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Image 1 — Pagenary June: Markdown → static sites grew into a publishing platform — blogs, a page-effects suite, meaning-based search (bundled Fortemi engine), docs-map, managed hosting
Image 2 — Pagenary June: Markdown → static sites grew into a publishing platform — blogs, a page-effects suite, meaning-based search (bundled Fortemi engine), docs-map, managed hosting
▲ 4 r/AIWG+1 crossposts

Pagenary June: Markdown → static sites grew into a publishing platform — blogs, a page-effects suite, meaning-based search (bundled Fortemi engine), docs-map, managed hosting

Pagenary turns Markdown into a fast static site — no server or database, cheap to host, and one setup can publish many branded sites from shared templates. June was a flagship month. (It publishes these monthly reports and the portfolio's docs sites.)

Blog publishing (Phase 1) — post pages, an index of clickable cards, next/previous + back-to-index, themed examples, and a "living scroll" reading style.

Page-effects suite — a per-page engine (loads only when the page is ready; respects reduce-motion): on-this-page contents strip, scroll-driven stories, snap sections, a depth effect, click-to-enlarge images, staggered reveal, fold-outs, and a reading-progress bar.

Meaning-based search — runs on a vendored copy of the Fortemi engine, bundled into the build (no separate server). A build-time check validates the search index before a site ships.

docs-map — an auto-drawn graph of how your pages link, self-arranging so linked pages sit near each other.

Managed hosting + robustness — managed hosting as a first-class option (self-hosting stays free), content-addressed filenames with automatic cache purge on deploy, sub-path serving, and proper page titles.

npm i @pagenary/publisher · GitHub · Full June report

What do you use to publish docs today, and what's the one feature that would make you switch?

u/Manitcor — 11 hours ago
▲ 3 r/AIWG+1 crossposts

Carbonyl v0.2.0-alpha.18 — terminal-native Chromium browser now tracks Chromium M150 stable (150.0.7871.47)

Carbonyl is a real Chromium browser that renders inside a terminal — actual web pages as text and color, no display server, drivable and readable by an agent. v0.2.0-alpha.18 updates the engine to track Chromium M150 stable (150.0.7871.47, released by Google 2026-06-30).

What changed:

  • Engine tracking — patches regenerated onto M150 (35 total), with refreshed runtime pins for Chromium / Skia / WebRTC.
  • Validated release — the full gate ran green before shipping: PR checks, patch validation, GHCR image publishing, release packaging, and package smokes. Package smokes passed for .deb / .rpm / .AppImage; GHCR smokes passed for the default and x11 images.
  • Cleaner native installs — Linux packages now declare their required X11 loader libraries, and X11 runtime builds properly configure ANGLE/WebRTC switches.
  • Hardened release-mirror handling.

Still preview/alpha, with no breaking CLI changes.

u/Manitcor — 1 day ago
▲ 5 r/AIWG+1 crossposts

June 2026: 30+ releases across our open Agentic OS stack — agent dashboard, secure runtime, agent memory (server + in-browser), publisher, terminal browser

June was a build month. Here's everything that shipped across the open portfolio — each line links the full write-up.

https://preview.redd.it/s1yjf3nvt8bh1.png?width=1200&format=png&auto=webp&s=7922695900ea07fd8d337e374560904c579f4a2e

AIWG (the agent toolkit) — Cockpit, a dashboard to watch/start/stop your coding agents; plain-words tool search; a leaner startup (~193K → ~110K token Claude context). 13 releases. Full report · GitHub · npm install -g aiwg

Agentic Sandbox (where agents run code safely) — secure-by-default transport, vsock VM enrollment, short-lived gateway SSH, live Observe/Drive terminal, 7 signed images. 18 releases. Full report · GitHub

Fortemi (self-hosted agent memory) — the "data coming in" milestone: streaming chat, signed webhooks, resumable bulk ingest + uploads, event feeds, plus a security/privacy hardening pass. Full report · GitHub

fortemi-react (Fortemi in the browser) — same memory, fully client-side: three load modes, off-thread search, deterministic note-graphs, a framework-free u/fortemi/graph. Powers Pagenary's search and Cockpit's tool-picker. 10 releases. Full report · GitHub

Pagenary (Markdown → static sites) — blog publishing, a scroll/motion effects suite, meaning-based search on the in-browser Fortemi engine, an auto page-map, managed hosting. It publishes these reports. Full report · GitHub

Carbonyl (a real Chromium browser in your terminal, no display) — 9 releases: headless structured extraction (accessibility tree, embedded-PDF text, PNG snapshots) so agents read page structure, input fixes (right-click, CJK/Cyrillic typing, modifier keys, Tab focus), a color-inversion shortcut, and raw-framebuffer output groundwork. Full report · GitHub

Everything's open source and on a steady CalVer cadence. Try any of them from https://aiwg.io.

