret Hygiene skill checks for leaked secrets and tells you what to rotate first.

Installs are checked on every Node version (24.1, 24.22 and 26.13).
Backups hold recent notes; Library ratings and highlights are saved in your VEKTOR folder.

Fixed

“Failed to fetch” after a successful picture from a free provider.
p5.js sketches in Animate run again.

The Plan tab’s 3D floor plan draws a tall room in front of the desks behind it.

Coder reads large files a section at a time.

Card pictures that were slow now appear, and a failed picture search retries after 90 seconds.

3D and animation pages in the Sandbox run with no internet.

Gmail mail shows sender, subject and date.

A run that reaches its step limit offers Continue.

The Desk box no longer covers the bottom of Studio.
Guide: new chapters T2–47 to T2–51.

Meoow

Run your own race, keep your data sovereign.
Version 2.0.6 is live in the downloads page: https:www.vekmem.com

Full list of changes: https://vektormemory.com/docs/changelog

Novel Technology · Privacy Enhanced · Data Sovereignty · Vektor Memory

600+ models BYOK · 60+ MCP tools · 16 Model Providers

VEKTOR 2.0.5: 88 MCP tool runs Improved—GLM-5.3-Flash with 1 million trial tokens

Vektor Memory — Sat, 07 Nov 2026 22:29:08 +0000

Studio Timeline Improved

Improvements to the 88 MCP Desk tools

We audited and improved our desk tools with models with small limits, still receiving a short list with full descriptions. They now also get one extra tool, call_tool, which runs any other tool by name. Its description lists the names that were left out, so the model can always find them.

The 36 tools now improved and fixed: (the tag browser, vaults, canvas boards, bases, templates, plugins and publish).

Two of them, vault create and switch, and “save a note from a template”, need the live memory, so they tell you to use the vault picker or the Notes panel instead of failing.

We checked all 88 with an empty call. The ones that need an argument now say which one, in a sentence, instead of throwing an error from deep inside.

A model that comes with your licence, and a way to top it up
VEKTOR Included gives you GLM-5.3-Flash as a model to use in the Cli/Gui. We changed the allowance: 1 million tokens for the trial week, then 10 million a month, and one Reset Quota press a month that gives another 10 million.

There is now a Top up button added. $5 buys 10 million tokens. Top-ups are spent after your month runs out, oldest first, and each one lasts two months from the day you buy it.

A new card at the top of Config shows what is true right now: your plan and when it expires, this month’s use and when it resets, and each top-up with what is left and the day it ends.

Memory you can check
Most memory tools tell you what they remember. Few show their working. We updated this into four parts for version 2.0.5.

Receipts. Every recall can leave a receipt: which memories fed the answer, in what order, and where each came from (you, derived, the agent or an outside source). You can reaffirm one, replace it with a correction, or dismiss it, and a corrected memory stops coming back.

A Review page. Open /review for one list of everything waiting on you: Faraday holds, half-finished records found at start-up, flagged outside content and proposed routines. Only a person can decide, and anything that came from outside content can never be approved “for good”.

Memory that self-improves. During sleep, groups of three or more related notes can be summed up as one belief that links back to its sources and carries a confidence score.

The sources are archived only after a week, never in the middle of a session, and you can revoke a belief to bring them back. On a synthetic 180-day test, the share of duplicate slots in the top five recalls fell from 65% to 8% and the tokens per distinct fact fell from 23.6 to 9.0, with the same recall.

A signed history. Memory writes and removals can be recorded in a log where each entry is signed and linked to the one before. Edit a row, delete one or swap in another key, and the check fails. A removal keeps the hash of what was deleted and the reason, but not the text.

Studio timeline
Three changes this round, all tested.

Freehand masks. Mask mode has a Polygon button. Drag a corner to reshape, drag the + on an edge to add a corner, double-click a corner to remove it. A clip can also carry several masks together.

Smooth motion paths. Press Smooth path and the straight lines between position keys become a curve through the same points, with no jolt at each key.

Reversed clips. Trimming, splitting and joining a clip that plays backwards used to cut the wrong end of the footage. The picture at the cut now stays where it was.

Smaller things you will notice

Workflow templates open as a form: Cover Letter Writer has its own boxes for job title, company and tone instead of one blank text field.

Pixabay videos show their thumbnails again (Pixabay moved where it keeps them). Enlarging a picture in Sample Media goes through the app, so sites that refuse direct links no longer break it, and large local pictures open up to 120 MB.

Compress now sits right under Convert in the Inspector as a combined panel.

Try it
Update with npm install -g ./vektor-slipstream-2.0.5.tgz and open the app. Config now starts with the usage card. Visit /review on the graph server to see the inbox. vektor doctor --timing measures how fast your computer finds a memory, using queries from your own memories, and sends nothing anywhere. Consolidation and reflex suggestions are off by default; the changelog says how to turn them on.

Everything else in 2.0.5
New

New: freehand masks on the timeline. A Polygon button in Mask mode makes a shape you can reshape: drag a corner, drag the + on an edge to add one, double-click a corner to remove it. A clip can also carry several masks together.

New: smooth motion paths. A Smooth path button turns the straight lines between position keys into a smooth curve through the same points, with no jolt at each key.

New: Usage and plan in Config. A card at the top of Config shows real figures from the VEKTOR server: your plan and when it expires, this month's allowance used and left, when it resets, when Reset Quota is available, and each top-up with how much is left and the day it expires, plus a Top up button. Refresh updates it.

New: vektor doctor --timing. Measures how fast this computer finds a memory (median, 95th percentile and slowest, on queries taken from your own memories) and says whether it is within the 50 ms target. It runs on this computer only; nothing is sent anywhere.

New: top up with a card. When you want more of VEKTOR Included, a Top up button on the Included card buys 10 million more tokens for $5 on a secure Polar page; your card is never typed into the app. The tokens are used after your month's 10 million, and each top-up lasts twelve months from the day you buy it. The card shows how much top-up credit is left and when the oldest expires.

New: two more free NVIDIA models. Mistral Large 4 (1T-total, 52B-active, text and image) and Nemotron 3.5 Bolt E4B (a small, fast model for reasoning and tool calling) are in the NVIDIA list. Both answered a live test, Bolt in under a second.

New: Review page. Open /review in the graph server for the HOLD inbox (approve once, approve and remember, deny), a "Why?" view that shows a recall's receipt with Reaffirm, Correct and Dismiss on each memory, and the profile with its version history and rollback. Local only, same guard as the other panels.

New: Windows key custody for the signed log. It protects against copying the key to another account or machine; it does not protect against malware running as you.

New: action traces for reflexes (opt-in). Arguments named like keys, tokens or passwords, and any value that looks like a key, are replaced by "[redacted]" before saving. Off by default.

New: Why did you say that? (recall receipts). Every recall can leave a receipt listing the memories that fed the answer, with their rank, score and where each came from. A memory can be reaffirmed, replaced by a correction, or dismissed; a corrected memory stops appearing in recall. Memories now carry an origin (you, derived, agent, external); weighting recall by origin is built but off.

New: HOLD inbox. One list for Faraday holds, audit gaps found at start-up, tainted-content flags, derived beliefs and reflexes waiting for approval. Only a person can decide; tainted items can never be remembered; "approve and remember" is scoped and expires after 7 days at most.

New: memory that forgets on purpose (consolidation). During sleep, groups of 3 or more related notes can be summed up into one derived belief that links back to its sources, has a confidence, and is marked tainted until you promote it. Source notes are archived only after a 7-day grace period and never mid-session, and a belief can be revoked to restore them. On a synthetic 180-day test the store shrank 43% (1260 to 716 nodes), duplicate slots in the top 5 fell from 65% to 8%, and tokens per distinct fact fell 62%, with the same recall.

New: signed, tamper-evident history. An append-only log of memory writes and removals, hash-chained and signed with Ed25519 (built-in crypto, no new packages). Edits, deleted rows and foreign signatures are detected. A removal keeps the content hash and the reason but drops the text.
New: sync without a cloud (building block). Each device keeps its own signed chain of memory events; devices swap what the other is missing, verify it, and rebuild the same state with fixed conflict rules (your edits beat the agent's; a removal beats a simultaneous edit unless you re-add it). Includes rewind, diff, restore and branches ("what did the agent believe on October 3"), and refuses to compare vectors from different embedding models.

New: import and export. Reads ChatGPT and Claude exports and a folder of markdown notes (preview first, resumable, no duplicates on re-run, who said it sets the origin). Exports to an open JSONL format and a markdown vault.

New: versioned profile. Profile facts can be proposed, accepted, pinned or retired. Automatic processes may only propose; pinned facts cannot be changed by them; every change needs a reason and is kept in a history you can diff and roll back.

New: two-way panels and goals as patches. Panels can be listed, read and written by the agent through the gate, with a revision check so an agent write can never overwrite a newer edit of yours. Your edits are recorded at the highest trust. Goals are a list of changes with reasons; the agent cannot change a goal you set.

New: gated connectors. Anything fetched from the web or a calendar feed comes back marked tainted, with hidden text (comments, hidden elements, zero-width characters) removed. After tainted content enters a session, any action with a side effect is held for you. In a 50-payload test, 50 of 50 actions were held and none of the hidden text got through.

New: tools work on models without tool calling. A capability table (native, constrained, text, none) with a 14-day cache, a text-protocol fallback that recovered 13 of 13 printed tool-call shapes with no false positives, a shortlist that sends 9 tool descriptions instead of 118, and a detector for "described the call instead of making it".

New: gated reflexes (opt-in building block). Repeated successful action sequences (5 or more clean runs; any failure or correction rules them out) can be proposed as a template with parameters limited to the values seen. A reflex needs your approval, runs every step through the gate, checks every result, stops and hands back to the model on any surprise, and never runs under tainted content.

New: poisoning red-team pack, markdown projection and intake router. Eight memory attacks run against these modules, 8 of 8 resisted. Memories can be written out as a folder of notes you or another tool can read; your edits come back as your own, highest-trust changes. A rule-based router files each new note as a decision, task, event, profile fact, preference, fact or drop (15 of 15 on a small test set).

New: Desk can check its own health and time its models. Say "health check" or "run a speed test" and Desk reads the app's database, providers and Ollama state, or times a short answer from the local model and each connected provider (time to first token, tokens per second, total time).
New: Studio timeline: on-picture editing. With a clip selected, Transform puts handles on the preview to move, scale and rotate it (Shift snaps the turn to 15 degrees). Mask draws a rectangle or ellipse mask with a Feather slider and Invert. Pin drags the four corners of the picture for a perspective look. A motion path shows the clip's position keys as a dashed line you can drag, and Key position here adds a key at the playhead. Every drag is one undo step.

New: Studio timeline: more clip tools. Play backwards (video clips; the sound is muted), a batch panel that edits scale, position, turn, opacity, speed, fades and blend on all selected clips at once, a Markers list (rename, recolour, jump to, delete), a Compare wipe between the picture with and without effects, Copy keys and Paste keys in the keyframe pane, and Alt+Left and Alt+Right to jump between keyframes.

New: rebindable timeline keys. The Keys button lists every shortcut; Set or Add a new key for any of them, Reset one or all. Q and W trim to the playhead, comma and full stop nudge a clip, and square brackets step between clips. Choices are kept in this browser.

New in the timeline: Find scenes, Auto reframe, Stabilize, Sync clips and Multicam buttons. Find scenes drops a marker at each cut of the selected video. Auto reframe crops a wide video to a tall project (or the other way) so the frame follows the action. Stabilize counter-moves a shaky shot. Sync clips lines cameras up by their sound, and Multicam cuts a new track between overlapping clips to whoever is loudest. Each is one undo.
New: Audio FX. An equaliser (bass, mid, treble), a compressor and a limiter for the whole mix, in the View group. The export includes them.
New: track sync lock. Press Sync on two or more tracks; a ripple delete or ripple trim on one then also moves the later clips on the others, as long as nothing would be cut or overlap.

Added: more timeline editing. Ten more blend modes with a Blend picker, coloured markers (right-click a marker to change its colour), a volume slider on every track, and Insert copy / Overwrite copy buttons that put the selected clip in at the playhead.

Added: more timeline looks and tools. Diamond, Blinds and Barn door transitions, Ripple and Hue shift effects, plus the maths behind auto-reframe (a crop that follows the action) and scene finding (cuts to markers), tested and ready to wire in.

New: Studio timeline: looks, layouts and text animations. Copy one clip's look (effects, blend, opacity, flip, crop, mask) and paste it on others; arrange clips side by side, stacked, in a grid or picture in picture; make a picture a corner watermark; add a lower third (name and title bar); choose a fade shape; and bring titles in with fade, slide, pop or drop.

New: Studio timeline: TTML captions. Export captions as a TTML file and import TTML next to SRT and VTT.

New: Studio timeline: EDL export and import. Save the timeline as a CMX 3600 EDL (drop-frame timecode, dissolves, speed changes and freezes, markers) and open an EDL as a timeline.

New: flip, crop, solo and track colours in the Studio timeline. Mirror a clip left-right or up-down and hide its edges in the new Frame section, solo a track to see or hear only that one, and give tracks a colour to find them fast.

New: a suggested reply sends with Enter. When Desk offers a next message in the empty box, Enter or the arrow sends it, the way Claude does. Tab still fills it in so you can edit first.

New: Studio timeline: new edit tools. Duplicate a clip (Ctrl+D), close gaps on a track, join two halves of a cut, slip the source under a clip, slide a clip between its neighbours, roll the cut between two clips, and stretch a clip to a new length with its speed adjusting to fit.
New: Studio timeline: motion and ducking. Add Drift, Sway, Breath, Spin or Shake to a clip; it is baked into keyframes you can edit. Duck a music track under voice so it dips while someone speaks.

New: Studio timeline: keyboard editing. Q and W cut a clip back to the playhead (Shift also closes the gap), comma and full stop nudge a clip a frame, and the square brackets step to the previous or next clip.
New: Studio timeline: copy, paste, lift and extract. Copy, cut and paste clips with Ctrl+C, X and V (they paste at the playhead, keeping their layout across tracks), and lift or extract the marked in and out range from every unlocked track.

Better
Better: a tool called without a required detail answers in a sentence. Instead of an error from deep inside the tool (for example cloak_fetch with no link), the tool says what it needs.

Better: workflow templates open as a real form. Running Cover Letter Writer, Ad Copy Generator or any of the 15 templates now shows a card with a labelled box for each detail (Job title, Company, Tone as a list, number boxes, tall boxes for code or work history) instead of one blank text box. Auto-fill with AI fills the boxes, and what you leave blank is simply left out.

Better: Compress sits right under Convert in the Inspector. The list of what can be converted and the installed helper tools moved to the bottom of the page.

Faster: repeated recall. Recall receipts add no measurable time.
Better: Convert and Compress are one page in the Inspector. Both sit under one tab, "Convert and compress", with Compress below Convert.
Better: the buttons in Health are in the same place in every card. Back up now and the two exports, and Run now for memory upkeep, sit at the top right, like Run now for memory consolidation.
Better: the Inspector Compress settings and the Compress button sit on one line. The settings are narrower so they fit; on a narrow window the line wraps.

Better: the Desk toolbar has panel tools instead of formatting buttons. The bold, italic, code, quote, heading, list and link buttons and the citation style menu are gone; Sandbox, Terminal, Files, Flowire and Command line sit in their place.

Faster: film strips on the timeline are remembered. The thumbnails inside a video clip are kept on this computer, so a reload no longer decodes the video again; one strip is made at a time.

Faster: sound waves on the timeline are remembered. The wave drawn inside a sound or video clip is kept on this computer next to the film strips, so a reload no longer decodes the whole audio file again.
Better: each keyframe has its own curve. Click a key in the Motion graph to select it; its ease (including a custom bezier curve) then shapes only the stretch up to the next key.

Better: the Inspector uses your theme colour. The selected tab, the Compress, Convert and Inspect buttons and the quality slider follow the theme instead of plain grey.

Better: every card has its close button on the right and its action buttons in the middle. Look, Show as text, Listen, Copy as Markdown, Save, Email and Print / PDF now line up the same way on all cards.

Better: broken pages are caught before they open. A cut-off HTML file, an unclosed script or a JavaScript syntax error is reported back to the model so it can fix the page, instead of opening a blank tab.

