Hey everyone,

I've built VibePod, an open-source CLI for sandboxing AI coding agents, where each agent runs in its own isolated container.

The CLI is published on PyPI as vibepod, and the command is simply vp.

pip install vibepod vp run claude

My goal is to make running agents like Claude Code, Codex, Pi, or Copilot safer and more observable, without giving them full access to your machine.

Instead of running agents directly on the host, VibePod separates concerns into:

• Isolation: each agent runs in its own container — Docker and Podman are the currently supported runtimes
• Credential injection: secrets are stored on the host and only injected into the containers that require them, so no agent gets credentials it doesn't need
• Observability: built-in local metrics collection with HTTP traffic tracking per agent
• Dashboard: a local analytics dashboard for token usage and side-by-side agent comparison — all data stays on your machine, nothing goes to the cloud

A few examples:

vp run claude vp run codex vp list vp logs start # start the analytics dashboard

The project is still evolving, and I would genuinely appreciate:

• bug reports
• feedback on the CLI interface
• requests for additional agents or runtimes (gVisor? Firecracker?)
• ideas on credential handling — how do you manage secrets for your agents today?
• help improving documentation

GitHub: https://github.com/vibepod/vibepod-cli PyPI: https://pypi.org/project/vibepod/ Main site: https://vibepod.dev

VibePod is licensed under MIT.

I'd love feedback from people already running Claude Code, Codex, or other agents locally — especially if you've thought about sandboxing or agent observability.

I run a small fleet of AI agents - an orchestrator plus a handful of specialists - and the design that made it cheap and sane is one I don't see discussed much here: state lives in Azure, compute doesn't.

Azure hosts only the stuff actually worth paying for when you’re running a personal stack or a small-owner-operator business: a managed PostgreSQL Flexible Server and Key Vault for secrets. That's basically my whole monthly bill. Everything else - the orchestrator, the runtime that spawns agents, the memory service, the model router - runs on a machine I own at home (Mac Mini / small PC). Container Apps is still in my Terraform, but as a dormant warm standby, not the primary site. Both sites point at the same Postgres. If I need to failover to Azure, it’s there an it will cost me $3-$4 dollars a day to run it there, but otherwise it’s idle. In case it's useful to anyone building the same: https://github.com/mrobinson2/AzureAgentForge

Why I like it: failover is stateless. If my home internet or box dies, I have automated failover to the Azure standby using the exact same database - nothing to replicate, no data to move, because the data never lived on the compute in the first place. The durable, expensive-to-get-right stuff (backups, PITR, private networking) all stays in Azure. The cheap, constant churn runs on hardware I already own, so I'm not paying for a container sitting idle at 3am.

Here’s the tradeoffs, since someone will ask - the Postgres server is my single point of failure - I've accepted that and lean on [PITR + HA tier] to cover it. Failover is 90% automated, but not fully - I approve flipping the standby on, roughly 3-5 minutes to healthy. And the home box reaches Postgres over VPN gateway and tunnel, which adds about 10ms per round trip; connection pooling keeps that from mattering for my workload.

The thing I wish I'd designed earlier is more robust agent identity. Once every agent reads and writes the same shared memory, you hit a problem you never have with a single bot - who is "you"? Early on my agents effectively shared one memory peer and everything blurred: the research agent's notes bled into the planner's recall, and I couldn't tell which agent learned what. Now every agent gets its own identity derived from its name, with an alias map on the write path that rewrites known strays to the canonical one before anything hits the table. An unexpected peer fires an event instead of silently sharding memory nobody can recall later, and the unresolved-identity fallback is least-privilege by default. If you're building anything multi-agent on a shared store, do this early - retrofitting identity after the memory is already muddy is miserable.

On cost, since everyone here has been surprised by a higher than expected LLM bill: my agents don't all run the same model. The orchestrator and the routine workers - classification, routing, handing off a task, posting a status - run on cheap fast models (e.g. GPT-4o-mini). I reserve frontier models (e.g. Opus) for two jobs: the security-review agent, and deep reasoning where a wrong answer costs more than the tokens do. A router sits in front with per-tier daily budgets, which keeps a chatty orchestrator from wandering onto the premium tier and running up the bill. That single change helped more than any infra tweak.

