DEV Community: Santiago Gonzalez Toral

Skunkworks Sloth: a set of agents automating my bookmarks review, keeping me focused and growing my portfolio and skills

Santiago Gonzalez Toral — Mon, 26 Oct 2026 04:30:01 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

In the last 8 years, I've been collecting a ton of bookmarks on different apps on interesting projects, tools and topics. At some point, the numbers of saved links have increased a lot that it turned impossible to review them by myself. Hacktoberfest 2026 Week 3 challenge theme inspired me to prototype a small swarm of agents that help me automate the curation of this huge amount of bookmarks sitting next to me, and build at the same time a personal skunkworks division that generates knowledge, help prototyping ideas and build a portfolio of skills. I took a sample of bookmarks from my Google Chrome (which currently holds ~8,130 bookmarks). One folder, Reading List: since March 2025 I have saved 1,017 links to it, every one of them "to learn by building". I have built nothing from it (so lazy).

That folder is the prototype's test bed: 1,017 of 8,130 bookmarks, about 12.5% of the pile. The other 7,113 are next.

Skunkworks Sloth is three small agents that do the part I kept putting off:

The Curator reads the whole list, drops the dead links and sorts the rest into build, read and Valhalla (the removal bucket).
The Coordinator turns the build pile into two-hour project cards, asks me once a week what I care about, offers a few cards every morning, and hands me one link a day from the reading pile.
The Worker builds the card I pick inside a hard two-hour box, writes down what it learned, and waits for my approval before anything reaches GitHub.

Each agent has its own Discord channel. My part of running it is three replies a day: tap a card, say go, say approve. The rest of my time goes into what the sloth can't do for me: reading the daily link and reviewing the code before I approve it.

Who gets to be lazier: anyone with a bookmark folder full of good intentions. I'm the first user.

Demo

All three agents are deployed with their independent profile in an Hermes instance, and communicate their work on specific channels within my personal Discord server.

The curator agent is in charge of processing batches of bookmark sources. Once it finished, it provides a summary reports, and highlights any links that needs a review from me

Above, the curator reports an odd link that I can check and either keep it or send it to valhalla.

A specific channel reports all links that were sent to valhalla for a final human triage. Here, I can confirm which ones to pick and the ones to dispose based on their status

The coordinator agent then turns the curator’s curated links into a skunkworks backlog. It uses Github projects to build a Kanban board that I can checkout from time to time

Moreover, the coordinator also builds a second brain using the Open Knowledge Format (OKF) format for keeping a knowledge base that can be used by me or other agents.

Every week, the coordinator will ask me for my preference around the topic of focus. He proposed a few based the knowledge and skunkworks kanban status.

Then, every day I get to pick a topic-specific project/idea to explore and communicate my decision to the coordinator. It then, forwards it to a worker agent that will assist me in the initial prototyping.

Additionally, the coordinator suggest me a daily reading post from those bookmarked links that are not touching any code.

Once the worker agent receives the ticket information from the coordinator, it awaits for my command to start a building session. I can tell it which model to use, either an open weights served by OpenRouter or a Anthropic model in case of more complex tasks.

Finally, the worker reports its works and verify that testing passes. I review the code and docs, make adjustments, and approve the prototype to be published as a repository on Github. The skunkworks kanban board and second brain update through the process.

Code

santteegt / HacktoberFest2026

AI-assisted prototypes I built during HacktoberFest 2026

Hacktoberfest 2026

Monorepo for my Hacktoberfest 2026 challenge entries and the knowledge base behind them.

Hacktoberfest 2026 is run by MLH and DEV, presented by DigitalOcean. The theme is "AI belongs to everyone": build with open-weight models and open-source AI. Pull requests no longer count. Rewards come from virtual stickers (see STICKERS.md), and the DEV Challenges carry cash prizes.

Challenges

Each challenge lives in its own folder under challenges/. I'm starting with the weekend challenge and adding the others as they open.

#	Challenge	Window (UTC)	Status
01	Weekend: Build for a Friend: RC Pit Companion	Oct 2 02:00 → Oct 5 06:59	✅ Published
02	Week 1: Touch Grass: Idea Leash	Oct 5 18:00 → Oct 12 06:59	✅ Published 2026-10-11 23:30 local
03	Week 2: Side Hustle: PartsPaddock	Oct 12 19:00 → Oct 19 06:59	✅ Published 2026-10-18 23:21 local
04

…

View on GitHub

The project lives in challenges/04-week3-slow-and-steady, MIT licensed. The three agents ship as Hermes profile distributions, so scripts/install.sh sets them up on another Hermes host in one step (or next to agents you already run).

How I Built It

The architecture in one picture

The green zone is my laptop, and everything that reads my links lives there:

Chrome's bookmark file is the input. The Curator only ever reads it.
sloth, a small Python CLI, does everything that has to be right: dedupe, rules, scoring, card moves, the time box, cost tracking, GitHub sync. It has four parts: curator, board, builder and core.
Ollama serves Gemma 4 12B locally. sloth calls it for language jobs only: triage verdicts, card wording, the one-line "why read this today".
Stagehand drives a real local Chrome for pages a plain fetch can't read, and asks the same local Gemma what's on the page.
Hermes Agent runs the three agents as separate profiles behind one gateway. Each agent has a skill that maps my Discord replies to sloth commands and passes the output back. The gateway also runs the cron jobs that start each morning.

The blue zone is hosted: the Worker's coding models, Postgres on Tiger Data, and GitHub. The purple zone is Discord, where I talk to the agents.

The rule I kept all week: models do language, code does rules. No agent decides what gets deleted, built or pushed; it runs a command, and the command checks.

A day in the skunkworks

Crons post first: at 07:00 the Curator triages anything new, at 08:00 the Coordinator posts today's reading, and at 09:00 it posts three or four cards weighted by my weekly focus. I tap one and say go in #sk-worker. The Worker builds for up to two hours, posts a summary with the test result and the cost, and waits. approve N pushes a private repo and moves the card to Built.

The Curator pipeline, with real numbers

1,017 bookmarks became 969 unique links (48 exact duplicates).
Enrichment read 440 pages over plain HTTP, 223 through the GitHub API (stars, archived flag, last push, README) and 197 in a real browser. 109 could only be judged by their title: login walls and very long X threads.
Code rules decide what is certain (dead, gone, archived, duplicate, stale): 48 links.
Gemma judged the rest: 921 decisions, each with a JSON schema, a verifier that checks the reason mentions something from the page, and one local retry. About 4.2 seconds of compute per link; the last 753 links took 71 minutes.
Result: 308 to build, 609 to read, 52 for Valhalla (51 proposed by the pipeline, 1 by me).

Valhalla is a proposal, never a deletion. I reply empty or restore 3, 7, and the sloth writes a removal list. It never edits Chrome's bookmark file.

What moves a card

The Coordinator clustered the build pile into 58 cards, each with a learning goal, a hypothesis, a done-means and kill criteria. Every arrow except the box timeout is one of my replies. A run that finishes waits in Building until I approve it, and a box that ends without meeting its done-means is Killed, with honest notes.

Agents, skills and schedules

Each agent is a Hermes profile with one job: its own model, skills, crons and channel. Deny rules block sudo, Docker and keychain commands, risky commands need my approval, and computer use is off. That's a command policy, not a sandbox: the agents still run as my user.

The Worker has two providers. By default it codes with Gemma 4 26B A4B, an open-weight model, through OpenRouter. When a card needs more, I say go deep and the same box runs on Anthropic Claude Sonnet 5.5. Every message about those runs says "(closed model)", and the cost is tracked either way. I can also name a model, go with qwen3-coder-next, from a short allowlist in the config. The desk refuses anything else, so a typo can't start an expensive run.

What gets stored

links is the hub. Triage keeps its history (1,098 rows for 969 links), so a page the browser reads later can change its verdict. events mirrors the #sk-logs channel, which is where every number in this post comes from.

How each partner tool fits

Gemma

What: Gemma 4 12B (QAT, Apache 2.0) through Ollama on my M4 Max, plus Gemma 4 26B A4B through OpenRouter as the Worker's default coder.
Why: the reading list is private and long. A local model can read all of it for the cost of electricity, and I can re-run it whenever the prompt improves.
How: one Ollama alias with a 64k context, shared by every caller so the model never reloads. Ollama's JSON-schema mode keeps output valid, and since the model never sees the schema, the prompt spells out every field and allowed value. A verifier and a local retry catch the rest.

Browserbase (Stagehand)

What: Stagehand's Python SDK drives a visible local Chrome. Browserbase's cloud isn't used.
Why: 137 links were on hosts a plain HTTP fetch can't read, X above all. Without a browser, those links get judged by title only.
How: extract() with a custom LLM callback pointed at the local Gemma, one browser session per batch, and a drain step after every timeout (see below). It read 197 pages, including 37 of 40 X posts in its last pass.

Tiger Data

What: Postgres with TimescaleDB on Tiger Cloud's free tier: 9 tables.
Why: three agents, crons and the GitHub sync all need one place that holds the truth. Plain SQL made every number in this post a query.
How: an idempotent schema applied with sloth apply-schema, triage history kept as rows, and a partial unique index for "one main reading per day".

What broke, and what I learned

Hermes refuses models under 64k context. My first Discord message to the Coordinator failed because I had served Gemma at 32k to save memory.
A browser timeout is not a cancellation. When Stagehand gave up on a long X thread after 45 seconds, Ollama kept working on it in its single slot, and every later request queued and timed out. Waiting for the abandoned request to drain fixed it.
Small models skip soft instructions. The Curator once answered "what's unsure?" from general knowledge instead of running its tool. A hard "load your skill first, run the matching command" rule in its persona fixed it on the next try.
Count cached tokens once. An early test run looked like $9.64. Hermes' input count already includes cached tokens; the real figure was about $1.02.
Crons don't have your keychain. A morning job failed because the database password sat in the macOS keychain, which a locked session can't open.

Why Does Open Innovation Matter?

My reading list is a diary of what I was curious about for a year and a half. With Gemma running locally, none of the 969 pages left my Mac to be judged. Triage, the browser step and the models behind the Curator and the Coordinator all run on the laptop; only their chat messages travel through Discord.

Local also means re-running is free. I re-ran triage on parts of the list several times this week as the prompt improved. With a hosted model each pass would be another bill; with Ollama it's electricity and a few minutes.

Open weights keep the Worker honest. Its default coder is Gemma 4 26B: hosted on OpenRouter and paid per run, but an open-weight model I could move home. When I choose go deep, I trade that for a stronger closed model, and the run says so.

The harness is open too. Hermes let me harden each profile and package all three agents as distributions, so the setup is scripts/install.sh on any other Hermes host instead of a page of instructions.

My Agent Session

The coordinator session, curated and scrubbed: from the first idea to three parallel lanes, the failures above, and the measured run.

Skunkworks Sloth: coordinating three parallel agent lanes to ship a Hermes + Gemma prototype in a weekend

You

Help me expand this idea: I've bookmarked a ton of 'learn by building' resources in Chrome, X, Facebook and Gmail. I want agents to review them, drop outdated ones, categorize them, build a board of ideas, pick one per day to prototype with an open-weights model, and feed a second brain. Explore how to extract the bookmarks and how to run a software skunkworks.

Agent

▸ bash count Chrome bookmarks by year and folder (no URLs printed)

Input

$ count Chrome bookmarks by year and folder (no URLs printed)

Output

bookmarks: 7845 · 2017: 2830 · 2018: 1528 · 2025: 1666 · 2026: 630 · top folders: Data Science 333, Machine Learning 141, ...

Expanded it into 'Skunkworks Sloth': collect → enrich → local Gemma triage → board → daily pick → 2-hour prototype box → second brain. Extraction per source: Chrome is a local JSON file (verified); X has a bookmarks API (tier unverified) with Stagehand as fallback; Facebook has no API for saved items (data export or Stagehand). Skunkworks rules: one boxed prototype at a time, a card before code, kill fast and keep notes.

You

Focus only on the 'Reading List' folder. Use Browserbase for links that need a browser. Three agents: Curator (extract, enrich, triage, a 'valhalla' bucket), Coordinator (weekly focus, daily pick, board), Worker (builds the pick). Each agent on its own Discord channel.

Agent

Wrote SPEC.md: three Hermes profiles, five channels, Postgres on Tiger Data, milestones M0-M5 with kill rules. Reading List = 1,017 links (427 from 2025, 590 from 2026).

You

You act as a coordinator. Adjust the spec to be executed by 2-3 workers in parallel.

Agent

Split into a coordinator + three lanes (Curator, Board, Builder) with disjoint owned paths, a one-hour contracts step (shared types, schema, notify, LLM helper, synthetic fixtures), single-writer rules for shared files, and merge checkpoints CP1-CP3. Also caught that the plan mislabelled Saturday as Friday and logged the correction.

Agent

▸ bash uv run sloth doctor

Input

$ uv run sloth doctor

Output

database: ok · ollama: ok (sloth-gemma4-12b) · discord: 5 channels set · 3 tokens set

▸ bash hermes -p <profile> send --to discord:<channel> 'wiring test' (x5), then read...

Input

$ hermes -p <profile> send --to discord:<channel> 'wiring test' (x5), then read message authors via the Discord API

Output

each message authored by the matching bot: per-profile send uses the profile's own token

C0 committed; started three lane sessions in their own worktrees.

Agent

CP1, all lanes green early: 1,017 → 969 unique links; triage 14.3 links/min uncontended on Gemma 4 12B; Stagehand with the local Gemma read 5/5 X posts; Worker model picked (Gemma 4 26B A4B, two-minute fixture run). Merged with no conflicts; ruled that only the Board lane moves card columns.

You

I've got an error message when texting the coordinator with 'focus'.

Agent

▸ bash read the Coordinator profile's errors.log

Input

$ read the Coordinator profile's errors.log

Output

ValueError: Model sloth-gemma4-12b has a context window of 32,768 tokens, which is below the minimum 64,000 required by Hermes Agent.

Recreated the Ollama alias at num_ctx 65536 and moved the shared config to the same size so callers never reload the model; asked the Curator lane to restart its jobs that had cached 32k.

Agent

Curator lane finding: the 'Ollama wedge' was the browser pass itself. Stagehand's 45 s timeout abandons a long X thread, but Ollama keeps working on it in its single slot, so every later request queues and times out. Fix: wait for the abandoned request to drain before the next page. The next pass read 37 of 40 X links.

You

I just asked 'what's unsure' to the curator agent. Check its response.

Agent

It answered from general knowledge with zero tool calls. The skill description didn't name that request and nothing forced the skill first. Added a hard 'load the skill, run the matching command, never answer from general knowledge' rule to its persona; after /new it loaded the skill, ran sloth curator unsure and answered in 41 s.

