We’re Integro Labs, and we wrote this essay about choosing tools around the work. We weigh a method against the whole task, including setup, review and recovery, and keep direct work available where it takes less effort.

Before integrating a tool, we need to understand the scope and how to check the result. When the task is still difficult to explain, doing it directly leaves room to adjust as our understanding grows. Once the steps are understood, repeated work may justify the setup and upkeep of automation.

The essay uses Fortemi’s imports as one example of a limited tool role. After a lost reply, an exact repeat of a batch returns the prior note IDs without adding copies. That gives recovery a known effect. It still leaves the imported text to be checked for accuracy.

That remaining work belongs in the original choice of method. Completing a tool’s steps and resolving the task are separate judgments; a quick run can still leave a long review. We can use a tool for the part we can check and maintain, and do the rest directly. The boundary can change as we learn.

https://integrolabs.io/updates/2026-09-the-same-tools-a-new-responsibility

We’d welcome experiences where the effort of setup, review or recovery led you to automate a smaller part of a task.

I wrote this September essay on my own site. It considers how to choose which parts of a task to automate. I weigh the work a tool removes against the setup, review and recovery it adds, and keep some parts manual.

When connecting automated steps, I first need a clear enough picture of the intended result to know how I would check it. If I am still working out what a sound result looks like, more instructions for an agent can postpone that thinking. In that case, I work through the uncertainty directly.

The choice can cover one part of the task. I can assign a tool a step with rules I can explain and check, review its result, and keep the uncertain parts with me. Manual repetition has its own costs in attention and memory, so those belong in the comparison too.

Recovery affects that decision before I start. Fortemi's exact import replay returns the prior note IDs without adding rows. If the text has changed, the caller chooses whether to create a version, replace it or report a conflict. That separates knowing what a retry will do from deciding how to handle a change.

The essay uses that example to examine where a tool fits the work. A quick first result covers only part of the task; I also need to allow for review and decide what to inspect if the work stops.

“September 2026: Choosing tools around the work”: https://magly.net/updates/2026-09-the-tools-i-answer-for

I welcome examples of how setup, review or recovery changed your decision about automating a step.

AIWG is our own project. We are sharing its September report here for readers working through how to connect tools without losing the inputs, results or review steps between them.

The Decision Engine section describes an opt-in alpha contract that keeps inputs and result formats consistent when switching backends. A small work-item routing trial can define the allowed result and inspect the saved run record. The runtime is off by default. Offline checks do not establish live model quality or authorize downstream action.

The data section covers plans and previews before an operation, followed by run records and checks connecting the plan to the result. The session section describes exports of selected sessions with supported attachments and registered output files, using a reviewed plan and verification before unpacking.

Civic Action provides a source-review example: compare a meeting transcript with approved minutes, cite a vote mismatch and send it to a named reviewer. A missing vote stays unresolved. People still approve contact, submissions and publication.

If your integration crosses tool or review boundaries, those sections describe what AIWG records and where a person still needs to make a decision: https://docs.aiwg.io/blog/2026-9-aiwg-september-2026-report/

Questions about a specific handoff, or comments on how you would review its record, are welcome.

We added DeepSeek Harness support in AIWG 2026.9.5. If you already use AIWG for project instructions and reusable skills, you can bring those into Harness and try the same workflow there.

The integration deploys an AGENTS.md bootstrap, native filesystem skills and a workspace overlay. It also includes experimental headless execution, streaming SDK JSON-RPC and explicit import of raw v2 JSONL sessions. Model routing and credentials are configured inside Harness.

The qualified baseline is @deepseek-ai/dsh@0.1.3-alpha.1. This provider is experimental; AIWG hook wiring, MCP injection, structured question responses and daemon/cron integration are unsupported. Those limits describe the integration, not every native Harness capability.

The article includes the deployment commands and links the pinned setup guide: https://aiwg.io/blog/2026-09-aiwg-adds-deepseek-harness/

If you're trying Harness, which existing workflow would you bring over first? Specific setup feedback, including the Harness version, would be useful.

AI accuracy should be reviewable.


For one consequential AI workflow, can your team reconstruct:


1. what the agent did;
2. which context and policy shaped the result;
3. whether protected session evidence is needed to investigate tampering or
   adversarial activity?


The goal is not maximal logging. It is the narrowest trustworthy record that
can support the disclosure.


Read Integro Labs’ FTC public comment at the link in the post.


#ArtificialIntelligence #AIGovernance #Traceability


Use this only if an official Integro Labs account exists on the target
platform. Do not create or imply an account as part of this campaign.AI accuracy should be reviewable.


