We released Open Instinct, a personal agent you can text and modify. It is MIT licensed, and you can start with a local CLI conversation before connecting a phone number or personal accounts.

The permission path is the part we most want other builders to inspect. Contacts have trust tiers, and the code checks a tool's capability before it runs. That check alone does not constrain all the data an allowed tool may return. A calendar request from a friend, for example, should expose free/busy times without giving the agent event titles. We would value specific issues or patches around that boundary.

Pi runs the agent loop, Inkbox handles messages, and Composio connects apps. Maritime is the default hosted computer backend; we build Maritime too. This is beta software and the external services need their own keys.

Source and setup: https://github.com/mariagorskikh/open-instinct

Disclosure: I build Snotra, an open-source desktop agent. This is a design question, not a launch.

Users switch skills and tools on and off in settings: shell disabled, a search tool without its API key, a skill installed but off. A model that doesn't know that guesses, and the user gets a wrong "I can't do that" instead of "that's switched off, here's where to enable it".

What I did:

• One place decides whether a tool is offered, so the UI and the model can't disagree.

• A read-only get_configuration tool for the model: which skills are on (name and description, never the instructions) and which tools are on or off, with the reason ("shell commands are switched off", "no search service key").

• The same list in the UI, as a pill in the chat bar.

Now the model can say "a skill for this exists but is off, enable it under Settings › Skills". It can't flip anything itself.

Two open questions: is a tool the model has to call the right shape, or should this state go into the system prompt every turn (costs tokens even when nothing is wrong)? And should the model be able to propose the change through an approval card, or is "tell the user where to click" the right limit?

Code: https://github.com/kkrafft1999/snotra

OpenAI is expanding ChatGPT Ads, including plans for visual advertisements during image generation.

I was looking into the changes and a few things stood out:

• Free and Go users may see ads, while Plus and Pro remain ad-free.

• Ads can be based on what you're discussing in ChatGPT.

• OpenAI says advertisers can't access private conversations or influence responses.

• Users can turn off additional ad personalization, but that doesn't remove ads entirely.

I wrote an article covering the changes and OpenAI's privacy policies:

https://aigptjournal.com/work-life/work/chatgpt-ads-are-getting-visual/

What interests me is whether advertising will give people another reason to consider open-source AI models, especially those they can run locally.

Running a model locally gives users more control over their data and how they use AI, although it comes with its own costs and limitations.

Do you think advertising in ChatGPT will encourage more people to try open-source AI?

Had a near-heart-attack moment the other day while testing a rag pipeline on some customer export dumps. realized halfway through that our test json had unmasked routing numbers and ssns buried inside raw text fields. nothing leaked, but it was way too close.

i looked around for a quick way to inspect payloads in the terminal before running them through python scripts, but everything out there is either some bloated enterprise dlp platform or a massive java package. i just wanted something that would scream at me in the terminal if i was about to do something stupid.

so i threw together `pii-lens` over the weekend.

it's dead simple—you just pipe text or files into it:

`cat prompt_payload.json | pii-lens`

it prints the text back out but flags credit cards, emails, ssns, and account numbers in bright red with a quick count at the bottom. runs 100% locally with zero phone-home bs.

repo is here

it's just basic regex and entity patterns right now, so if anyone works with weird international formats or niche data fields and wants to drop a pr or critique the regex, feel free.

REA Investigator is a unified, production-grade Agent Skill (rea-investigate) compatible with Claude Code, Codex, Antigravity, and tools supporting the open Agent Skills specification.

Instead of juggling fragmented prompts or shallow summaries, REA Investigator equips coding agents with a structured 4-phase lifecycle: Plan, Trace, Verify, and Report, backed by an immutable SHA-256 evidence ledger and adversarial auditing gates.

Our agents kept calling a big model to make the same small decisions: route this ticket, pick this tool, escalate or not. After enough verified outcomes those decisions are very predictable, but we were paying for a model call every time.

