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A shadow evaluation protocol for ERP agents

FuturPulse — Mon, 12 Oct 2026 11:13:13 +0000

A shadow evaluation protocol for ERP agents

An ERP assistant can produce a convincing replenishment recommendation while relying on information that was unavailable when the decision mattered. Evaluating it against today's database can quietly reward hindsight. Before giving the assistant purchasing permissions, evaluate what it would have recommended from a time-bounded view of the business.

The protocol below is a proposed engineering method, not a report of measured results. It focuses on recommendation quality; transaction safety and authorization still require separate checks.

Freeze the evidence at decision time

Choose one workflow, such as suggesting replenishment for stocked maintenance parts. For each evaluation case, record the decision timestamp, inventory snapshot, open orders, supplier quotes and policy version. Keep both the effective date of each fact and the time your system actually received it.

A delivery notice entered on Friday must not appear in a replay of Thursday's decision, even if its effective date is Wednesday. Exclude later invoice corrections, retrospectively updated forecasts and notes that reveal the final outcome.

Store references to these snapshots with the case. Pin the retrieval configuration, prompt and model version where possible. If a provider cannot pin a model, record the returned version metadata and evaluation date. Otherwise, a changed answer may reflect changed evidence rather than improved reasoning.

Separate development cases from evaluation cases

Use one set of cases to refine prompts and retrieval, then reserve a later period for evaluation. Keep related events, such as revisions of the same purchase request, together so that an almost identical case does not appear on both sides.

The scikit-learn guidance on data leakage explains why information unavailable at prediction time creates overly optimistic evaluation. The same principle applies when an agent retrieves ERP records instead of reading a conventional feature table.

Sample ordinary requests and exceptions: unavailable suppliers, contradictory quantities, missing quotes and inventory corrections. Report how cases were selected. A handpicked collection of easy approvals cannot establish readiness for the whole workflow.

Review proposals without executing them

Run the assistant with read-only tools and route its proposed actions to an evaluation store. Use an isolated replay environment, not a live purchasing queue. Give reviewers the same time-bounded evidence and hide which system produced each proposal when practical.

For each case, classify the result as acceptable, requiring correction, requiring escalation or unsupported. Record the violated rule and the evidence used. Review disagreements separately; historical human decisions are useful comparisons, not automatically correct labels.

For context on the processes being evaluated, our overview examines how agentic AI changes ERP workflows. This protocol adds a concrete way to evaluate those proposed capabilities.

Measure usefulness alongside failure

Track unsupported recommendations per evaluated case, mandatory escalations missed, unnecessary escalations and reviewer minutes per case. Count skipped cases and tool failures explicitly. Break results down by supplier, exception type and purchasing category; an acceptable aggregate can conceal a weak subgroup.

Predefine acceptance criteria with the process owner before examining the reserved results. A read-only replay can assess recommendation quality, but it cannot establish realized savings or the effects of actions that were never taken.

After launch, compare incoming case distributions with the evaluation set. Google's model monitoring overview distinguishes changes between training and serving data from changes in serving data over time. Applied here, distribution checks signal when the evaluation may no longer represent current work. They should trigger investigation and fresh review, not automatic approval of a new model.

Test the handoffs before trusting an always-on AI coworker

FuturPulse — Mon, 12 Oct 2026 10:41:09 +0000

Test the handoffs before trusting an always-on AI coworker

An AI coworker demo usually shows a successful conversation. An operational evaluation should show what happens when that conversation moves between a web application, a team channel and a background task. The difficult boundary is often the handoff: who is speaking, which permissions apply, and what happens to work that is already running?

Consider an internal assistant that investigates a failed deployment and drafts a status update. Before giving it production access, run the following exercise with synthetic incidents and a test workspace. The goal is to expose state and permission failures without depending on a particular model's eloquence.

Start with an ownership map

Write down who operates the conversation store, scheduler, model connection, tool execution and messaging integration. For every component, name the person or team responsible for recovery. A hosted interface does not answer all of these questions, and deploying source code does not establish an operating process.

Our comparison of OpenAI Dots and CopilotKit OpenDots is useful background for this ownership discussion. Use it to frame procurement questions, then verify the behavior of the actual deployment you will use. The tests below are proposed acceptance criteria, not reported test results for either product.