Better: reasoning models get more room to answer. The minimum token budget rises from 2,048 to 4,096, and more reasoning models are recognised, so a model that thinks first has space left for the answer.
Better: your model choice is kept. Desk now answers with the model you picked. Nothing swaps it for a cheaper or faster one, and a second provider is no longer raced against it. Another provider steps in only when yours has no working key or stops answering, and the answer says so.
Better: Animate puts the picture first. The picture area is large on the right and the controls sit together on the left, so a photo gets room to show.

Better: diagrams stay a sensible size. A diagram in an answer is a standard card, and Expand (or a click) opens it large.

Better: Studio Explore uses the full width. of the page, lined up with the Shelf bar above it.

Better: the key status panel is wider, with Test my keys, Find best models and Add or change keys in one row.
Better: JOT card buttons sit in one even row. (Save, Plan, Improve, Summarize, Extract Actions, Save to Memory), all the same size.

Better: model cards are half the width, so twice as many fit on a row in Config.

Fixed
Fixed: trimming, splitting and joining a reversed clip. The cut now takes the right end of the footage, so the picture at the cut stays where it was.

Fixed: 36 Desk tools answered "Unknown tool". The tag browser, vaults, canvas boards, bases, templates, plugins and publish tools were offered to the model but never given the database they need, so every call failed. They now run (vault create and switch and "save a note from a template" say plainly to use the vault picker or the Notes panel, because they need the live memory). All 88 Desk tools were checked with an empty call.

Fixed: browser identities. cloak_identity_create passed its whole input as the name and cloak_identity_use called a function that did not exist. Both work now: create saves a named identity, use loads it (and fetches a page if you give a link).

Fixed: the Top up button could stay hidden after updating. The Included bar's script is now reloaded with the new version.

Fixed: Desk could not reach most of its 88 tools on free and small models. To keep requests small, those models were shown about 20 tools, and Desk then said it had no tool such as cloak_fetch. Every model now also gets one extra tool, call_tool, that runs any other tool by name; its description lists the names that were left out. A tool you name is still added to the short list directly. Checked live: a model ran a tool that was not on its short list through call_tool and returned the real result.

Fixed: Pixabay videos showed empty boxes. Pixabay changed where it keeps a video's picture; the thumbnails now come from its new field, so the video results show again.

Fixed: enlarging a picture in Sample Media could show a broken image. Pictures from Pixabay, Pexels and the other sites are now fetched through the app (the sites refuse direct links from another page), pictures on this computer can be up to 120 MB when enlarged (25 MB when added to a prompt), and if the big version still cannot load, the small picture is shown with a short reason.

Fixed: Desk sometimes answered with its own answer rules. After a web search a weaker model printed the rules block back as its whole reply. Desk now cuts the rules out of any reply; if nothing else is left it shows what the tool returned, or says the model repeated its instructions and offers a retry.

Fixed: web search in Desk and JOT works with any search key. It now tries Serper first and then each other service in turn, and says which one answered. Desk's own agent search also uses every provider and the keys saved in Preferences, not just two environment variables.

Fixed: Desk could not use many of its own tools. Asking Desk to fetch a page with cloak_fetch got "I don't have that tool". A question that looked like plain chat skipped the tools completely (now a web address, a tool name, "mcp", a search service, or a speed or health question keeps them), and 36 of the 88 tools were never offered to Desk at all (the note, vault, canvas, bases and template tools, and the note transforms). Four more were offered but said "not available" when called: the timeline and date tools and the two TurboQuant tools. All 88 are now offered and run.

Fixed: Desk said it had no search tool. Desk can now search the web with a new web_search tool.

Fixed: Desk said it had no Serper or web search tool. When you ask about searching, Serper, Google, Tavily, Brave or the web, the search tools (cloak_search, cloak_search_deep, cloak_fetch) are now offered to the model, also on the shorter tool list used by smaller and fallback models.
Fixed: the Bauhaus controls in Art did nothing on other designs. They only reach the Bauhaus designs and the line overlays, so they now grey out, with a note, when another design is chosen.
Fixed: Ledger buttons ran past the edge of the Records card. Each record now fits the card at any width, and Mark paid, Exclude, Rate and Delete stay on one line.

Fixed: the model list in the + menu of the Coder (and Desk) now opens in full. It was cut off to a thin strip; the list now opens inside the menu, with its search box, and the menu scrolls.
Fixed: Studio pop-up cards now follow the theme. The Audio FX, Markers, Keys and ease-curve cards and the snip editor use the theme's card colours, and their sliders, tick boxes and buttons match it in every light and dark theme.

Fixed: chat search only worked with Serper. The search tool in Desk and JOT chat (cloak_search, and the deep search built on it) now uses every search service you have a key for: Keenable, Serper, Ceramic, SerpApi, Brave, Tavily, Firecrawl, and keyless DuckDuckGo as a last resort, with each one's daily limit. With no key at all, the error says which keys to add.

Fixed: Save picture on the code animation did nothing for some animations. It now saves WebGL animations (they came out blank), animations with no canvas (drawn from the page), and says what went wrong or that the preview did not answer instead of staying silent.

Fixed: the chat said it had no web fetch tool. With many tools connected, the short list sent to the model could leave out the one you named or the web tools. A tool you name (like cloak_fetch), and the web tools when you give a site address or ask about a website, are now always included.

Fixed: "Open it in the sandbox" now opens HTML pages. Run in sandbox opens a .html or .htm file in your default browser. Before, only Python, JavaScript, PowerShell, batch and shell files could run.

Fixed: tool calls a model typed as text now run. When a fallback model wrote a tool call as plain text, such as run_verified(...) or a write_file block, nothing happened and Coder stopped as if the model had quit. Desk and Coder now turn those into real tool calls.
Fixed: expanding a diagram showed a tiny white box. The large view now always sets an explicit size.

Fixed: 3D pages in the Code panel stayed black. A page that listens to the camera controls (for example a physics simulation that redraws on "change") stopped with "controls.addEventListener is not a function". The built-in orbit controls now accept listeners and fire start, change and end, keep a camera the page has moved, follow a changed target, and honour auto-rotate, enabled and the distance and angle limits.
Fixed: a flowchart written on one line now draws. Some models write a whole card on a single line; it used to show as plain text and now draws as the diagram it is.

Fixed: sequence, class and state diagrams draw instead of showing as text. Desk now asks models to write those as Mermaid, which draws as a diagram card.

Run your own race, keep your data sovereign.

Version 2.0.5 is live in the downloads page: https:www.vekmem.com

Full list of changes: https://vektormemory.com/docs/changelog

Novel Technology · Privacy Enhanced · Data Sovereignty · Vektor Memory

600+ models BYOK · 60+ MCP tools · 16 Model Providers

VEKTOR 2.0.4: Hacker News Books/Videos added—Node.js & SQLite security upgrades

Vektor Memory — Mon, 02 Nov 2026 23:33:32 +0000

Another gigantic update. We have now added Hacker News books and video recommendations into the library.

We added 1 million trial tokens that are included on all new trial signups to test out the systems via credit card.

We now include model GLM-5.3-Flash with 20 million tokens per month, per user on the $9 per month paid plans, as newer, faster models are released, we will upgrade to them. You can still use the free model provider options and Ollama local GPU as well, it’s your choice.

We upgraded to Node v26 engine for the latest security updates with automatic fallback to your own Node version and the Vektor upgrade node once ready.

We also upgraded to Better-sqlite3 v13: one binary for Node 22, 24 and 26.

We also upgraded to the latest ONNX version is now 1.30.0.

We added in procedural memory nodes to make a 5-layer graph: entity, causal, temporal, semantic, and procedural.

From the 9th of November, Ollama Cloud becomes a pay-as-you-go provider, as per their notice, we have updated this in our models section.

Books and videos from Hacker News that people recommended
Hacker News comments are one of the best reading lists on the internet, and searching them can be difficult. We combined both into our own list from the public comments: about 9,100 books and 27,000 videos, each with how many different people recommended it and when it was first mentioned.

It lives in the Library under HN SHELVES.

You can pick books or videos, view in Open Library (Today’s shelf, Top all-time, Rising, Recently recommended, Newest or Oldest first mention, Title A-Z), and filter by publication year, first-mentioned year, topic and features.

Click a card and the Explain panel tells you what the book is about and who it is for via an instant LLM response, using the model you already chose.

Pictures are off until you switch them on. When they are on, covers load only for the cards you scroll past, are shrunk to about 4 KB on your computer and are kept in a local cache.

Nothing is stored on our servers. If a book has no cover, or the source sends a blank one, you get a Bauhaus drawing made from the title, so the same book always looks the same.

We added procedural memory for how to do things
Facts are only half of what you remember. The other half is how: the steps that finally got a release out the door, the order you check a spreadsheet, the way you answer a hard email.

Procedural memory keeps those as routines. A routine has a short name, when to use it, numbered steps and what done looks like. A new one starts as a draft. After two successes it becomes active, and after three failures in a row it is retired. Secrets are cut out of every field before saving.

When a question fits an active routine, the steps are added at the end of the prompt, where a small model’s limited window keeps them.

You can write one by hand, paste what worked and let the model draft it, or turn a good routine into a reusable skill. The graph has a Procedural layer that links each routine to the memories it came from, and agents get five tools to find and save them.

Does it help? We ran a small test: ten simulated data tasks, each with specifics only the routine could supply, three runs each, on two models.

With no routine: 0 of 30 correct on both models.

With the right routine found by the real retrieval code: 22 of 30 on both (73%).

With a routine learned from a short account of a run that worked: 21 of 30 and 17 of 30.

With a routine for the wrong task: 0 of 30, the same as nothing.

The tasks are simulated, the scoring is literal, and the intervals are wide, so read this as “the idea works and the plumbing is right”, not as a benchmark to quote, as we will test this again with full benchmarks once fine-tuned next week with more data.

A model that comes with a small quota of 20 million tokens
Since the provider free tiers keep shifting and degrading, we added a provider called VEKTOR Included. Pick it in the model list and you chat on GLM-5.3-Flash.

The allowance: 1 million tokens for the trial week, then 10 million a month. When that is used up, a Reset Quota button gives you another 10 million once per month, so a total of 20 million per user.

Where you see it: a bar under the context window shows what is left and when it resets. Hover the card for the rules.

Privacy: requests pass through our server to reach the model. The server keeps one number per licence, a token count, and nothing you wrote. The provider is labelled in the picker and is never used as a hidden fallback.

Speed: the first real word of an answer arrives in about 1.3 to 2.4 seconds in our live tests. The model thinks first and shows the answer after.

We will try our best to increase quotas as newer, more efficient models are released and inference costs continue to decline. We can appreciate users want larger, higher-quality models and more tokens included.

Faster answers when the model is in the cloud
A question in Desk used to cost two model calls. With tools on, Desk first asked the model to decide on tools, threw away the answer it got back when no tool was needed, and asked again for the final text.

Now the first reply is used when it is the answer, and every round streams, so the first words show up as they are written. A question makes one call instead of two.

We also checked the NVIDIA free endpoint model by model. The default we shipped, gpt-oss-20b, timed out at 90 seconds. nemotron-3-nano gave a real answer in 1.1 to 1.3 seconds. The default is now nemotron-3-nano.

Studio: more of what editors expect
The Studio timeline had 20 effects, 8 transitions, captions, voiceover and a shorts finder added. The new one is Chroma key: it makes a green or blue screen transparent, with controls for how soft the edge is and how much of the green glow to pull out of hair and edges.

We also compared the timeline with other tools, feature by feature, and wrote down a plan to upgrade these over time: the first four are now one-click buttons in the timeline toolbar.

Auto cut removes quiet stretches and closes the gaps (an 8 second test clip with 2 seconds of silence came out 1.8 seconds shorter), Beats puts a marker on every beat, Duck lowers music under a voice clip, and Level sets a clip’s volume from its loudness. Each is one Undo.

Editing by deleting words in the transcript, noise clean-up and subject-following reframe follow. A permissively licensed open source suite turned out to describe most of these methods, which saved us from guessing.

Everything else in 2.0.4
New

HN SHELVES in the Library: about 9,100 books and 27,000 videos with recommend counts, filters, optional local pictures and Bauhaus covers.

Procedural memory: routines, the Procedural graph layer, five agent tools, graduate a routine to a skill.

VEKTOR Included: a model with your licence, a usage bar, a monthly Reset Quota button.

Chroma key in the Studio timeline.

One-click Studio edits: Auto cut, Beats, Duck, Level.

Answer guard: Desk answers end with a short list of rules, and a check for links nobody supplied. In a test of 14 invented questions run three times on two models, answers to requests for links nobody had supplied were clean 6 of 18 times without the rules and 18 of 18 with them on one model, and 4 of 18 against 17 of 18 on the other.

Faster

One model call instead of two for plain Desk questions on cloud models; answers stream as they are written.

NVIDIA default is now nemotron-3-nano. Latency shows in seconds in desk
A managed Node 26 engine with automatic fallback to your own Node, and vektor upgrade-node.

better-sqlite3 13: one binary for Node 22, 24 and 26.

Providers and cost

Ollama Cloud: all 24 models listed with prices, a PAID tag from 9 November 2026, and not used as a silent fallback after that day.

Download size: the voice code no longer installs a second copy of the speech runtime. The install is about 300 MB smaller and the unsupported-package warning is gone. Voices download once from our own mirror.

Look and feel

Quality editorial diagrams with a dark skin, and a bundled diagram-design skill.

Vek answers in its own voice: no forced one-sentence replies, no stock openers, and a short note of what you were working on lately.

Config: balanced two-column layout with no gaps, a half-width mascot tile, runtime checks for Node, ONNX and the database in About.

Explain on Hacker News cards, a round send arrow in Desk that becomes a stop button while it works.

Upgrade now:
Update with npm install -g ./vektor-slipstream-2.0.4.tgz and open the app.

Library, then HN SHELVES, is the quickest tour path.

Procedural memory is in Config, with an Add 5 examples button if you want to see the graph layer fill in.

The VEKTOR included model quota is in the model list and needs the licence you already have.

Run your own race, keep your data sovereign.
Version 2.0.4 is live in the downloads page: https:www.vekmem.com

Full list of changes: https://vektormemory.com/docs/changelog

Novel Technology · Privacy Enhanced · Data Sovereignty · Vektor Memory

600+ models BYOK · 60+ MCP tools · 16 Model Providers

VEKTOR 2.0.3 - Studio: Motion and Timeline video creation tools

Vektor Memory — Sun, 01 Nov 2026 11:08:40 +0000

Novel Technology · Privacy Enhanced · Data Sovereignty · Vektor Memory

600+ models BYOK · 60+ MCP tools · Ollama Local & Cloud · 16 Providers

Studio Panel

We combined our motion and video timeline into one editing tool.

We are now more than just a memory layer; with Studio, you can now create clips, pictures, text and sound on tracks, trim and split, add captions and a voiceover, preview, and export. Compound clips and linked audio and video work, with undo and redo.

There are 540 ready-made templates across business, events and social formats.

The shelf is a new feature strip under the Studio tabs. Drop a picture, video or sound on it and send it to Timeline, Motion, Dither, Animate, Design, Transcribe or Media Factory without saving and re-adding it. A Back button returns to the tab you came from, up to ten steps.

Shorts takes a long talk or video and finds the strongest 30 to 60 second moments, titles them, and builds a vertical project with captions you can edit and export one clip at a time. It centre-crops. Face tracking is not in yet.

Auto in Motion starts from one line, such as “a 20 second launch teaser for our new product”. It works in stages you can watch: it reads the idea (length, shape, tempo and look, with the reasons written out), writes the plan, sets the tempo and look, checks the quality, and hands it over ready to play.

Studio also learns your checks, you teach a rule once, for example, a scene with more than 3 points, and it runs with Check quality on every video after. Pin notes on a scene or the whole video and press Revise. The assistant proposes edits for every note and you tick the ones you want.

Coder edit refinement that cannot quietly break a file

The agent has a new edit_file tool. You give it the exact old text and the new text, and the old text must match once. A miss tells the agent how to fix the request.

An edit that would leave the file with a syntax error is refused, with the error and the lines shown, and the file stays as it was. Rewriting an existing file of more than 50 lines in one go is refused too, unless you say you mean it. Every successful write ends with a quick syntax check, and in Coder the project’s own tests run after each write.