Agent

▸ bash inspect the first Worker run's token usage

Input

$ inspect the first Worker run's token usage

Output

input 4,311,522 · cache_read 4,203,979 · cache_write 107,423 · output 33,218

The runner reported $9.64 because it added cached tokens on top of an input count that already included them. Real cost about $1.02, confirmed against the agent harness's own estimate; fixed with a test pinned to these numbers.

Agent

Found a background gateway that also served an old WhatsApp profile for an hour (it ignored messages from non-allowlisted users and sent nothing). Parked that profile, then hardened every profile: deny rules for sudo/docker/keychain, manual approvals, computer use off. Logged it as a lesson: one multiplexed gateway serves every unparked profile.

You

Let's design an architecture and workflow diagrams of the prototype. Very visual, every component, skills and partner integrations.

Agent

Published a blueprint with seven figures (system map, daily workflow swimlane, Curator pipeline with real counts, card states, agents/skills/crons, data model, partner map), then exported each as a PNG with headless Chrome for the post.

Agent

Sunday 18:24 status check: deadline about 7.5 h away, no measured run yet, and the morning cron had failed because a locked keychain hid the database password. Re-planned in parallel: the author picks and says go; one lane packages the agents as Hermes profile distributions with install/export scripts; I draft the post from the challenge memory.

Agent

▸ bash make test (runner) vs make test with the login-shell PATH

Input

$ make test (runner) vs make test with the login-shell PATH

Output

runner: aztec: No such file or directory · login-shell PATH: 2 tests passed, demo exit 0

The measured open-weights run built a Noir contract in 4.9 min for $0.11, but the runner marked it killed: its check ran without the Aztec toolchain on PATH. Fixed verification to use the PATH the agent's terminal gets, added sloth work --verify, and noted honestly that the tests run in Aztec's TXE, not a live Sandbox node as the model's notes claimed.

You

Build the model picker feature. And the second brain shows card ids in Obsidian's graph instead of topics → subtopics and how they connect. It should tag content properly.

Agent

Model picker: an allowlist in config; go with <model> in Discord; anything else refused. Brain: notes named by title inside the vault, subtopics named by local Gemma and assigned by a local embedding model, 465 wikilinks with none dangling, nested tags type/vertical/topic/status, graph colours by type.

The Lazy Payoff

	Before	After
Bookmarks waiting	8,130 in Chrome, 1,017 of them in the Reading List	the Reading List fully triaged: 969 links, a reason each; 7,113 bookmarks still to go
Prototypes built from the list	0	1 on open weights, approved and pushed (plus 1 labelled closed-model test)
From "what should I build?" to a tested repo	never happened	about 20 minutes: pick, `go`, a 4.9-minute build, `approve`
Cost of that build	n/a	$0.11 on Gemma 4 26B A4B (OpenRouter)
My time per day	(not doing it)	a few replies to run the loop; 30-120 minutes in total including reading and reviewing the generated code before `approve`
Reading	a pile of 609	one link a day, picked for this week's focus

The first open-weights build was the card I picked on Sunday evening, Private Aztec Sandbox Smart Contract. The Worker wrote a Noir counter contract with private state, two tests and a demo in 4.9 minutes. My first look said it failed: the runner checked make test without the Aztec toolchain on its PATH. With the same PATH the Worker had, both tests passed. They run in Aztec's test environment (TXE), not against a live Sandbox node, which is less than the Worker's own notes claimed. That gap is why the runner checks every build itself instead of trusting the agent's report.

Future Work

My saved links don't only live in Chrome. The next sources are the bookmark piles on X and Facebook, flowing into the same links table (it already has a source column), so everything downstream stays the same.

X bookmarks. The official route is the X API v2 bookmarks endpoint (GET /2/users/:id/bookmarks, OAuth 2.0 with the bookmark.read scope, the 800 most recent). The fallback needs nothing new: Stagehand already reads X posts, so it can scroll x.com/i/bookmarks in my own logged-in local Chrome and extract each post.
Facebook saved posts. There is no API for saved items. One path is Facebook's own data export (Accounts Center → Download your information, JSON). The other is Stagehand on facebook.com/saved in the local browser.

Either way, the browser stays on my machine, the model stays local, and the agents don't change.

Prize Categories

Gemma: Gemma 4 12B runs all triage, card wording and daily-reading lines locally; Gemma 4 26B A4B is the Worker's default coding model.
Browserbase: Stagehand reads the 197 pages a plain fetch couldn't, on a local Chrome with the local model.
Tiger Data: Postgres with TimescaleDB holds the links, triage history, cards, runs, readings and the event timeline (9 tables).

PartsPaddock: a WhatsApp parts desk agent for RC clubs - P2P / B2C market connecting hobbyists

Santiago Gonzalez Toral — Mon, 19 Oct 2026 04:21:18 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

I race 1/10 touring cars in my home country. Almost nothing we run is sold here, so parts come mostly from the US, and every club member has a drawer of spares: a spur gear bought twice, the arms left over from a conversion, an old chassis kit. Meanwhile someone else in the club is asking the WhatsApp group for exactly that part, and the message scrolls away. When nobody has it, the member asks whoever brings parts in, and that person prices the import by hand.

PartsPaddock is an agent that lives in the club's WhatsApp group and does both jobs.

Swap and sell inside the club (free, P2P). "Tengo un spur 68T de TT-02 nuevo, $10" becomes a listing. "@bot busco un TAM54500" becomes a request. When a request meets a listing, the bot writes to both members in private.
Import quotes (paid, B2C). When the club has nothing, the bot looks the part up at an online hobby shop and answers with the store price, the weight the store lists, our per-pound fee, and two totals: one if the member orders it and ships it to our forwarding address, one if we order it for them (+12%). Once the member wants to confirm the purchase, if we order it, they need to send a deposit receipt PDF, which the agent forwards to the manager DMs to confirm the deposit; if they order it themselves, they send the store's tracking number. Confirmed import requests then go into the next order batch (e.g. daily batches). The manager gets one summary per batch with links, quantities and what to charge whom.

Who pays: the club member who wants a part brought in. The fee per pound and the 12% for placing the order are the side hustle; the free matching is what keeps the bot in the group. It is small and specific on purpose: one club, one store, one person running imports.

Demo

Code

santteegt / HacktoberFest2026

Hacktoberfest 2026

Monorepo for my Hacktoberfest 2026 challenge entries and the knowledge base behind them.

Challenges

Each challenge lives in its own folder under challenges/. I'm starting with the weekend challenge and adding the others as they open.

#	Challenge	Window (UTC)	Status
01	Weekend: Build for a Friend: RC Pit Companion	Oct 2 02:00 → Oct 5 06:59	✅ Published
02	Week 1: Touch Grass: Idea Leash	Oct 5 18:00 → Oct 12 06:59	✅ Published 2026-10-11 23:30 local
03	Week 2: Side Hustle: PartsPaddock	Oct 12 19:00 → Oct 19 06:59	✅ Published 2026-10-18 23:21 local
04

…

View on GitHub

The project is in challenges/03-week2-side-hustle. MIT licensed.

How I Built It

Three pieces, and a strict line between what the model does and what code does.

The agent: Hermes Agent with Gemma 4. Hermes Agent is an open-source agent harness from Nous Research. It already has a WhatsApp gateway (a Baileys bridge that links a number like WhatsApp Web), skills, cron jobs and per-profile configuration. The bot is a Hermes profile distribution: a persona file, a config, two skills (rc-listings and rc-quote) and a manifest, so the whole agent installs on another Hermes machine with one script. The model is Gemma 4 26B A4B, an open-weight mixture-of-experts model with about 4B active parameters, served by OpenRouter. The bot has a dedicated prepaid SIM, answers in the group only when mentioned, and only allowlisted numbers can talk to it.

The skills: two SKILL.md files, one per business. A Hermes skill is a markdown file with a short description the agent sees in every turn; when a message fits it, the agent opens the skill and follows it. I split the bot into two skills because the free side and the paid side have different rules, and a smaller skill is easier for a mid-size model to follow than one long manual.

rc-listings (P2P, free). What: recording what members have and need, finding matches, answering "who has X?", and closing a listing when a member says "ya lo vendí". Why: this is the only place where the model's language skills matter: it has to turn "tengo un spur 68T de TT-02 nuevo, lo doy en 10 dólares" or "I'm looking for a rear lower arm for the X4" into one small JSON object. How: the skill gives the JSON shape with an example, says "copy the part number only if the member wrote one, never invent it", and maps each command result to one next step: ok → send reply as written; missing_fields → ask the one question in reply, then run it again; ambiguous → ask which listing. Validation, part-number normalization, matching, the private match messages and the "sold" notice to the other member all happen in rcparts listing add / search / listing close.
rc-quote (B2C, paid). What: import quotes from the hobby shop, accepting a quote, the deposit receipt, the self-order tracking number, and the maintainer's "confirmar" / "rechazar". Why: every number here is money, so the model must never compute or round anything, and the steps that move money (paid, confirmed) need a person. How: the skill tells the model to pass the member's words to rcparts quote, quote accept, deposit submit --file <the PDF path Hermes gives it> and tracking submit --text "<their message>", and to send reply verbatim. The quote math, the two totals, reading the amount and reference from the PDF, the carrier from the tracking number, and the rule that only the maintainer's number can confirm a payment are code. The model cannot get around that rule, because the sender comes from the WhatsApp session, not from what the model passes. A quote asked in the group is sent by DM, so prices and the forwarding address never sit in the group chat, and "mis pedidos" lists a member's confirmed purchases with their status and tracking number.

Around the skills, SOUL.md holds the persona and the rules that apply everywhere: answer in the member's language, keep group replies to two lines, never post phone numbers in the group.

The code: a small Python CLI, rcparts. The skills tell the model to run rcparts listing add --json '…' or rcparts quote --query TAM54500 and to send back the reply field as written. Every command prints one JSON object with ok, reply and action (the next step, which a mid-size model follows better than a paragraph in a skill). The CLI validates the listing, normalizes part numbers, saves it, finds matches, computes quotes from the store's own JSON, and sends the private messages with hermes send. The order batch is a Hermes cron job that runs a script with no model turn at all.

The data: Postgres on Tiger Cloud. Clubs, members, listings, matches, quotes, deposits and orders live in a Tiger Cloud service. Matching is an exact part-number match first, then Postgres full-text search over the glossary concepts (with unaccent and a pg_trgm similarity tiebreak). A different brand, chassis or part number on both sides vetoes a text match, and so do opposite positions such as front and rear. The agent can also read the database through Tiger's MCP server for questions the CLI does not cover, with only the read-only db_query tool enabled.

What the model does: turn "tengo un spur 68T de TT-02 nuevo, lo doy en 10 dólares" into {"kind":"HAVE","chassis":"TT-02","description":"spur 68T","condition":"new","price_usd":10}, choose the skill, and relay the reply. What it never does: decide who is writing, compute a price, or message someone on its own.

Lessons learned

Four things the real tests changed:

The model invented phone numbers. My first skill asked the model to pass the sender's phone. In the first real DM it searched the members table by WhatsApp display name for ten tool calls, then ran the command with --phone 1234567890 and later 593000000000, and told me I was not registered. Hermes already puts the sender of each turn in the environment of the shell it runs, so the CLI now reads it there and ignores any number the model passes. The skills don't mention phones anymore. That also means a member can't ask the bot to act as someone else.
The model drifted from the JSON field names. It wrote type instead of kind, price instead of price_usd, explicit nulls and a markdown code fence around the JSON. The values were right, so the validator accepts those variants in code instead of asking the member again.
Members should only see answers. Hermes shows tool progress in chats by default ("running rc-listings…"). In a club group that is noise, so the profile turns off tool progress, reasoning and interim messages for WhatsApp; the calls still go to the log.
The shop's search is literal. "kit Xray XB2D 2026" found nothing, while "Xray XB2D 2026" found the kit in stock. The quote command now tries up to four rewrites of the member's words (filler words removed, model codes only, brand first) and picks the hit whose title matches best.

A real quote from the build: TAM54500, TT-02 68T spur set, $13.00 at the store, 113 listed as its weight, 0.25 lb × $7.00/lb = $1.74 fee, $14.74 if the member orders it, $16.30 if we order it (+12%, $1.56). Those numbers come from the store JSON and rcparts; the model only passed them on.

Why Does Open Innovation Matter?

Because the business has to cost almost nothing to run. The fee on a $13 part is a couple of dollars. Gemma 4 26B A4B costs $0.0675 per million input tokens on OpenRouter. A model call in this bot is about 20,000 tokens, most of it Hermes's own instructions and tool definitions, so a call is about a seventh of a cent and a full "add a listing" turn is well under one cent. A closed frontier model at many times that price would eat the margin of a club-sized business.

Because I can leave. The weights are open. OpenRouter also lists a free :free version of the same model; during the build its shared pool was rate-limited, so I pay for the regular one. If the price changes or the provider disappears, the same model family runs on Ollama on my own machine and the profile changes one block. I describe that swap; I have not tested it in this build.

Because the agent itself is open. Hermes gave me the WhatsApp bridge, skills, cron and a profile format I can version in git and install on my home server. When something went wrong I could read the session database and the exact tool calls the model made, which is how I found the invented phone numbers.

Because the club's data stays with the club. Listings live in a Postgres database I control. I also looked at self-hosted Cognee, an open-source memory layer, for remembering members' cars and habits. It can run on the same Postgres with open embeddings, but its extraction step makes several model calls per message and it was too much for this week, so it is the next step, not part of this build.

What is not open: WhatsApp itself, and the bridge is unofficial. A version for many clubs needs the official WhatsApp Business API.

My First User

The first user besides me is a fellow racer and member of ACRO (the local RC racing club), who used the bot from their own phone in the test group and in a private chat. They asked for an import quote for a rear anti-roll bar, chose "we order it", sent the deposit receipt as a PDF in the chat, and the order went into the batch after I confirmed it. The receipt was a generated test PDF from a fictional bank, so no money moved in this test.

He was amazed at how the club chatroom history can be leveraged to connect people's spare parts HAVE/NEEDS through an agent that automates this process. The imports service also caught his interest, as club members often opt to make individual imports to cover their needs and end up paying extra fees. If a club starts batching orders and sharing fees, the hobby becomes more accessible.

Prize Categories

Best Use of Gemma. Gemma 4 26B A4B is the only model in the project. It reads every message in the club group and in DMs, turns HAVE and NEED messages in Spanish or English into listing JSON, picks the skill and relays the replies.

Best Use of Tiger Data. The listings, matches, quotes and orders live in a Tiger Cloud Postgres service (Postgres 18, TimescaleDB 2.30). Matching uses Postgres full-text search with unaccent and pg_trgm, driven by a bilingual RC glossary, and the agent has Tiger's MCP server with the read-only db_query tool for questions the CLI does not cover; all writes go through the CLI. pgvector and pg_textsearch (BM25) are available on the same service and are the next step for words the glossary does not cover.