Test identity across two surfaces

Give one test user access to an incident record and deny another user access. Ask both to request the same summary from the web interface and the team channel. The denied request must stay denied, including when the second user quotes a previous answer or supplies the incident identifier directly.

CopilotKit's Channels documentation describes application-controlled mapping between channel users and application users. Treat that mapping as a security boundary. Log the resolved application identity and permission decision, rather than trusting a display name carried in message text.

Also test group conversations. A person may be authorized to read an incident privately while the channel audience is not. Retrieval permission and permission to disclose the result to everyone in a room should be separate checks.

Interrupt a task at an inconvenient moment

Start the investigation, pause it after evidence retrieval, and restart the worker. Require a visible task identifier and a clear record of the last completed step. The assistant should resume from recorded state or explain that it cannot safely continue.

Deliver the same event twice. The system should recognize the duplicate or safely repeat a read; it should not generate two external status updates. Then revoke the user's incident access while the task is paused. Resumption should reevaluate authorization before retrieving more evidence or disclosing stored material.

Set a deadline and a maximum number of tool calls for the exercise. Confirm that cancellation prevents new work from being scheduled, and explicitly document any in-flight operation that cannot be interrupted.

Bind approval to a concrete action

Show the proposed status message, destination and evidence timestamp together. If the draft or destination changes after approval, require a fresh decision. A generic acknowledgment such as "looks good" should not authorize an unspecified future message.

Finish by producing a compact receipt: initiating identity, task identifier, evidence references, approval decision and external result. Keep sensitive document contents out of routine operational logs.

Score the exercise by permission consistency, duplicate handling, recovery behavior and the completeness of that receipt. A fluent answer is useful, but a coworker becomes dependable when a human can reconstruct what it did across every handoff.

A repeatable acceptance test for a local GGUF model

FuturPulse — Mon, 12 Oct 2026 10:40:14 +0000

A repeatable acceptance test for a local GGUF model

A local language model can load successfully and still be unsuitable for your application. A helpdesk classifier needs consistent labels; a coding assistant needs useful changes; a document extractor needs evidence. Choosing a download by parameter count alone skips the question that matters: can this exact model, quantization and runtime complete your workload within your machine's limits?

Here is a small acceptance test you can run before integrating a model into a product. It is an evaluation design, not a claim that a particular model has passed it.

Freeze the candidate before measuring it

Record the model repository revision, GGUF filename, file checksum, runtime version and launch settings. Include the chat template, context limit and accelerator configuration. If any of these changes, treat the result as a different candidate.

GGUF stores tensors and metadata, as explained in the Hugging Face format documentation. That does not make every GGUF file compatible with every runtime build. Check the publisher's required loader and supported architecture before interpreting a load error as insufficient memory.

Keep the model's license alongside that record. Downloadable weights and unrestricted use are separate questions. Our practical shortlist of GGUF models for local inference provides starting candidates and compatibility considerations; the acceptance test should decide what stays on your machine.

Build a workload with explicit failure conditions

Start with thirty inputs drawn from the task you actually intend to automate. For an extraction service, include ten ordinary documents, ten incomplete or ambiguous documents and ten unusually long examples. Remove sensitive information before sharing the evaluation set.

Define the expected behavior before running anything. A missing invoice date should produce a null field, not a plausible date. An unsupported category should produce an explicit rejection. A response that parses as JSON but invents a value should fail the content check.

Use a separate development set for prompt tuning. Repeatedly editing the prompt against the same thirty acceptance cases turns the test into a rehearsal. Keep several unseen cases for the final comparison.

Measure the whole interaction

Time cold startup separately from a warm request. For each request, record time to first token, total completion time, output length, peak memory and the task result. Record prompt-processing speed separately from generation speed when the runtime exposes both.

Repeat the same inputs several times using fixed settings. Report the spread as well as the median: an occasional long pause may matter more to an interactive application than a slightly faster typical answer. Do not claim a meaningful tail-latency estimate from a tiny sample.

Then repeat with a longer context and with the other applications you normally keep open. The model file size is only part of the memory budget; runtime buffers, context state and other processes also need room. Monitor swapping rather than treating a successful load as proof of comfortable operation.

Make the decision auditable

Choose acceptance limits before looking at the results. For example, require every output to parse, forbid invented values in the selected extraction cases and set a latency budget appropriate to the interface. These are project requirements, not universal benchmark thresholds.