In the Coder editor the same idea reaches you. The Fix button on a problem asks the model for a corrected version and shows it as red and green lines. Accept puts it in the file as one edit that Undo can reverse, then checks again.

Reject drops it. If the file changed while you were deciding, Accept tells you instead of overwriting.

The agent also watches itself for wasted motion. It is nudged, then stopped, when it makes copies of files such as app_fixed.js instead of editing, rewrites a file much smaller, rewrites the same file three times in a row, or hits the same failure again and again.

If it calls the same tool with the same arguments and gets the same result, it is warned on the third time and stopped cleanly on the fifth, with progress saved so you can continue.

Routing that learns your accounts

Earlier routing assumed the accounts we use. This release learns yours.

When a provider limits or refuses a model, VEKTOR tries the other free models that provider lists: OpenRouter’s :free models, NVIDIA's free set, Cloudflare models that are not plan-only, and Ollama Cloud's list. It remembers what worked, what needs a plan and what was withdrawn, for you only.

If two different models hit a limit together, it concludes the limit belongs to the account and moves on. The provider order puts free providers first, and paid ones join only when paid fallback is on.

Keys are handled the same way. When a key hits a limit or is refused, it rests for ten minutes and the next one is tried. That covers numbered keys entries in the Passport.

Two measuring tools sit behind this. Find best models reads each connected provider’s current list, ranks it by size and recency, and checks the biggest few with a plain answer, a tool call with the right numbers, and a short function it must get right. It keeps the best model that passed, tries the next if that one stops working, and rechecks weekly.

The provider probe sends one tiny question and one tool call to each provider and records uptime and speed, never a key. A fast, reliable provider goes first. One that is out of usage goes to the back.

When a provider is slow, Desk hedges. If it has not started answering after a couple of seconds, Desk also asks the next one and uses whichever answers first, with a short note. It never uses a local model or a paid provider you have not allowed, and deskHedge set to false turns it off.

The Model keys panel puts this in words. A banner names any provider that has run out or had a key refused. Each row says what is wrong (out of usage, key refused, needs a plan) and links to the account page. A provider with several keys shows “6 of 10 keys work”, and Test my keys asks each key one tiny question.

We rebuilt our coder module to plan in steps

Coder writes a plan you can read and change, covering the files, the steps and how it will be tested. It makes a project folder and has the agent write the code. Then it checks the result for real: it runs the tests it wrote, checks every script and page, and flags anything the plan promised but never wrote.

Failures go back to the agent for up to three fix rounds. A second reader reviews the code against the plan. The finished app opens in Preview, and a checklist shows each stage.

You can choose a stronger model for the plan, the fixes and the review while the Coder model does the bulk writing. The run writes inside its own project folder without asking for each file, because you approved the plan.

If a model cannot use the file tools, Coder writes the files straight from the model’s answer. If the model cannot answer at all, it says so plainly.

Preview shows the workspace’s index.html in a sandboxed frame that cannot reach the app. Projects that need a dev server, such as React or Vite, tell you to start it and type its address.

Knowing your memory is healthy

Three new tools in Verify answer different questions for users.

The memory value report answers what your memory did. It is a one-page, printable summary: memories kept and written, what was recalled, how the memory gate kept things clean (new, merged, duplicates blocked), your busiest agents and spaces, and how searches went. It uses counts only, never your memory text, and nothing leaves your machine.

The memory quality check answers whether it is in good shape. It gives a score out of 100 and a plain list of what to fix. It asks for 24 of your own memories using a few of their words and counts how many come back.

It also looks for repeats, faded memories nobody uses, newer facts that contradict older ones, and untidy entries. It reads your memory and never changes it.

The clean-up tools act on what the check finds by reviewing repeated memories lists duplicates, keeps the oldest copy and lets you untick any before the rest are removed. A backup is made first and pinned memories are never touched.

Entity resolution finds names written several ways (plurals, capitals, spacing), merges them under one name, and lets you undo a merge or split a spelling back out, again with a backup first and no change to memory text. Decision lineage shows where a memory came from, why it was saved, which versions came before and after it, and which searches it went on to answer.

After an embedding-model upgrade, searches could return the same few memories whatever you asked. Searches and new memories now use the upgraded model, and the quality check can rebuild search numbers that no longer match their text.

You can ask Desk for an animation, a simulation, or a 3D scene

It now tells the model what it can draw, then opens the model’s code in the Sandbox with a live preview. three.js and p5.js are built in, so it runs with no internet. Pages that load them from the web or use an import map are fixed up automatically.

Once a program is open in the Sandbox code box, every model, small local ones too, is shown it and told to edit that program. Ask for “add rings” or “make it faster” and the model replies with the whole updated program.

Desk puts it into the code box, the preview runs it, and an Undo button sits under the reply. A snippet or a plain question about the code is never applied.

As an example, asking about the solar system gives you a moving model under the answer: drag to turn and tilt, scroll to zoom, click a planet for facts. Change review applies the same visibility to edits. When the agent changes files, you see the lines that changed in red and green and can undo any edit.

Several things that used to load from other sites now ship within the app:

To keep the app security and privacy controlled, we now host and load our own fonts, charts, the p5 drawing library, and the reCAPTCHA script. The package-safety lookup for skills runs only if you turn it on, and the anonymous model lookup is gone.

Speech, search and safety models now download from VEKTOR’s own host. Every file is checked against a fixed fingerprint, and a private mirror can be set in the config. The games in the library open full screen in a locked frame with no network, so they cannot reach your files or your notes.
**
Long chats and slow models speeds improved**

Older turns in a long chat are now shrunk to a short stub instead of being left out, so a fact from early in the conversation can still be recalled. The same handling now covers local Ollama chats in Lightning mode, which used to keep only the last four turns.

Streaming no longer cuts an answer on a fixed 60-second clock. An answer is cut off only when the model goes quiet: 30 seconds in the middle, 90 before it starts. If a model stalls partway, the part you already have is kept with a note and a Continue button. If a cut-off answer appears earlier in a chat, the model is told, so it does not treat the half-finished text as complete.

What we measured

Numbers from the release checks and our private benchmark scripts, run on one desktop (Ryzen 5 5600X, 48 GB RAM, Windows). Where a number is a replay and not live traffic, it says so.

A slow provider no longer slows Desk

Hedging asks a second provider when the first has not started answering. We tuned it by replaying 40,000 turns drawn from 30 measured first-token times per model.

OpenRouter free 550B alone: the slowest 5 in 100 turns fell from 13.6 s to 2.2 s.
NVIDIA 550B with the same model on OpenRouter as backup: the slowest 1 in 100 fell from 11.7 s to about 3 s, and the mean dropped 20 to 25%.
The cost: an extra call on 6 to 23% of turns, and another provider answering on 3 to 6%.
Live check, 8 runs, NVIDIA 550B with Groq as backup: median first chunk 3568 ms with hedging off, 2366 ms on.
The limits are real. The replay draws independent samples, while real queues are correlated, and a duplicate call can worsen a rate limit. Hedging doubles load on slow turns only.

On a local 9B model, median of 6 runs, time to the first streamed text.

With tools off Desk adds nothing measurable. The plain lane adds about 190 ms for the profile and memory lookup. Tool definitions add about 280 ms of prompt reading on a small model. Twelve turns of history add 5 to 25 ms. So a slow answer is almost never Desk. It is the model or the provider queue.

A trivial question, no Desk, median of 3 runs, time to a full one-sentence answer. Single runs spiked to 2 to 10 s on several of these. That variance is what hedging removes.

The guard runs per tool call, not per chat message. Median cost: 0.14 ms before a tool runs, 0.09 ms on a 500 character result, 1.1 ms on 10,000 characters, 10.4 ms on 100,000 characters, and 0.13 ms on a result carrying an injection attempt.

Other speed changes

Connectors used to take about 3 seconds to open. It now shows its last answer at once and refreshes behind.
Transparency draws 60 memories at a time instead of 200, about a third of the page elements.
A stalled stream is cut after 30 quiet seconds mid-answer or 90 before it starts, not on a fixed 60-second clock.
A provider whose keys are all out of usage is skipped instead of waited for.
Full regression tests pass on every release
The release gate ran 313 checks with none failed, covering unit, tool, security and integrity suites.
The live tool server passed 22 of 22 profile checks and the memory tools 19 of 19.
The extension manifest lists 71 tools and matches the source.
V2.0.3 — Changelog additions:
The agent and Coder

A run now survives a bad moment. Short rate limits are waited out, dropped connections and timeouts are retried up to three times, and progress is saved after every step.
If VEKTOR closes or crashes mid-run, the Agent panel offers to resume where it stopped.
Edits are safer. The agent replaces exact text, a change that would break the file’s syntax is refused, and rewriting a long file in one go is refused.
It notices when it is busy but going nowhere, nudges itself, then stops cleanly.
Build an app: describe an idea, get a plan you can edit, a project folder, written code, real checks, up to three fix rounds and a second reader, then a Preview.
A step limit is no longer the end. Continue resumes the saved run with 40 more steps.
Models and keys

Free providers go first. Paid ones join only when you allow paid fallback.
Routing learns your accounts: it tries the other free models a provider lists and remembers what worked.
A spare key takes over for ten minutes when one hits a limit or is refused.
Find best models checks the biggest models each provider offers and keeps the best one that passed.
If a provider has not started answering after a couple of seconds, Desk asks the next one too and uses whichever answers first.
The Model keys panel says in words what is wrong with an account and links to its page.
Studio

Timeline and Motion are one tool, with 540 ready-made templates.
The shelf moves a picture, video or sound between tabs without saving it first.
Shorts finds the strongest 30 to 60 second moments in a long video and builds vertical clips.
Auto turns one line of description into a finished video, in stages you can watch.
Studio learns your quality checks and revises a video from the notes you pin.
Memory

A memory value report gives you counts of what your memory did. It never uses your memory text.
A quality check scores your memory out of 100 and lists what to fix.
You can review repeated memories, merge names written several ways, and trace where a memory came from.
Separate spaces keep two clients from seeing each other’s memories.
Desk and everyday use

Desk opens animations and 3D in the Sandbox with a live preview that works offline.
Flashcards are real cards: flip, slide, mark Knew it or Again, then study only the misses.
Change review shows what the agent edited as red and green lines, with undo.
You can type while VEKTOR answers, and pick a permission mode for the chat.
Long chats keep early facts, and a stalled answer keeps the part you already have.
Fewer outside connections: fonts, charts and the drawing library now ship with the app.

**
**
600+ models BYOK · 60+ MCP tools · Ollama Local & Cloud · 16 Providers

Version 2.0.3 is live in the downloads page: https:www.vekmem.com

Full list of changes: https://vektormemory.com/docs/changelog#v203

Novel Technology · Privacy Enhanced · Data Sovereignty · Vektor Memory

Video Production

LLM

Superintelligence

Agentic Ai

VEKTOR 2.0.2: Memory benchmark LongMemEval results

Vektor Memory — Sun, 25 Oct 2026 06:25:18 +0000

Desk Trip Planner

VEKTOR 2.0.2 How we got there, via our custom Minimax matrix and a lot of testing

We measured where answers were being lost, changed one thing at a time, and kept a no-change control beside every test.

With the same open models throughout, the shipped build scored 89.2% on a paired check of 120 random LongMemEval questions, against 84.2% for the build before it. On the full 500 questions it scored 84.0%, the full run with the 30 preference questions taken from the shipped build (27 of 30).

Minmax: Each row is a candidate setting and each column is a kind of question:

Temporal, counting, updated facts, single-session lookups, preferences.

We run a candidate on a small set of questions from every column and score it by its worst column, since a setting that lifts one category by breaking another is not an improvement.

The winner is the row whose weakest column is highest. Each candidate runs next to a no-change control, because the same run twice moves a few answers by chance.

Each also runs against a guard sample of questions that were already right. Extraction is cached per question, so a test that only changes recall or the answering step takes minutes, not hours.

We tune on one set of questions and confirm on another. That is how the recall sizes (12, 80 and 120), the saving instruction, and the answering effort were chosen, and how the reranker, the paper-style expansion and duplicate removal were ruled out.

The newly built switch is in the preferences panel. The rest of the release upgrades are listed at the bottom.

Benchmark results: https://vektormemory.com/research

LongMemEval is a public benchmark of long chat histories with questions about them. It has six categories and 500 questions. We ran every question through the real memory code, with a fresh database per question, and graded the answers with the benchmark’s own rules.

Every test had a control that changed nothing, because the same run twice moves questions by chance: on 90 missed questions, 8 turned right on a plain repeat. And every change was checked against a sample of questions that were already right, so a gain in one place could not hide a loss in another.

Dates written by code

The first deduction came from reading the misses. The model was asked to turn “last Friday” into a date, and it guessed. So code now does it, from the conversation’s own date: “last Friday” becomes 2023–03–10, “four weeks ago” becomes a target day, and the events near it are marked in a sorted timeline. Temporal questions went from 63.8% to 80.3% in the first lab run (graded by Llama 3.3 70B).

In Preferences the switch is called “Remember when things happened”. Every saved fact also starts with the day it was said, at no extra cost.

The right amount of memory for the question

The second deduction came from counting the misses by cause. Of 61 multi-session misses, 52 were “how many” questions that had been shown only 12 memories. The answer was in the history, and only part of it was returned.

Recall size now depends on the question. Plain questions return 12 memories (it was 5). Counting and totalling questions about your own history return up to 80. Order, duration and relative-day questions return up to 120. A short tested check recognises each kind, so a plain lookup such as “how much does the plan cost” keeps the small size.

The results, on the same saved memory and the same judges: multi-session questions went from 54.1% correct at 12 memories to 75.9% at 80. Knowledge updates, where a fact changed over time, went from 74.4% at 5 memories to 85.9% at 12 (that curve graded by Llama 3.3 70B). Recall took the same time at 5 and at 12.

Saving the specifics

The third deduction: about 34 of the 90 questions still wrong were never saved at all. The saving step wrote summaries and dropped the named items. One added instruction now keeps every named item from both sides of a conversation, with list items and their position, and never keeps secrets or health records.

On the 52 previously missed questions it fixed 16, against 5 for a repeat with no change, and 39 of 40 right answers stayed right. It stores about 1.7 times as many memories and makes no extra model calls.

We measured the path users actually run

Our test harness had been calling the raw memory core, not the recall the app uses, which fuses keyword, meaning, graph and sentence search.

Measured through the app’s own recall, 29 of the 90 misses moved to right, against 8 for the control. Together with the saving change, 47 of the 90 were fixed against 8, and 30 of 32 guard questions stayed right.

On a paired check of 120 random questions, the shipped build scored 89.2% against 84.2% for the earlier build with the gpt-oss judge, and 85.8% against 82.5% on the majority of three judges.

Finding near-duplicates

The remaining misses are mostly lost counts. We traced them to the exact conversation turns that hold the evidence. Across 40 multi-session questions and 101 evidence turns, the saving step kept 94.1%, the app’s recall returned 77.2% at its normal size and 90.1% at double the size.

Then we tested the ideas that should have closed that gap. A cross-encoder reranker kept the same score at 24 memories and lost at 12. Expansion borrowed from the SmartSearch paper found no new evidence on top of our graph and sentence search.

Removing near-duplicates lifted the evidence returned from 77.2% to 84.2%, but the answers did not improve. A period computed in code for “in the past two weeks” made no difference. A preference-focused saving instruction scored no better than the control.

Each result narrowed the search. With nearly all the evidence in front of it, the answering model still miscounts, so counting is limited by the answerer, not by retrieval.

Calibrating the judges

Preference questions are graded against a rubric, and two judges disagreed. We read all 30 answers ourselves. The gpt-oss judge agreed with a careful reading on 25 of 30, and the Qwen judge on about two in three. We graded preference with the judge that matched the audit and published the audit file. Under that judge, preference is 83 to 90%, close to the 90% other memory systems report.

We also stopped re-running everything. Weighting a small set of missed questions and a sample of right ones to their real sizes reproduces a full run (81.9% estimated against 82.0% measured), so a change can be judged in minutes.

Update to 2.0.2, open Preferences and turn on “Remember when things happened”. Then ask your memory a question about time, or how many of something you did.

What the numbers say from the oracle

We used the oracle variant of LongMemEval, which hands the system only the conversations that hold the evidence. The full variant buries them among about 50 sessions. Scores on the two are not comparable, and published results are usually on the full one.