**Which of these would you actually use? And what's missing from your agent stack right now?**June was a build month. Here's everything that shipped across the open portfolio — each line links the full write-up.

AIWG (the agent toolkit) — Cockpit, a dashboard to watch/start/stop your coding agents; plain-words tool search; a leaner startup (~193K → ~110K token Claude context). 13 releases. Full report · GitHub · npm install -g aiwg

Agentic Sandbox (where agents run code safely) — secure-by-default transport, vsock VM enrollment, short-lived gateway SSH, live Observe/Drive terminal, 7 signed images. 18 releases. Full report · GitHub

Fortemi (self-hosted agent memory) — the "data coming in" milestone: streaming chat, signed webhooks, resumable bulk ingest + uploads, event feeds, plus a security/privacy hardening pass. Full report · GitHub

fortemi-react (Fortemi in the browser) — same memory, fully client-side: three load modes, off-thread search, deterministic note-graphs, a framework-free @fortemi/graph. Powers Pagenary's search and Cockpit's tool-picker. 10 releases. Full report · GitHub

Pagenary (Markdown → static sites) — blog publishing, a scroll/motion effects suite, meaning-based search on the in-browser Fortemi engine, an auto page-map, managed hosting. It publishes these reports. Full report · GitHub

Carbonyl (a real Chromium browser in your terminal, no display) — 9 releases: headless structured extraction (accessibility tree, embedded-PDF text, PNG snapshots) so agents read page structure, input fixes (right-click, CJK/Cyrillic typing, modifier keys, Tab focus), a color-inversion shortcut, and raw-framebuffer output groundwork. Full report · GitHub

Everything's open source and on a steady CalVer cadence. Try any of them from https://aiwg.io.

Which of these would you actually use? And what's missing from your agent stack right now?

reddit.com
u/Manitcor — 2 days ago
▲ 171 r/integratedai+1 crossposts

The OpenClaw crisis is the most complete case study of agentic AI security failure. Here's the full timeline and technical breakdown.

OpenClaw the open source AI agent platform with 346K+ GitHub stars had four chainable CVEs disclosed on May 15. But that was just the latest chapter. The crisis started in january and it's worse than most people realize.

The numbers

  • 245,000 instances exposed to the public internet (Shodan + ZoomEye scans)
  • 30,000+ actively compromised and used by attackers (Flare)
  • 1,184 malicious marketplace skills across 12 publisher accounts (Antiy Labs)
  • 12% of the entire ClawHub marketplace was compromised
  • 4 chainable CVEs including a CVSS 9.6 sandbox write escape (Cyera Research)
  • 9 CVEs disclosed in a 4-day window in March
  • 50,000+ instances exploitable via one-click RCE (CVE-2026-25253)

The Claw Chain (Cyera Research, May 15)

Four CVEs that chain together into a complete kill chain

  1. CVE-2026-44113 (CVSS 7.7) - TOCTOU filesystem read escape. Race condition lets you swap paths with symlinks to read outside the sandbox
  2. CVE-2026-44115 (CVSS 8.8) - Credential disclosure. Gap between command validation and shell execution leaks API keys through unquoted heredocs
  3. CVE-2026-44118 (CVSS 7.8) - MCP loopback privilege escalation. Trusts client-controlled senderIsOwner flag without session validation
  4. CVE-2026-44112 (CVSS 9.6) - Filesystem write escape. Same TOCTOU race in write ops. Backdoor placement on the host

The chain malicious plugin -> read escape + credential theft -> privilege escalation -> persistent backdoor. Every step mimics normal agent behavior. Traditional monitoring cannot distinguish this from legitimate operations.