Limits

The WhatsApp bridge is unofficial, and WhatsApp can restrict the bot's number.
Inference is at OpenRouter, not local. The self-hosted path is described, not tested.
The demo club and its listings are seeded test data, and the deposit receipt in the demo is a generated test PDF.
The store does not say what unit its weight is in. 113 for the spur set is consistent with grams, and the quotes say "weight as listed by the store"; I have not checked it against a scale yet.
One online hobby shop I tried to include in the prototype disallows search in robots.txt.
No payments: quotes and orders are records, and I collect and place orders by hand. ACP, UCP, x402 protocols can be explored in the future.
A turn takes 20 to 60 seconds, mostly model calls on a 20,000-token prompt.
Matching is words, not meaning. Spanish and English meet through a 42-concept RC glossary; words outside it will not match across languages until embeddings are added.

Idea Leash: an open-source walk buddy with a local model for a brain

Santiago Gonzalez Toral — Mon, 12 Oct 2026 04:30:20 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Idea Leash is how I take a break from work and touch grass. I go out with my dog, or for an evening walk with a podcast in my ears, and I wanted those walks to leave something behind: what I heard, the ideas that show up while I'm moving, and a small reward for going out. It is for anyone who walks with a podcast and wants to keep some of it.

The AI runs on open models on my Mac: Gemma 4 12B through Ollama, the Hermes agent, and whisper.cpp for speech. On the phone I use two apps. The Leash app records the route and my voice, and Telegram is where I talk to the Leash agent.

The Leash agent is an ambient agent: it speaks first. When a podcast is ready, when a walk has been saved and when I answer a quiz question, the Mac messages me in Telegram without being asked, and I reply there. It can also bring back one concept and one older note from my wiki when I ask. I have not built scheduled nudges, so it speaks when something happened, not on a timer.

The loop:

Before the walk I send an episode link to a Telegram bot. The Mac transcribes it, writes notes into a markdown wiki (my second brain) and prepares a five question quiz. Every question quotes the transcript, and a script checks the quote.
I walk with the phone in my pocket and listen in my usual podcast player. The web app records the route, and one big button records a voice idea.
When I stop, the Mac sends one Telegram message: distance, time, my ideas, and the top links for any note that started with "search the web for...".
I say "quiz me". Four multiple choice questions arrive as Telegram buttons. A podcast competes with the street for my attention, so the fifth asks me to connect an idea from the episode to something I saw on the walk.
Sixteen badges reward kilometers, time with the dog, streaks, voice ideas and quiz results. Code computes them, not the model.

The theme asks for the screen to be the shortest part. On the walk I touch the phone only to start, to record a note and to stop. The screen does stay on behind a black cover, because in Chrome a hidden web page gets no GPS.

Demo

A walkthrough of how I use the Leash app and its ambient agent to rest from work and touch grass, going out with my dog or out to exercise. Along the way it keeps a second brain of what I learned and the ideas I had, with the AI running on my Mac.

I pick the kind of walk (podcast, dog or plain), pocket the phone behind the black cover, and hold a corner for three seconds to stop.

Afterwards the Activity view shows my totals, the streak and recent walks.

The Badges view shows what I have earned and how far I am from the next badge.

This is the Telegram message I've got from the agent after Sunday morning's offline dog walk:

Dog walk saved: 921 m in 24 min.
The phone could not track 2 min of it (screen off or another app in front).
2 voice ideas saved:
  - Remind me to check out the milestones of the project as we are back f…
  - Search on the web about libraries for 2D gaming and physics engine.
Searched "libraries for 2D gaming and physics engine":
  1. Most Popular C[++] Open-Source Physics Engines
     https://www.reddit.com/r/gamedev/comments/qu1y2y/most_popular_c_opensource_physics_engines/
  2. physics-engine · GitHub Topics
     https://github.com/topics/physics-engine
  3. Free 2D game engine library for teaching purposes
     https://www.facebook.com/groups/132728896890594/posts/2143324715830992/

Code

santteegt / HacktoberFest2026

Hacktoberfest 2026

Monorepo for my Hacktoberfest 2026 challenge entries and the knowledge base behind them.

Challenges

Each challenge lives in its own folder under challenges/. I'm starting with the weekend challenge and adding the others as they open.

#	Challenge	Window (UTC)	Status
01	Weekend: Build for a Friend: RC Pit Companion	Oct 2 02:00 → Oct 5 06:59	✅ Published
02	Week 1: Touch Grass: Idea Leash	Oct 5 18:00 → Oct 12 06:59	✅ Published 2026-10-11 23:30 local
03	Week 2: Side Hustle: PartsPaddock	Oct 12 19:00 → Oct 19 06:59	✅ Published 2026-10-18 23:21 local
04

…

View on GitHub

The code, the setup steps and the dated log of everything can be found in the challenges/02-week1-touch-grass folder. MIT licensed.

How I Built It

The numbers in the diagram follow one loop:

A link I send in Telegram reaches the Leash agent, a Hermes profile in a macOS VM. The agent runs leashctl, which calls the tool server on the Mac through an SSH reverse tunnel. The server fetches the audio from the podcast host, graphify transcribes it, Gemma writes the notes and the quiz, the notes go into the wiki, and Piper makes spoken versions of the questions (not used in the Telegram quiz yet).
The same tool server serves the phone app over Tailscale HTTPS, reachable only from my tailnet. A finished walk uploads its GPS track, and the Mac recomputes distance and time.
Voice clips travel with the walk. whisper.cpp transcribes each voice note (a 15 second clip in 0.95 s) and it is saved as an idea in the wiki. A note that starts with a search phrase becomes one SerpApi search.
Code builds the after-walk message, and the Mac sends it to Telegram.
"Quiz me" goes to the agent, which asks each multiple choice question as buttons.
Each answer goes back through leashctl. The Mac grades it and sends the grade itself.

The stack:

Job	What runs it	License	Where
Language model (notes, quiz, grading wording, chat)	Gemma 4 12B, 4 bit QAT build, through Ollama	Apache 2.0	my Mac
Agent in Telegram	Hermes Agent v0.21.6, one profile	MIT	a macOS VM on the Mac, run by Lume (MIT)
Speech to text for voice notes	whisper.cpp with Whisper large-v3-turbo	MIT	my Mac
Podcast transcripts	graphify with faster-whisper	Apache 2.0 / MIT	my Mac
Spoken versions of the quiz questions (made at ingest, not used in the Telegram quiz yet)	Piper, run as a separate process	GPL-3.0-or-later	my Mac
Tool server: walks, badges, quiz checks, messages	Python, standard library only	MIT (this project)	my Mac
Phone app	one HTML file, no framework	MIT (this project)	Chrome on Android
Phone to Mac link	Tailscale HTTPS, tailnet only	not open	phone and Mac
The second brain	an OKF markdown wiki	plain files I own	my Mac

Two agent skills

The agent's behaviour lives in two skills in the repo's skills/ folder. A skill is a SKILL.md of instructions plus scripts the agent can run.

podcast-quiz turns a transcript into four multiple-choice questions and one field-link question, and grades spoken answers afterwards. Its scripts check every question's quote against the transcript, the spoken length and the answer-length bias, and shuffle the answer positions.
leash-tools tells the agent how to reach the Mac with one command, leashctl: ingest, next, answer, idea, search, resurface, walks and audio.

I used the skill-creator skill workflow for implementing them: write the skill, run it on three test transcripts with the local Gemma model, read the failures, change the skill, run again. It took four rounds. The checking script did more than the prose: with it, the skill passed 27 of 27 checks, against 20 of 27 for a plain prompt.

Telegram taught me a second lesson. The 12B model did not follow a paragraph in a skill and wrote a question as plain text instead of calling the buttons tool. So the instruction moved into the tool's reply, in an action field the agent only has to follow. agent/setup-profile.sh installs the Hermes profile with both skills.

The rule I ended up with: the model does language jobs, and code does everything else. Distances, scores, badges, streaks, scheduling, and every message that has to be true.

Where it ended up

Every part of the loop has run on a real phone and a real walk: the link and the notes, the walk, the offline upload, the after-walk message, the quiz in Telegram and voice search. In one quiz retake after the grades moved to the Mac, the model stayed silent after each multiple-choice answer. By Sunday, the app had counted four walks: 7.66 km, 112 minutes, nine voice ideas, a three-day streak and 7 of 16 badges. That is one person and one phone.

Limits:

The 12B model is not a reliable tool user. In my tests, it sometimes skipped a command and described it as done.
Grading of the open question is unmeasured. I have three data points.
English only, and speech recognition mishears names.
The Mac has to be awake. While it sleeps, the bot is offline, and a finished walk waits on the phone. Used caffeinate to keep my laptop awake while going out.
The screen stays on during a walk, behind the black cover.

Why Does Open Innovation Matter?

Idea Leash asks for a lot of trust. It hears my voice, keeps the routes I walk with my dog, and builds a private memory of what I learn. Open models and an open agent harness made it buildable on those terms, because every part that thinks runs on my own Mac, where I can look inside it and change it.

No hosted model hears my voice or sees my route. Gemma 4 12B answers through Ollama, whisper.cpp transcribes voice notes on the Mac (a 15-second clip in 0.95 s), and the GPS track is stored there. Ollama's log from the agent's first day, tests included, shows 183 chat requests, all to the local model. My reasoning, not a test: with a hosted model, every voice idea and episode transcript would pass through a server I do not control.

Free calls shaped the design. The quiz generator checks every quote against the transcript and regenerates up to three times when a check fails. With a bill per call, I would have weighed each retry and each test run against the cost, and I suspect I would have tested less.

I could open it up. Ollama shows which model is loaded and with what context, which is how I caught it reloading at every hand-off. Hermes keeps its chat history in SQLite, so I read what the model actually did instead of trusting its summary. The agent is a folder of files (persona, allowed tools, two skills), versioned and tested like code, so I decided which jobs the model gets. Grades now come from code. The model is one alias in one file, and the speech model, the voice and the harness can each be swapped.

The second brain is plain OKF markdown, so any tool or agent can read it, and nothing I learn is locked in a product. One script installs the agent profile with both skills on a Hermes instance. I have run it in one VM only, so "someone else could run their own Leash on their own Mac" is a design goal, not a result. It is also how one person put this together inside the challenge week, from open parts that each do one job.

Not all of it is open. Telegram carries my messages and voice notes, Tailscale links the phone to the Mac, search words go to SerpApi, the audio comes from the podcast host, and the agent's VM runs macOS.

My Agent Session

I built this with Claude Code, with subagents for brainstorming and for the app's icon, badge and layout design. The session below is an abridged transcript of 106 messages: my prompts and the final reply of each turn, shortened, with tool calls and private details removed. The first Telegram tests are the most useful part: a token the shell tool never got, and an old error the model replayed from its chat history.

Building Idea Leash: a walk-and-podcast quiz agent with Gemma, Hermes and a phone app

You

let's scaffold the folder for Week1 challenge

Agent

The Week 1 (Touch Grass) folder is scaffolded at challenges/02-week1-touch-grass. I haven't tried to build or run anything, and I haven't committed.

Challenge facts (live data, event 79)
- The theme is Touch Grass. The project must put open-source AI at the core and get people off the screen and into the world. Bonus points if you take it outside and report how it went.
- The deadline is Oct 12 06:59 UTC, which is Sun Oct 11, 11:59 PM PDT.
- The tag is #hf26challenge.
- The prizes and the 16 partner categories are unchanged from the weekend. Judging weights the write-up most heavily.
- Start-time mismatch: the DEV API now says Oct 5 18:00 UTC. The schedule feed said 07:00 and an earlier API read said 16:00.

[trimmed for the transcript]

You

check if anyone on DEV have reported how to use a gemma model on mobile for custom agentic apps

Agent

Yes. DEV has a lot on this, mostly from the Gemma 4 Challenge in May and since. These are the posts worth reading, grouped by how you'd build.

Native Android with Google's LiteRT-LM (the most direct route)

On-Device AI with the Google AI Edge Gallery and Gemma 4 (Google AI, Apr 2026) is the official starting point.
- The open-source Gallery app (Kotlin and Swift) runs Gemma 4 E2B/E4B on both Android and iOS.
- It has an Agent Skills system (SKILL.md files with JavaScript or native intents), a Function Calling Guide using @Tool/@ToolParam, and a fine-tuned FunctionGemma 270M for device control.

[trimmed for the transcript]

You

I'm going to give you a quick overview of my outdoor everyday activities. Spin up a brainstorming agent using Opus 5.5 to propose 3-5 ideas for the this week challenge based on that context and the challenge theme. Ideas should be fun, might include my dog, might be helpful to reduce the stress while keeping my attention levels high so I can keep motivated to continue working on projects, maybe help me with critical thinking and foster creativity, help me exercise to trade-off the hours I spent in front of my laptop:

I commute from home to the my aunt's house ~20-min by car in the morning. Nearby there's a park
Take a 20-25 min with my dog around the park
Come back to my aunt's and do home office until 1pm. She has a small garden where I play with my dog from time to time to relax between work sessions
In the afternoon, I work at home. At night, around 7:30pm, I drive to the same park again and do some stretching exercises, and then a 30-min fast walk

Agent

The Opus 5.5 brainstorming agent finished. It read the rules, the playbook and the wiki, and it wrote no files. The ideas below are its output; I've shortened them and added a few of my own notes.

One design choice runs through all of them. The phone only records and plays audio, and the AI runs on your Mac before you leave or after you're back. That avoids the phone-inference problem from the weekend. The catch is that nothing runs in the park, so the post has to say so plainly. The "why open" argument becomes that your voice, work and location stay on your Mac and it costs nothing to run daily.

[trimmed for the transcript]

You

let's do another ideas brainstorming iteration:

it should include some king of outside activity
if the model runs on the laptop pre/post going outside, the phone could act as a multi-modal sensor device. This data could be used together with some public APIs if possible, to include gamification into the AI side of the idea

Agent

The brainstorming agent is working on the second round with your new constraints. They are: an outdoor activity at the core, the phone as a sensor, public APIs, and gamification driven by the AI. I'll pass along the ideas when its report comes back.

You

is the agent using Opus 5.5 ?

Agent

Round 2 is back from the brainstorming agent. Nothing was written to disk. The agent worked from memory on API terms, so each uncertain claim is marked (check) and still needs a day-one test. I've condensed the report.

Setup common to all four ideas
- The phone is only a sensor outside: photos, voice notes, and GPS.
- Getting files to the Mac: LocalSend works on both iOS and Android with no cloud. The agent says AirDrop and some share sheets can strip photo location, so check on day one that GPS survives.
- API privacy rule: only read-only calls from the Mac, with coordinates rounded to about 1 km, and no photo or audio uploads.