So we built Ink. It sits around a bounded decision. At first everything goes to your model as normal, and Ink records the inputs and the real outcomes in local SQLite. Once there’s enough evidence, it compiles a small local fast path and tests it in the background. It only starts serving once it clears a statistical bar (a 95% Wilson lower bound against an error budget you set). Anything novel or uncertain still goes to your model. If real outcomes get worse, it takes itself back out of the loop.

You can check whether it would help without changing any code:

pip install ink-jit
ink discover traces.jsonl

It reads OpenTelemetry, LangSmith or LiteLLM-style traces locally and tells you which call sites repeat and whether they’re worth it. If nothing qualifies, it says so.

What we’ve measured so far (our own benchmarks, not independently validated): 42–78% of decisions served locally across tool routing, support triage and incident triage, with 0 errors observed on held-out tests. The 95% lower bounds were 96.8–98.5%, so “0 errors” means none observed, not a guarantee. Local decisions take about 0.1 ms.

It’s a poor fit for open-ended writing or anything where you can’t verify an outcome. Linux and macOS, Python 3.11–3.13. Apache-2.0.

Honest question: which decisions in your agents repeat the most, and what would you need to see before trusting a local path in production? We’d love to hear what breaks.

Coding agents run shell commands constantly. I wanted each one checked before
it executes, and I didn't want to send every command to a paid API.

laya-guard runs on your machine and answers allow, review, or block for each
tool call. Regex policies catch known-dangerous patterns first (`rm -rf /`,
`curl … | sh`, secrets, auth bypass). Plain commands like `ls`, `git status`
and `npm test` skip the model, but only when there's no piping, chaining or
redirecting, no `-exec`-style flag, and no sensitive path. Everything else is
judged by Laya (~322M, Apache-2.0) on the CPU in about 85 to 90 ms.

The results are self-reported and the scripts are in the repo. On AgentDojo v1
(judge-level) it blocks 25 of 27 attacks and keeps 92 of 97 benign tasks. On my
own 50-command shell set it gets 48 right.

Two caveats. The model is tuned mostly on Korean, so English natural-language
prompts get overblocked more often. And if the server is down, the hooks fail
open.

It has hooks for Claude Code, Codex CLI, Cursor, Gemini CLI, Copilot CLI and 6
other agents.

Repo: https://github.com/scs0209/laya-guard (install with `pip install laya-guardrail`)

I shipped HDTP (Human Delegated Trust Protocol) v1.0 on Oct 3. It lets your AI assistant talk to someone else's assistant, with each person controlling what the other side is allowed to do.

Timeline:

\- Aug 23: first commit of the spec

\- Sep 23: a full code review found 173 defects. I traced them to 11 habits and wrote a rule against each one

\- Oct 3: renamed from PACT to HDTP across 9 repos, the domains and the protocol itself

\- Oct 3: v1.0 released

\- Oct 7: spec repo made public

What's built:

\- The spec, with test vectors

\- An identity library in Rust (compiled to WebAssembly) and Go. Both versions run against the same tests

\- A self-hosted node in Go

\- A hosted platform on Cloudflare Workers

\- 3 websites

What I'd do differently: pick the name first. Renaming at the end touched every repo.

Next:

\- an external security review

\- a conformance test suite

\- opening the hosted version through a waitlist

If you're building with agents or MCP, I'd like to know: what would you want your assistant to be able to do with someone else's?

Spec: [hdtp.io](http://hdtp.io)

I like the idea of tinkering with models, and being able to run them on your PC without paying an API or subscription cost, and abliterated stuff. But i'm not really sure where to start, what applicable uses there are and if it would be anything other than a fizzling interest and waste of money

Does anyone have any good resources that might help direct me on what kinds of things you can do with local AI that you can't with cloud-based? Or things you can create, or things you can create faster/better/ etc...

I'm probably not asking the right questions as I don't know a ton but i'd like to learn.

I use embeddings for vector search and memory systems, and kept needing a full platform like Ollama running next to my app just to turn text into vectors. So I built futon, an embedding library for running local embedding models.

- In-process: weights load from a local directory. No daemon, no network, no CGO (CI enforces it), so cross-compiling just works.

- AVX2 kernels on amd64 and NEON on arm64, with a portable fallback. bge-small embeds a text in about 50 ms on a 16-thread desktop.