ClawHavoc supply chain attack (Jan-Feb 2026)

  • First malicious skill appeared January 27
  • By February 5, 1,184 malicious packages identified
  • Skills disguised as crypto bots and productivity tools
  • Installed keyloggers on Windows, Atomic Stealer on macOS
  • 76 distinct malicious payloads
  • ClawHub had zero verification for skill publishers until March 26 - eight weeks after the attack started

Timeline

  • Jan 27 - First malicious skill on ClawHub
  • Feb 1 - Koi Security names "ClawHavoc"
  • Feb 3 - CVE-2026-25253 (one-click RCE) disclosed
  • Feb 5 - 1,184 malicious skills identified
  • Feb 9 - 135K exposed instances found
  • Feb 18 - 312K+ instances on default port
  • Mar 18-21 - 9 CVEs in 4 days
  • Mar 26 - ClawHub adds verified screening
  • Apr 23 - Claw Chain patches released
  • May 15 - Claw Chain research published

What this means for all AI agent deployments the underlying problems are not unique to OpenClaw

  1. Agents running with user's full credentials across every connected system
  2. Marketplace/plugin ecosystems with no security review
  3. Sandbox implementations with race condition vulnerabilities
  4. No behavioral monitoring to detect multi-step attacks that mimic normal behavior
  5. Default configs exposing agents to the internet with no auth

If you're running any AI agents in production, the OpenClaw crisis is your case study. Scan inputs at runtime. Isolate credentials per agent. Monitor behavior patterns, not just system metrics.

reddit.com
u/Manitcor — 1 month ago
▲ 1 r/integratedai

Visualizing transformers and attention | Talk for TNG Big Tech Day '24 - YouTube

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.

youtu.be
u/Manitcor — 1 month ago
▲ 2 r/integratedai

The Brain’s Learning Algorithm Isn’t Backpropagation - YouTube

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

youtube.com
u/Manitcor — 1 month ago
▲ 2 r/integratedai

Verbalized Sampling

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.

verbalized-sampling.com
u/Manitcor — 1 month ago
▲ 5 r/AIWG+1 crossposts

AIWG v2026.5.11: better provider detection, local-first issue management, and transcript sidecars

Hey everyone, I just released AIWG v2026.5.11.

If you're new here, AIWG is an open-source framework that gives AI coding assistants (Codex, Claude Code, Cursor, Copilot, OpenCode, Warp) structured workflows, provider-specific guidance, reusable skills, and project artifacts.

This release focuses on fixing the friction points that show up once you start heavily relying on agentic tooling in real projects:

  • Tighter provider detection. Handles mixed-provider workspaces much better. It now prefers active runtime/process evidence over passive provider files. If you're running a Codex session, AIWG treats it as Codex, even if you have Claude or Cursor files sitting in the repo.
  • Local-first issue management. Manage issues as plain files in your repo — no heavyweight issue server required. Documented import/export to a remote tracker, with sync performed on demand (only when you ask, not continuously). The project config is the definitive source of truth for where issue state lives.
  • Media workflows. Added transcribe-media for timestamped transcript sidecars — media+transcript hashes, source metadata, optional speaker labels, and explicit degraded plans if your local transcription tools aren't available.
  • Strict release gates. Release tags now route through a signed tools/release/cut-tag.sh gate: version lockstep checks, changelog/announcement presence, and a preserved split between commit-signing and release-tag-signing keys.

Install: npm install -g aiwg@2026.5.11 Release notes: https://github.com/jmagly/aiwg/releases/tag/v2026.5.11

Links:

Feedback request: if you use more than one AI assistant in the same repo, does the new provider routing match how you expect the active session to behave?

u/Manitcor — 1 month ago
▲ 5 r/integratedai+1 crossposts

Project GrowBot. Raspberry Pi Zero 2, two servos, a camera, an IMU, a battery, some 3D printed parts - Youtube

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  

youtube.com
u/Manitcor — 1 month ago
▲ 3 r/integratedai

How AIWG hardend itself against the Shai-Hulud NPM Worm

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.

https://preview.redd.it/uqqv8b13z51h1.png?width=1024&format=png&auto=webp&s=3a6c56e9b65777e56b8bdff67983819440bcf11c

reddit.com
u/Manitcor — 2 months ago
▲ 3 r/integratedai

If your team uses multiple AI coding tools — here's a framework that runs on all 10 of them with the same install command

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!

u/Manitcor — 2 months ago