[trimmed for the transcript]

1 message not shown

Agent

The three public APIs all answered without a key. I used the 1 km radius around your coordinates and a 350 m radius for Overpass. The results change the idea ranking in a few places.

iNaturalist (no key, HTTP 200)
- There's enough data for Parkdex. Within 1 km there are 566 observations covering 329 species across all months.
- October alone is thin: only 18 research-grade species. At 2,500 m near the equator there's little seasonal pattern, so I'd drop the month filter and use all-year records.
- The top October results are butterflies and plants, not birds. Those are hard for Gemma to identify from a photo, so squares should stay category-level.

[trimmed for the transcript]

You

I have two Android phones. If you need more details in terms of resources and capacity, let me know

Agent

Two Android phones is a good setup for the sensor plan. LocalSend, phyphox and GPSLogger all run on Android, and you avoid the iOS restrictions on browser sensors and the light sensor. One caveat: Google Photos sharing strips location, so move the original files (from the camera's DCIM folder) to the Mac.

For each phone I'd need:
1. Make and model. That tells me what sensors it has, and many mid-range Androids have no barometer.
2. Android version and free storage.
3. Camera settings. Is "location tags" switched on in the camera app?

[trimmed for the transcript]

You

Make and model: Redmi Note 11, Freedom Factory DGen1
Android Version: 13, 15
Free storage: 10 Gb, 230 Gb
Camera settings: not sure

Agent

The Redmi Note 11 is fine as a capture device. I couldn't find any specs for the Freedom Factory DGen1, so I won't guess about it.

Redmi Note 11 (per GSMArena and other spec sites)
- It has a Snapdragon 680, 4 or 6 GB RAM, a 50 MP main camera, and an accelerometer, gyroscope, compass and light sensor.
- It has no barometer.
- It isn't a candidate for running Gemma on the phone. Google's E2B guide asks for 6 GB or more, and with a weaker chip you'd get slow output. This fits the plan that the phone only captures data and the Mac does the AI work.

[trimmed for the transcript]

You

let's do another brainstorming iteration. Let's include the following thoughts:

might include a way to establish a random route in the and min steps as goals when going out for a walk with the dog or going out explicitly to exercise. The more I move, the more goals achieved, the score increases. Weekly Score tiers unlocks the chance of treating well and buying sweets, snacks, or other food etc so the user has to earn eating them by completing the goals, or feel disappointed to eat something extra if otherwise
Spin up an agent on an open source harness like Hermes. Then, using an open-weights model (hosted not local) and connecting it to a messaging app like Telegram. Me as a user can make him a call and maintain a hands-free voice call around a topic:
- ai-generated meditation for when I'm driving
- ai-generated content related to a topic. I load and pick topics of discussion. Then the agents talks for a few minutes and test the user's understanding from time to time.

Agent

Round 3 is back from the brainstorming agent. It checked the facts that the voice and Hermes idea depends on, and the news there isn't great. It wrote no files, and I haven't verified its findings myself.

What it found about your Hermes and Telegram idea

Telegram bots can't hold a live voice call. The Bot API only sends voice notes and audio files. Real calls would need a "userbot" driving your personal Telegram account, which the agent ruled out as unrealistic in 5 days and risky for your account.
Hermes Agent is MIT licensed, has a single gateway for Telegram, Discord and others, has cron jobs, and connects to any OpenAI-compatible model endpoint.

[trimmed for the transcript]

You

look if anyone on DEV has spun up an voice-over agent using Hermes or similar through a messaging app like telegram, discord

Agent

Several DEV posts cover pieces of this. I found none that holds a live hands-free voice call through Telegram. The closest are Discord voice with Hermes, which is slow, and Telegram voice-note loops. I read 7 posts in full. Treat anything else I mention as title-only.

The closest matches

Hermes with live voice
- Hermes agent: Connect to Discord is the only real-time voice conversation with Hermes I found.
- The author joined a Discord voice channel and talked to the bot, and called the result "super slow".
- They say real-time conversation needs streaming, which Hermes doesn't have.

[trimmed for the transcript]

You

check Hermes docs, but also look for similar use cases using openclaw

Agent

Hermes's docs describe more than the DEV posts suggested, and OpenClaw has one thing Hermes lacks: a real phone-call plugin. I also found two new Week 1 entries that overlap with the photo and walk ideas.

Hermes docs

Telegram is voice-note only. It transcribes your voice messages and replies with voice bubbles. Hermes has no calls, and the voice page says phone calls and telephony are unsupported.
Continuous talk works in the command line, Discord voice channels, and a desktop app. The command line has push-to-talk. Discord voice channels listen continuously.

[trimmed for the transcript]

You

can you use gps data to calculate steps, distance, calories ?

Agent

Yes for distance, partly for steps, and only roughly for calories. Treat steps and calories as estimates.

Distance (good)
- Sum the great-circle distance between consecutive GPS points (the haversine formula).
- Raw GPS inflates distance, especially while you stand still or move slowly. A dog's sniff stops are exactly that case.
- Fix it in code by dropping points with poor accuracy and ignoring tiny movements (for example under 3 to 5 m between points). Light smoothing helps too.
- Tree cover reduces accuracy, and a local park has plenty.

[trimmed for the transcript]

You

let's go over another brainstorm iteration. Here are some resources and ideas that can be useful:

a Time article on AI and the real world
Ideas around an ambient agents using an open source harness like hermes and other parter tools
Build something on top of Jarvis
Idea: an agent that collects user thoughts offiline and build a second brain via sync when back online. Foster creativity by allowing a user to quickly save ideas that comes to his mind when outside. In order to reduce screen time, it could be voice over, like tap and record.
I just bought a ESP32 kit + MicroSD card module + HiLetgo GY-521 3 Axis Accelerometer Gyroscope Module 6 DOF 6-axis Accelerometer Gyroscope Sensor Module

Agent

Round 4 is back from the brainstorming agent. It wrote no files. The "checked" items below are its own claims, which I haven't verified.

What it found about your resources

ESP32 kit has no microphone, speaker or battery holder. It does have PIR, ultrasonic, temperature and light sensors, an LCD, buzzer, keypad and buttons.
- Recording audio on the ESP32 itself isn't possible without a mic module you don't have. Audio stays on the phone.
- The ESP32 can be a button, a beeper, a motion and light logger and a Wi-Fi uploader.

[trimmed for the transcript]

You

resources are not bound to each other (e.g. tap and record only via esp32 is wrong, tap and record on a phone. ESP32 for anything sensor-based). Brainstorm agent can come up with multiple ideas based on the list of resources. Given his report, is it needed to made amendments?

Agent

Round 5 is back from the brainstorming agent. It wrote no files, and the hardware details are its own and untested. One design rule applies wherever ESP32 logs meet phone files. The ESP32 has no real-time clock, so it takes network time over Wi-Fi before you leave.

The ideas

1. Idea Leash v2: capture thoughts outside, build the second brain later. Phone only; no ESP32.
- Capture: you record ideas on the phone's recorder or in a Telegram voice note on the walks. The Mac transcribes them (whisper.cpp) and files them as wiki notes (Hermes or plain Python, with local Gemma).

[trimmed for the transcript]

You

let's get into the Idea leash V2 project. Here are a list of additional features that could drive its design:

During my exercise walks at night I like to listen to podcasts. I'd like to be incentivized to walk and listen to podcast while getting tested of my understanding on the topics discussed. The workflow should be like:
- before going out, I give the agent the youtube link to the podcast episode. The agent downloads the audio version of it and process it to include main ideas, definitions, facts and insights into the 2nd brain wiki. Then prepares a 5 question quiz to test my knowledge after the exercise
- An offline-first PWA registers my walk, total distance and path taken (to be loaded in OSM) in the background. I can use a tap and talk or directly send an audio message in telegram that wait to be sent when back online
- After exercising, the agents test my knowledge and scores both time spent exercising and right answers. All gets registered as a session.

Agent

Idea Leash v2 is now the chosen project. I've marked it in AGENTS.md (with your podcast-quiz loop written out as the design brief) and logged the decision and supporting findings in docs/CHALLENGE-MEMORY.md. Nothing is built yet.

It's a big scope for the time left. I checked what's on your Mac and found six technical risks that shape the plan.

What's on your Mac

Already installed: Ollama, ffmpeg, Node 24, Python 3.14, uv. The hardware is an M4 Max with 64 GB RAM.
Models already pulled: Gemma 4 (e2b, e4b, 12b), gpt-oss:20b, qwen3.5:9b, glm-4.7-flash, plus embedding models.
Missing: yt-dlp, whisper.cpp, Piper (speech), Hermes and Syncthing.

[trimmed for the transcript]

You

Here are my answers:

English only
Redmi note 11
Yes
Yes, but try to install it at the project scope level

Other feedback:

Make sure you use an open source harness like Hermes for certain features instead of reinventing the wheel.
Do not forget to judge the whole idea around the challenge prompt and theme
Try to maximize parter tools usage so the project qualifies for some prizes. You can run an ideation and scoing agent using Opus 5.5 to carry this creativity process

Agent

The tools are installed inside the project, and the scoring agent's report is back. I've logged all of it in docs/CHALLENGE-MEMORY.md. Nothing is committed yet.

Set up inside `challenges/02-week1-touch-grass`

Python environment: a uv project pinned to Python 3.12 with yt-dlp, piper-tts and cmake. Nothing is installed globally.
whisper.cpp: built into vendor/ with the Metal backend, plus the large-v3-turbo-q5_0 model (547 MB).
Speech voice: Piper en_US-lessac-medium (60 MB).
Not installed: Hermes. The official installer writes to global paths, so I'll check whether it can install into the project first.

[trimmed for the transcript]

You

here's my feedback:

For the podcast audio transcription, explore using graphify. It already has a feature to transcript audio files and event extract youtube videos. It it can be integrated further for other features you may find interesting, propose it
Podcast content to be included in the demo are: Bankless, InTheLoop, and TheDeFiReport
How GPSLogger is going to be intregrated into the project? Having to run another app separately introduces friction
Create a system diagram artifact that includes the tech stack and how your ideas improves the project's idea alignment with the challenge theme
We should prioritize features that incentivize to get people off the screen and into the world with the help of AI and open tech
During development, Hermes will run locally within a VM. We should use Lume

Agent

The diagram is published as a private Artifact. It has the system map, a minute-by-minute walk timeline, the tech-stack table with measured versus planned status, and the theme-alignment argument with score estimates. Those scores are the brainstorming agent's estimates, not a judge's view. Only you can open the page unless you share it.

I also logged your decisions and findings in docs/CHALLENGE-MEMORY.md.

Answers to your feedback

[trimmed for the transcript]

You

here's my feedback:

Graphify deeper integration: sounds good, however, I think semantic extraction will be very slow on local models, so leave it as optional feature, while still having okf-wiki like content for the agent to get content from. Be critical on this fact I'm putting. Am I confusing features or right?
Podcast content: the project extract audio transcripts only for growing the second brain wiki and for creating the quiz. The podcast itself will be listened using the official app (e.g. Youtube Premium allows to download the episode and listened while the app is in the background)
What do you mean the okf-wiki skill predates the challenge window?
Let's use Tailscale for connecting devices via private VPN

Agent

Is semantic extraction too slow on local models?

You're right about semantic extraction, and I measured it:
- graphify's headless extraction with gemma4:12b-it-qat on a 1,724-word transcript took 604 seconds, then reported no usable nodes.
- That's one run on default settings, and I didn't try other models. An hour-long episode is about 6 to 8 times longer, so it can't sit on the critical path.
- Where I'd correct you: graphify's transcription is a different feature, and it doesn't use an LLM. It took 47 s for 699 s of audio on base. I'd keep that part.

[trimmed for the transcript]

You

here's my feedback:

It's fine to create a macOS VM for now
Keep the new version of the graphify skill
In the PWA, make the top-left corner text more visible while the screen is black, and put a "Record" button in the middle that triggers the voice notes
While the VPN is enabled, let's upload the test PWA to vercel

Agent

All four are done. One caveat: the real phone test (wake lock, GPS with YouTube playing) is still open.

Walk test page

The black screen now shows a clearly boxed "Hold 3 s to stop" corner with bright text. It turns amber while you hold and says "keep holding…". A big Record button sits in the middle. Tap it to start a voice clip and tap again to stop, with an elapsed-time line below it. I ran a full cycle in the browser pane with stubbed GPS and microphone: clip saved, report generated, no script errors. The pane denies the wake lock, so that part is the Redmi's job tonight.

[trimmed for the transcript]

You

Tailscale tailnet is setup in my mac and mobile
A SerpAPI key is already in .env
Telegram bot token and user id are setup in .env

Agent

Everything on your list checks out, with three small steps left for you. The VM is also still finishing its setup.

What I verified

.env: every key is filled in. I checked key names only and never read the values. It's gitignored.
Tailscale: it was installed but stopped on the Mac. I turned it on with your existing settings. Your Redmi is in the tailnet but showed offline.
Name privacy: the HTTPS certificate will publish the machine name in public certificate logs. It has no personal name in it, so I'm fine proceeding with it.

[trimmed for the transcript]

You

Tailscale Serve is ready to use
HTTPs certificates are enabled in the console
Tailscale is turned on on my phone

Agent

Both of the first two steps are done, and the VM is the one open problem.

Tailscale and the walk page

Serve works: the page loads from the Mac with HTTP 200 and a valid certificate. It's tailnet-only, so anyone outside it can't reach it.
Phone: it's online and answers tailscale ping. The Vercel page also works for tonight.
One reliability problem: the small server behind Tailscale had died once, which caused a 502 error. I restarted it. The real app needs a proper supervised server.

[trimmed for the transcript]

You

PWA app loaded from the Tailscale URL. I'll use Vercel in tonight's tests
Proceed with the features you can make progress. Regarding the quiz generation, would be a great to write a skill for that using the skill-creator so hermes or any harness uses it

Agent

The quiz skill is built, tested and valid. The VM exists but needs about five minutes of your input.

The quiz skill

It's in .agents/skills/podcast-quiz/, where Hermes, Claude Code and the local pipeline all read it. The skill-creator validator reports it as valid.
- Generate mode: a transcript goes in (plain text or SRT). Out come 4 spoken-friendly multiple-choice questions plus 1 "field link" question to answer outdoors, each with a verbatim transcript quote.
- Grade mode: it scores your spoken answers 0 to 2 with one sentence of feedback. Multiple-choice answers are graded by code.

[trimmed for the transcript]

You

I enabled remote login on the leash-hermes VM
Implemented skills should be put under a skills subfolder to then be installed either on .agents or hermes skills directory
Later when defining the leash agent behavior, check the docs on how to setup multiple profiles in Hermes. All config & definition files should be added to a folder in the challenge directory. While there should be instructions and even a setup script to easily setup the agent profile on an hermes instance

Agent

Both changes are in. One step needs you before I can continue with the VM.