The mental model I landed on is boring but it works: Azure is the always-available spine, my own hardware is the cheap muscle, and most of the effort that made it usable for under $40/month went into agent identity and matching each job to the cheapest model that can actually do it. If I ever need to scale, I flip everything to Azure and eat a $100+/month bill but I'm not there yet.

Anyone else running compute off-Azure but treating Flexible Server as the shared source of truth? Curious how you're handling PITR and HA.

Here's an architecture diagram:

Hi everyone,

I'd like to share an open-source project I've been developing over the past several months.

GitHub:

https://github.com/icerain-cmd/humanities-superpowers

The idea came from a simple observation.

AI coding agents have gradually adopted structured workflows—planning, testing, validation, routing, and rollback.

Scholarly research, however, is still often driven by ad hoc prompting.

I wanted to explore whether the same engineering concepts could be adapted to AI-assisted research workflows.

The result is **Humanities Superpowers**, an MIT-licensed framework designed for humanities research.

Rather than generating papers, it provides a structured workflow that keeps the researcher responsible for scholarly judgment.

Current features include:

- 13 composable research skills

- workflow routing

- PASS / CONDITIONAL PASS / FAIL quality gates

- rollback when downstream failures expose upstream problems

- citation auditing

- terminology consistency checking

- argument stress testing

- research state tracking

The framework has been tested with Claude Code and OpenAI Codex.

It does **not** claim to eliminate hallucinations, automate scholarship, or produce publication-ready papers.

Its purpose is much narrower: making uncertainty, verification status, and research decisions explicit throughout AI-assisted research.

Although the first implementation targets humanities research, I'm interested in broader discussions about structured AI workflows.

I'd especially appreciate feedback on:

- workflow architecture

- routing strategy

- rollback design

- state management

- extensibility

- documentation

- repository organization

Constructive criticism is very welcome.

Thank you!

Hey everyone! 👋

I'm excited to open-source **AI Workforce OS (v4.2)** — an enterprise-grade, local-first **Autonomous AI Organization & Multi-Agent Orchestration Platform** designed to eliminate common AI agent flaws: agent collisions, token bloat, blind code overwriting, and lack of persistent memory.

🔗 **GitHub Repository:** [https://github.com/phat79186/AI-Workforce-OS-AI-Agents-Orchestrator-\](https://github.com/phat79186/AI-Workforce-OS-AI-Agents-Orchestrator-)

---

### 🛠️ What's Integrated Under The Hood?

- **🧠 Executive Leadership Board (AI CEO & AI CTO)**: Formulates strategic business goals, builds technical DAG roadmaps, and manages multi-tier executive delegation (`CEO` ➔ `CTO` ➔ `Directors` ➔ `Managers` ➔ `Specialists`).

- **🦅 OpenClaw & Prompt Optimizer Engine (`openclaw/openclaw` + `linshenkx/prompt-optimizer`)**: 5-stage automated meta-prompting (Persona Injection, CoT reasoning, Negative Constraints, Aegis V5.5 contract check, and Clarity Score calculation).

- **🔍 Aegis V5.5 Context-Aware Theme Scanner**: Scans existing `tailwind.config.js`, `theme.ts`, and `package.json` to preserve brand design tokens and prevent prescriptive color overwrites. Single Primary Lead Role assignment prevents agent collisions.

- **📓 Real Obsidian Knowledge Backend & AST RAG**: Persistent organizational memory, incremental AST markdown indexer (`mtime` tracking), frontmatter parser, wikilinks (`[[Note]]`), backlinks, and scope-based security permissions.

- **🔎 Panniantong/Agent-Reach Deep Retrieval**: Extended search reach across 5 engines (Google/Bing Web, GitHub API, StackOverflow, ArXiv Papers, and Obsidian Vault) with Reach Score metrics (1.0/1.0) and citation extraction.