Save the inputs, outputs and run configuration together. Select the smallest candidate that satisfies the requirements, then rerun the same checks whenever you change the model or runtime. A modest model with a reproducible pass is easier to operate than an impressive download with an unrecorded configuration.

Running transformers in the browser got serious, and the bottleneck moved

FuturPulse — Fri, 25 Sep 2026 12:40:23 +0000

Running transformers in the browser got serious, and the bottleneck moved

Client-side inference in JavaScript spent years being a demo. You could load a
small model, you could show it working, and then you looked at the numbers and
shipped the server call anyway. That calculation has changed, and not because
the models got smaller — because the runtime underneath them stopped being a
thin wrapper and became an actual compiled engine.

The shift: a runtime written for the hardware, not for the language

The interesting move in the current generation of JavaScript ML libraries is the
WebGPU runtime being rewritten in C++ and compiled down, rather than implemented
in JS on top of WebGPU bindings. That sounds like an internal detail. It is not.
It changes what kind of model is viable in a tab, because the per-op overhead
that used to dominate small-batch inference largely disappears, and it makes the
same runtime behave consistently across browsers and Node instead of forking on
platform quirks.

The second-order effect matters more to most teams: once the runtime is a
compiled artifact with a stable interface, the library can adopt architectures
that were previously impractical to express efficiently in JS — multi-head
latent attention, mixture-of-experts routing — without each one becoming a
bespoke optimisation project.

Build tooling is not a footnote here

Migrating a library of this size from Webpack to esbuild is the kind of change
that gets one line in a changelog and reshapes contributor experience entirely.
An order-of-magnitude build-time improvement is the difference between a
contributor iterating on a model implementation and a contributor waiting.
Libraries that take a year to reach a major version are usually paying that year
partly in build latency, and fixing it is how the next year goes faster.

The typing story is the same category of unglamorous-but-decisive work: dynamic
pipeline types mean your editor knows what a pipeline returns before you run it,
and splitting tokenization into its own lightweight, type-safe package means a
project that only needs tokenizers no longer drags an inference engine into its
bundle. FuturPulse went through the release in detail —
what actually shipped in Transformers.js v4 and how to install it
— including the single-command NPM install and the full feature list.

Where browser inference still loses

Be honest about the remaining constraints before you rewrite an architecture
around this:

First-load weight transfer. WebGPU does not make a model file smaller. The user still downloads it, and cold-start on a mobile connection is the number that kills these deployments.
Memory ceilings. A tab's GPU memory budget is not your machine's GPU memory, and it varies by browser, platform and what else is open.
WebGPU availability. Better than it was, still not universal, and the fallback path is where your performance assumptions go to die.
Debuggability. When a compiled runtime misbehaves, you are debugging through two layers you did not write.

None of these are reasons to avoid client-side inference. They are reasons to
measure it on the devices your users actually have, rather than on the laptop
you developed it on.

The decision this changes

The old default — "run it server-side unless the model is tiny" — was correct
when the runtime overhead dominated. With a compiled WebGPU runtime, the
crossover point moves, and it moves furthest for the workloads where
server-side inference is most annoying: per-keystroke embeddings, privacy-bound
text, anything where a round-trip is worse than a slow local pass.

Recalculate it with your own model and your own devices. The general answer is
now genuinely "it depends", which it was not two years ago.

FuturPulse covers AI engineering releases with that separation in mind: what a
version actually ships, and what it changes about the decision in front of you.
Ongoing coverage is at FuturPulse.

Budgeting a video model: the numbers that actually appear on the invoice

FuturPulse — Fri, 25 Sep 2026 10:59:07 +0000

Budgeting a video model: the numbers that actually appear on the invoice

Most comparisons of AI video generators rank the output. That is the wrong first
question for anyone who has to sign off on the bill, because the output quality
gap between the current generation is narrow and the billing structures are not.
A model that charges per generated second behaves very differently in a monthly
budget than one that charges per clip, and the difference shows up long before
anyone argues about which render looks better.

Per second, per clip, or per seat

Three billing shapes are in circulation right now, and they reward completely
different workflows.

Per generated second. You pay for duration, so a twenty-second clip costs
roughly twice a ten-second one. Iteration is the expensive part: five attempts at
eight seconds costs the same as one forty-second final. Teams on this model learn
to storyboard before they render.

Per clip or per prompt. A fixed charge regardless of length, usually with a
hard cap on duration. Cheap to iterate, expensive to produce anything long,
because length is not something you can buy — it is a ceiling.