Done

Skills: podcast-quiz now lives in challenges/02-week1-touch-grass/skills/, and the pipeline reads it from there. scripts/install-skills.sh installs skills in two ways:
- For Claude Code and other agents: --target agents creates a relative symlink in .agents/skills.
- For Hermes: --target hermes --profile leash copies it into that profile's skills/ folder and leaves the test files out.
Quiz check: the quiz still runs from the new location (2 attempts, 44 s).

[trimmed for the transcript]

You

I enabled the Local Network to Claude and the command you requested to run returned "open".
VM username: hermes

Agent

I'm setting up SSH access to the VM. One step is yours: type the VM password.

What I found

Your Local Network switch worked for the app's terminal panel, which reaches the VM (port 22 is open). My own shell is still blocked, probably until the Claude app restarts. I don't need that restart: I'll run VM commands through the panel, where you can watch them.
I opened a panel tab. It created a dedicated key and is now waiting for the hermes account's password. Please type it in that tab and press Enter. I won't see it, and I'm not asking for it in chat.

[trimmed for the transcript]

You

I entered the password, go ahead

Agent

Hermes is installing in the VM, and the Ollama link already works.

Where things are

SSH to the VM works. Your hermes user logs in with the key, and leash-hermes is an alias in your SSH config.
The tunnel works. The guest reaches your Ollama at [REDACTED]:11434 through a reverse SSH tunnel and lists the models. Ollama stays bound to the Mac's loopback only.
Hermes is installing in a tab. It's slow (the repo is about 120 MiB, plus Python 3.14, node, ffmpeg and browser tooling).

[trimmed for the transcript]

You

Hermes install finished, go ahead

Agent

The agent works end to end inside the VM, and it passed the first real tool-call test.

What I ran

Hermes v0.21.6 in the leash-hermes VM, with the profile leash on your leash-gemma4-12b model reached through the SSH tunnel. I asked it to make a quiz from an SRT transcript using the podcast-quiz skill, then run the verifier and the shuffle script. It did all of that on its own:
- Time: 3 minutes 14 seconds, on the first try.
- Verifier: "OK - 5 questions". I re-ran the verifier on the Mac against the file it wrote: exit 0, with correct answers in positions 2, 0, 1 and 3, so the shuffle ran.
- Limits: that's one run, so I can't say how reliable tool calls are across many runs.

[trimmed for the transcript]

You

Build the ingest step first and then define an initial version of the agent's behavior. I already have a few phone walk reports that I'll provide later with some details I found

Agent

The ingest step works on a real YouTube episode, and I've defined a first version of the agent's behavior. The agent isn't fully reliable yet, so I list what I measured below.

Ingest step

python -m leash ingest <link | audio file | transcript> takes a podcast and produces:
- Transcript (graphify's transcriber, with an optional faster whisper.cpp engine).
- Notes: main ideas, definitions, facts and insights. Each item must carry a quote that is actually found in its section, and unsupported items are dropped (2 were dropped on the real episode).
- Second-brain pages (OKF format: source page, concept pages, index, log and hashes).
- The quiz from the podcast-quiz skill, plus spoken audio for each question made with Piper.

[trimmed for the transcript]

You

go ahead with the telegram gateway setup
stay with gemma 12B. Bigger models do not work reliably in my Mac
I'll decide on the vault bootstrap and provide the walk reports later

Agent

The Telegram gateway is live, and it's ready for you to try.

What's running

Gateway: the Hermes gateway in the VM reports "Connected to Telegram (polling mode)". hermes gateway list shows your leash profile as "served by the default multiplexer", so it serves the profile without a separate process.
Why not -p leash gateway run: Hermes v0.21.6 allows only one gateway per host, which serves all profiles, so I started it from the default profile.
Model: it stays on the 12B, as you decided.

[trimmed for the transcript]

You

I started testing telegram bu got another kind of message when sent a "quiz me" message:

I'm sorry, but it looks like I can't reach the local tools on your Mac right now because a required connection token is missing. Please let me know if you'd like to try looking for a podcast or sharing an idea instead.

Agent

The bot should work now. I haven't confirmed it, though, because the fix needs a real message from you.

Cause: Hermes doesn't pass the profile's secrets to the shell tool. The bot's leashctl calls ran without LEASH_UPLOAD_TOKEN, so the tool failed. The model turned that error into the message you saw. The gateway log shows the exact error. My earlier tests didn't catch it because I had set the token by hand in the test shell.

Fix: I made two changes, both as the Hermes docs describe:
- terminal.env_passthrough in agent/config.yaml now lists LEASH_UPLOAD_TOKEN and LEASH_SERVER.
- The leash-tools skill now declares the token as a required variable.

[trimmed for the transcript]

You

I've got the following message when submitting quiz me one more time

I'm sorry, but it looks like the connection token is still missing, so I can't access your saved episodes to start the quiz. You might need to check your local setup to ensure the LEASH_UPLOAD_TOKEN is correctly configured.

Agent

This second failure didn't come from the token. The model never ran the command.

No request reached the Mac. The server log has no new entries.
No new tool error in the gateway log. The only new line is the old tool-call ID from your first attempt.
What happened: the 12B model reused the earlier failure from your Telegram chat history and answered from that instead of trying again. So the token fix is still unverified.

I added a rule to agent/SOUL.md telling the bot to re-run a command whenever an earlier error is in the chat, and I redeployed it to the VM.

[trimmed for the transcript]

You

I've got the first questions prompted. Make sure the agent uses telegram widgets to display questions an candidate answers

Agent

The quiz now runs through Hermes' own button tool, but it isn't verified yet. I haven't tapped the buttons in Telegram, and I don't know if the 12B model will call the tool reliably.

What changed
- Buttons: the quiz skill and SOUL.md now tell the agent to ask each multiple-choice question with the clarify tool. Telegram shows the four choices as tappable buttons, plus an "Other" option, and the correct answer is never shown.
- Open question: the last "field link" question stays a plain message, so you can answer it with a voice note.
- Tap handling: the server now accepts a tapped button's text, a number, or a letter, and still takes spoken phrases like "my answer is B".

[trimmed for the transcript]

You

the model asked in plain text instead of using buttons. the test ran a while ago and the host hibernated, so probably the vm, the ssh tunnel or the hermes gateway need a restart

Agent

The tunnel and gateway are back up and the quiz flow is changed. I haven't confirmed the buttons appear in Telegram, because that needs a real message from you.

Restarted
- Tunnel: it had died, so I restarted it. Ollama and the Leash server on the Mac were fine, and the VM could reach both through the tunnel.
- Gateway: it wasn't running in the VM. I restarted it, and the log shows "Connected to Telegram (polling mode)".
- Tool server: I restarted it on the Mac with the test vault.

[trimmed for the transcript]

You

here's my feedback:

I ran a full quiz session. It worked as intended. Check the responses in the logs if you can.
I tried sending voice messages like I'm driving, quiz me and a voice note with some ideas and the answer I've got from the agent were I'm sorry, but I still can't hear your voice message automatically...
I couldn't test the Send a voice note to check it's saved and sending a voice note asking to search something
the "sleep problem": yea, a startup script and launchd services will be good. Put them in the challenge directory with instructions on how to setup them
What project components are currently run on the host vs within the VM?

Agent

Everything you asked for is done except one item. Voice notes still need a real test from you, because I could only test the transcription path with an audio file on the Mac.

1. Your full quiz session

The server log shows one next call and one answer call per question, all returning 200. The scores were 1, 0, 1, 1, 2, which is 5 out of 6.

Latency is a problem for hands-free use. Ollama's log shows most turns taking 0.7 to 7 seconds. The first turn after idle took 37 to 50 seconds, and three turns near the end took 1m05s, 1m13s and 2m29s. I don't know the cause yet. All 183 agent requests went to your local Gemma through Ollama, so no hosted model was involved.

[trimmed for the transcript]

You

here's my feedback to your latest work:

Latency: you may see some longer time spent on certain quiz questions because I wasn't paying much attention, so some of them answered after a few minutes
I tested a few voice notes. Check out the server logs

here's my report on using the phone PWA (Vercel deployment) through different walking sessions

I ran 4 different walking sessions (1 to 4)
- Walk #1: I opened the app, enabled GPS and microphone and tap the "start walk" button while keeping the browser open and the phone screen alive during my walk.
- Walk #2: followed the same procedure as above but also started the podcast episode on YouTube before starting the session. At some point I mistakenly touched the screen inside my pocket and the phone switched to another app. When I returned to the browser, it requested permissions again and the running session was closed.
- Walk #3: I gave full gps/mic permissions, left the browser opened and turned off the screen to check whether the app kept the gps working while the phone screen is off.
- Walk #4: a walk with my dog, screen on, phone in my pocket.

Agent

The phone walk results changed the design, and the app is built and tested in a desktop browser. It is not yet on your phone. It needs Tailscale switched on first.

Latency

That explains the long gaps between your answers. It doesn't explain three slow model requests of 1m05s, 1m13s and 2m29s in Ollama's log, which are processing time for a single request. I logged that as an unexplained outlier.

[trimmed for the transcript]

You

Tailscale is on, go ahead with phone-serve and pairing

Agent

The QR code is in a terminal tab. I haven't read that tab's output on purpose, because the token is inside the code.

To pair
1. Open the Tailscale app on the phone and connect it.
2. Scan the QR code with the phone camera and open the link. The app should say "Connected to your Mac."
3. Start a walk, and allow location and microphone while visiting the site, not "this time".
4. When you're back, send me what happened, including whether the screen stayed on, any interruptions and how the resume worked.

[trimmed for the transcript]

You

The phone is paired and connected. I'll come back with a report after my exercise session

Agent

The QR tab was already closed, so there's nothing left to clean up. The token was never printed as text, and the code is gone from the screen.

The stack is running for your session: the tool server, the tunnel, the VM, the gateway and the published app. When you're back, send whatever you noticed: how the screen behaved in your pocket, any interruptions, and how the voice notes went.

You

I just came back a walking session using the app. Check the server logs for the walk

Agent

The walk is fully recorded and the new app held up. Here's what the Mac received.

The walk (20:33 to 21:09, podcast kind, screen on in your pocket)


Duration	35m17s, tracked 2116 s of 2117 s
Distance	3151 m as the Mac counts it (3329 m unfiltered)
GPS	4321 fixes at about 2 per second, median accuracy 6.9 m, none worse than 18 m
Interruptions	page hidden 0 s, 0 gaps, no errors
Battery	91% to 85%

[trimmed for the transcript]

You

the after-walk message sounds like a great addition to the ambient agent idea
should we keep the test-vault until I make another test tomorrow with the phone in offline-mode during the walk?

Agent

The after-walk message is built and tested everywhere except the real delivery to your chat. Tomorrow's walk will be the first real delivery.

The test vault

Yes, keep it until tomorrow's offline walk. Switching mid-test would only split your ideas across two folders.

How the message works

When a walk reaches the Mac, the server sends you one Telegram message, built by plain code with no model involved.

[trimmed for the transcript]

You

yes, send the test message

Agent

I sent the test message, and Telegram accepted it.

It is labelled "TEST MESSAGE (sample of the after-walk summary, not a real walk)" and uses sample numbers, so please don't read them as a real walk.

I logged this in the challenge memory as "accepted by Telegram", not yet as "seen by you".

[trimmed for the transcript]

You

in the meantime, get a brief summary of the implemented features and a list of the ones that are pending

Agent

Here is the state as of tonight, 2026-10-09, with about 51 hours left until the deadline.

Implemented

Area	What exists	Evidence
Ingest	A podcast link goes to audio download, local transcription, verified notes, a 5-question quiz and a wiki page.	A 30-minute episode took 477 s end to end.
Quiz	The `podcast-quiz` skill checks every question against a transcript quote and shuffles the answer positions.	27 of 27 checks passed, against 20 of 27 without the skill.
Quiz in chat	The server keeps your place in the quiz. Multiple choice shows as Telegram buttons.	A full session on your phone scored 5 of 6.

[trimmed for the transcript]

You

go ahead with the README, license and claims table

Agent

I've written the licence, README and claims table. The README's setup commands have not been run from scratch on a clean machine.

What's new

LICENSE: MIT, 2026, written from the standard text.
README.md: replaces the scaffold. It covers what the project does, why open matters using only what the logs show, a status table, run steps, a stack table with licences, honest limits, and credits and prior work.
Claims table in docs/CHALLENGE-MEMORY.md: 19 "may claim" rows, each tied to a log entry, with a matching "must not claim" for each.

[trimmed for the transcript]

You

let's improve the UX of the PWA. Make it more appealing and introduce icons or generated sprites to represents and compliment certain text labels like walk, dog, etc. Addtionally, you can include a menu bar to enble additional page views like activity stats and badges earned. For the latter, design some simple badge images. You can spin up a design and UX subagent using Opus 5.5 to assist you with this

Agent

The app now has three views (Walk, Activity, Badges), a bottom navigation bar, 20 icons and 16 badges. I tested it in a desktop browser against your real data. It isn't on your phone yet, and I haven't seen it there.

How the work was split

Design subagent (Opus): I started it with the opus alias, so I can't confirm the exact model version. It wrote the icon sprite, the badge sprite, a review page and a 700-line design spec. It did not touch the app.
Me: I built the /stats data it needed, checked its SVGs in the browser, and wrote the integration into the app.

[trimmed for the transcript]

You

I'm going out for a dog walk in a bit. Check everything is up and remind me what should I've been testing (e.g. offline mode)

Agent

The Mac side is ready for your walk. The one thing I can't see is the phone: Tailscale lists it as offline, last seen a few minutes ago.

What's up

Tailscale and the app: the app is published on your tailnet. The new version, the sprites and the manifest all load, and the API refuses requests without the token.
Services and model: the tool server, the tunnel and Ollama are running.
VM and gateway: the VM answers, the gateway serves the leash profile, and the tunnel works both ways.
Telegram: the bot token is valid (a read-only check, nothing sent).

[trimmed for the transcript]

You

I just got back from the walk, check the server logs:

Offline mode worked smoothly
Telegram message arrived after the walk
New design and UX is good
Two voice notes asked to do a web search. Should that triggered something?

Other feedback: there's should be a minimum walk distance requirement to save the session. Set it to 5 minutes

Agent

I've reviewed the server logs and made the 5-minute change. One question is left for you about search.

What the logs show

The walk: 11:40 to 11:59 (18m55s), a dog walk with the phone offline. 722 m, 1810 fixes, median accuracy 4 m, 1131 of 1135 s tracked.
Offline worked as designed: the walk reached the Mac about 30 s after you stopped, the three voice notes were transcribed within about 2 s each, and the Telegram message went out at 11:59:39.

Did the search notes trigger anything?

No. There was no POST /search in the log.