Each row is one run, and cloud models are not fully deterministic. Every claim above rests on a control or a paired run.

Settings were tuned on the same questions we report. The random paired check reduces that overlap but does not remove it.

Everything here uses open-weight models available. Saving and answering: gpt-oss:120b. Grading: Qwen3.8–27B, gpt-oss:120b and nemotron-3-ultra. The earlier GPT-4o-mini grader has been retired and hence not used on this run.

Judges are models, and the preference audit is one reader’s. The file is public so you can check it.

We tried a larger answering model (beam, 501 billion parameters) on the questions still wrong. It fixed more of them, and the difference from gpt-oss was within the noise of the test, so it is not the shipped setting.

We would put forth a perspective that these tests were reproduced on smaller models; better recall/retrieval results will be achieved with larger frontier models in the field.

Everything else upgraded in 2.0.2

Cards and Desk

Answers as cards: plans, comparisons, checklists, timelines, charts, slide decks, photo strips and tiles, journey maps and where-to-eat sections. A Look button cycles four looks, and a fifth uses your Studio brand.

A plan is a living document. Press a button on a plan card and the reply updates that same card as it arrives.

Add and suggest inside the card. Checklists, trip days, steps, timelines, card sets, boards and food lists take new entries by hand, or show four ideas that fit the plan. Tables, comparisons, numbers and budget sliders take entries but no ideas, since ideas there would be invented figures.

Start-screen cards: a Today row for what is overdue or due soon, forms for goals, tasks, reminders, projects, trips, reviews, recaps and budgets, and a box for your own notes or numbers.

Save (as a note in Jot) and Email on every card answer. Long answers become a report card with Expand, Share and a side reader.

Copy table and Copy as picture on every table, so a table keeps its shape in Docs, Word, Notion, Sheets and Medium.

Panels and tools

Find anything (Ctrl+K): panels, memories, notes, tasks and books in one pop-up, with tabs, recent picks and a New menu.

Customise the left rail: hide panels, reorder them, saved in your browser.

Connectors as a tile grid. A Connect an app button on the MCP panel with the one command and a Copy button.

Agent activity feed in Transparency. Routines gallery in Workflows with eight ready-made routines. Persona cards in the Agent Profile.

Four more Convert tools: TypeScript to JavaScript, JSON5 to JSON, JSON with comments to JSON, and Markdown with code blocks to one working page.

Memory and answers

Better answers when a fact has changed: ordinary recall returns 12 memories instead of 5.

Better answers to counting and totalling questions: up to 80 memories, so “how many” questions see the whole history.

Order questions such as “which happened first” are now recognised, so they get the timeline and the wider recall.

Off by default: keep the original wording of past chats with VEKTOR_RAW_CONVERSATION=on. Keys, tokens, card numbers and passwords are blanked, lines that read like health records are never kept, and pieces are deleted after 180 days. On the 56 assistant questions it scored 53 against 51 without it, so it is a choice, not a default.

Models and speed

The local model reads the start of a turn up to about 30% sooner. Set VEKTOR_OLLAMA_NUM_BATCH=default to go back.

OpenRouter: more than a dozen newer models, plus Kimi K3.1 Pro, Meta Muse Wonder and LiquidAI LFM2.5 24B. Ollama Cloud: Beam.

Saving a conversation can use Ollama Cloud.

Polish

One icon style across the app. A lighter, greyer teal theme. The notes side panel stays closed until you open it.

Card buttons act at once and no longer leave their sentence in the message box.

Version 2.0.2 is live in the downloads page: www.vekmem.com

Run your own race, and keep your data sovereign

Vektor Memory

Generative Ai Tools

Longmemeval

Ai Memory

VEKTOR 2.0.1: Desk routing went from 11% to 90%. Measured via Minimax matrix

Vektor Memory — Wed, 21 Oct 2026 00:13:13 +0000

Before this release, Desk’s automatic routing ran the right mode for about 11 of every 100 messages that needed one. In our test it was 15 of 133.

Desk has eight modes: ordinary chat, Council, Critique, Collab, Agent, Coach, Crucible and Cadence. Council compares options. Critique challenges a plan. Coach helps you sort out your priorities. Each one gives better answers to the right kind of question, but each sat behind a choice you had to remember to make.

A message like "Should we choose Jev or Clef, give me different viewpoints” gets a better answer from Council, and only someone who knows Council exists will open it.

So for 2.0.1 we changed who makes the choice. Desk now reads your message, picks the mode, and tells you what it did. This post covers three things: what we built, why we trust it enough to let it run without asking, and what we measured, including where it still gets things wrong.

Updated Desk v2.0.1

What we measured
We tested this on 330 messages we wrote ourselves. About 9 in 10 of the messages that needed a mode went to the right one, up from about 1 in 10 before. Fewer than 2 in 100 automatic runs were wrong. The decision takes about a third of a second and happens on your own computer, so the text of your message stays there.

Our test messages are ours, so real use will probably land a few points lower. The undo chip counts every time someone reverses a routing, and that count will show us the real rate.

Desk routing: before and now

Speed, and the fast tier for small jobs

Vektor decide: sort your own text into your own labels

Final choice on CLINC150: the minimax matrix

A. Full results: Desk routing and decision speed

B. Full results: the fast tier and release checks

C. Full results: CLINC150

D. Full results: When2Call and BANKING77

E. Full results: the router head and other tries that did not ship

F. Full results: Vektor decided and hosted pickers

G. Final choice on CLINC150: the minimax matrix (with the hosted model named)

How it works
Four signals, one decision. Desk looks at the words you used, a set of rules for strong phrases, a small embedding model that shortlists the three most likely heavy modes, and the local language model, which picks one from that shortlist or says “ordinary chat”.

The model answers one narrow question. Choosing among four options is far easier than open-ended chat, which is why it takes about 0.3 seconds on a local GPU.

A policy decides run or suggest. If you name a mode, it runs. Coach, Critique, Crucible and Cadence run on the model’s word, because a wrong guess costs one extra reply. Council and Collab run only when the model is at least 90% sure of its own pick. In our tests its stated confidence matched how often it was right, within 3.2 points.

Agent never runs on one signal. It can change things, so it always needs a second one, and you get a one-click chip instead.

Everything else becomes a chip. If the signals disagree, you see a suggestion and nothing runs.

Every automatic run can be undone, and the app counts how often it is. Turn the whole feature off with “Let Desk run the mode for me” in Preferences.

The fast tier is separate and opt-in. If you pick a cheaper model for small jobs, a local yes/no question decides which jobs qualify. Anything about your own history (“what did I say about…”) always stays on the main model, because that is where your memory is.

Release checks. More than 200 test suites pass before a build ships. We replay 330 saved answers through the routing rules, and a live check with the real model fails the release if wrong automatic runs pass 3% or coverage drops by 2 points.

Privacy. By default nothing leaves your computer. An optional hosted decision model exists, but it needs a consent box, sends only the text of the one message (up to 1,200 characters), and falls back to the local model if it fails or runs out of free allowance.

How we chose this approach
Every design question in this release was settled with the same small method, a minimax matrix skill file included.

The setup. Each question is a table. The rows are the competing methods, and the method we already ship is always one of them, so a new idea has to beat the current one. The columns are only the numbers that question cares about, such as right answers, wrong answers and milliseconds.

Three stages. We look at errors and pick thresholds on a tune set. We check on a second set. We score a hold-out set once and never change anything after seeing it.

Small samples are scored at the low end of their interval. A lucky 100% on 40 messages does not win. The script also tells us whether the winner survives if we double or halve the noise allowance.

Hard limits only where a wrong answer costs something. Sending an ordinary message to a heavy mode is a real cost, so it gets a limit. Speed gets a weight, not a limit.

What the matrix decided:

When Desk may run a mode for you. Six policies on 146 messages. Showing the model’s suggestion as a chip only routed 12.5% of the hold-out set.

Running every pick routed 98% but would also start the costly modes on a single guess. The policy we shipped, which asks for a second signal before the costly modes and lets the cheap single-reply modes run, routed 87.5% with no wrong runs.

At a strict 2% limit no row passed, because 68 messages cannot prove a rate that low. The script said so, and we kept the decision conservative.
Which jobs count as small. Word lists found 21% of the small jobs. The embedding model found 63%. A yes/no question to the local model found all of them, with no big job sent to the cheap model.

With every number weighted equally the script preferred the embedding model, purely because it is instant. We weighted accuracy three times higher, because a hard job on a small model costs quality and 0.27 seconds does not. That is a judgement, and we state it.

One prompt or two. Asking both questions in one prompt looked simpler. It scored worse on routing (95% down to 86%), made 5 wrong runs instead of 1, and was not faster. We rejected it.

Local or hosted. A hosted decision model got one more message right out of 68, within noise, took about 470 ms against 259 ms, and would send message text off your computer. We kept local and offer the hosted one as an opt-in.

More data for the shortlist model. We tried adding 250, 1,000 and 3,000 public everyday requests as extra examples of ordinary chat. The shortlist model separated ordinary chat from mode-worthy messages slightly worse each time (0.986 down to 0.963 to 0.983), and found fewer heavy messages at the same false-alarm rate (90% down to 69 to 83%).

Our own 148 messages already do that job. Adding more of our own data did not move it either, so we kept the model as it is and rely on real undo-chip feedback for the next round of data.

Later rounds. Letting Council and Collab run when the model is 90% sure added correct runs and no wrong ones, so it shipped. Adding example messages to the prompt changed 3 of 330 picks and made none better overall, so it did not ship. A reworded “ask or act” step for Vek scored 75.6% against 77.4%, so we reverted it.

Why this was the right way to work: it stopped us shipping three ideas that looked sensible and measured worse, and it made us say out loud where we overrode the numbers. Live runs also found two bugs that 37 unit tests missed, a cold model that never finished loading and a deleted line that returned the model’s answer.

Customers can also use the idea directly. 2.0.1 adds vektor decide: write a small JSON file with your text and your own labels, and your local model answers with a choice, a yes or no, or a score, each with a probability.

It is the same request shape the hosted decision services use, so a file written for it carries over. On a public set of 151 categories it was right about 9 times in 10, in about a third of a second, and the --eval option measures it on your own labelled sample. Nothing leaves your computer.

You can use the same method yourself. The minimax-matrix skill ships with 2.0.1.

Ask Desk a question like “keyword search or embeddings, we care about accuracy and speed” and it builds the matrix, tests in stages, scores small samples cautiously, and tells you whether to switch or keep what you have.

The skill’s reference page walks through the small-jobs decision above, step by step, with the real numbers.

Everything else updated in v2.0.1

Desk and routing

Desk is much less likely to start a mode you did not want. Everyday phrases such as “compare these two numbers” or “what does cadence mean in music” stay ordinary chat.

The first message after your local model goes cold is faster, because routing now loads the model in the background.

New Decision model card in Config: choose who answers Desk’s two quick questions. The default is your own computer. Cloudflare is an opt-in, with a consent box.

The Fast tier now uses a quick local yes/no check to spot small jobs. Questions about your own history always stay on the main model.

Collab workers start the moment the workers they depend on finish, so one slow branch no longer holds back the fast ones.

A Via tab appears in Desk if you have Via installed: open tasks with Start and Done buttons, an add box and recent activity.

Desk finds the right tool from how you phrase a request, including word endings (“translating” finds the translate tool).

Vek

Ask “what changed since last week?” and Vek lists, with dates, the facts that were replaced, the decisions made and the tasks you finished.

Two new quiet check-ins: a changed fact that may affect a recorded decision, and a decision left open for 30 days (and again at 90).

When you are away: if nobody answers a question within the time you pick, Vek takes the recommended option, carries on and tells you what it chose.

It never approves an action for you.

Secrets for agents: a placeholder such as {{secret:GITHUB_TOKEN}} is swapped for the real value only when the request goes to a host you approved, now on SSH servers too. Add a secret from Config.

Vek starts speaking sooner, and its answer bubble no longer hides behind the Ask box.

Library and reading

Top shelf: your model suggests must-read books, each checked on Open Library and ranked by real reader ratings. Copy the shelf as a list, remove a single book, or save free books from Internet Archive.

Papers: search free arXiv papers and save one to your Library with a click.

The PDF reader opens faster, Tone tints the page itself, and highlighting stops where you stop.

Kindle .mobi books open and convert.

Move the Explain box anywhere on the page.

Your own decisions

New vektor decide: sort your own text into your own categories, answer yes or no, or rate on a scale, with a probability for each, on your own computer.

Add examples per label for better answers, and run --eval on a labelled sample to see accuracy and calibration. --eval also tests a temperature fix for over-confident answers on one half of your sample and checks it on the other half, and only recommends a change if it helps.

The same request works at /api/decide on the local app and as the vektor_decide tool for your connected AI apps.

Tools for writing and showing

Humanize: select text and rewrite it in plainer words from a small bar, or press Ctrl+Shift+H.

Snip with pointer tools: Ctrl+Alt+S dims the screen, you drag over an area, and mark it up with arrows, circles and text.

A guided tour in three levels (5, 9 and 16 steps), from “Show me around” on the Desk home or by asking Vek.

Setup and providers

Activation now has Basic, Standard and Advanced levels. Basic is the short path.

Cloudflare Workers AI is available as a model provider, through your own account, with 24 chat models (the ones that need the paid plan are tagged, and VEKTOR answers on the default model if you pick one on the free plan). Claude Haiku 5.5 is in the Claude list.

The local model stays on your graphics card more reliably. VEKTOR asks Ollama for flash attention and a compact memory cache, which roughly halves the video memory a long conversation needs.

The first reply with a local model is faster, because the fixed start of the Desk prompt is read in the background.

Fixes

No more plain browser pop-ups: every question and message box uses a themed VEKTOR box.

Trivia now writes all 20 questions, accepts more model replies, and tells you when your setup is the reason it is slow.

Text is centred in the agent’s model pill and the Transparency buttons, and Vek’s faces no longer show broken characters.

Live chat text follows the voice sentence by sentence.

After an update, the Library tells you to close VEKTOR fully and start it again, instead of showing a technical error.

Stories and writing

Story panel: a story’s world, characters, style and your own notes in layers. Lock canon so it cannot change by accident, record who knows what, and list the promises the story still owes the reader.

Check a passage against the story: it names the locked rule a scene breaks. The nightly memory clean-up leaves locked canon alone.

Reverse engineer a book: attach a book or paste a link and get a point-form report on length, chapters, year, readability, characters and how deep they are, style, themes and why it works. Each point says whether it was measured, found in the text or comes from general knowledge, and made-up figures and names are removed.

The report is written section by section with a progress bar, so you can read the top while the rest fills in.

Remix a studied book into a blueprint for your own story, in the same genre or a different one.

Help that shows you

Ask Vek how to do something and an arrow points at each button on the real screen, one step at a time. It never clicks for you.

Say what is wrong (“Ollama is not running”, “my key fails”) and Vek checks your computer first, shows the fix, then checks again to see if it worked.

Studio

Motion videos: camera moves, a quality check before you save, a version strip, thumbnails, a video as the background, route scenes, and translate and dub into eight languages with every number kept exactly.
Decks from your own notes, in three styles, with no number that is not in your text.

Art and Dither: dozens of designs, layers, 17 dither patterns, photo looks and your own LUT files, and animation saved as WebM or MP4.
Script lab, Transcribe a file, and Ledger for reading invoices with a sums check.

Voice

Speak in your own voice, made on your computer, for reading aloud, Vek and Jot.

Voice tab: pauses, Save as audio, a waveform with every word marked, captions as SRT and VTT, and projects with several speakers and an audiobook file.

Vek can have its own voice, and one Stop ends all speech.

Tools and look

Companions: every function of Via, Vek-Sync, Prov and Vex, with a search for MCP servers.

Key health shows which of your keys work. A Local models card lists everything that runs on your computer.

Desk shows tokens per second and time to the first word under each answer.

If your local model cannot answer, Desk can try your connected cloud providers, free ones first.

Tic-tac-toe and Connect four against Vek, Word guess, Trivia duel and a Memory quiz.

New faces for Vek (VM-3 and Phosphor), and buttons and selected states that look the same in every theme.