Per seat, with a generation quota. Predictable monthly cost, unpredictable
capacity. Fine until a deadline week, when the quota turns into the constraint
nobody planned for.

The practical consequence: the model you should pick depends on whether your
bottleneck is iteration count or finished duration. That is a question about your
process, not about the model.

Self-hosting changes the arithmetic, and the risk

Several current video models publish weights for self-hosting, often under a
community licence with a revenue threshold. Below the threshold there is no
per-generation charge; above it, a commercial licence applies. This turns a
variable cost into a fixed one — you pay for GPUs whether you render or not —
and it moves the risk from the invoice to the infrastructure.

It is worth doing the crossover calculation honestly. Take your real monthly
render volume in seconds, multiply by the hosted per-second rate, and compare
against what the equivalent GPU hours cost you including idle time. For low,
bursty volumes the hosted API usually wins. The break-even arrives sooner than
most teams expect once rendering becomes a daily habit rather than an experiment.

Read the duration ceiling before the price

The specification that quietly decides most of this is maximum clip length, and
it varies between tiers of the same product — a "fast" tier sometimes allows
longer clips than the "pro" tier above it, because the constraint is compute per
second, not quality. FuturPulse worked through the current rates and limits in
a per-second comparison of LTX-2.5 against the alternatives,
including which tier actually permits twenty-second output.

Check the ceiling first. If it is below what you need to deliver, the price is
irrelevant — you are going to be stitching clips, and stitching costs editing
time that no pricing page lists.

A short checklist before you commit

What is your monthly render volume, in seconds, measured rather than guessed?
What is the maximum single-clip duration you need to deliver?
How many attempts does a usable clip take in practice, on your prompts?
Does the licence threshold apply to your revenue, and what happens when you cross it?
Can you export and re-render elsewhere if the price changes?

FuturPulse covers AI tooling with that kind of separation in mind: what a vendor
publishes as a rate, and what it costs once a real workflow runs through it.
Ongoing coverage is at FuturPulse.

What an AI incident channel needs before it counts as one

FuturPulse — Fri, 25 Sep 2026 10:01:48 +0000

What an AI incident channel needs before it counts as one

Governments are starting to talk about notifying each other when an AI system does something dangerous. The word that gets used is "hotline", and it carries an image: a line that rings, someone who picks up, a decision that follows. Almost none of the proposals on the table describe that. They describe an intention to describe it later.

The current example is concrete. Ahead of the 24 September 2026 Trump–Xi meeting, the United States put forward a mechanism for notifying China about AI incidents. Beijing confirmed through Xinhua that AI was discussed. It did not say the proposal had been accepted. FuturPulse worked through what Trump was actually being asked to approve before the Washington summit, and the honest summary is that the published record establishes a topic, not a channel.

That is not a criticism of the idea. It is a description of how far along it is — and the distance is worth making explicit, because anyone building incident response inside a company already knows what the remaining work looks like.

The four things any incident channel has to specify

If you have ever run an on-call rotation, this list will be familiar. It is the same list at the level of states.

A trigger. What counts as an incident? A model that exceeds a capability threshold, an autonomous system that acts outside its authorised scope, a deployment that produces physical harm? Until the trigger is written down, every party gets to decide privately that nothing has happened yet. A channel with no defined trigger cannot fire, and — more importantly — cannot visibly fail to fire, which means nobody can be held to it.

A named endpoint. Who receives the message, at what address, with what authority to act on it? "The two governments" is not an endpoint. Real channels name a desk and a deputy, because the person who answers at 03:00 is rarely the person who negotiated the agreement.

A message format. What has to be in the notification? A timestamp, a system identifier, an observed behaviour, a confidence level, a recommended action. Without a format, the first real notification arrives as prose, is read as a diplomatic signal rather than a technical report, and is answered in kind.

A response deadline. How long may the other side take to acknowledge? An unbounded acknowledgement window converts an incident channel into a suggestion box.

No public document sets out any of the four for the US–China proposal. That is the measure of where it stands.

Why the gap keeps being missed in coverage

Because the label does the work. "Hotline" is a familiar object, so readers fill in the operational detail from the word rather than from the document, and the story gets covered as though a mechanism now exists. The same move happens with "task force", "czar" and "framework". The correction is mechanical: before treating an announcement as an instrument, ask which of the four items above appear in a published text, and note the ones that do not.