[trimmed for the transcript]

You

here's my feedback:

do the search-related notes using your recommended approach
Yeah, 5-minutes duration should be the requirement
I tested the search feature right from telegram using a voice note and it worked

Agent

Voice-search notes are done and tested, and I've confirmed the 5-minute rule is a duration requirement. The search path has not been used on a real walk yet.

How voice-search notes work

Trigger: the Mac acts only on a note that starts with an explicit phrase: "search for…", "search the web for…", "do a web search on…", "web search…" or "look up…".
Result: after you stop, the top 3 results arrive as plain titles and links in the Telegram message. A model doesn't write them.
Limits: at most 3 searches per walk, and it counts against the daily cap of 8.

[trimmed for the transcript]

You

give me an overview of all things needed to close this week challenge project

Agent

Here is the closing overview. You have about 33 hours left: the deadline is Monday Oct 12, 01:59 local (06:59 UTC).

Where things stand

Built and tested: the ingest pipeline, the quiz in Telegram, voice notes, the walk app with three views, offline walks, the after-walk message, voice search, the 5-minute rule and the services. All test files pass.

Not done: the post, the demo, the audit, and some housekeeping and decisions below.

[trimmed for the transcript]

You

Regarding the decisions table:

Still hold on the test-vault until the last offline test walk with podcast
Hold the commit until I report back the last test walk
Regarding YouTube terms, make sure to mention podcasts in general in the submission post. The yt-dlp usage stays in the README
Hold post draft writing and submission until I say so
I accept what you recommended for the other items in the table

Things I've done

Already record a demo during the test dog walk. I'll do another one with a podcast tonight. Final demo video should be edited by me by tomorrow morning tops.
I enabled automatic log in to the hermes VM

Your next actions

Everything you listed in Things I can do now except for vault promotion, post draft and staging

Agent

The closing items are done, and everything you put on hold is untouched. Nothing is committed or pushed.

What I did

Walking-route privacy: your raw GPX tracks stay local, and the tests use anonymised copies (rotated, moved about 3,800 km and re-timed).
Walk test evidence: the four report logs are committed as docs/walk-tests/*.txt, stats only.
Index rows: the challenge index and root README now say "Idea Leash", built and tested, demo and post pending, not published.
README final pass and feature freeze logged in the memory file.

[trimmed for the transcript]

You

make sure everything is up so I can test the app tonight. Make sure the mac stays up with caffeinate for two hours. That'll give me enough time until I come back. Then give me a quick sample message I can send in telegram to load a podcast via a youtube link

Agent

Everything is up for tonight, with one thing only you can do: connect the phone's Tailscale.

Status (19:55 local)

Mac awake: caffeinate runs for 2 hours. The Mac is on AC at 100%.
App and services: served over HTTPS at version 3, the tool server, tunnel and Ollama are running, and the VM and gateway answer.
Models: loaded and kept warm, so the first message won't be slow.
Search: 3 of 8 searches used today.

[trimmed for the transcript]

You

i just came back from my exercise session. Check the logs

Agent

The evening test mostly worked, and it also exposed four real defects, which I've fixed. The part I couldn't fix myself is that the 12B model told you a wrong answer was right.

What went right

Podcast prep: your link became a 56.7-minute Bankless episode, done in 18 min 10 s, about a third of its length.
Walk: 34m19s, 2,863 m, 4,149 fixes, median accuracy 5 m, 2,059 of 2,061 s tracked, no gaps.
Quiz: all five questions were served with buttons and graded: 3 of 6.
Latency: agent turns had a median of 2.3 s and a maximum of 65 s, with no multi-minute turns.

[trimmed for the transcript]

You

Do both: send grading directly and notify when ready

Agent

Both are built, tested with a fake Telegram, and deployed. Neither has been seen on a real phone yet.

What changed

1. Grades are sent by the Mac itself
- Each answer produces a Telegram message built by code, for example Question 2 of 5: Not quite. The answer was: The pace is too fast.
- The last answer carries the total in the same message: Quiz finished: 3 out of 6.
- Fallback: if Telegram can't be reached, the old behaviour returns, with exact text for the agent to send. That case is tested too.

[trimmed for the transcript]

You

how should I retake the quiz to record it for the demo? And what's the criteria to get a good grade on open question #5 ?

Agent

Retaking the quiz for the demo

You need one reset per take, because last night's attempt counts as finished. Sessions roll over at 19:00 local (midnight UTC), so before then "quiz me" would just say it's done.

I changed restart so it archives your previous attempt instead of deleting it. In Telegram, send /new and then Quiz me.

[trimmed for the transcript]

You

help me make sure everything is up on my laptop. I want to upload the last offline walking session

Agent

Everything on the Mac is up and waiting for the walk. The phone is the missing piece.

Mac side

Sleep protection: a fresh caffeinate for 2 hours. The Mac is on AC.
App and services: served over HTTPS, the tool server, tunnel and Ollama are running, the VM answers, and the gateway serves the leash profile.
Model: warm, so the first reply isn't slow.

[trimmed for the transcript]

You

The walk is uploaded, check the server logs

Agent

The upload worked end to end, and the walk contains a real interruption that the app handled correctly.

The walk (dog walk, 10:44 to 11:08)

Duration: 23m53s. Distance: 921 m (counted by the Mac). 2,114 fixes, median accuracy 5 m.
Tracked: 1,290 of 1,433 s. 143 s are untracked, in one gap.
Errors: none, and no permission prompt.

[trimmed for the transcript]

Prize Categories

Gemma: Gemma 4 12B QAT, served by Ollama on my Mac, writes the notes, the quiz and the grading wording, and is the model behind the Telegram agent.
SerpApi: voice-requested web search. A walk note that starts with "search the web for..." runs one search after the walk and puts the top three links in the Telegram message (the example above); a Telegram voice note can ask too. The key stays on the Mac, and a daily cap lives in code.

A setup coach for my friend's RC car that stays on his laptop

Santiago Gonzalez Toral — Mon, 05 Oct 2026 05:26:57 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

My friend races a 1/10 touring car, a Yokomo BD12. On practice days he changes something, drives a few laps, changes something else, and by the afternoon he can't say which change helped. When the championship race comes he has no record of what worked at which track.

That is what he asked me for. In his words: "this app will be very useful for practice days so I can quickly store my settings and get feedback from what works, what might have affected my run."

RC Pit Companion does three things:

The coach: he says how the car feels ("the track is very slippery", "once i hit the breaks into the corner the car oversteers"). It picks one change, says what to expect and how to check it over the next few laps, and shows the change on a small 3D car.
The practice log: each change is stored with a better, same or worse outcome, next to the track conditions. It tells him whether a lap-time difference is bigger than the usual run-to-run noise.
Race day: he enters the conditions and gets his saved setups ranked by how close the track was to the one he's racing.

Here is one real turn. Session on a low-grip carpet track, and I said: "the back steps out when I get on the power out of the hairpin." The model labelled it loose on power (confidence 0.95). A hand-written table then picked the lever: softer rear diff oil, toward 3,000 to 4,000 cSt. The model read it back as: "We are softening the rear diff oil to around 3,000 to 4,000 cSt to let the rear wheels turn independently for more side grip. This might feel looser into the corner, so check power-on exits over three to five laps while confirming turn-in hasn't gotten looser." The whole turn took 4.0 seconds on my Mac with the E4B model.

Here is the app from first launch to race day, on a clean database with the small E2B model that runs on his laptop, one screenshot per step. The track ("Club carpet") and the "better" verdict are made up for the screenshots. Nothing in the images is edited.

1. Start a session. It asks for the track, the surface and the grip, because some advice depends on them: the softer diff oil row only applies on low grip.

2. Say what the car feels like. I typed "the back steps out when I get on the power out of the hairpin". It reads that as loose on power, lists three quick checks to do first (belts, tyres, cross-tweak), then offers one change with the trade-off, how to check it and the notes it came from. It doesn't know his current diff oil, so it tells him to enter it in Setup. It won't guess a number.

3. Or tap a chip. Chips skip the model's understanding step, which makes them faster on his laptop. "Push in" gives "Reduce caster, 4 to 3 degrees", the trade-off ("more twitchy on the straight"), and the 3D view of the change.

4. Apply it, drive 3 to 5 laps, say how it felt.

5. If it was better, save the setup with today's conditions.

6. Every change is kept with its outcome.

With runs logged, each run shows the change made before it, and "What affected run 3?" asks the app. This one is the demo data from npm run seed:demo, labelled DEMO in the app.

7. On race day, rank the saved setups for the conditions he enters. This frame was taken after loading the labelled DEMO setups, so my one live setup ("Club carpet", score 100) sits next to them. Setups saved on a different surface are listed separately and never rank above a matching one.

The 3D view is how the app explains a setting, because a number like "rear shock position 2 to 3" doesn't mean much until you can see it. Each suggestion plays on a small schematic car: the faint copy is the setting before and the blue one is after. There are twelve of these explainers, ten in 3D and two flat diagrams. Four of them, captured from the scene's own test page:

Camber, seen from the front. The tops of the tyres lean in less.

Toe, seen from above. Front toe-out points the wheel fronts out.

Ride height, seen from the side.

Shock position: the outer hole stands the shock more upright. This is the move behind one of the reviewed rows in the table.

Every frame is labelled "Schematic, not to scale" and says how much the movement is exaggerated, because a 0.5 degree change would be invisible at true size.

It is a laptop app. He would have preferred his phone, and I tried: Gemma 4 E2B downloaded in a phone browser, but it lagged too much to use. I wrote that down, dropped the phone, and built for the 2019 MacBook Pro he also has.

I installed it on his laptop and recorded the demo there. He was very happy with it, and he can't wait to use it at the next practice sessions.

Demo

The video was recorded on his laptop. It is a walkthrough of the app with Wi-Fi turned off, using voice in and out. The screenshots above show the same steps one by one, and the sections below say what I did and did not verify.

Code

santteegt / HacktoberFest2026

Hacktoberfest 2026

Monorepo for my Hacktoberfest 2026 challenge entries and the knowledge base behind them.

Challenges

Each challenge lives in its own folder under challenges/. I'm starting with the weekend challenge and adding the others as they open.

#	Challenge	Window (UTC)	Status
01	Weekend: Build for a Friend: RC Pit Companion	Oct 2 02:00 → Oct 5 06:59	✅ Published
02	Week 1: Touch Grass: Idea Leash	Oct 5 18:00 → Oct 12 06:59	✅ Published 2026-10-11 23:30 local
03	Week 2: Side Hustle: PartsPaddock	Oct 12 19:00 → Oct 19 06:59	✅ Published 2026-10-18 23:21 local
04

…

View on GitHub

The project is in the challenges/01-weekend-build-for-a-friend folder of that repo. MIT licensed. The README has run steps (Node 22.13+, Ollama, two commands) and the full list of limits, and docs/GETTING-STARTED.md walks through install, first launch, a practice day, voice and the common problems.

How I Built It

The model does two small jobs. Everything else is plain code.

Gemma 4 turns what he said into one of 12 symptoms, or into "not something my notes cover."
After the advice is chosen, Gemma phrases it as a short spoken paragraph.

Between those two steps there is no model. A table of 61 adjustments (levers.json) says what to change, in which direction, by how much, and what it costs elsewhere on the car. I reviewed 12 of those rows by hand, and the app marks the other 49 as drafts. The model never makes up a number: a guard checks the wording against the table, and a template answer replaces the model's text if it strays. Every answer cites the notes it came from. A question outside the notes gets a refusal, not a guess.

Here is how a turn moves through the app. Only the two green boxes call the model. Everything else is plain code, and everything runs on his laptop.

The stack:

Gemma 4 E4B (QAT build) on my Mac, served by Ollama, using Ollama's JSON-schema output so the symptom label is always a valid id. His 2019 laptop has no usable GPU for this, so it runs on the CPU. I asked him for a raw speed test with the small E2B model (gemma4:e2b-it-qat) and got one reading: about 9 tokens per second writing and about 39 tokens per second reading a prompt.
Mastra runs the coach as a workflow with two pause points: one waiting for him to accept the suggestion, one waiting for the outcome after the laps. There is a plain TypeScript fallback behind the same interface, in case the workflow misbehaves.
A Hono server and Preact UI on localhost, three.js for the 3D scenes, one LibSQL file for the vault, MiniSearch for finding the right passage in the notes.
Voice: push-to-talk in, speech out. In the demo video both work with Wi-Fi off. Speech recognition runs on the device in desktop Chrome, after a one-time download of the English speech pack.

The touring-car notes are not new. They are a frozen snapshot of my earlier notes, which I distilled from other people's videos and manuals, and the repo credits that separately and checks it against a hash. All the code is new and written during the window. I did not reuse my earlier 3D guide or setup-sheet app.

On testing: the symptom test has 25 cases, 20 in my wording and 5 in my friend's. I ran it on the bigger E4B model first, and Gemma got 24 right. Then I installed it on my friend's laptop, which runs the smaller E2B model, and hit a bug my test had hidden: whatever I asked, the coach answered "reduce caster". When the small model can't place a sentence it falls back to the same symptom, even for "hello". On the same 25 cases E2B gets 21 right, and it refused only 6 of 12 off-topic sentences I tried.

I added a plain-code check in front of the model. A sentence with no word about driving or setup gets the refusal without calling the model at all, which also saves a few seconds on his laptop. With it E2B gets 23 of 25, and "hello", "what time is it" and "how should I run cli commands on a macbook" are all refused. A sentence that does contain a driving word, like "how many laps is the main race", still goes through to the model. I widened the word list once after it wrongly refused one of my slangy test cases, so that 23 is partly tuned on the test and not a clean number.

On his own five sentences E2B got 5 right and E4B got 4. The E4B miss was "it's understeering out of the corner": I'd labelled it as pushing on corner entry, the model said corner exit, and I think the model's reading is fair. A plain keyword matcher got 17 of 25. On 14 fresh phrasings I'd written down answers for before running them, E4B got 13. Finding the right passage in the notes hit 9 of 10 on questions I wrote before tuning the search. These are small tests, written by me, run on an Apple M4 Max with the model loaded. They say the app works for the sentences I tried; they do not say it understands everyone. His laptop is several times slower than my Mac, as the next list shows.

What fell short:

It is slow on his laptop. A typed sentence goes through the model twice, and from his one speed reading I estimate about 25 seconds for a full answer, and about 15 seconds for a symptom chip, which skips one of the two model steps. Text streams in while it is written. That is an estimate from the speed test, not a timing of the app, which I haven't done on his laptop yet. There is a setting that turns the model's wording off and shows the plain template text instead: the advice and numbers are the same, a chip answers straight away, and a typed sentence only waits for the model to understand it.
The 3D scenes are schematic. The directions come from the notes and some sizes are exaggerated so you can see them. The label says so.
Voice needs desktop Chrome and the on-device English speech pack, which downloads once. The embedded browser I used while building reported on-device recognition as unavailable, so I only saw it work in the demo video, once. Typing and tapping a symptom chip work without it.
Setup needs Node and Ollama. There's no installer.