Model lists: Cloudflare now has 24 chat models, with a tag on the ones that need the paid plan (VEKTOR answers on the default model if you pick one on the free plan). The OpenRouter list includes the models on its usage leaderboard.

The arrow in a guided fix moves on by itself when the step is done: a check passes, or you press the button it points at.

Right-click a snip for Copy, Save and Ask Vek. Config settings sit two to a row, and the plain Config buttons (Check keys, Refresh, Play and the rest) now look like real buttons.

Story and Ledger are centred on the page, and an attached book can be removed before you study it.

What happens next

We will keep adding functions as we get feedback. Chrome automation, enterprise controls, more games and a game-pack format, and faster local inference are on the list for the next release.

Version 2.0.1 is on the downloads page: https:www.vekmem.com

Run your own race, and keep your own data.

VEKTOR 2.0: Private local memory and sovereign data

Vektor Memory — Fri, 09 Oct 2026 22:27:19 +0000

VEKTOR 2.0 is out of the workshop, and the list of what is new runs longer than we expected.

Skim the highlights below, or read the technical section if you want to see how it works.

What we believe in:

Cutting Edge Privacy Enhancing Technology

Sovereign local data principals

Protecting our customers' anonymity

Real-time LLM trivia game

Why we build it this way
Our motto has not changed since day one: thinking memory, not retrieving.

VEKTOR is a memory and assistant layer that runs on your own computer, works with frontier and open models, and keeps your data private where you store it.

This is also a story about how much one small team can now do. A developer anywhere in the world, with an idea, a laptop and an internet connection, can now build serious software using a mix of open and closed models. We think that is good for everyone, and VEKTOR is our proof of concept.

We are a small, self-funded team. We do not have rows of engineers or rented GPU data center inference farms, and we are not trying to be everything to everyone. We are putting our best efforts forward where a small team can lead: a four-layer memory graph, a useful harness around 600+ models, transparency about what the software does, and privacy by design.

The code is our own. VEKTOR is written in JavaScript, about 170,000 lines of it, to fit our own design rules. The few open-source parts we ship, such as PDF.js for reading PDFs, ONNX Runtime and the Silero model for voice detection, are named in the package with their licenses.

Testing every panel
Before this release we went through every panel the way a race team goes through a lap, looking for lost milliseconds. Panels that list your tools now share one copy, long lists load in pages, and the slow paths we found were tuned. Even a test Library of 1,400 books and 237,000 passages now opens its settings page in approx. two seconds.

The place where we see the most room left is local inference. Running models on your own machine through Ollama is the slowest step in the chain, and the next gains there will come from smaller models, speculative decoding and smarter routing between local and cloud models. That is where our attention goes next.

What VEKTOR does and does not try to be
People will ask whether VEKTOR does everything the big agent assistants do. It does not, and it is not meant to. It is a focused business tool: it remembers, it acts on your behalf with your permission, and it shows its work. We would rather do those things well, in the open, than promise everything.

Choice and privacy
We think the choice of AI tools belongs to the people who use them, in the same way you choose where your files live and who can read them. Free services are often paid for with your data. We took a different route: you subscribe to the software at a reasonable price, and your memory stays in a file on your own computer, complete with data sovereignty.

If you choose a cloud model, your prompts go to that provider, and we say so in the app. If you choose a local model, nothing leaves your machine. The decision is always visible and always yours to choose in the settings.

Why should you trust us? Because you can check our policies. The rules we hold ourselves to are written in our tenets, our privacy statement and the code itself, and the built-in action log shows what the software ran, what it asked about and what it was stopped from doing.

Technical highlights
Memory: a SQLite file on your computer, with four linked layers (semantic, causal, temporal and entity). Recall combines meaning, keywords and names, and now also scores single sentences, so a memory is found when the answer is one line inside it. In our tests retrieval takes single-digit milliseconds on a file of about 2,300 memories (retrieval only, not the model’s answer).

Changed facts: when two memories give different values for the same thing, they are listed with Replace and Undo. A fact restated in very different words is flagged as a possible change for you to review. Nothing is merged or hidden for you.

Voice, all local if you want it: speech to text with Whisper on your machine, read-aloud with Kokoro, and a small voice-detection model (Silero, about 14 MB) that runs in your browser to tell speech from a cough or a door.

Pictures: Vek tries your chosen provider first, then a picture-capable model on your own computer, then your other cloud keys. A key that has just failed is skipped for 15 minutes.

Permissions: an MCP tool asks before it runs unless its server marked it read-only. Scheduled routines pause after ten failures in a row, and one switch stops everything that would leave your computer.

Local-only server: the app runs on your machine, locks its window with a content security policy, and checks where each request comes from before it answers.

Quality gate: every build passes a 63-check release gate, and the packaged build is tested end to end against a copy of real data before we ship it.

What is new in 2.0
Vek, the assistant

Talk to Vek hands-free with Live voice, with turn detection, interruption, and a voice detector that ignores coughs and background noise.

Show Vek your camera or screen with Look: one picture, only when you ask, never saved. (Live Beta, with future improvements)
Four buttons under the ask box: Live, Camera, Screen and Chat.

Vek cards: checklists, step guides, option tiles and numbered lists, with icons, your own pictures, optional Wikipedia photos and small diagrams.

Vek opens Library books by title, at a chapter or a page, and opens any of 17 ready-made templates by name.

Vek reads the page you are reading and answers from it.

Email from Vek’s own address, with your approval on every send, plus an optional read-only mailbox from senders you trust.

Talk to Vek from your phone through Telegram.

Ask for a background task in one sentence, and Vek sets the name and schedule.

Eight mascots with their own voices and motion: the robot, a flat Orb with a colour picker, a glass orb with oscilloscope lines, Cloud, VEK 9000, Ghost, Game pad and the Magic 8 Ball.

Meetings and notes

Meeting notes in Jot: record your microphone and, if you wish, the other side of a call, or import an audio file.

Time-stamped transcripts with names, and notes from six templates: meeting summary, stand-up, client call, interview, brainstorm and lecture.

Calendar join: read an .ics file to fill in the meeting title and invitees.

Remember button to save the notes to memory, only when you press it.
Desk can save a decision when you state one, and Workflows keeps a Decisions list with an as-of date.

Library and reading

PDFs open in the same reader as books, with selectable text, highlights, zoom and find.

Library starter pack of 14 reference files, an index button for new files and another for a full rebuild, text editing with kept versions, and a Remove from reading action.

Convert tool with 81 conversions, a built-in guide of 63 chapters, and a Desk bar with Library search and Convert one click away.

Workflows and business tools

Projects: a goal, running notes and the result of each run, with the last plan reused on the next run.

Goals with steps that follow your task list, and optional steps suggested by Vek, labelled as Vek’s.

Ideas tab of things Vek can really do today, an Inbox tab that turns a pasted list into tasks, and Recipes for known code fixes applied the same way every time.

Triggers: run a Workflow on a schedule, when files in a folder change, when a commit lands, or when a CI run fails. Steer a run while it works.

Task due dates, reminders and a to-do list you manage by typing in Desk.

Companions page for Via, Vek-Sync, Prov and Vex, each with a preview before any action.

Flowire opens a file and lights up the files a change touches.
Memory

Causal links updated between memories, drawn as arrows in the graph.
Insights (beta, off by default): short summaries of related memories, kept apart and labelled.

Optional key facts: three short facts per Desk answer, off by default.
Real dates in place of “yesterday” and “last Friday” when memory is handed to the model.

Daily limits for web search, and Firecrawl and Keenable search keys.
Security and transparency

Action log: one list of what VEKTOR ran, asked about or was stopped from doing, with the rule that applied.

Pause all outside messages, a single switch for Telegram, email and delivery.

A safety message instead of a coaching run when someone writes about self-harm.

For play and for focus

Trivia on the Game pad: 8 topics, 3 levels, 20 questions written fresh by your own model, a timer, streak scoring, sounds and confetti.
Magic 8 Ball with a real 20-sided die and a fair draw of the 20 classic answers.

Ghost, Cloud, Game pad, Orb and VEK 9000 each have their own funny quips.
Models and skills

Seven more newly released models in the OpenRouter list, which still refreshes itself.

Two new skills: a plan-build-verify loop and a minimax-matrix for choosing between options on accuracy and speed.

What happens next

We will keep adding functions as we get feedback. Chrome automation, enterprise controls, more games and a game-pack format, and faster local inference are on the list for 2.1.

Try it, tell us what works and what does not, and hold us to the standard above.

Run your own race, and keep your sovereign data private and local.

Upgrading

Drop-in from any earlier version. Your memory database is untouched, and v2.0.0 includes everything from v1.9.10

The full changelog is at vektormemory.com/docs/changelog. The forum is the fastest way to reach us with questions or feedback.

VEKTOR Memory builds local-first persistent memory infrastructure for AI agents. Documentation and downloads are at vektormemory.com.

The SI Wealth Transfer Is Becoming the Largest Rug Pull in History.

Vektor Memory — Tue, 06 Oct 2026 23:48:32 +0000

We are all involved in this machine; don’t get upset by this message. Look at what you can do to make things more “evenly distributed”.

The most important question about superintelligence is no longer simply whether the technology works.

The deeper question is who will own the infrastructure, who will control access to it, who will receive the wealth it creates, and what happens to the people whose work makes the system valuable.

SI is often described as an open revolution. Models are released publicly. Code is shared on GitHub. Datasets and experiments appear on Hugging Face. Developers around the world contribute improvements, documentation, and research.

But openness at the production layer does not necessarily mean openness at the ownership layer.

A project can be open source while the platform hosting it is privately owned. A model can be freely downloadable while the hardware and APIs needed to run it are controlled by a small group of companies. A developer can publish an idea openly while a much larger corporation turns that idea into a closed commercial product.

This creates the possibility of an enormous transfer of wealth from distributed creators to concentrated platform owners.

Sitting Ducks — Michael Bedard 1977

The emerging ownership structure
The SI economy is forming around several strategic layers.

There are the chip companies that manufacture the hardware. There are cloud companies that rent the computing capacity. There are model companies that train and operate the systems. There are developer platforms that host code, models and datasets. Finally, there are the applications that convert SI capabilities into products and services.

Many of these layers are controlled by a surprisingly small number of companies.

Nvidia’s agreement to acquire Hugging Face for approximately $12.9 billion is a major example. Hugging Face has become one of the central platforms for sharing open-source models, datasets and tools. Nvidia says the platform will remain open and that developers will not be required to use Nvidia hardware. However, ownership still gives Nvidia a major position inside the distribution and discovery layer of SI development.

Microsoft already owns GitHub, one of the most important repositories of source code and developer activity in the world. GitHub reportedly has more than 150 million users and hosts more than 420 million projects.

This does not mean Microsoft owns every project on GitHub or Nvidia owns every model on Hugging Face. It means that two enormous corporations have influence over where software and models are stored, discovered, ranked, tested, discussed and commercialised.

That distinction matters.

The future may not be controlled by the company that owns every piece of code. It may be controlled by the company that owns the roads through which code travels, the infrastructure.

The platform is the new landlord
A useful way to understand this is to think of digital platforms as landlords.

The developer creates the intellectual property. The platform provides the building:

hosting

identity

collaboration

rankings

analytics

deployment

payments

enterprise access

recommendations.

At first, the platform may be generous. Free accounts encourage participation. Open repositories create network effects. Developers invite other developers. Models become easier to find. The ecosystem grows.

Once the platform becomes essential, the balance of power changes.

The platform can introduce fees, change visibility, promote its own products, alter search rankings, place ads, limit competing services or change the terms of use. It may not need to ban a project. Making the project difficult to discover could be enough.

This is how practical control works in a digital economy. Formal ownership is only one kind of power. Visibility, distribution and default placement can be just as important.

A model may technically be available to everyone, but if it is ranked below a company-owned alternative, excluded from recommended lists or made difficult to deploy, its practical reach can be severely reduced.

The creator may legally give away the business
The open-source licensing model makes this particularly important.

If a developer releases a project under the MIT or Apache 2.0 licence, a company can generally use the code commercially, modify it, combine it with proprietary software and sell the result.

The company usually does not have to publish its modifications or contribute improvements back. It normally only has to preserve required notices and comply with the licence terms. Apache 2.0 adds clearer rules around attribution, notices and patents, but it still allows commercial use.

This is not necessarily theft under the law. It is often the intended purpose of a permissive licence.

The problem is that many creators do not fully appreciate what they are giving away. They think they are sharing a tool. In reality, they may be releasing:

the implementation

the technical architecture

the market concept

the product roadmap

the evidence of customer demand

the lessons from failed experiments

the knowledge needed to build a competing product.

A corporation may take the repository, preserve the licence notices and build a paid service around it.

The creator may retain copyright in the original code, but that does not mean the creator retains commercial exclusivity.

This is the difference between legal ownership and economic power.

Ideas are easier to borrow than code
Copyright generally protects the expression of an idea more clearly than the idea itself.

Suppose an independent developer publishes an open-source memory system for SI agents. The system includes a database schema, retrieval engine, API and documentation.

A large company might:

copy portions of the code under the licence

rewrite the system in another language

build a similar architecture independently

use the public documentation to understand the market

use its cloud, distribution and sales resources to reach customers first.

Even if the company does not copy the code directly, it may still benefit from the creator’s public research.

The original developer has taken the risk. They have paid for the experiments, written the documentation, attracted early users and demonstrated that the problem matters.

The larger company may then arrive after the uncertainty has been removed.

This is one of the quiet forms of intellectual-property transfer in the technology industry. The original creator may not lose ownership of a specific file. They lose the opportunity to be the only person able to commercialise the insight.

The new form of extraction
Traditional resource extraction involved taking oil, minerals or agricultural products from one place and concentrating the value elsewhere.

The digital equivalent can involve taking:

code

research

ideas

user behaviour

model evaluations

community knowledge

developer time

open datasets

The platform supplies infrastructure and monetises the resulting activity.

This does not mean platforms contribute nothing. Hosting, security, compute, storage, search and collaboration are expensive. The issue is the imbalance between the value contributed by millions of participants and the bargaining power of the company operating the platform.

A developer might contribute years of work to an open project. The platform can earn revenue from enterprise subscriptions, advertising, compute usage, developer tools and strategic intelligence.

The creator receives visibility and perhaps reputation. The platform receives recurring revenue and a strategic view of the entire ecosystem.

That is an uneven exchange when the platform becomes indispensable.

The state is entering the ownership structure
The government is now becoming part of this story.

US officials have discussed whether the government should receive equity stakes in major AI companies. Reports have described proposals involving OpenAI and a public investment vehicle that could distribute future returns to American households. Reuters also reported that Anthropic was not involved in discussions about providing the government with equity at that stage.

This is important, but it should not be described too simply as nationalisation or as a public utility.

A government holding shares in a private company is not the same as the company becoming a regulated public service. The company may still set prices, control access, exclude users and pursue private commercial objectives.

The key question is what the public receives in exchange for public involvement.

There are several possible arrangements:

the government buys shares with taxpayer funds

the company donates shares

the government exchanges subsidies or contracts for equity

the state receives board representation

the state receives only passive ownership

future returns are distributed through a public wealth fund

taxpayers absorb losses while executives retain operational control

These arrangements are economically very different.

If the government takes a stake using public money, taxpayers carry investment risk. If the company donates shares, the public may receive future upside without paying directly, but the donation could still be exchanged for political influence, regulatory protection or public contracts.

Either way, ownership alone does not guarantee public control.

Public risk and private control
The most dangerous arrangement would be one where public money supports private companies without creating meaningful public rights.

Consider the possible sequence:

Venture capital funds the early company

Government subsidies support chips, energy, data centres or research

Public agencies become major customers

The company becomes too strategically important to fail

The government accepts equity or provides emergency support

Taxpayers absorb part of the downside

Executives and private investors retain control over operations and pricing

This would amount to socialising risk while preserving private control.

The government may describe the arrangement as an investment. But taxpayers may not receive the same protections as private investors. They may have no board seat, no meaningful ability to influence strategy and no guarantee that returns will be distributed to the public.

A small passive stake can create the appearance of public participation without giving the public real power.

The Intel arrangement is a useful warning. The US government agreed to purchase an approximately 9.9% stake in Intel, with the investment associated with federal semiconductor funding. The announced structure was described as passive and did not provide ordinary governance control.

That model may be appropriate in some circumstances, but it demonstrates the difference between owning part of a company and controlling its decisions.