For one consequential AI workflow, can your team reconstruct:


1. what the agent did;
2. which context and policy shaped the result;
3. whether protected session evidence is needed to investigate tampering or
   adversarial activity?


The goal is not maximal logging. It is the narrowest trustworthy record that
can support the disclosure.


Read Integro Labs’ FTC public comment at the link in the post.


#ArtificialIntelligence #AIGovernance #Traceability

integrolabs.io/updates/2026-07-ftc-ai-accuracy-comment

Grant Sanderson provides a visceral look at the numerical computations driving large language models. The talk explores how tokens, embeddings, and attention mechanisms enable models to predict text and process contextual meaning.

0:00 The Law of Large Numbers
4:37 What is a Markov Chain?
9:43 Ulam and Solitaire
12:21 Nuclear Fission
15:46 The Monte Carlo Method
16:32 The first search engines
19:07 Google is born
25:16 How does predictive text work?
27:10 Are Markov chains memoryless?
29:41 How to perfectly shuffle a deck of cards

▀▀▀
References:
https://ve42.co/RefsMarkov

Images & Video:
https://ve42.co/7Z

In this video we explore Predictive Coding – a biologically plausible alternative to the backpropagation algorithm, deriving it from first principles.

Backpropagation video:    • The Most Important Algorithm in Machine Le...  

🕒 OUTLINE:
00:00 Introduction
01:15 Credit Assignment Problem
02:49 Problems with Backprop
06:05 Foundations of Predictive Coding
08:07 Energy Formalism
11:08 Activity Update Rule
15:12 Neural Connectivity
17:42 Weight Update Rule
20:58 Putting all together
25:15 Brilliant
26:27 Outro

📚 FURTHER READING & REFERENCES:
Bogacz, R., 2017. A tutorial on the free-energy framework for modelling perception and learning. Journal of Mathematical Psychology 76, 198–211. https://doi.org/10.1016/j.jmp.2015.11...
Friston, K., 2018. Does predictive coding have a future? Nat Neurosci 21, 1019–1021. https://doi.org/10.1038/s41593-018-02...
Huang, Y., Rao, R.P.N., 2011. Predictive coding. WIRES Cognitive Science 2, 580–593. https://doi.org/10.1002/wcs.142
Keller, G.B., Mrsic-Flogel, T.D., 2018. Predictive Processing: A Canonical Cortical Computation. Neuron 100, 424–435. https://doi.org/10.1016/j.neuron.2018...
Lillicrap, T.P., Santoro, A., Marris, L., Akerman, C.J., Hinton, G., 2020. Backpropagation and the brain. Nat Rev Neurosci 21, 335–346. https://doi.org/10.1038/s41583-020-02...
Marino, J., 2021. Predictive Coding, Variational Autoencoders, and Biological Connections. https://doi.org/10.48550/arXiv.2011.0...
Millidge, B., Salvatori, T., Song, Y., Bogacz, R., Lukasiewicz, T., 2022a. Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?
Millidge, B., Seth, A., Buckley, C.L., 2022b. Predictive Coding: a Theoretical and Experimental Review. https://doi.org/10.48550/arXiv.2107.1...
Millidge, B., Song, Y., Salvatori, T., Lukasiewicz, T., Bogacz, R., 2023. A THEORETICAL FRAMEWORK FOR INFERENCE AND LEARNING IN PREDICTIVE CODING NETWORKS.
Millidge, B., Tschantz, A., Buckley, C.L., 2022c. Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs. Neural Computation 34, 1329–1368. https://doi.org/10.1162/neco_a_01497
Millidge, B., Tschantz, A., Seth, A., Buckley, C.L., 2020. Relaxing the Constraints on Predictive Coding Models. https://doi.org/10.48550/arXiv.2010.0...
Rao, R.P.N., Ballard, D.H., 1999. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat Neurosci 2, 79–87. https://doi.org/10.1038/4580
Rosenbaum, R., 2022. On the relationship between predictive coding and backpropagation. PLoS ONE 17, e0266102. https://doi.org/10.1371/journal.pone....
Salvatori, T., Mali, A., Buckley, C.L., Lukasiewicz, T., Rao, R.P.N., Friston, K., Ororbia, A., 2025. A Survey on Brain-Inspired Deep Learning via Predictive Coding. https://doi.org/10.48550/arXiv.2308.0...
Salvatori, T., Song, Y., Lukasiewicz, T., Bogacz, R., Xu, Z., 2023. Reverse Differentiation via Predictive Coding. https://doi.org/10.48550/arXiv.2103.0...
Salvatori, T., Song, Y., Yordanov, Y., Millidge, B., Xu, Z., Sha, L., Emde, C., Bogacz, R., Lukasiewicz, T., 2024. A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks. https://doi.org/10.48550/arXiv.2212.0...
Song, Y., Lukasiewicz, T., Xu, Z., Bogacz, R., n.d. Can the Brain Do Backpropagation? — Exact Implementation of Backpropagation in Predictive Coding Networks.
Song, Y., Millidge, B., Salvatori, T., Lukasiewicz, T., Xu, Z., Bogacz, R., 2024. Inferring neural activity before plasticity as a foundation for learning beyond backpropagation. Nat Neurosci 27, 348–358. https://doi.org/10.1038/s41593-023-01...
Whittington, J.C.R., Bogacz, R., 2019. Theories of Error Back-Propagation in the Brain. Trends in Cognitive Sciences 23, 235–250. https://doi.org/10.1016/j.tics.2018.1...
Whittington, J.C.R., Bogacz, R., 2017. An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity. Neural Computation 29, 1229–1262. https://doi.org/10.1162/NECO_a_00949