Why Does Open Innovation Matter?

Three things here depend on the model being open.

First, it runs where he races. A pit table is not a place I can count on a good connection. The model, the notes, the vault and the search are all on his laptop, so the app doesn't need a connection to answer. In the demo video, recorded on his laptop, Wi-Fi is off and it still answers. I also checked that the server and Ollama opened only local connections during a full loop on my Mac. Installing it needs the internet once, for the packages, the model and the speech pack.

Second, his setups stay with him. What he runs at a track, and how it goes, is his own record. The vault is one file on his disk.

Third, it costs nothing per question, and I can see why it answers what it answers. A closed API would bill per turn and would send his habits somewhere. With open weights I could also shrink the prompt until it fit a slow CPU (252 tokens, down from 389, with accuracy unchanged on my 25 cases) and find out that Ollama's JSON schema doesn't reach the model: a version that relied on the schema alone scored 3 of 25. The advice itself isn't hidden in model weights either. It's in a table and a set of notes he or I can open and correct in an editor. I did not fine-tune Gemma, because tuned weights can't cite a source or be fixed by editing a file.

My Agent Session

Building RC Pit Companion: an offline setup coach for a friend's RC car with Gemma on his laptop (Hacktoberfest 2026)

You

Help me setup the initial repository structure (README.md, AGENTS.md, agent skills, etc), knowledge base for the Hacktoberfest challenges:

Use the devrelay skills to gather information about the hacktoberfest 2026 challenges. The current dir will act as a monorepo and will assign a separate folder per challenges under the challenges subfolder
A knowledge base will be created using the okf-wiki skill in order to maintain some context regarding brainstorming ideas, tools, frameworks, etc
…

Agent

The repo is set up. The weekend challenge, "Build for a Friend", closes Oct 5 at 06:59 UTC, which is Sun Oct 4, 11:59 PM PDT. When I checked it was about 25 hours away, and nothing has been built for it yet.

What's in place
- Root files: README.md, AGENTS.md (the harness-neutral entry point), and STICKERS.md. CLAUDE.md is a symlink to AGENTS.md. I ran git init but made no commit.
- Skills: everything lives in .agents/skills/, and .claude/skills is a symlink back to it. I copied in the nine devrelay-* skills and okf-wiki, and wrote one new skill, hf26-challenge. It covers scaffolding a challenge folder, a …

You

Previous prompt was incomplete. Go over the following list and complete what's left:

Use the devrelay skills to gather information about the hacktoberfest 2026 challenges. The current dir will act as a monorepo and will assign a separate folder per challenges under the challenges subfolder
A knowledge base will be created using the okf-wiki skill in order to maintain some context regarding brainstorming ideas, tools, frameworks, etc.
This repository will be used by different harnesses …

Agent

▸ Agent Partner tech analysis and ideas report

Input

Partner tech analysis and ideas report

Output

The partner report is written and the challenge AGENTS.md is in place. About 25 hours remained when I checked the clock before launching the subagent, so the deadline (Oct 5, 06:59 UTC) is getting close.

What's new
- AGENTS.md in the challenge folder has the brief, hard requirements, judging weights, the 16-partner list, the credit rules, the submission steps, and the official template verbatim. CLAUDE.md there is a symlink to it, and the root AGENTS.md now tells agents to read it first.
- SUBMISSION.md now uses t …

You

do you think the parter report is enough to cover tooling capabilities. It might be worth exploring each parter tool separately and adding them to the knowledge base. Give me your thoughts

Agent

Short answer: no, the report isn't enough for building, but it does its job as a triage tool. Per-partner pages in the knowledge base are worth doing, with some caveats about timing.

Why the report falls short
- It gives each partner one table row. That's enough to decide whether a partner fits an idea, but not to integrate it. It has no setup steps, SDK or API shapes, model and version specifics, quotas, or known failure modes.
- It lists about ten unverified items, and several of them are the ones that decide whether an idea works. Examples are whether Qwen3.5 adapters can be served locally and whether Ollama accepts audio input for Ge …

You

okay. let's explore the following ideas. Assign an Opus 5.5 subagent to dive deep into each, expand the ideas, which parter tools best fit into it, and analyze how it should fit into a rapid prototype to be submitted to the current challenge before the closed deadline. Finally generate a final report.

Community Skunkworks agent

Skunkworks within a dev-shop agency like RaidGuild, is a small, autonomous team operating outside normal bureaucratic structures to r …

Agent

▸ Agent Deep dive: Community Skunkworks agent

Input

Deep dive: Community Skunkworks agent

Output

▸ Agent Deep dive: RC Pit Companion PWA

Input

Deep dive: RC Pit Companion PWA

Output

Both deep dives and the final report are written, and I recommend building the RC Pit Companion. About 14 hours remain until the 06:59 UTC deadline. The reports come from Opus subagents; I read them in full but didn't re-verify their web and package claims.

ideas/FINAL-REPORT.md is the comparison. The two deep dives are 01-community-skunkworks-agent.md and 02-rc-pit-companion.md.

Why RC edges ahead
- Theme fit: one clubmate at the pit table asking "why does the rear step out on power?" is one person and one moment. Skunkworks reads …

You

let's go with the rc pit companion idea. Here's my friend persona:

he has a 1/10th RC Touring car (Yokomo BD12) likes to go to the track on practice sessions. He wants to make these practices worth the effort, know what settings to tweak the car based on feeling, get setup recommendations and store the best settings and track conditions so these can be retrieved during race championships to know which setup settings have work
he prefer using the agent companion on this mobile phone, but als …

Agent

The scaffold is committed as 657599b on main, and the working tree is clean. npm run build (typecheck plus production build) passes. The commit has 77 files, and I found no secrets when I scanned them.

What's in the commit
- Monorepo: AGENTS.md (with CLAUDE.md as a symlink), the READMEs, the sticker tracker, the skills in .agents/skills/ (with .claude/skills as a symlink), the knowledge base, and challenges/.
- RC Pit Companion scaffold:
- A Vite + TypeScript PWA shell with a manifest, icons and a service worker, and a mobile-first Coach / Setup vault / About layout.
- Typed stubs only, with no feature logic y …

You

what do you mean by notes snapshot?

Agent

The "notes snapshot" is the touring-car knowledge the coach needs to give advice. I mean a frozen copy of the RC touring-car pages from your OpenGrid wiki.

What it is
- Your OpenGrid wiki at ~/OpenGrid/knowledge-base/opengrid/wiki/ has pages like touring-car-setup-procedure.md, touring-car-suspension-tuning.md, touring-car-steering-geometry.md, touring-car-weight-balance.md and yokomo-bd12.md. They come from your earlier work, including the NotebookLM material.
- The coach has to answer from the RC knowledge those pages hold, such as which adjustments change entry understeer and what each does. So I'd copy only the touring-ca …

You

let's start by testing if we can run the agent with gemma on a phone, otherwise we should stick to laptop and that opens up the opportunity to integrate other parter tools like Mastra and other knowledge-base management tool to store the field knowledge base

Agent

The spike page is ready and served over HTTPS on this Mac. I haven't run the full model test myself. It needs a 2 GB download, and I'd rather you decide that than have me pull it into the in-app browser. The page also isn't committed yet.

What I verified here
- The page loads over HTTPS from this Mac.
- The device check runs. The in-app Chromium has WebGPU with shader-f16 and subgroups, which the model runtime wants.
- The WASM runtime loads from this server, with no request to jsDelivr. The package's default is jsDelivr, which would have broken any offline claim, so the files are self-hosted.

What the page tests on the phone
1. …

You

page is not reachable on my phone using the same network

Agent

Nothing on the Mac side explains it: the firewall is off, and both servers answer on 192.168.x.x when I request them from this Mac. The ARP table also lists five other devices on the same network, several with randomized MAC addresses. Some of those are probably phones, so the router isn't blocking device-to-device traffic outright. I can't tell which one is yours.

I started a second server with no HTTPS, to separate a network problem from a certificate problem. Please try these on the phone in order:

http://192.168.x.x:5174/, which is plain http, port 5174.
- If it loads, t …

You

model is downloading on my phone. I'll send the report once it ends

Agent

Good. A few things will help the test come out cleanly:

Keep the page open and the screen awake until the download finishes. Mobile browsers often suspend or kill background tabs, and a 2 GB download can't resume. If it dies, you restart from zero.
Order matters for the offline test: after the download, press 3 (init runtime) while still on Wi-Fi, because the WASM files come from this Mac. Press 4 (create engine from cache), then turn Wi-Fi and mobile data off and press 5.
If step 4 fails or the tab reloads, copy the report anyway (step 6). A crash on the phone is a useful result, and the error text says which l …

You

my phone is too slow and lags when running step 4 and 5. Given the time we have, let's stick with a consumer laptop solution. Store this agent session to DEV so we can tell how we tested mobile compatibility when writing the report

Agent

The session is saved to DEV as private session 504, but DEV shows 0 messages parsed for it, so I can't yet say it renders. We're now on the laptop-only plan. The time is about 20:12 UTC, so roughly 10.5 hours remain.

Saved session
- Where: the session on DEV. It is private and unpublished.
- What's in it: your 10 prompts and my replies, with 235 transcript records. It is cut at your save request.
- What I removed: my reasoning blocks, the harness system reminders, personal paths and your usern …

You

go with ollama. Then analyze Mastra and other partner tools for field knowledge base snapshot.

For the notes snapshot, do you think it is better to capture the knowledge base from OpenGrid or better right from the notebooklm. For the latter, we'll need to install the same notebooklm skill used on the other project in order to plugin it into the project. That can be marked as no pre-existing work

Finally, how useful and feasible (given the short deadline) you think will be to tune the gemma mod …

Agent

Ollama is now the runtime; I recorded it in the README, AGENTS.md and the provider stub, and the typecheck passes. On your three questions: use Mastra with one local SQLite file, take the wiki as the snapshot (NotebookLM only as a short extra), and skip fine-tuning.

1. Mastra and partner tools for the field knowledge

The knowledge base is tiny, about 10k words or 150 chunks. It is not a vector-database problem. There are two separate kinds of data:

Data	Needs	Fit
Field knowledge (frozen, cited)	Search, with citations	Keyword search (BM25 or SQLite full-text) is enough for v1. Embeddings are optional. …

You

Here's my feedback:

yes, download the gemma model you believe fits best for the solution
Let's skip model fine tuning on this challenge
notebookLM extras after the loop works, sounds perfect. It would double check any useful info is not missing
How are you going to store the kb snaphot? Using the same okf-wiki format or other ?
In Mastra, does embedding generation adds added value to the kb snaphost?

Agent

The pull is running in the background (6.1 GB; I'll run a baseline check when it finishes). I'll take your two questions in order.

How the snapshot is stored

I'd use the same OKF format for the frozen copy and a different format for what the app reads at runtime.

Layer	Format	Why
Frozen snapshot (`kb/source/`)	OKF markdown, byte-for-byte copies of the OpenGrid touring-car pages with their frontmatter	It preserves provenance exactly: `kb/PROVENANCE.md` lists each page with its sha256 and original date. It stays readable by your friend and a judge, and a corrected fact is a diff to a text file.
** …

You

yes, import the snapshot while the model downloads

Agent

The snapshot is imported and verified, but nothing is committed yet. 11 pages are frozen in kb/source/ and compile to 106 retrieval chunks (about 7,500 words, median 68 words per chunk, no duplicate ids).

What's in place
- Scope: the 8 touring-car pages, vehicle-dynamics fundamentals, the BD12 page and the lap-time statistics page. I left out the ESC, motor, telemetry and logger pages and the raw/ extractions.
- Frozen: files are byte-for-byte copies. kb/snapshot-manifest.json has a sha256 for each. npm run kb:verify fails if one changes, and I tested that it catches a tampered file.
- Build: npm run kb:build compil …

You

yes, make the two commits

Agent

Both commits are made, and the working tree is clean:

c0c2d08 feat(kb): import frozen, credited snapshot of prior touring-car notes
c4bd117 feat: phone feasibility spike, switch to laptop + Ollama
657599b chore: initial monorepo structure and RC Pit Companion scaffold

c4bd117 has the phone spike (page, self-hosted WASM script, docs/PHONE-SPIKE.md), the Ollama decision, and the doc and config updates. It contains nothing from kb/.
c0c2d08 has only the 11 frozen pages, the sha256 manifest, kb/PROVENANCE.md, kb/additions/, and the two scripts (kb:verify, kb:build). History now shows the credited prior knowle …

You

maintain a challenge memory that saves relevant ideas/findings that should be included in the final report, so these don't get lost throughout implementation. Set it as a rule in the main AGENTS.md

Agent

gemma4:e4b-it-qat classified 10 of 10 test utterances correctly. That result is much weaker evidence than the number suggests. I logged it in the challenge memory with its caveats.

Baseline on this Mac (M4 Max, 64 GB, Ollama 0.35.1, temperature 0, Ollama JSON-schema output, model warmed up first)

Measure	Result
Correct symptom id	10/10
Median time to first token	138 ms (first call 379 ms)
Median decode speed	85.8 tokens/s
Memory in use	3.7 GB, 100% on GPU

Why not to quote 100% yet
- Small and easy: there are only 10 cases, written in plain wording, not the friend's real phrasing.
- …

You

regarding the open items mentioned before:

my friend's laptop is a Macbook Pro 2019 - (CPU): 9th-generation Intel Core i7 (6-core 2.6 GHz) and Memory (RAM): 16 GB of 2666 MHz DDR4 memory
Friend's quote: "this app will be very useful for practice days so I can quickly store my settings and get feedback from what works, what might have affected my run"

Agent

Both items are logged. The laptop is the bigger of the two, because it changes the model choice and the latency budget.

Quote: it's in the challenge memory as a QUOTE entry. It stresses that the setup vault and "what might have affected my run" matter as much as the symptom coach, and that storing settings has to be quick. Does he agree to be quoted in the public post, and do you want him named? I left that as a TODO-REPORT.

His laptop
- No GPU acceleration: per Ollama's docs, Intel Macs run CPU-only and need macOS 14 (Sonoma) or newer. I don't yet know his macOS version or GPU. If he is on something older, Ollama won't inst …

You

I forgot to mention that my friend's Macbook has a AMD Radeon Pro 5300M (4GB GDDR6)alongside Intel UHD Graphics 630

Right now I cannot reach to him. Let's leave the benchmarking for later

Agent

I've recorded the GPU and the deferred benchmark in the challenge memory and in the challenge AGENTS.md.