From technology oligopoly to political oligarchy
The concern is not only economic concentration. It is the interaction between economic concentration and political power.

A Silicon Valley oligarchy would not necessarily look like a government dictatorship. It would operate through ownership, influence and dependency.

A small number of companies could control:

the chips

the cloud

the models

the developer platforms

the software distribution

the enterprise contracts

the government relationships

the funding channels

They would not need to own every startup. They could own enough infrastructure to shape the choices available to startups.

**Independent companies might have to buy compute from the same **corporations that compete with them. They might have to distribute models through platforms owned by major infrastructure providers. They might rely on cloud credits, marketplace rankings and enterprise partnerships controlled by potential competitors.

This produces a system in which competition is allowed, but the terms of competition are privately administered.

The smaller company can exist, but it may never become independent enough to challenge the system.

The potential near-future outcomes
Several outcomes are plausible over the next few years.

The controlled public wealth model
Governments receive minority stakes in major AI companies. Those stakes are placed into public funds and future returns are distributed to citizens.

This could give the public a genuine claim on AI-generated wealth. But it may also legitimise a small group of private companies as permanent national champions.

The result could be public dividends combined with private oligopoly.

The protected national champion model
Governments decide that frontier AI is too important to leave to ordinary competition. They provide subsidies, contracts, energy access and regulatory protection to a handful of companies.

Smaller competitors remain technically free to operate but cannot match the capital and infrastructure advantages of the protected firms.

This would resemble an industrial policy system centred on AI.

The platform enclosure model
Open models and code remain publicly accessible, but discovery, compute and deployment become concentrated.

The open ecosystem continues to exist, but the economically valuable part is captured by the platform owners. Developers can contribute to the system, but cannot easily monetise their work independently.

The AI bubble and rescue model
Large investments in data centres and AI companies fail to generate the expected returns. Some firms collapse. Governments intervene because the infrastructure is considered strategically important.

Taxpayers then fund rescues, while the surviving companies acquire failed competitors and become even more concentrated.

This would be the clearest version of the feared rug pull.

The decentralisation response
Developers begin moving toward self-hosted Git platforms, independent model registries, federated identity, local inference and cooperative infrastructure.

Open-source projects distribute their repositories and models across multiple locations. Creators adopt more restrictive or commercially structured licences.

This outcome would reduce platform power, but it would require developers to sacrifice some convenience.

The problem with calling everything theft
There is a danger in using the word “theft” too broadly.

If a creator releases code under MIT or Apache 2.0, commercial reuse is generally authorised. The company may be behaving aggressively, opportunistically or unfairly, but not necessarily unlawfully.

The stronger argument is about bargaining power.

A creator may voluntarily publish the code, yet still be operating inside a system where:

the licence is poorly understood

the platform owns the audience

the company has superior legal resources

the company can see early signals of commercial demand

the creator cannot afford enforcement

the code is legally reusable but the creator’s business model is not protected.

This is better described as asymmetric extraction than simple theft.

The transaction may be legal while the economic outcome is deeply unequal.

How creators can protect themselves
Open-source creators do not need to abandon openness, but they should separate what they want to share from what they need to own.

Possible strategies include:

releasing a useful core while retaining a proprietary hosted service

using dual licensing for commercial customers

considering AGPL where network-service sharing is important

keeping proprietary data and evaluation sets private

protecting the product name with a trademark

maintaining multiple repository mirrors

avoiding dependence on a single model registry

keeping customer relationships outside platform marketplaces

publishing only the components that benefit from adoption

using contributor agreements that clarify ownership

separating public demonstrations from commercially valuable infrastructure.

A repository should be treated as a strategic asset, not merely as a code dump.

The question should be:

What must be open for adoption, and what must remain controlled for the business to survive?

The deeper question
The future of SI will not be decided only by model intelligence.

It will be decided by ownership.

Who owns the compute? Who owns the data? Who owns the distribution channel? Who controls the ranking system? Who receives public subsidies? Who determines which projects receive visibility? Who captures the productivity gains when SI replaces or augments human work?

If the answers remain concentrated among a few technology companies and their financial partners, SI may produce tremendous technical progress while also creating one of the largest wealth transfers in modern history.

The public may receive better software and cheaper services, but still lose economic power. Independent creators may produce the innovations, while platforms capture the recurring revenue. Governments may support the infrastructure, while private companies retain the strategic control.

That would not be a conventional revolution in which power is distributed.

It would be a transfer from many creators to a small number of owners.

Conclusion
The central danger is not that SI companies will suddenly steal every piece of code or that open source will instantly disappear.

The danger is more gradual.

Millions of developers may create the intellectual foundation of the SI economy. A small number of companies may then control the hardware, cloud infrastructure, model platforms and distribution channels through which that work becomes valuable.

Government equity stakes could either give citizens a share of the upside or help legitimise and protect a private oligopoly. The difference depends on governance, transparency, competition and whether public risk produces public control.

Open source can remain legally open while becoming economically enclosed.

That is the issue to watch.

The critical test for the next decade will be whether SI becomes a widely owned productive infrastructure, or whether it becomes a privately controlled utility-like system in which society supplies the data, labour, capital and public support while a small Silicon Valley oligarchy captures the wealth.

Sources and factual context
Reuters reported that US officials had held preliminary discussions about the possibility of the federal government acquiring stakes in major artificial-intelligence companies. The report described the discussions as preliminary, rather than confirming that the government had completed an investment in OpenAI or Anthropic.

Reuters reported that OpenAI had discussed a possible 5% stake for the US government, according to reporting by the Financial Times. The proposal was described as part of broader discussions about national-interest involvement in advanced SI.

Reuters separately reported that the Trump administration and Anthropic had not discussed the government taking a stake in Anthropic at that time. This distinction is important because public statements and media reports have sometimes grouped OpenAI and Anthropic together despite the different status of the discussions.

Nvidia agreed to acquire Hugging Face for approximately $12.9 billion. Hugging Face is a major platform for hosting and distributing open-source SI models, datasets and development tools. The acquisition gives Nvidia a significant position in the SI software and developer ecosystem.

GitHub states that its platform is used by more than 150 million developers and hosts more than 420 million projects. This helps explain why control of GitHub has strategic importance beyond ordinary code hosting.

The MIT licence permits commercial use, copying, modification and redistribution, provided the required copyright and licence notices are preserved. The Apache 2.0 licence also permits commercial use and modification, while adding more detailed requirements concerning notices, modifications and patent rights.

GitHub’s documentation explains that software without an open-source licence remains protected by ordinary copyright law. A public repository is not automatically free for unrestricted commercial reuse merely because it can be viewed online.

GitHub’s generative SI terms state that GitHub will not use customer inputs or outputs to train generative SI models under the relevant service terms. However, developers should distinguish between different GitHub products, account types, settings and contractual terms rather than assuming one universal data policy.

The US government announced an agreement to purchase approximately 9.9% of Intel for about $8.9 billion. The arrangement was described as a passive investment without ordinary board representation or governance control, illustrating that public ownership does not automatically equal operational control.

The facts are developing, but the direction is already visible: SI is becoming not only a technology race, but a contest over ownership, distribution and political power.

Founder’s note
The article’s claims about wealth transfer, platform enclosure, intellectual-property extraction and Silicon Valley oligarchy are analytical arguments rather than settled facts. The cited material supports the underlying events and legal or commercial structures, while the conclusions about future control and wealth concentration remain potential forecasts and not financial, legal, political or personal advice.

VEKTOR Memory builds local-first persistent memory infrastructure for AI agents. Documentation and downloads are at vektormemory.com.

Open Source

LLM

Hugging Face

Github

The Road to VEKTOR v2.0: How we Refined our Research Methods for Speed

Vektor Memory — Sun, 04 Oct 2026 00:16:41 +0000

What 1.9.9 fixed, how we tested, and what we are building next.

14 min read · vektormemory.com

The pace is changing

The field keeps renaming itself: assistants, agents, and now SI “super intelligence."

I always liked “the everything machine” better, because it says what we want from the thing. If it is going to merge into the one "thing," it has to be built from parts that all work in unison, accurately with speed.

A few months ago, VEKTOR was a tiny 4-layer memory store with a lot of lofty ideas around it, unbuilt modules on a Kanban board, and saved to memory as future planning projects.

Today we can ask an assistant to run a workflow, watch it ask for permission (Hitl), and read afterwards exactly what it did, saved to the memory layers graph, with full transparency.

A search that took half a second now takes a few milliseconds, shaving off time from the dozens of iterations we made. A memory that used to pile up now notices when a fact has changed. None of the ideas are ever finished, and we list what is still rough around the edges below. But the pace has changed, and I think I know why.

Building the foundations was the hardest part.

Memory, context, skills, MCP tools, and the connectors all had to be built and work properly and agree with each other before anything built on top could be trusted. For a long time that meant slow, careful back-end plumbing that nobody sees, wiring, event handlers, and smoke testing 100’s of LLMs constantly changing and improving.

Now the majority of those pieces hold, and each new feature starts with more already in place. The assistant can search your notes, read the custom Vektor guide in the library we made for users and LLMs, see which panel you have open, and use the same 60+ MCP tools and memory Claude via sees. We stopped re-explaining things to it, so new work lands faster. We expect the pace to keep climbing, as the Frontier models improve in coding and the open-source models as well.

There is a second reason. Somewhere in the last few weeks we reviewed our design and research methods and started measuring it more efficiently, primarily because we are inpatient, with a small framework we describe below and will release as a skill once refined.

It borrows from John von Neumann and adds a few habits of our own that we learned from watching LLMs struggle with benchmarking and looping. It’s not claiming new science or a white paper, just a remix of old and new ideas into the agentic tools we use now. It culls the bad ideas early and cuts our test time from days to hours via microtests in a matrix.

**The real problem
**The field has moved so fast in the last two months from Openclaw and Hermes agents.

Now with OpenAI launching “dots” on 29 September: always-on personal agents with their own cloud computer, connected to more than 4,000 apps, working read-only in the background and asking before they send or change anything. xAI shipped Grok Bot in August, an agent that operates apps by looking at the screen and remembers your corrections, amazing. Meta’s Muse Spark plans a job and hands the pieces to parallel sub-agents, with cute characters and payments.

All three live in the vendor’s cloud data centers, highly polished with millions spent on devs, and all three keep your memory there, and all have agent credit card payments built in, some even work in your Tesla EV, controlling real vehicle settings, the future promised now.

We look at where the market is going, and we are building the other trade-off: assistant and memory on your own machine, any model you like, with a human in the loop by default, security, hitl, privacy, soveriegn data.

Those choices cost something, complete agent autonomy mode. Currently a small local model is worse than a frontier model at deciding when to use a tool, the cloud computer option to lean on, or built-in credit card payments, which we can see as being an absolute cluster if an agent conducts incorrect transactions, loop errors, and customer support issues in the future.

Now a billion-dollar company will have thousands of offshore support staff agents that can handle 1000s of tickets for insurance and cashback claims when the agents go rogue and order 200 pairs of shoes instead of 2.

So the question for this release was not “which payment feature is next" because that is where the market is at. but “which parts of the foundations work well for us based on our own ethos."

It's all a sacrifice to maintain our local philosophy, but then again, it avoids a lot of the foreseeable payment headaches. Maybe we are right or wrong; sitting on sidelines and watching it all play out, we can learn from the mistakes and implement and revise as needed at our own pace.

Vek Panel

How we test: grids, gates, worst-case rules
Von Neumann minmax matrix

Every “which method is best” question now goes through the same three steps.

The grid. Rows are methods, and the shipped method is always the first row. Columns are accuracy, speed, and accuracy on a holdout set. Each cell is one small experiment. Because each cell takes seconds, we can try thirty ideas in an afternoon.

The gates. Each gate is a yes or no. We tune on 10 questions and keep a change only if the ranking score rises and the top-10 hits do not fall.

We then re-check the kept change on a separate 20-question form, and it must not be worse there. The remaining 70 questions are never used while tuning, and they are the only numbers we report.

Ten questions are noisy: one question moves the score by 10 points. The second gate exists to catch luck. Several window-size changes passed the first gate and failed the second.

The worst-case rule. For each row we compute two regrets: how much accuracy it gives up against the best row, and how much speed it gives up against the fastest row. We take the worse of the two and pick the row with the smallest worst case.

Here is a real example from the library search matrix:

Matrix

The 30-candidate row is the most accurate, but it pays for that in speed. The 20-candidate row gives up 5% of the accuracy and loses far less time, so it wins. A fast method that is wrong and an accurate method that is slow both lose under this rule. Timing is noisy, so the milliseconds moved by a few between runs. The ordering held.

Where the idea comes from. In 1928 von Neumann proved the minimax theorem: in a two-player zero-sum game, you can pick the strategy that limits your worst-case loss. We borrowed the decision rule, not the game.

Nobody is playing against the test set. The “opponent” is our own habit of picking the method that wins on the metric we happen to like, whilst refining and improving on our ideas, reiterate quickly, fail fast, and win fast, stacking upwards into the matrix.

Why do this? Because the market is moving that fast, there is little point watching the LLMs hammering tokens and performing benchmark tests over and over to find out the original theory and ideas were wrong in the first place and bear no tangible results…

It is also worth knowing what the research says about people and minimax.

In 2009 Levitt, List and Reiley tested whether professional poker and soccer players actually play minimax in laboratory games (NBER working paper 15609). They mostly did not, and the skill did not transfer from the field to the lab.

Our reading is modest: do not assume theory describes the behavior, and use worst-case regret as a simple tie-breaker when no single metric can be trusted.

What the research says about the pieces
A few results shaped what we built and what we left alone.

Keyword search stays the base. A 2026 scaling study, “BM25 Wins at Scale”, ran 28 corpus sizes up to about 600 million tokens. Keyword search overtook dense retrieval around 10 million tokens and led at every larger size. So the Library keeps keyword search as its foundation and adds meaning on top.

Fusion by rank. To combine two ranked lists we use reciprocal rank fusion (Cormack, Clarke and Büttcher, 2009). It adds up 1 divided by each list’s rank, so a result that is high in both lists wins, and no one has to compare raw scores from different systems.

A model-written search, only when needed. HyDE (Gao and colleagues, 2022) has a model write a hypothetical answer and searches with that. It costs a model call, so VEKTOR runs it only when the quick searches disagree, and waits about a second at most.

The embedding window. The small embedding model reads about 128 tokens, which is roughly 512 characters of English. Our library passages average 2,600. That one fact explains why plain re-ranking by meaning hurt at first, and why scoring overlapping windows fixed it.

Dates at search time. Memory tools describe handling time questions with rule-based pattern matching and no extra model calls, using dates stored with each memory. We store when each fact was replaced, and we plan to test our own version of that for the ledger described near the end.
The initial assumption is not always correct

The Library holds about 154,000 passages from our test books. Search took 545 ms on our test queries, which you notice when an assistant searches three times before it answers.

We assumed the ranking code was the cost. A profile said otherwise: one line counted every passage in the index before each search, and that count alone took about 470 ms.

Reading the total the index already stores brought it under a millisecond. Searching first for passages that contain all the meaningful words, and widening only when needed, took ten short queries to 5.6 ms on average. One hundred full-sentence questions average 96 ms.

Speed was the easy half. To measure accuracy, we had a local model write 100 natural questions from random passages, each tied to the passage that answers it. Plain keyword search put the right passage in the top five for 63% of them.

We tried five ideas to improve that. Dropping search terms for speed cut it to somewhere between 23% and 52%. Splitting passages into smaller pieces cut it to 53%. Letting a local 9-billion-parameter model pick the best passage made first place worse on a 30-question check, from 47% to 27%.

One thing helped: re-ranking the top 20 by meaning. Because of the window limit above, we score overlapping 480-character windows and keep the best one.

On 70 questions we never tuned on, the ranking score went from 0.41 to 0.50, about 22% better, at the same speed once the vectors are stored. They fill in quietly as you search.

Limits: a local model wrote the questions, 70 questions leaves roughly five points of noise, and your first search on a new topic is ordinary keyword search.

A memory that says “that changed”
Save “the meeting moved to Thursday” and the old “Tuesday” memory should stop answering.

VEKTOR now compares each new memory with the closest older ones. A different date, number, owner or address, or a “no longer”, links the pair with a “contradicts” edge. It uses wording and the stored embeddings only, with no model call, so it is private and free.