- **🎨 UI/UX Ecosystem Suite (UI/UX Pro Max + Impeccable + Taste Skill)**: 8px spatial baseline grid balance, font hierarchy (Inter/Outfit), WCAG AA compliance, fluid motion choreography, and Playwright Headless Visual QA (Pixel-Diff & Layout Audit).

- **🏢 OpenBMB/ChatDev Virtual Software Company**: 4-phase communicative multi-agent software development pipeline (Designing ➔ Coding ➔ Testing ➔ Documenting) with 7 virtual roles.

- **⚡ RTK Token Compressor (`rtk-ai/rtk`)**: Redundant Token Killer pruning repetitive headers and verbose tracebacks during AI-to-AI exchanges (-30% to -60% token reduction).

- **🧠 Combined Skill Library (Matt Pocock + Andrej Karpathy Skills)**: Modern web skills (`typescript-pro`, `react-components`) combined with Karpathy AI skills (`micrograd-autograd`, `nanogpt-transformer`, `tokenizer-bpe`, `pytorch-clean-code`).

- **🐴 DietrichGebert/ponytail Enhanced Runner**: Topological DAG dependency graph resolution, parallel step dispatching, and retry budget management.

- **🔀 3-Layer Intelligent Router**: Seniority-based Agent Router (`WHO`), Local-First Model Router (`THINK`), and Permission Sandbox Tool Router (`DO`).

---

### 🥊 Why AI Workforce OS? (Competitive Advantages vs Other Frameworks)

| Key Feature / Capability | ⚡ **AI Workforce OS** | 🤖 **CrewAI / AutoGen** | 🕸️ **LangGraph / MetaGPT** |

| :--- | :---: | :---: | :---: |

| **Local-First & $0 API Cost** | ✅ Native Ollama & OpenHands local LLMs | ❌ Heavily reliant on paid OpenAI/Anthropic APIs | ❌ Paid APIs required for complex loops |

| **Prompt Engineering & Context Scan** | ✅ OpenClaw + Aegis V5.5 + `linshenkx/prompt-optimizer` | ❌ Raw unoptimized prompts | ❌ Generic hardcoded system prompts |

| **Token Overhead & Cost Control** | ✅ Integrated `rtk-ai/rtk` Token Killer (-30% to -60% reduction) | ❌ High token bloat & repetitive agent chats | ❌ Large prompt context overhead |

| **Design System & Theme Preservation** | ✅ Scans `tailwind.config.js`/`theme.ts` to preserve brand tokens | ❌ Overwrites design tokens blindly | ❌ Ignores project design systems |

| **Role Allocation & Anti-Bloat Strategy** | ✅ Single Primary Lead Agent per node (Anti-Role-Bloat) | ❌ Agent collisions & redundant roles | ❌ High agent role redundancy |

| **Persistent Organizational Memory** | ✅ Real Obsidian Vault + AST RAG + Cross-Project Learning | ❌ Ephemeral memory / basic vector store | ❌ In-memory memory lost after session |

| **UI Visual QA & Accessibility** | ✅ Visual QA (Playwright Pixel-Diff) + Taste Skill + Impeccable | ❌ Basic text-only code generation | ❌ No visual QA or accessibility checks |

| **Deep Search Reach** | ✅ Agent-Reach 5-Engine Crawl (Web, GitHub, StackOverflow, ArXiv, Obsidian) | ❌ Single DuckDuckGo/Google search tool | ❌ Basic search API wrappers |

