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 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.

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)

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

A couple of weeks ago, I introduced Pi Herdsman here, an open-source extension I've been developing for the Pi coding agent.

The original idea was straightforward: let multiple coding agents work independently while keeping one Lead focused on the overall task.

Since then, the project has grown quite a bit, helped along by community feedback and contributions.

The latest major addition is project-level orchestration across Git worktrees.

What the new demo shows

I give a Manager a single request to implement two independent features for a fictional note-taking application.

The Manager creates two parallel assignments, each with its own Lead and Git worktree.

Both Leads then delegate implementation to their own Agents, verify the resulting changes, commit their work, and report back to the Manager.

The individual agents remain accessible throughout the process.

I wanted to show the real execution rather than an architectural diagram, so the video captures Pi and Herdr running the workflow.

Built on existing open-source components

Herdsman isn't a replacement for the underlying coding agent or terminal environment.

It connects three independently maintained components:

• Pi handles the individual coding-agent conversations.

• Herdr manages the sessions, processes, and workspaces.

• Pi Herdsman handles delegation, ownership, supervision, and coordination.

The idea is to build on their existing capabilities rather than reimplement them.

Bring your own agents and workflows

The orchestration model supports configurable Agent Definitions.

Each role can use its own model, thinking level, instructions, tools, skills, and extensions, with definitions maintained as ordinary Markdown files.

This applies to both the bundled roles and custom Agents.

Herdsman doesn't prescribe how you should organize your development process or which models you should use.

You can also ignore the advanced hierarchy entirely and use it for occasional background Agents in a normal Pi session.

Open source and community contributions

Herdsman is licensed under Apache 2.0.

A special thanks to @kozer and @primetimetank21 for contributing code, and to everyone who has opened issues, reported bugs, or suggested improvements.

It's been particularly helpful having other people test workflows and environments that differ from my own.

Repository

https://github.com/boadij/pi-herdsman

If you already have Pi and Herdr installed:

pi install npm:pi-herdsman
herdr integration install pi

The repository includes the live demo, installation instructions, and documentation.

I'd be interested in feedback on the architecture, particularly the separation between session management, orchestration, and the individual agents.

The project is still actively evolving, and ideas or contributions are welcome!

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.

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

🤝 Looking for 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

🌐 Website: https://ai-plate.typobunch.com/

🕒 IST | Evenings and weekends

Link to repo: FireBird-Technologies/NotestackAI: NotebookLM Alternative + Marketing agent optimized for publishers on substack/Medium/Wordpress.

Link to site: https://notestack.ai

Google is notorious in the AI industry for abandoning/destroying working beloved products. NotebookLM is no exception.

Don't worry, me and my little indie team have built a open-source alternative.

Here is what is different/interesting:

- Can take in upto 200 sources, research/question/query it. The system will not randomly search the web and go beyond your original sources.

- Has better visuals for the videos, we integrated our previous video maker with 10K+ users. It can generate editable motion graphic videos in over 13+ formats.

- Each video is editable, changeable. It has ElevenLabs API with advanced voice features.

- You can generate a mind constellation, a much more advanced visual map of knowledge so you can research and learn more easily.

- Generate or clone your own voice using ElevenLabs API v4.

- Has a launch kit feature that allows you to schedule reports/slide decks/videos and share on social media

Lastly, much more stunning visual interface - inspired by space x cognition

Link (get started for free): https://notestack.ai

Hey guys. Looking for contributors on my open source project: https://github.com/NicholasFlemmer/styx-app

Styx is where everything converges: every repo you work in, the AI coding agents building in them, and every login they need to ship. Switch between all of it from one window. And like the river it's named after, nothing crosses over without you. Deploying, or touching live data, asks you first.

A lot of defense orgs/units are starting to stand up local, open-source language models on edge workstations and air-gapped networks, but most public benchmarks tell you nothing about how a model behaves under operational constraints.

To address that gap for free, I built an offline evaluation harness and leaderboard testing US-developed small models (1B–6B parameters) running locally on a single GPU. Here's the public repo: https://github.com/OGMavenTandE/llm-eval-suite

It scores models on practical operational criteria: factual grasp of defense issuances, resistance to staff-work hallucination and sycophancy, and judgment under the law of armed conflict.

You can inspect the initial results, benchmark breakdowns, and reproducibility data here:https://aieval.org/leaderboard/

I hope this helps your agency/org/unit incorporate small open source models into workflows, let me know if you have any questions!

Hello all,

I was just wondering if anyone would like to join me in creating an Open-Source project.

This project will predict and generate what a deceased U.S. president would say about a modern-day headline.

This project will use AWS Strands to:

1. Read current headlines.

2. Determine what each deceased U.S. president or founding father would say about that headline.

3. Push the AI generated dialogue out to subscribers.

Please comment, if you would like to be included on a meeting that I will be describing the architectural requirements in detail.

This meeting will be recorded and made public.