By default nothing is replaced or deleted. An optional setting retires the older fact, which stays in your file.

The first version was wrong in a useful way. On a test set of 46 pairs it caught 19 of 20 changes with no false alarms. On our real test file of 2,256 memories it flagged 37% of everything, because the file is full of long chat transcripts and test timestamps whose numbers differ constantly.

We restricted it to short plain statements, ignored long numbers and anything shaped like code, and required clear change wording before a bare number counts. It now flags 10 memories, 0.4%.

One gap we cannot close yet: the test file has no recorded “this replaced that” pairs, so we cannot tell you how many real changes it misses. That is why it flags and does not rewrite.

An assistant must not claim what it did not do
Vek the mini assistant can now use tools: search your notes, memory, the guide and the Library, open a panel, save a note, run a Workflow.

You choose how much it may do. Read means it only looks, Ask (the default) shows each change on a card and waits for Approve or Deny, and Auto just does it. Every action lands in a list called “What Vek did”.

No tool can buy anything in cyberspace with a credit card, that's the compromise we made so far.

Our test model is a local 9B model through Ollama, and it does not call tools reliably, yet.

So the loop does three things:

It tells the model which tools exist and gives an example. If you asked for an action and the answer only says it was done, the loop replies that nothing happened and asks once more. Plain chat answers get a rule that they cannot run anything and must not say they did.

Over 21 requests across seven kinds of task, the model used the right tool 20 times. The miss was a claim that a workflow did not exist.

Privacy you can switch on or off
An internal auditor run we did critiqued our privacy chapter and asked what the “few small automatic requests” were, and noted that weather and currency widgets also call out.

The chapter lists four: the update check, licence activation, first-time model downloads and the model list. The widgets were not on the list.

They are now, and there is a switch. In Config, under Privacy, “Block automatic network requests” stops everything VEKTOR does on its own. A test records every outside address and shows zero requests with the switch on.

The app sends no telemetry in either state. We searched the code again for analytics and error-reporting addresses and found none.

What is in 1.9.9
Memory:

Keeps itself up to date in the background: backup first, never deletes a memory, resumes if you close it

Claude, the app and the terminal share one memory

A richer Graph, linked as you save: by names, by time order, by meaning
Searches follow names, and the slower model-written search step runs only when the quick searches disagree

Replaced facts stay replaced, and the new contradiction check

Vaults work everywhere: Claude and the terminal follow the vault you pick
Fading memories now covers every memory, with safeguards, and vektor cold shows and undoes it

New terminal commands: vektor recall, backup, export, tags. db-merge imports into your normal file

Vek assistant:

Tools with Read, Ask and Auto levels, an approval card and an activity list

Reads public links and your notes, runs Workflows, answers how-to questions from the guide

Two-part answers, personalities that change how it answers, an Ask box under it, spoken answers, orb avatars and a larger face

Library, guide, workflows:

The VEKTOR guide ships as a 55-chapter book in your Library, with matching MCP tools (60+ in all)

Library search much faster (545 ms to under 6 ms on short queries) and re-ordered by meaning. EPUB pictures and a wider reader

Workflows cards fill the page, with All, Scheduled and On demand views, search, a details window and last-run time. Results can go to Slack or email

Setup and privacy:

The privacy switch described above

Both setup wizards offer all 18 providers, and your question goes only to the provider you chose

API keys set from the terminal go into the encrypted vault

A 15-tool profile for apps that cap how many tools they accept

Terminal commands start in 0.15 seconds instead of 0.9

The road to v2.0
These are our plans, in the order we expect them being built now over the next month:

A decision ledger. Ask “what did we decide about the Quokka plan, and what changed since?” or “what did I believe in March?” The memory already records when a fact was replaced. The ledger would tie decisions to their sources.

Memory that works while you sleep. Merging scattered notes into short insights that link back to their sources. We will test it on a copy first, because a model rewriting your memory is where trust gets lost.
One front door. One assistant decides who handles a request. The Desk modes stay as manual overrides.

The benchmark rerun. We are re-running our memory benchmarks and plan to publish full results with 2.0, including the categories where we need most improvements.

Phone and text. Only after we find the most private method to do it. Local-only means your computer has to be on.

A simpler start. Opening the app without a terminal command.

On the shelf: letting Vek see your screen through a small local vision model, strictly opt-in. We want the privacy rules written before the we start coding and testing how it works.

Where VEKTOR fits
None of this needed a bigger model. It needed measuring each step, keeping what held up on questions we had not tuned against, and dropping what did not.

Two of our best-sounding ideas lost to the baseline in the first test, leading to quicker iterations and pivots.

That is the method we plan to keep through 2.0: a small test, a yes or no, then move on. Memory, context, skills, MCP and the connectors give each test something solid to stand on, and we think that is why this stretch moved so fast and wil continue this way into the future.

If you run local models, we would like to hear what you would let an assistant do without asking, and what you would never want it to touch.

Those answers determine the default levels and where and what we build next.

Upgrading
Drop-in from any earlier version. Your memory database is untouched, and v1.9.9 includes everything from v1.9.8.

npm install -g ./vektor-slipstream-1.9.9.tgz
The full changelog is at vektormemory.com/docs/changelog. The forum is the fastest way to reach us with questions or feedback.

VEKTOR Memory builds local-first persistent memory infrastructure for AI agents. Documentation and downloads are at vektormemory.com.

Sources:

OpenAI dots launch coverage (androidheadlines.com/2026/09/openai-launches-dots-always-on-ai-agents.html), xAI Grok Bot (layer3labs.io/guides/what-is-grok-bot), Meta Muse Spark 1.1 (siliconangle.com/2026/07/09/meta-launches-flagship-muse-spark-1–1-model-multi-agent-upgrades), “BM25 Wins at Scale” (arxiv.org/abs/2607.26497), Levitt, List and Reiley, “What Happens in the Field Stays in the Field”, NBER working paper 15609 (nber.org/papers/w15609), Mem0 on temporal reasoning (mem0.ai/blog/introducing-temporal-reasoning-in-mem0), Cormack, Clarke and Büttcher, “Reciprocal Rank Fusion outperforms Condorcet and individual rank learning methods”, SIGIR 2009, Gao et al., “Precise Zero-Shot Dense Retrieval without Relevance Labels”, 2022.

AI Agent

LLM

Memory Improvement

Minmax

VEKTOR v1.9.8 is live: an LLM library that builds itself, a converter for nearly every file, and security you can actually see

Vektor Memory — Wed, 30 Sep 2026 23:28:03 +0000

Local-first memory for AI agents, now with your books, your documents and a much harder look at what your AI reads.

We shipped more in the last month than we did in the previous twelve.

Every system in VEKTOR now lives in one SDK and follows the same rules. Adding a module used to mean wiring it into five places and arguing with each one. Now the foundations are there, and a new idea has somewhere to go on day one.

The clearest proof is the Library. We sat down with a rough idea: point VEKTOR at a folder of books and notes, and get something you would want to open every day. Design, plan, build, verify, test and refine all happened in a few hours. It was one of the fastest and most enjoyable things we have built.

That's just RAG? Kinda, like RAG but on automode at hyperspeed.

That speed is the story of this release. v1.9.8 has a Library, a reader, a converter for documents and media, and a security layer that now asks your permission before it lets an AI do anything risky. Below is what each of them does, what we believe about privacy, the technical detail for people who want it, and a full list of every change.

Library mode

The Library: your folders, as a place you want to be

Add any folder under Config and it appears as a Bauhaus art cover grid. Every book gets its own custom cover drawn on the spot, so nothing slows down as you scroll, and PDFs and EPUBs show their real cover when they have one or sourced online. You can search, sort, filter by format and rate books from one to five stars. The PDF, EPUB and text copies of the same book sit on one card.

Hover a book and you get its title, author, year and a one-line summary from your local model. If a detail is wrong an LLM will regenerate, edit it and your edit stays. Right-click for more books like this one, a fresh summary, or a trash folder you can restore from.

Then there is the reader. Text, Markdown, EPUB, Kindle formats and PDFs open as a two-page book, a single page, a whitepaper or a continuous scroll, in a paper, sepia or night tone.

Highlight a passage and send it to Jot or Desk, or press Explain to get a plain-language version that appears as it is written via LLM. Or have the book read to you, in short natural sections, with a sleep timer.

It is a rag-style folder with generated covers and summaries, and using it is genuinely a pleasure. We did not expect to feel that about a file browser.

Convert: one tool for the files people actually have

This week a screenshot landed with a hand-drawn circle around a dropdown. The format menu on the new Convert tab was showing a single item out of a long list. That is a fair summary of how this feature grew: fast, then a real person pointed at the rough edge, then we fixed it the same day.

The new Convert tab turns documents, spreadsheets, slides, e-books, fonts, archives, pictures, audio and video into other formats. Most of it needs nothing installed. Pictures, audio and video are converted inside your browser, so the files never leave your machine. There are more than 80 conversions, and every result tells you plainly what it could not carry over.

A few highlights:

PDF tools. Split a PDF into one file per page, keep only pages like 1–3, 5, or join several PDFs into one.

Rare formats. Old Word files, Photoshop files, CAD drawings, disc images and bzip2 archives.

Media. Save a video frame, make an animated GIF, build a contact sheet, pull out the audio, or turn a camera RAW file into the JPEG preview stored inside it.

Your installed programs. If you already have Pandoc, LibreOffice, ImageMagick, 7-Zip or FFmpeg, you can switch each one on with an Allow button and VEKTOR will use it for better layout and more formats. If one is missing, a Get button opens its official download page. VEKTOR never downloads or installs anything by itself.

You can also right-click a book in the Library and choose Convert, and Desk now reads documents you attach: PDF, Word, OpenDocument, RTF, slides, sheets and plain text. Library search reaches inside PDFs and Word files too, which used to be a blind spot.

What we believe about your data

We reviewed every file in the package and wrote down exactly what leaves VEKTOR. The result is a plain-English page, PRIVACY.md, listing what stays local, what goes to a provider only when you use one, and the few small requests VEKTOR makes by itself.

The app page itself now makes no outside requests. The typefaces, the code editor and the flow chart panel ship inside the install instead of loading from third-party servers.

We want to be a leader in local privacy tech, so we are upfront; the only truly air-gapped setup is a local Ollama model with no tool calls for web search or connectors. That is a real trade-off, and it is your call. We give you the choice and we show you what each option does, rather than deciding for you.

The same four ideas guide every build, and we cross-check each release against them:

Local first, sovereign data. We do not store your data, we do not sell it, and there are no ads in any of our products. We will never partner with a company that sells user data. Telemetry is kept to the minimum.

Faraday security. Faraday is our layer against rug pulls, taint, prompt injection and unsafe skill files. The next section shows what it does in practice.

Customer privacy. Nothing about you is kept on our servers. We do not watch how you use the app and we do not collect usage statistics, apart from the software update check.

Novel technology. Tech moves quickly, so we ship the cutting edge once it has been tested and verified. We focus on business tools that are quick to learn: a simple interface, clear icons, customisation, and the complexity kept behind the panel.

Frontier models now take on jobs that used to be edited and coded by hand, and we also test against small local models to see how far their tool use can go. Some pass and some fail. We expect that to improve every month as smaller models get better at tool calling.

Faraday now asks before it acts

The biggest change in security is that risky actions wait for you. When Faraday holds an action in Desk, such as sending an email, writing a file or running code, you get an Approve once or Deny card. Only you can approve it. The AI cannot approve its own action.

Faraday also got better at noticing trouble. It checks documents you add, email, connector results and attached pictures before an AI uses them. It flags polite requests hidden inside emails and pages, holds an action that tries to send something to an address it only saw in untrusted text, and holds any send or post you never asked for when you only asked it to read or summarise.

Outside text now reaches the AI marked as data, which made a small local model far less likely to follow instructions hidden in that text in our tests.

Three Collab tasks can also run at once now, each in its own terminal lane, and the whole interface loads far lighter than before.

Technical info

For the people who read the source first, here is how the main pieces work and how we checked them.

Convert is built as reader, model, writer. Each format is read into a small common document model, and writers produce the target. That is how one set of readers feeds DOCX, ODT, RTF, EPUB, PDF and more without a pile of pairwise converters.

Everything runs on Node built-ins. The PDF writer uses standard fonts and is Latin-only. Scanned PDFs have no text and would need OCR, which is not included.

We tested against other programs, not only ourselves. The WOFF2 reader decodes the compact glyph format that almost every web font uses. We compared its output with Chrome’s own decoder on six fonts and got zero differing pixels, and a font library opened every glyph without errors.

PDF split and merge output was read back by two independent PDF libraries. Photoshop files matched ImageMagick to within one level per channel. The disc image reader matched 7-Zip’s listing. Our FLAC writer is checked frame by frame, with valid checksums on every frame, and Chrome decodes it back to identical samples. In total there are over 270 automated checks for the Convert, Library and Desk-document work.

Installed programs run in a sandbox of our own making. A program runs only if it was detected and you allowed it, and that is checked again on every run. It gets a private temporary folder that is deleted afterwards, arguments are passed as a list with no shell, a timeout kills the whole process tree, and jobs for the same program queue one at a time.

Pandoc runs with its --sandbox flag, and a test confirmed it will not read a picture path that points at another file on your computer. ImageMagick is told the input format explicitly, so a script file renamed to .png is refused. LibreOffice gets a fresh profile with macros and link updates switched off. Nothing is probed until you allow it.

Untrusted text is scanned passage by passage. Attached documents and Library files go through the same ingest guard. One bad paragraph is replaced with a notice and the rest of the document still comes through, including when an instruction is wrapped across two lines. Invisible tag characters are stripped. In a check on 25 real PDFs from our own research folder (126 MB), indexing took 5.2 seconds.

Known limits. Poppler, which does better PDF text, has only been tested through its command lines, because it was not installed on our test machine. DXF drawings lose hatch fills and 3D, and ZIP-with-prediction Photoshop files are refused. Engine jobs have timeouts but no cancel button yet.

Every update in v1.9.8
Library and reader
New Library: add folders under Config and browse them as a cover grid
Covers drawn on the spot, real covers for PDFs and EPUBs, all saved as files in your VEKTOR folder

Search, sort, format filters, 1 to 5 star ratings, and copies of a book grouped on one card

Right-click: more like this, one-line summary, edit details, move to a restorable trash folder, Convert

Reader for text, Markdown, EPUB, MOBI, AZW3 and PDF with four layouts, your own font, spacing, margins and paper, sepia or night tone
Highlights, in-page search, chapter jumps and exact reading place
Listen with pause, skip and a sleep timer; Explain in three depths, with a note on whether it ran locally

Hover card with title, author, year and summary; your edits are kept
IN BOOKS searches inside indexed books; NOTES gathers every highlight
Library search now reads PDF, Word (.docx and .doc), OpenDocument text and RTF

Held-back passages list in Config so you can allow or hold each one
Convert

New Convert tab with more than 80 conversions and a plain note on what each one drops

Documents, data, e-books, fonts (including WOFF2) and archives (zip, tar, gz, bzip2, disc images)

Pictures in your browser: BMP, GIF, ICO, PDF, keep-under-N-KB, batch to ZIP, SVG and camera RAW preview

Audio in your browser: WAV, FLAC, AIFF, AU, CAF with trim, mono and sample rate

Video in your browser: frame, animated GIF, contact sheet, audio, trimmed WebM

PDF split, keep pages and join; old Word text; Photoshop to PNG; DXF to SVG

Optional Pandoc, LibreOffice, ImageMagick, 7-Zip, FFmpeg and Poppler, each behind its own Allow switch, with Get links for any that are missing
Desk reads attached documents locally, with oversized or suspicious passages handled safely

Faraday security

Approve once or Deny cards for risky actions in Desk; the AI cannot approve its own action

Actions you did not ask for, and actions aimed at addresses from untrusted text, wait for your OK

Emails, pages and library passages reach the AI marked as data
Out-of-place requests flagged, with a wider set of known jailbreak phrasings

A second, quieter sentence-level check with a small local model, flag-only by default

Content from documents, email, connectors and attached pictures is checked before an AI uses it

Approvals show the real change, and credentials stay out of what goes to providers

Desk, Collab and Agent

Three lanes: up to three Collab or Agent tasks at once, each with its own terminal pane and stop button

Desk chooses the mode for plain messages and shows why, with an option to ask in chat instead

run_verified: syntax check, run and report with real machine facts, plus detach for GUIs and games

Undo a whole Collab run, or start a fix run from the real error
Sandbox ribbon tools, paste-and-run code, wider lint coverage and improved terminals

Vek is now a quiet business assistant, with personality mode opt-in
Privacy, install and speed

PRIVACY.md: a plain-English page on what stays local and what does not
The app page makes no outside requests; fonts, editor and flow chart ship inside the install

Lighter install with fewer packages, and a GUI page compressed from about 2.6 MB to about 0.5 MB

CLI brought in line with the GUI for lint, run and model lists

Fixes

Collab planner replies that were not valid JSON no longer abort the run
Internal MODEL: markers no longer appear at the end of CASCADE and CRUCIBLE replies

Lost characters in the CLI and a missed mojibake case are fixed
Collab live events no longer show one lane’s activity on another lane’s card

Upgrading

Drop-in from any earlier version. Your memory database is untouched, and v1.9.8 includes everything from v1.9.7.

npm install -g ./vektor-slipstream-1.9.8.tgz
The full changelog is at vektormemory.com/docs/changelog. The forum is the fastest way to reach us with questions or feedback.