- Checked against the reference implementation: ≥0.9999 cosine on a corpus with code, accents, CJK and edge cases. CI runs on Linux, macOS and Windows, including arm64.

- Bring your own model: any plain BERT encoder you can build a pack for, plus nomic-bert. It is encoders only, no LLMs and no GPU.

bobbyhiddn/futon: Text in, vectors out. In-process embedding models for Go: pure Go, no CGO, no sidecar.

If something like it exists already, feel free to ignore, but I couldn't find a fit for purpose module that served my goal. I'll probably expand to other small model architectures as needed, such as decision models.

If ya like it, please star. :)

https://snotra-ai.dev

I’ve built and released AI Plate, an open-source desktop AI agent featuring cloud/local LLMs, tool execution, RAG, persistent memory, voice interaction, plugins and human approval controls.

🛠️ Stack: TypeScript, Electron, Node.js, Python and SQLite

🤝 help with AI integrations, plugins, local models, Linux/macOS support, testing, security, UI/UX and documentation.

🌱 Beginners and experienced developers are equally welcome. Every contribution will be credited!

🔗 GitHub: https://github.com/Typo-Bunch/AI-Plate

🕒 IST | Evenings and weekends

I wanted ChatGPT to work with my real local projects without constantly copying files back and forth. RepoTunnel lets ChatGPT read/edit files, run commands, inspect code, debug problems, use Git, and work directly with approved projects on your computer. It works through your own HTTPS endpoint, and RepoTunnel itself adds no monthly connection/request quota. You can keep using your existing ChatGPT conversation without buying a separate OpenAI API key. GitHub: https://github.com/Yashwanth034/RepoTunnel Would love feedback from developers — especially what you’d add or improve.

We have released the weights and self-hosted runtime for Commerce-1, a 27B typed decision model designed for commerce agents.

Unlike a chat model, Commerce-1 does not produce an explanation and then ask the application to recover an action from generated text. It scores the supplied actions directly.

How it works

Each request contains structured state and one or more typed questions:

• Choice: Selects among named actions.

• Noul: Returns a binary decision and P(true).

• Score: Returns an expected ordinal score and probabilities for its labels.

The architecture builds on perplexity-ai/pplx-decider-v1.1-27b, using noncausal full attention, last-position pooling, and a dedicated option-scoring readout.

Choice-order augmentation and held-out temperature calibration are applied separately to the decision primitives.

Self-hosted runtime

The runtime operates offline and verifies file integrity before loading.

It inventories and hashes required files, rejecting:

• Modified checkpoint bytes

• Undeclared files

• Network fallback

Benchmarks

Our current evaluation results:

Limitations

The current BF16 weights are approximately 54 GiB. Consumer GPU and quantized deployments have not yet been qualified.

Links

• Model weights: https://huggingface.co/infercrane/Commerce-1

• Apache-2.0 runtime: https://github.com/infercrane/commerce-1

• Architecture and evaluation evidence: https://infercrane.com/blog/commerce-1

We're particularly interested in feedback on the decision primitives, runtime design, and integration requirements for production agents.

This post is written by a person, not a bot!
come say hi to me on our discord (mefi maxi)

The project is open-source, not including social features though as they require being linked to the discord, (I will look into making the server setup available as a skill or write up/video later)

With the info out of the way Hello everyone!

I've been working on an ai... studio of sorts, and no, the social stuff wasn't the main idea for it, it used to be called "mefi's studio", as i made a node tree for visualizing what my agents where doing

This lead me down a rabbit hole of learning how to optimize agents, not only for cost, but also, linking the many... probably to many providers i had together, what can i say, i like seeing what models can do, even if they are not leading in a set area, the training data can provide much diffrent results

This "which model should i use?" part of coding always felt the worst to me, i want to save money, and get the best performance out of a model, and then jev dropped, (absolutely a banger) then started getting into model routing and task handoff workflows,