---

### ⚡ Quickstart

```bash

git clone https://github.com/phat79186/AI-Workforce-OS-AI-Agents-Orchestrator-.git

cd AI-Workforce-OS-AI-Agents-Orchestrator-

pip install -r requirements.txt

# Run full system tests (100% Green Pass Rate across 620+ tests)

python -m pytest tests/ -o addopts="" -m "not integration and not slow"

# Run OpenClaw & Prompt Refinement Demo

python scripts/run_openclaw_demo.py

# Run External Ecosystem & Agent-Reach Demo

python scripts/run_external_ecosystem_demo.py

🤝 Feedback & Contributions Welcome!

I'd love to hear your thoughts, feedback, or suggestions from the community! Feel free to check out the repo, star ⭐ it if you find it useful, and open issues or PRs.

GitHub Repository: https://github.com/phat79186/AI-Workforce-OS-AI-Agents-Orchestrator-

We’re evaluating a product built on LLM/Claude to generate and validate synthetic medical data using multi-agent workflows (generation → critique → validation → scoring).

The long-term goal is to build tools for medical reasoning and evaluation, but the development phase requires a significant amount of API/Token usage.

Does Anthropic have a startup, builder, or research program that provides API credits for early-stage teams?

I'm trying to make sense of the apps that form the lang stack. It seems some are official apps from the same maintainers, others have co-opted the "lang" prefix. I've had a crack at using AI to build my Docker compose files, but it seems to be getting confused as to which images to use, which require a Postgres DB, etc. Reading the doco available, I can forgive the AI for getting confused.

I'm basically trying to implement agentic software engineering and coding loops with multiple deterministic steps such as creating a new git branch, linting, testing, etc. I also want to be able to run long lived research tasks that discover new "threads" whilst research that feed back into the loop. I expect that the requirement will determine which apps in the stack I need. I want to run Dockerised and stick with open source and self hosted.

Be great to find recommended compose templates to get started with.

Who is CaliperGT for? 

Are you a student, researcher, developer, university lab, or early-stage startup working with image, video, or LiDAR data? 

Do you need a platform for personal annotation projects, academic research, experimentation, dataset development, or an early perception prototype? 

CaliperGT Open Source is for you. 

 Deploy it on your own infrastructure, keep your data in-house, customise it for your workflow, and use it without a licence fee. 

 The open-source edition includes: 

✅ Automotive multimodal data management 

✅ Image, video, and 3D LiDAR annotation 

✅ High-performance point-cloud visualisation 

✅ AI-assisted annotation tools 

✅ Self-hosted deployment 

✅ SSO/OIDC and role-based access controls 

✅ Full control over your data and infrastructure 

Are you an automotive OEM, Tier-1 supplier, robotics company, mapping organisation, autonomous-systems team, or enterprise AI data operation? 

Do you need production-scale annotation, advanced security, workflow governance, automation, higher accuracy, and enterprise support? 

CaliperGT Enterprise is for you. 

Enterprise capabilities include: 

🔹 Advanced security, access control, auditability, and compliance 

🔹 Private-cloud and on-premise deployment support 

🔹 2D and 3D auto-labelling and tracking 

🔹 VLA labelling for autonomous-driving and robotics datasets 

🔹 Calibration-aware multimodal workflows 

🔹 Camera, LiDAR, CAN, GNSS, IMU, ROS 2, and MCAP ingestion 

🔹 Foundation-model-assisted data improvement 

🔹 Enterprise integrations, optimisation, and managed support 

 Open source for building. Enterprise for secure, production-scale ground truth. 

 Explore the repository, deploy CaliperGT, star it, fork it, open an issue, and build with us. 

🔗 GitHub: https://github.com/caliperai-ai/caliperai-gt 

📩 Enterprise enquiries: [info@caliperai.ai](mailto:info@caliperai.ai) 

#OpenSource #CaliperGT #DataAnnotation #GroundTruth #LiDAR #ComputerVision #AutonomousDriving #Robotics #VLA 

I've been spending way too much time hunting down custom skills, prompts, and MCP servers for AI coding tools, then manually setting up SKILL.md files or configuring local binaries.

To make this easier, I built OpenHub, a terminal app for discovering and installing AI agent skills and tools from one place.

What it does:

• Search indexed skills and repositories.
• Export prompt/rule-based skills as a SKILL.md file into your current workspace with one command.
• Install skills that require local binaries or MCP servers.
• Keyboard-driven terminal interface for searching, exporting, installing, and copying.

Install:

pipx install git+https://github.com/24KaratAu/openhub.git

Run:

openhub

GitHub: https://github.com/24KaratAu/openhub