The same test applies to your own vendors

If you run AI systems in production and depend on a provider, you have an incident channel with them too — and it is worth checking against the same four points. Most status pages define no trigger for model-behaviour incidents, name no endpoint beyond a support queue, specify no format, and promise no deadline. The state-level version of this problem is the one in the headlines; the vendor-level one is the one that will actually page you.

FuturPulse covers AI policy with that separation in mind: what a published document establishes, and what it merely announces. Ongoing coverage is at FuturPulse.

When your AI agent hits a wall, robots.txt is usually not the reason

FuturPulse — Fri, 25 Sep 2026 09:57:28 +0000

When your AI agent hits a wall, robots.txt is usually not the reason

If you build browser-driving agents, you have met this failure: the agent searches fine, opens product pages fine, then dies at checkout with a terms-of-service error. It is tempting to blame robots.txt and move on. That diagnosis is almost always wrong, and it sends you fixing the wrong layer.

A current example is worth walking through, because it shows how little is usually established when an "agent is blocked" story breaks. Meta launched its Muse personal AI agent in the US on 8 September 2026, with browser-based shopping among its stated uses. Users then reported an Amazon Conditions of Use error when Muse tried to complete a purchase. FuturPulse worked through what is actually on the record about the Amazon access problems reported by Muse users, and the answer is: less than the headlines imply. No published Amazon statement confirms a Muse-specific enforcement action. No report reproduces the full error text, its first appearance date, or an HTTP response code.

The four gates an agent passes, and what each one enforces

robots.txt is a crawler directive. It asks automated crawlers which paths they should not fetch. It is advisory, it is aimed at indexing bots, and it has nothing to say about an authenticated session completing a transaction. When a purchase fails, you are almost certainly past that layer. In practice an agent crosses four separate gates:

Fetch. Can you retrieve the page at all? This is where robots.txt, IP reputation and user-agent filtering live.
Render. Does the page produce usable DOM for a headless browser, or does it require a real client fingerprint?
Session. Is there a logged-in account, and does the site's terms of use permit automated access to it? This is a contractual gate, not a technical one.
Transaction. Does the payment or order step accept a request that did not come from a human-driven session? Fraud and abuse systems sit here, and they are the strictest of the four.

Each gate fails differently, and the error surface is misleading: a terms-of-service message at gate 4 is not evidence of a crawler block at gate 1.

Debugging in the right order

Log the gate, not the symptom. Record which of the four steps produced the failure, with the status code and response body. "Blocked" with no gate attached is not a bug report.
Separate search from checkout in your tests. A shopping agent can read catalogue pages long after transactions stop working. If you only test end to end, you cannot tell which gate moved.
Read the terms, not just the headers. Gate 3 failures are decided in a contract, not in a config file. No amount of user-agent tuning fixes them, and trying is how integrations get permanently cut off.
Treat unattributed reports as unconfirmed. If neither the platform nor the agent vendor has said what happened, your incident timeline is a guess. Write it down as one.

Why the distinction matters commercially

Agent products are being sold on the promise of completing purchases, and the gate that decides whether that promise holds is the one nobody publishes rules for. A crawler directive is public and machine-readable. A transaction-level policy is neither, it can change without notice, and it is where the business risk actually sits.

FuturPulse covers agent infrastructure with that separation in mind, and marks explicitly what the published record does not establish. The full breakdown of the Muse case is here: what is known about the Amazon access problems reported by Meta Muse users. For continuing coverage of agents, model releases and AI infrastructure pricing, see FuturPulse.

Open Jev alternatives: matching the feature set, part by part

FuturPulse — Fri, 25 Sep 2026 08:05:21 +0000

Open Jev alternatives: matching the feature set, part by part

EN : FuturPulse was built on a simple observation: most AI coverage repeats press releases. We do the opposite — every article starts from a primary source, a captured page or a reproducible test, and shows its dates. Recent work includes verified million-token API pricing from OpenAI, Google and xAI; a builder's pattern library for AI agent skills; and evidence-based guides on local reasoning models. Coverage spans AI, agents, tools, automation, robotics, research and the business of AI. Free to read, with the week's most useful pieces in a free Monday digest.

What it does

FuturPulse.

In short: Independent AI news, analysis and practical guides.

Find out more

Details, live data and access: which parts of Jev the open projects actually match.