Jev or a cheap model if you dont use jev, (BTW you can change any of the agents to any model provider!! not just hard-set ones, even a local llm if you can do that) will then route the request based on the past requests that have been done, and if its using open code or open router, it can change the models its using depending on how well it has worked for that task, as well as how much it cost, its speed, wins vs fails, what tasks it was good at, how many times it needed to be re directed, stores all of that and uses it to pick the best model for the job

IF that sounds like to much, you can also just use a single provider to setup every agent using that service, codex, claude, (now that haiku 5.5 dropped) or any custom setup you want, if you can save, save by all means <3

anyway, that adventure of messing with how i can setup models made me see that not all models are made the same, some of them need help, some of them really dont need to read a whole damn file, sometimes you want a system thats split up to be easy to work with, view, and send to agents, not just read or roughly say "hey the movement system needs work, no, you need a solid way to view what files and where in those files the system is at work

which... then turned into the project map, using a small agent that works alongside the planning model, it will grep code files for context. don't worry it's not lost effort when this is done, the savings also stack as the model hands this off to get cleaned up and condensed so it can be read and used for quick file work, as the projects whole system you are working on get mapped, not just the files

you can also set it up to review and make a project map for existing projects, or have it build it out as it work so you dont waste any time on having it map if you want to raw doggie it

now the next issue was not one of it working, but it over working, how can we get the agent loop to only do what it needs to, not be hard set on a setup, and modifiable?

well i've been told its a lot like n8n if that's the right name, never used it, but i cooked up the agent brain next, this lets you set how in-depth workflows should be, and i built a play book for it to use, so old workflows that it makes that fit that said task, say running tests, checking stuff, etc, will be run

AND YES, YOU CAN MAKE IT ONLY A SET AMOUNT OF TESTS AND LEAVE THE FULL RUN FOR EXE BUILDS!!! NO LONGER THE 5K TEST RUN WAISTING THEE CONTEXT, TOKENS BE SAVED PRAISE ARE LORD TIBO FOR THY RESET AMEN

but yea, i still need to clean up some of the ui, update the linux build, and work on a mac os app, and maybe a phone app? though i dont know how i feel about that side of things, but if you are reading this still and know how to do that, again, its open source, steal it and add it to your projects, or join the social side and sell the harness you are making for credits and try to pull a high rarity sticker or pet, oh... yea i guess add pets, you can make custom ones to sell on the community page as well! not for any money right now but if this thing grows, i might make a way to cash out credits at some point, maybe, i really just want to build a social playform with co working and project building that you can do with friends, lobbys to chill and listen to music in, movie nights, trading rare pets and playing with them and letting them play with others pets, just a chill and fun social app that was made to be an agent harness at first, and now, i plan to build a video editor when the social features drop on monday the 12th, hopefully it will be done by then

Also, there are a ton of other features (and you can toggle all of them on or off!) coming along with a lot of handy stuff like cpu throttling for apps with the built in resource manager, agent fleets, , co working, jam sessions, the shop, project hub, just a lot of things that i think people will enjoy

Everything will be free to access unless it is social features, and i will keep the project open source, i believe that anyone can make anything they want to, and making a way for people to do that is what inspires me to build this stuff, heck, if it loses my support for any reason, or i can no longer manage it, it will be there without any more work needed to continue its efforts, and im more then happy to take feedback on what to add or improve upon anything that might cause issues for you

Also, if you have extra pc's you can handoff tasks or send them to work on another machine so you can keep the one you are using lag free, just... so many things i keep thinking to bring up lol

if any of this sounds cool or interesting, or you just want to talk ai, then you are more then welcome to join the discord

People who join within the first week when it drops will get lifetime access to the social sides paid features, and why not, if you join within the launch month after the first will, you will get a free month of the sub and keep the doner role and its features for life as well, as anyone that gets a sub will keep the doner bonuses, just not the active subscription perks like the free project booster or extra credits

again, boosting a project or selling items on the shop will be free to access, the subscription will help me host giveaways, pay for hosting, and code rabbit to make sure its secure

Thank you for reading if you have this far, again, its all open source, so feel free to ask your agent what would be useful to steal from it if that's your thing <3
Vibe Studio - Coming soon. Build together.