What the GPU means for the plan
- No change to the plan: Ollama's docs only promise CPU inference on Intel Macs. Any AMD GPU speed-up would be an unverified bonus, and 4 GB of video memory couldn't hold the E4B model anyway.
- Which model: I'm still planning on Gemma E2B QAT for his machine.
- Which MacBook: the 5300M suggests the 16-inch 2019 model. That is my inference, not confirmed, and that model should be able to run macOS 14 or newer, so Ollama should be installable. Please ask him for his macOS version when you n …

You

let's spin up a Opus 5.5 project planner agent that reviews the current state of the challenge idea and code and build a full implementation plan, decides tooling, libraries to use, UI setup settings, settings interactive visual explanation views, etc. This final plan should be then used for implementation (sequencially or split to various subagents (Sonnet 5.5 or Opus for harder features like visual interactive explainers)

Agent

▸ Agent Opus planner: full implementation plan

Input

Opus planner: full implementation plan

Output

The plan is written and logged in the challenge memory. It is docs/IMPLEMENTATION-PLAN.md, about 17,000 words including the JSON appendices. The planner read everything I pointed it to and did not open your earlier 3D guide. I read most of it myself, but I haven't re-verified its web-sourced claims (voice APIs, package versions, speech-pack support).

Headline decisions
- Shape: the planner proposes a local Node server (Hono) plus a Preact UI in Chrome on localhost, with Ollama on the same machine. It drops the PWA and service worker, since they add stale-cache bugs and no value here.
- Mastra: it ru …

You

here's my feedback:

commit the pending work before starting with implementation
Keep entire partner tool out due to workload. We'll use Devrelay agent session tool when writing the submission article

Regarding data questions:

Keep the generic droop gauge
explain the contradictions found regarding diff oil and rear traction so I can decide which one is right and use it as a lever

After clearing the above, let's review the lever rows you mention

Agent

Everything is committed and your Entire and droop decisions are recorded. The diff oil question is below, for your decision. The data question: I found three statements about diff oil in your notes, not two, and they only clash on low grip.

Done
- The pending work is committed in three commits, plus one that logs the diff-oil finding. The tree is clean. I also replaced one local path in the plan before committing.
- Entire is dropped: the plan, the memory file and the partner list now say so. Categories are Gemma, plus Mastra only if the coach workflow actually ships on it. The session is shared through DevRelay's agent-session tool …

You

here's the answer from notebooklm:


<pasted_content id="1efc">
### Comparison: Thinner vs. Thicker Rear Gear Diff Oil on Asphalt

The handling effects of rear gear differential oil viscosity depend heavily on track traction levels, affecting on-power side grip, exit acceleration, and corner rotation:

| Performance Metric | Thinner / Softer Diff Oil (e.g., 3,000 cSt) | Thicker / Harder Diff Oil (e.g., 7,000–10,000 cSt) |
| :--- | :--- | :--- |
| **On-Power Rear Side Grip** | **Increases si …

Agent

Everything is committed and the tree is clean. Here are the rows to review: the first three for each of the four demo symptoms (13 rows in all). That is the part the demo actually shows, and the remaining rows rarely surface because the engine offers only a primary change and two alternatives.

exit-oversteer (loose on power out of corners)

#	Change	Effect	Trade-off	Check
1	More rear toe-in, +0.5° (2.0–3.5°)	More rear traction on power	Entry response vs straight-line stability	The BD12 factory value is already 3.5°, the top of the range. The engine will skip this row on a stock BD …

You

approve all 13 rows, architecture approved, start T0

Agent

▸ Agent T0: contracts and scaffold

Input

T0: contracts and scaffold

Output

▸ Agent T1: server core and vault

Input

T1: server core and vault

Output

You

continue where you left off

Agent

▸ Agent T7: walking skeleton integration

Input

T7: walking skeleton integration

Output

▸ Agent T6: voice input and output

Input

T6: voice input and output

Output

The app is built and committed, about 7.5 hours before the deadline (it's 23:30 UTC). T10 is committed as 9d9f8ea, and I checked it myself, including a clean-clone test from the commit.

Clean-clone test
- What I ran: a fresh clone, npm ci (3 seconds), then npm start.
- What worked: the knowledge index built automatically, the server started on the Mastra engine, and /api/health, /api/meta and the built UI all answered. A chip turn and a typed turn both completed.
- What T10 had to fix first: before its changes, a fresh clone failed to build and then crashed on the first coach turn, because the generated knowledge index wasn't in the repo. The install and start commands now build it, and the server exits with one clear line if it is missing.
- Other checks: whole-repo typecheck is clean, 92 tests pass, and the build succeeds.

Also in T10
- Out-of-range values: a current value outside the notes' range is now flagged for confirmation. It is no longer clamped into a huge jump.
- UI and scene: the caster view frames the whole car, scene buttons are 48 px, and OPEN SETUP lands on the right Setup row.
- Demo data: npm run seed:demo loads clearly labelled DEMO data.
- The runs show one within-noise comparison and one above-noise comparison.
- Race day ranks the demo setups 100 and 38, with the carpet setup under "Other surface".
- I deleted the dev database: it held only an agent test session, so the demo starts empty.

A real accuracy signal, and it's less flattering than 100%.
- Setup: the 100% score is on cases we wrote. I tested 14 fresh phrasings, with the expected answers written before I ran them.
- Result: 13 of 14 were right. The miss was "the car pushes wide on the way into the hairpin", labelled mid-understeer. I'd read it as entry-understeer, and it reproduced.

You

how much time left we have before the deadline. Make sure you use my local timezone

Agent

About 4 hours 55 minutes left. It's Sun Oct 4, 21:04 local time (UTC−5). The deadline is Mon Oct 5, 01:59 AM your time, which is 06:59 UTC.

My earlier "≈7.5 h" figure came from a 23:30 UTC clock reading that was off. The system clock now reads 02:04 UTC, so the real margin is about 2.5 hours shorter than I said.

You

what about the Reword the five eval cases you mentioned before?

Agent

Those five cases are in the 25-case classifier test, data/eval/symptom-utterances.json. I wrote their wording, so they test my phrasing and not your friend's. The result is only worth quoting if the sentences sound like him.

You

here are my friend's own wording:

it's understeering out of the corner
once i hit the breaks into the corner the car oversteers
the track is very slippery
the car pushing power goes down in the last few laps
steering feels deviated to one side

Agent

Your friend's five phrases scored 4 of 5, and I've committed the result (559af20).

Case	His words	Result
c11	"it's understeering out of the corner"	Missed: I labelled it entry-understeer and the model said exit-understeer
c12	"once i hit the breaks into the corner the car oversteers"	ok (entry-oversteer)
c16	"the track is very slippery"	ok (low-grip)
c17	"the car pushing power goes down in the last few laps"	ok (fade-late-run)
c18	"steering feels deviated to one side"	ok (left-right-difference)

The miss is partly my label's fault. I wrote c11 for "pushing into the hairpin" (corner entry). "Out of the corner" most naturally means corner exit, so exit-understeer is a defensible answer.

You

continue

Agent

The README and the first full draft of the DEV post are both committed, and the humanizer pass is done. I haven't staged anything on DEV. It's 22:24 your time, so about 3 h 35 min remain (deadline 01:59).

My git add -A slip: it also committed the humanizer skill folder, which you or another process had installed, plus skills-lock.json. I didn't intend that. It's MIT-licensed, nothing is pushed, and I can split it into its own commit if you'd rather.

You

I added the repo remote url to git. Push it so I can run it on my friend's laptop.

Agent

The repo is pushed to main and public: https://github.com/santteegt/HacktoberFest2026. It includes one extra commit, 96f0c84, which adds the clone step to the README.

I checked what went up. There are no keys or tokens, ideas/ and your .env and database files are not tracked.

You

while I'm installing the project on my friend's laptop, stage the draft on DEV as unpublished, work on an HTML artifact that can help users getting started to use the app.

Regarding post submission, are you including screenshots on how the app works?

Agent

▸ create_article An offline-by-design setup coach and setup vault for a 1/10 touring car, buil...

Input

An offline-by-design setup coach and setup vault for a 1/10 touring car, built around Gemma 4 on Oll …

Output

All three are done. It's 22:52 your time, so about 3 h 07 min remain until 01:59.

1. Draft staged on DEV (unpublished). It's article 4798987, tagged devchallenge, weekendchallenge and hf26challenge, and marked some_ai.

3. Screenshots: yes, the post now has three. They show the Coach screen, the Session log, and Race day. They come from the app running on its labelled demo data, and the post says so.

You

update the submission post to include a bit more of the visual artifacts that explain the setup settings

Agent

The staged DEV draft (article 4798987) now has a section with four 3D explainer frames, and it's still unpublished. The new section explains that a number like "rear shock position 2 to 3" doesn't mean much until you can see it. The app plays each suggestion on a small schematic car, with the faint copy as the setting before and blue as after.

You

here are the model performance on my friend's laptop:

Prompt: how should I run cli commands on a macbook

Results

total duration: 2m39s, prompt eval rate 38.69 tokens/s, eval count 1462 tokens, eval rate 9.21 tokens/s

Agent

Your friend's laptop gave the first real reading. His laptop reads a prompt at about 39 tokens/s and writes at 9.2 tokens/s. That is within the range I predicted yesterday (5 to 15 for writing, 20 to 50 for reading), and about 6× slower at writing than my Mac on CPU alone.

My estimate, not measured: a typed sentence takes about 25 s from end to end. A symptom chip takes about 15 s, which skips one of the two model steps.

You

Here's my feedback:

yes, the reading was from gemma4:e2b-it-qat
BTW, you have permissions to quote my friend

Before running a demo on my friend's laptop In the app, I cannot use the STT feature, when I click the hold to talk button I get the following message: Voice input is off in Settings. Type or tap a chip. Is this a bug or should I do something to enable this in a macbook

Agent

It wasn't a crash. It was a bad default, and I've fixed it. Hold to talk printed that message because voice input started out set to Off, and nothing told a new user where to turn it on. That made the hands-free feature a dead end on first run.

What I changed (pushed as b73888f, 92 tests pass): voice input now starts on On this computer; the off message now says where to turn it on.

You

when using the app, I can ask anything, the coach agent always recommends "reduce caster"

Agent

Found and fixed. The coach recommended "reduce caster" for everything because your friend's laptop runs the small model, and I had only ever tested the bigger one.

What was wrong
- The two models: he runs gemma4:e2b-it-qat. Every accuracy test I'd run, including the 24 of 25, used the larger gemma4:e4b-it-qat model.
- The reproduction: I pulled E2B to my Mac and tested it. When the small model can't place a sentence, it falls back to the first symptom on its list, and that symptom's first suggestion is "reduce caster". "hello" and "what time is it" both got it.
- The off-topic test: E4B refused all 12 off-topic sentences I tried. E2B refused only 6 of 12.

What I changed: a plain-code check now runs before the model. A sentence with no word about driving or setup gets the "not in my notes" refusal immediately, with no model call. With it E2B scores 23 of 25 on the test cases.

You

fix worked.

Using the shot list you suggested above, update the app screenshots in the submission post to give an image-based demo walkthrough

Agent

The post now has an image walkthrough built from your shot list, and the DEV draft is synced and still unpublished.

I scripted a pass through the real app: a fresh database, the real buttons, and the gemma4:e2b-it-qat model your friend runs. Nothing in the images is edited. Seven steps, one frame each: start a session, say it, tap a chip, apply and rate, save setup, session log, race day.

You

here's my feedback:

Yes, video demo shows wifi is off, and voice input/output working

Check if there are any remaining TODOs in the post draft

Agent

There are two TODO comments left in the DEV draft, and I've fixed the wording they were guarding.

What's left
1. Agent session embed (My Agent Session). The post needs a public DevRelay session link.
2. His reaction (end of "What I Built"). The post says he hasn't tried it himself yet.

You

the video was recorded on his laptop.
My friend was very happy with the product. He cannot wait to use it on the next practice sessions.
Upload the agent session. Include it if needed in the submission post and publish it

I built this with Claude Code: one planner agent, then builder and reviewer subagents working on separate parts of the code. The session shows the phone test that failed, the frozen-notes decision, and how I checked the agents' claims, including a fresh-clone test that found the project didn't install cleanly and led to a fix.

This is a curated copy, not the raw log. It keeps every prompt I wrote and the assistant's final reply to each, shortened, and leaves out the subagents' reports, tool output and reasoning. Paths, my email and local IP addresses are scrubbed. It ends where I asked for the upload, so the last few steps (the 3D explainers, the architecture diagram and the Wi-Fi-off demo check) are in the post and the repo's docs/CHALLENGE-MEMORY.md, not in the session.

Prize Categories

Gemma: Gemma 4 (E4B QAT on my machine, E2B QAT on his) is the model behind both language steps, served by Ollama.
Mastra: Mastra 1.74 runs the coach workflow, including the two pause points.

I looked at the other partners and left them out. Most would have meant a cloud service in a project that is meant to run on one person's laptop.

DEV Community: Santiago Gonzalez Toral

Skunkworks Sloth: a set of agents automating my bookmarks review, keeping me focused and growing my portfolio and skills

What I Built

Demo

Code

santteegt / HacktoberFest2026

AI-assisted prototypes I built during HacktoberFest 2026

Hacktoberfest 2026

Challenges

How I Built It

The architecture in one picture

A day in the skunkworks

The Curator pipeline, with real numbers

What moves a card

Agents, skills and schedules

What gets stored

How each partner tool fits

What broke, and what I learned

Why Does Open Innovation Matter?

My Agent Session

The Lazy Payoff

Future Work

Prize Categories

PartsPaddock: a WhatsApp parts desk agent for RC clubs - P2P / B2C market connecting hobbyists

What I Built

Demo

Code

santteegt / HacktoberFest2026

Hacktoberfest 2026

Challenges

How I Built It

Lessons learned

Why Does Open Innovation Matter?

My First User

Prize Categories

Limits

Idea Leash: an open-source walk buddy with a local model for a brain

What I Built

Demo

Code

santteegt / HacktoberFest2026

Hacktoberfest 2026

Challenges

How I Built It

Two agent skills

Where it ended up

Why Does Open Innovation Matter?

My Agent Session

Native Android with Google's LiteRT-LM (the most direct route)

What it found about your Hermes and Telegram idea

The closest matches

Hermes docs

What it found about your resources

The ideas

What's on your Mac

Set up inside challenges/02-week1-touch-grass

Answers to your feedback

Is semantic extraction too slow on local models?

Walk test page

What I verified

Tailscale and the walk page

The quiz skill

Done

What I found

Where things are

What I ran

Ingest step

What's running

1. Your full quiz session

Latency

The walk (20:33 to 21:09, podcast kind, screen on in your pocket)

The test vault

How the message works

Implemented

What's new

How the work was split

What's up

What the logs show

Did the search notes trigger anything?

How voice-search notes work

Set up inside `challenges/02-week1-touch-grass`