VEKTOR Memory builds local-first persistent memory infrastructure for AI agents. Documentation and downloads are at vektormemory.com.

VEKTOR v1.9.7 is live: A tiny mascot that thinks, Ollama cloud & Kokoro STT included

Vektor Memory — Mon, 28 Sep 2026 05:21:07 +0000

Apologies for the mind dump of updates below. We’ve been so busy building that I haven’t been able to keep up with the changes. It seems every day a new LLM or a new feature is released, so it’s hard to keep track.

We’ve put a real focus on improving our UX and fine-tuning the LLMs’ outputs. This is a core challenge, as every LLM gives different answers, and getting fine-tuning, guardrails, and consistent prompt accuracy right is mind-bogglingly hard. It’s very much like a finger in a dam: fix one thing and another issue arises. Providing quality outputs across 600+ LLMs is not easy to perfect in a harness.

We left personalization until last, with SOUL.md integration for a small bot that learns about the user and offers quips and advice. It’s still expanding, learning from the memories in the system itself, and becoming more aware every day, but the technology has come far enough that it’s relatively secure and not too annoying. Don’t like it; turn it off in settings.

We’re in two minds about whether it’s worth adding payments and Muse, OpenClaw or Hermes-level functions and still weighing whether that’s worth the hassle and the issues it causes, as rogue bots with payment portals and your credit card are dangerous without hitl; instead, we are staying more business-tools focused for now.

Depends on user feedback as to what people want and utilize, will review and go slow till all the unknowns are safe and solved. Being local first, private and secure is always our main focus.

This is a two-in-one update. v1.9.6 made the multi-agent side of VEKTOR visible and hands-on: you can watch Collab agents write files and run commands live, and they now work with real code in the same terminals you see. It also added a tenth free model provider, offline text-to-speech and a Business Coach.

v1.9.7 is about your local model functions. It can now see and use the majority of VEKTOR panels. Before this release, a small Ollama model would tell you it had “no access to a library system” while sitting on 632 indexed files. That’s now fixed, and as more models are entered, we will revisit often as LLM’s update.

There’s also a new personality in the sidebar. Vek, the little monitor robot, now talks through your local model, knows your name and your weather, and every so often shares a thought of its own. It never recites facts at you. Our motto for it is the one we use for the whole product: thinking, not retrieving.

Vek may get more features depending on feedback.

None of your data is stored by us. No ads, no telemetry, no embedding costs, no data caps, and nothing of yours is used for training. The mascot’s voice and thoughts run on your local model only, so they cost nothing and never leave your machine.

VEKTOR v1.9.6: What’s New

Collab you can watch
See Collab agents work live. File writes and terminal commands now show up in the interface the moment they happen, and the final answer streams in word by word. Before, you saw nothing until the whole task finished.

Collab agents work with real code. Worker agents can read, write and run code, and they drive the same terminal panes you see on screen, not a hidden shell. Risky actions, like deleting a file or running a destructive command, wait for your approval.

Quality checks are a vote, not one opinion. When a result is uncertain, up to 3 models check it instead of trusting a single verdict. Clear-cut results still resolve quickly and cheaply.

Collab recovers instead of failing. A run could fail completely over one bad provider key, even after every agent had finished and passed its checks. The final merge now tries each configured provider in turn. An old, discarded attempt from a retried agent also no longer leaks into the final answer.

New providers, voices and tools
Ollama Cloud, a tenth free provider. Free hosted models (gpt-oss 120b and 20b, gemma4, nemotron-3) are now a provider of their own, separate from local Ollama. No GPU needed.

Offline text-to-speech with Kokoro. A local voice that needs no API key and runs fully offline after its first download. Hume and Cartesia joined the cloud voice options.

Every model gets real tools. Some models, including Ollama Cloud and local Ollama, claimed they had no tools even when tools were set up. They now get the same tools as every other model, and SSH tools work with every provider.

A real weather widget. The Health tab shows current conditions, a 5-day forecast and humidity, and it can detect your location automatically. Asking about the weather in chat now actually looks it up.

Business Coach
Three coaching modes inside Desk. COACH turns a brain-dump into a reflection and up to 3 priorities written straight to your Tasks board.

CRUCIBLE is a guided decision dialogue (5-Whys, pre-mortem, weighted trade-offs) that saves decisions with 30 and 90 day review reminders.

CADENCE is a weekly habit and decision check-in.

Small fixes
Saving an API key in Config now shows it as saved straight away, with no page refresh.

The copy button on memory cards has a visible border again.

Desk has a persistent file-tree panel, matching the other side panels.

VEKTOR v1.9.7: What’s New
Local models that use their tools
Local models now reach your library, memory, config and Faraday. Desk used to send small local models all 81 tools at once, with the library tool at position 65. The model gave up and answered from guesswork. Now a local model gets a focused shortlist: about 12 core VEKTOR tools plus whatever matches the words in your question, capped at 20. Hosted models still get the full set.

Tool choice is steadier on local models. At Desk’s default temperature of 0.7, the model used the right tool in 2 of 3 identical runs. The first tool-choosing round now runs at 0.2, which took it to 3 of 3.

Desk knows its own app. Ask it to list the 10 Desk modes, the 15 nav panels or where your skills live, and it answers correctly. The map is generated from the real interface file, so it can’t drift from what you see on screen. It stopped inventing documents like a “skill_files.md” that never existed.

Browse your book library. “List a few books in my library” used to run a keyword search for the words “list” and “books” and come back empty. Now a browse request returns a sample of real titles plus the total count.

Desk as a code workspace
Desk can build straight into the Sandbox. Ask for a landing page, a component or an SVG, and the model opens it in the Sandbox editor next to the chat with a live preview. Under the hood a new sandbox_open tool streams a sandbox_load event that the interface picks up.

Files the model writes open in the Files panel. A successful file write now sends a file_written event, which opens the file live in the Files panel. It’s the same path Collab workers already used.

More code tools in Desk. Desk gets the Agent tab’s code search, grep, semantic code search, blast-radius and repo-architecture tools. They run through the same approval checks the Agent tab uses.

The mascot
It has a voice. After each answer, Vek reacts with a short line from your local model. Click it and you get one useful nudge grounded in your real memories. A hard rule stops it from mentioning anything that isn’t in the memories it was given.

It has thoughts of its own. Every 7 to 14 minutes it may share one small original thought, sparked by the time of day, your weather or what you’re working on. It only speaks while you’ve been active in the last 20 minutes and the app is on screen, never mid-answer.

Robot jokes and sci-fi nods. About a third of its thoughts are a self-aware robot joke or a nod to a film like Blade Runner or Star Wars. One real line from testing: “Fixed the panel, but did I just see the replicants among the files?” If it copies one of its example lines word for word, it regenerates once.

It has a SOUL.md. Vek’s personality lives in a plain markdown file at ~/.vektor/mascot/SOUL.md, which you can edit in Config. The six personalities (cool, funny, caring, helpful, intelligent, smartass) sit on top as a mood, so the tone shifts but the soul stays the same.

You can rename it. The default name is Vek. Change it in Config and the speech bubble label and the mascot’s own sense of its name both update.

It knows your name and your weather. Your name comes from your Profile, falling back to your system username. The weather comes from the location you set in the Health tab, cached for 20 minutes so a reaction never waits on the weather service.

A 2.5D look in your theme colours. The mascot is now a flat-shaded monitor robot built from three surface tones, so it follows all eight themes. It has a bigger screen face and body language that matches its expression: it hops, tilts, shrugs and wiggles.

Faces stay on the screen. Its little screen shows only short keyboard faces like ^^ and ><. Anything wordy goes to the speech bubble, and longer faces shrink to fit.

Don’t like it; turn it off in settings. With more customization to come in the future.

Voice
Listen starts almost instantly and reads the whole reply, via a local LLM. Replies used to go to text-to-speech as one request, capped at 4,000 characters. Now they’re spoken sentence by sentence: the first plays as soon as it’s ready while the next ones are prepared in the background. On a normal Windows machine with Kokoro running locally, a 357-character chunk took 1.4 seconds to prepare, far quicker than it takes to play.

Cleaner speech. Code blocks, links and markdown symbols are skipped, so it no longer reads out asterisks or source code. Hovering the Listen button warms up the local voice model, since loading the model was most of the delay.

4 voices added; if more voices are created, we will add them in later on.

Workflows
Tasks, Skills and Prompts in one place. Saved Collab tasks can be grouped into your own categories with filter tabs. Agent Skills are managed on the same page, and a new Prompt Gallery sits alongside them.

A saved workflow uses its preferred model. The preferred model on a workflow used to be a label only. Now it’s the model that actually runs.

Type / for a command palette. Typing / in the chat box opens a searchable list of every mode, including LIGHTNING, COLLAB and COUNCIL.

Updated a lot of workflows and templates

Interface polish
Softer, deeper, more consistent. Borders dropped from 26 to 40 percent opacity down to about 9 to 15 percent, and buttons, cards and the composer got a subtle top highlight and shadow. There’s one filled accent button per view, with squared-off corners and a keyboard focus ring everywhere.

Cards that line up. Every workflow card now uses the same fixed layout, so buttons, tags and descriptions sit at the same height across the whole grid.

Readable text sizes. 512 font sizes below 10 pixels, some as small as 7, were raised so nothing sits below 9. Buttons now inherit the text size around them instead of the browser’s default.

Fixes
The code-preview sandbox no longer goes blank white after you close and reopen it. Closing it now unloads the page, so every reopen starts fresh.

Notes saved from AI output no longer show a raw code dump as their title.

The Files panel tree now fills the panel when no file is open. It used to stop at 240 pixels.

The Sandbox, Terminal, Files and Flowire buttons no longer turn dark after you close their panel.

BUILD mode no longer produces pages with literal \n text when a smaller model mis-escapes its output.

Flowire-assisted edits are now checked for real syntax and type errors right after they’re applied.

A Collab run’s answer no longer fails to render silently.

Upgrading
Drop-in from any prior version, same as always, and v1.9.7 includes everything from v1.9.6. Your memory database stays untouched.

npm install -g ./vektor-slipstream-1.9.7.tgz
Full changelog with everything not covered here is at vektormemory.com/docs/changelog. Questions or feedback, the forum’s the fastest way to reach us.

VEKTOR Memory builds local-first persistent memory infrastructure for AI agents. Documentation and downloads at vektormemory.com.

LLM

Agentic Ai

Agentic Rag

Chatbots

VEKTOR v1.9.5 is live: your choice of over 600 LLMs across 9 providers

Vektor Memory — Wed, 23 Sep 2026 04:54:33 +0000

VEKTOR v1.9.5 is here, and it moves the platform from a fast-moving memory add-on into an agentic orchestration tool for solo devs and medium-sized teams who value local install and privacy with a choice of over 600 LLMs across 9 providers with all the latest models updated.

Some models are also free to use with API keys or use Ollama locally for complete privacy.

None of your data is stored by us, no ads, no telemetry, and no on-selling or using in training.

This release touches security, coordination, and daily usability in equal measure. Faraday now scans a skill package before it ever runs, catching dangerous code, known vulnerabilities, and hidden binaries that used to slip past every text-based check.

The multi-agent Collab system no longer trusts a model’s opinion of its own work; it verifies against real compiler output, and when a task keeps failing, it can now rewrite its own approach or restructure the plan entirely instead of retrying blindly.

VEKTOR v1.9.5 — What’s New

Every fix below was found, reproduced, and verified against live data before it shipped.

Security
Built a full pre-install skill scanner. It checks a skill package before you ever run it, not after.

Real Python AST analysis catches dangerous calls: exec, eval, subprocess with shell=True, pickle.loads.

Live CVE lookups against OSV.dev, so known-vulnerable dependencies get flagged with real advisory IDs.

Malware pattern detection for webshells, cryptominers, and reverse shell one-liners.

Supply chain checks: unpinned dependencies, malicious postinstall hooks, typosquat package names.

New binary and executable detection. A compiled .exe, .dll, or .so file used to slip through every text-based scanner untouched. Now it gets flagged, even if someone renames the file to hide it, because the check reads the actual file header, not just the extension.

SARIF and JSON output modes, plus proper exit codes, so this plugs into any CI pipeline.

MITRE ATT&CK technique tags now show up directly in the live event feed, not just buried in a status JSON blob.

The event graph endpoint had a hard cap of 300 events. Now it is adjustable, so a busy history doesn’t get silently truncated.

Model Selection
Improved where the “Newest” and “Oldest” sort buttons did nothing for OpenAI, Groq, Cerebras, and most other providers. Only OpenRouter had real release dates before this. Every one of these providers actually returns a real creation date in their own API, it just wasn’t being read. Now it is.

Verified live against a real account: 116 OpenAI models, all sorting correctly by true release date.

Live, right now (via the app’s real-time model discovery), across the 9 providers with an Active Model tab: 642 models — driven almost entirely by OpenRouter (454) and OpenAI (116) refreshing live from their real APIs; Claude, Gemini, Groq, Mistral, xAI, Cerebras fill out the rest.

Static catalog baseline (every provider VEKTOR knows how to talk to, including ones without a live discovery feed — DeepSeek, Together, Cohere, MiniMax, NVIDIA, Perplexity, LM Studio, LiteLLM): 101 models, across 17 providers total.

Voice and Speech
Added language and emotion/style controls to the voice preferences panel. Previously the server could accept these settings, but nothing in the interface sent them.

MiniMax and ElevenLabs both get real per-provider tuning now: language boost, emotion tags, and a style intensity slider.

Speech-to-text shipped with three real tiers: free browser recognition, cloud (Groq or OpenAI Whisper), and fully offline local recognition, so nobody is locked into one approach.

Multi-Agent Collaboration (Collab)
Replaced pure LLM opinion with real ground truth verification. A failed build or a broken downstream file now gets caught by an actual compiler check, not just another model’s guess.

Added Conductor re-planning. If a task fails repeatedly with the same approach, the system can now rewrite the approach itself instead of just retrying the same thing forever.

Full topology re-planning for the hardest failures: new nodes get added to route around a dead end in the task graph.

Real coordination primitives between agents working the same task: signal, listen, and challenge, so one agent can flag a concern about another’s output before final judgment.

Upgraded from a text convention to genuine tool calling across every configured provider, Claude included.

Full trajectory recording and replay, so a past run can be re-run and compared side by side.

Desk Interface.
Fixed the profile page jumping the scroll position every time new information was added.

Fully built out the Preferences panel: answer font, keyboard shortcut style, response language, response length, autosuggest, notifications, and the full voice settings above.

Model picker now has real search and sort, with capability badges for vision, voice, video, tool use, and mixture of experts.

Upgrading
Drop-in from any prior version, same as always. Your memory database stays untouched.

npm install -g ./vektor-slipstream-1.9.5.tgz

Full changelog with everything not covered here is at vektormemory.com/docs/changelog. Questions or feedback, the forum’s the fastest way to reach us.

VEKTOR Memory builds local-first persistent memory infrastructure for AI agents. Documentation and downloads at vektormemory.com.

LLM

Llm Agent

Memory Management

Ollama

Agentic Ai