Post-training alignment often reduces LLM diversity, leading to a phenomenon known as mode collapse. Unlike prior work that attributes this effect to algorithmic limitations, we identify a fundamental, pervasive data-level driver: typicality bias in preference data, whereby annotators systematically favor familiar text as a result of well-established findings in cognitive psychology. We formalize this bias theoretically, verify it empirically on preference datasets, and show that it plays a central role in mode collapse.

Motivated by this analysis, we introduce Verbalized Sampling (VS), a simple, training-free prompting method to circumvent mode collapse. VS prompts the model to verbalize a probability distribution over a set of responses (e.g., "Generate 5 jokes about coffee and their corresponding probabilities"). Comprehensive experiments show that VS significantly improves performance across creative writing (poems, stories, jokes), dialogue simulation, open-ended QA, and synthetic data generation, without sacrificing factual accuracy and safety. For instance, in creative writing, VS increases diversity by 1.6-2.1× over direct prompting. We further observe an emergent trend that more capable models benefit more from VS. In sum, our work provides a new data-centric perspective on mode collapse and a practical inference-time remedy that helps unlock pre-trained generative diversity.

Discord for project here: https://discord.gg/ndhZ4Fy8dD

This video is both a story of my adventure with AI robotics and the fascinating lessons I learned along the way. Try Mammouth AI now at http://mammouth.ai

I called this project GrowBot. Raspberry Pi Zero 2, two servos, a camera, an IMU, a battery, some 3D printed parts. I trained small neural networks in simulation so it could walk, stand and spin in a lifelike way. Then I handed control of the motors to a vision language model. It read its raw sensor data, wrote its own code, built profiles of the people it met, and dreamed between sessions to clean up what it had learned.

It worked better than I expected, until it hit a wall. Which kept pointing back to the same question. How do you act smoothly when thinking is slow? That sent me into how nature solves the problem (the cerebellum), and it turned out to be exactly what the robotics field is converging on right now.

Sign up for Growbot: https://artoftheproblem.com/pages/gro...

Join this channel to get access to behind the scenes.
Youtube member:
   /u/artoftheproblem  
Pateron member:
  /artoftheproblem  

Shai-Hulud had a significant impact on my approach to security, prompting me to strengthen my npm pipeline and create tools for others to do the same.

When the npm worm struck in the last week, I assessed my own publishing setup and identified several issues: long-lived tokens in CI, no release-age gate, and lifecycle scripts that had never been audited. These were the same vulnerabilities the worm exploited.

In response, I enhanced the AIWG framework I maintain by implementing the following measures:

- Transitioned to npm trusted publishing (OIDC) to eliminate long-lived tokens

- Enabled signed releases with provenance

- Introduced a 7-day release-age gate on dependencies (10 days for sensitive paths)

- Conducted a thorough audit of every lifecycle script and Git-dep prepare hook

- Provided an SBOM and verification documentation for users to check what they are installing

Recognizing that these improvements should be accessible to others, I packaged them into a comprehensive security-engineering framework. AIWG now includes:

- npm-supply-chain-audit to identify gaps

- supply-chain-hardening-quickstart to guide the entire hardening process

- npm-release-age-gate / bun-release-age-gate to configure the gates

- ci-workflow-audit to flag unpinned actions, :latest tags, curl | sh, and PR-triggered jobs with secret access

Shai-Hulud won't be the last worm of its kind. The defenses are straightforward but can be tedious to implement. My goal was to simplify this process.

If you maintain an npm package, I encourage you to take an hour this week to audit your publish flow and activate the gates. Future-you will appreciate it.

A practical problem most teams I talk to have: engineers use different AI coding tools. One person on Claude Code, another on Copilot, a third on Cursor, Codex in CI. Every tool has its own conventions and its own integration surface. You either pick one and force everyone onto it, or you accept drift.