Built by which parts of Jev the open projects actually match — FuturPulse.

Decision models vs LLMs: when a typed output beats a token stream

FuturPulse — Fri, 25 Sep 2026 08:05:20 +0000

Decision models vs LLMs: when a typed output beats a token stream

EN : FuturPulse was built on a simple observation: most AI coverage repeats press releases. We do the opposite — every article starts from a primary source, a captured page or a reproducible test, and shows its dates. Recent work includes verified million-token API pricing from OpenAI, Google and xAI; a builder's pattern library for AI agent skills; and evidence-based guides on local reasoning models. Coverage spans AI, agents, tools, automation, robotics, research and the business of AI. Free to read, with the week's most useful pieces in a free Monday digest.

What it does

FuturPulse.

In short: Independent AI news, analysis and practical guides.

Find out more

Details, live data and access: where typed outputs beat tokens in production.

Built by where typed outputs beat tokens in production — FuturPulse.

AI stop-sign cameras are a traffic-enforcement idea worth scrutinizing, not just deploying

FuturPulse — Thu, 24 Sep 2026 17:37:33 +0000

AI stop-sign cameras are a traffic-enforcement idea worth scrutinizing, not just deploying

Obvio's pitch is straightforward: mount AI-powered cameras near stop signs, automatically detect vehicles that roll through or blow past them, and issue citations without a police officer present. Framed purely as a road-safety measure, it is easy to see the appeal — stop-sign violations are a real and persistent cause of intersection collisions.

The part that deserves more scrutiny than the safety pitch alone is the enforcement pipeline behind it. Automated detection at scale means false positives get generated at scale too, and the appeals process for a citation triggered by a computer vision model is rarely as accessible as contesting a ticket written by a person who can be questioned about judgment calls. There's also the question of who audits the model's accuracy over time, and against what independent standard, once it is generating revenue for a municipality or a vendor.

None of this makes the underlying safety problem less real. Stop-sign running is dangerous and hard to police continuously with human officers. The question is whether automated enforcement systems are being deployed with the same due-process guardrails that limit human-issued citations, or whether the AI label ends up substituting for that scrutiny.

FuturPulse covered the rollout and what Obvio's cameras actually do: Obvio's AI-powered stop sign cameras enhance road safety.

When AI reconstructs a voice, whose voice is it really

FuturPulse — Thu, 24 Sep 2026 17:36:59 +0000

When AI reconstructs a voice, whose voice is it really

ALS gradually removes a person's ability to speak, and until recently the alternative was a synthetic text-to-speech voice that sounded nothing like the person using it. A recent case of a musician regaining a version of his own voice through AI voice reconstruction is a useful test of what current speech synthesis can and cannot do.

The technical part is the least interesting part. Voice cloning from limited source audio is a mature capability at this point — the harder questions are downstream. How much old recorded audio is actually needed to get a usable model. Whether the reconstructed voice can carry emotional range, or just flat statements. Who owns the resulting voice model, and what happens to it if the platform behind it shuts down or changes its terms.

For assistive technology specifically, the bar is not "does it sound realistic" in the abstract — it's whether the person's family and friends recognize it as them, and whether the person retains the same level of expressiveness they had learning to communicate that way for the first time again.

FuturPulse's coverage of the case has the specifics: How AI helped a musician regain his voice after ALS.

The broader pattern worth tracking is that voice-preservation tools are increasingly marketed pre-emptively — record your voice now, in case you need a synthetic version later — which raises the same data-custody questions long before there is any diagnosis to react to.

FuturPulse: Independent AI news, analysis and practical guides

FuturPulse — Thu, 24 Sep 2026 17:31:19 +0000

FuturPulse: Independent AI news, analysis and practical guides

EN : FuturPulse was built on a simple observation: most AI coverage repeats press releases. We do the opposite — every article starts from a primary source, a captured page or a reproducible test, and shows its dates. Recent work includes verified million-token API pricing from OpenAI, Google and xAI; a builder's pattern library for AI agent skills; and evidence-based guides on local reasoning models. Coverage spans AI, agents, tools, automation, robotics, research and the business of AI. Free to read, with the week's most useful pieces in a free Monday digest.

What it does

FuturPulse.

In short: Independent AI news, analysis and practical guides.

Find out more

Details, live data and access: FuturPulse.

Built by FuturPulse — FuturPulse.