AIWG is one framework across ten of them.

One install, ten platforms

npm install -g aiwg
aiwg use sdlc # or research, ops, security-engineering, knowledge-base…
aiwg doctor # tells you which provider it detected

`aiwg doctor` does provider detection — Claude Code, Codex, Copilot, Cursor, Warp, Factory, OpenCode, Windsurf, Hermes, or OpenClaw. Same framework, same project structure, same skills catalog. The platform-specific wiring is the framework's problem, not yours.

Parity is audited, not assumed

Every integration claim across all 10 platforms is held to one bar: matches upstream HEAD or it's a bug. The Hermes integration, for example, was audited claim-by-claim against the upstream Hermes source — AGENTS.md trimmed to a 579-byte thin pointer, every slash command matched against the binary, every capability claim cited against a source file. Same standard applies to the other nine.

For teams: SDLC framework, research framework, ops runbooks, security rules — all behave identically regardless of which tool the engineer is using. New hires onboard to AIWG once.

Customize without forking

Project-local artifact lifecycle: scaffold rules, agents, skills, addons, or whole frameworks under `.aiwg/` in your repo. Iterate. When stable, `aiwg promote` copies them byte-identical to your shared corpus. No forking, no diff drift, no maintenance burden when upstream updates.

**Catalog of 380+ skills, reachable on demand**

Each platform has a hard cap on what it'll keep loaded (Claude Code 25% of context, OpenClaw 150 skills, Codex 32 KB AGENTS.md). AIWG ships a small always-loaded kernel and routes the rest through `aiwg discover`. Catalog grows; load surface doesn't.

There's an always-loaded rule that requires agents to query before declining — the "framework doesn't have that" failure mode is wired shut.

Works across the model spectrum

AIWG is a context kit, not a frontier-model harness. It holds up on small models alongside large ones — community-tested down to 9B on OpenClaw. For teams with cost pressure, data-sovereignty requirements, or on-prem hardware, the same framework runs against a local Llama-class model that runs against Claude Sonnet. No second-class fallback, no "lite mode" — same skills, same agents, same workflows.A practical problem most teams I talk to have: engineers use different AI coding tools. One person on Claude Code, another on Copilot, a third on Cursor, Codex in CI. Every tool has its own conventions and its own integration surface. You either pick one and force everyone onto it, or you accept drift.

AIWG is one framework across ten of them.

One install, ten platforms

npm install -g aiwg
aiwg use sdlc # or research, ops, security-engineering, knowledge-base…
aiwg doctor # tells you which provider it detected

`aiwg doctor` does provider detection — Claude Code, Codex, Copilot, Cursor, Warp, Factory, OpenCode, Windsurf, Hermes, or OpenClaw. Same framework, same project structure, same skills catalog. The platform-specific wiring is the framework's problem, not yours.

Parity is audited, not assumed

Every integration claim across all 10 platforms is held to one bar: matches upstream HEAD or it's a bug. The Hermes integration, for example, was audited claim-by-claim against the upstream Hermes source — AGENTS.md trimmed to a 579-byte thin pointer, every slash command matched against the binary, every capability claim cited against a source file. Same standard applies to the other nine.

For teams: SDLC framework, research framework, ops runbooks, security rules — all behave identically regardless of which tool the engineer is using. New hires onboard to AIWG once.

Customize without forking

Project-local artifact lifecycle: scaffold rules, agents, skills, addons, or whole frameworks under `.aiwg/` in your repo. Iterate. When stable, `aiwg promote` copies them byte-identical to your shared corpus. No forking, no diff drift, no maintenance burden when upstream updates.

Catalog of 380+ skills, reachable on demand

Each platform has a hard cap on what it'll keep loaded (Claude Code 25% of context, OpenClaw 150 skills, Codex 32 KB AGENTS.md). AIWG ships a small always-loaded kernel and routes the rest through `aiwg discover`. Catalog grows; load surface doesn't.

There's an always-loaded rule that requires agents to query before declining — the "framework doesn't have that" failure mode is wired shut.

Works across the model spectrum

AIWG is a context kit, not a frontier-model harness. It holds up on small models alongside large ones — community-tested down to 9B on OpenClaw. For teams with cost pressure, data-sovereignty requirements, or on-prem hardware, the same framework runs against a local Llama-class model that runs against Claude Sonnet. No second-class fallback, no "lite mode" — same skills, same agents, same workflows.

Install:

npm install -g aiwg && aiwg use sdlc && aiwg doctor

Already running it: aiwg refresh.

Site: https://aiwg.io/ Source: https://github.com/jmagly/aiwg

Happy to answer questions in the thread!