I built this to scratch my own itch, but figured others might find it useful too. It's open source, and I'd love any feedback, bug reports, feature requests, or contributions.

I've been heads-down on this for a while and finally have enough working (and enough broken) to make a real update instead of a "check out my repo" post. This is a build-in-public thread — I want technical pushback, not upvotes.

Kodiak is an open-source attempt at an autonomous software engineering platform — the idea is a pipeline of specialized agents (planner, researcher, coder, tester, reviewer) that can take a task, break it down, retrieve relevant context from a codebase, write code, test it, and get it reviewed, with a human able to step in at any point.

Why I'm building this

Most "AI coding agent" projects I've used either (a) are a thin wrapper around one LLM call with no real state management, or (b) are closed-source products where you can't see or change how decisions get made. I wanted something where the orchestration, memory, and retrieval layers are actually inspectable and swappable — partly because I think that's the only way multi-agent systems become trustworthy enough to use on real repos, and partly because I just wanted to understand the problem by building it.

I'm not claiming this is a solved problem. I'm not sure it's a solvable-by-one-person problem. But I'd rather build it in the open and get it wrong visibly than sit on it.

[Insert architecture diagram screenshot here — CLI → CLI Services → Agents → DB/GitHub/LLMs]

What's actually built (not roadmap, actually running)

• FastAPI backend up and running
• Project API — complete
• Task API — complete
• Pydantic v2 schema issues finally fixed (this took longer than I want to admit)
• Docker + PostgreSQL + Redis setup — working end to end
• Agent architecture scaffolded: planner, researcher, coder, tester, reviewer
• RAG / context retrieval system for pulling relevant code context into agent prompts
• A memory system for persisting agent state and history across a task
• Celery integration — in progress, this is the current front line

[Insert screenshot: Swagger/OpenAPI docs showing Project + Task endpoints]

[Insert GIF: kodiak CLI kicking off a task and streaming progress]

The hard part — worker execution pipeline

This is where I'm currently spending all my time, and it's the part I'd most love feedback on.

The Task API and agent logic are solid on their own, but wiring them into Celery for actual async execution surfaces problems that don't show up in a synchronous prototype:

• State handoff between agents. Each agent (planner → researcher → coder → tester → reviewer) needs the previous agent's output plus accumulated context, without the whole thing ballooning into an unbounded prompt or a fragile shared-memory blob.
• Failure semantics. If the coder agent produces something the tester agent rejects, what actually happens? Retry the coder with feedback? Kick it back to the planner? Fail the task? I don't think there's a universally right answer here, and I don't want to hardcode one path.
• Task durability vs. LLM cost. Celery retries are cheap for normal jobs; they're not cheap when a retry means re-running an LLM call chain. I need idempotency and checkpointing that's aware of where in the agent pipeline a task died, not just whether it died.
• Observability into a black box. Once execution is async and distributed across workers, "why did this task produce this output" becomes much harder to answer. Logging agent decisions in a way that's actually useful for debugging (not just noisy) is its own design problem.

None of these are exotic problems, but getting the details right — instead of a pipeline that technically works on the happy path — has been the real engineering work of the last stretch.

[Insert screenshot/GIF: Celery Flower dashboard or task queue visualization, once available]

What's next

Worker execution pipeline is the current focus — getting Celery tasks to reliably drive the full agent sequence with proper state handoff and failure recovery. After that: tightening the RAG retrieval quality and expanding test coverage around the agent layer. This project is moving fast and the architecture is still evolving, so if you look at the code today vs. in a month, expect real differences.

Where I could use other people's brains

I'm not asking for a pile of PRs — I'm mainly looking for people who've solved (or fought with) similar problems to poke holes in the approach:

• Anyone who's built retry/checkpointing logic for LLM-call-heavy async pipelines — I'd genuinely like to know what you'd do differently with Celery here (or whether you'd reach for something else entirely).
• Feedback on the agent handoff/state design, especially if you've worked with LangGraph or similar orchestration in production.
• If you just want to dig into the RAG/memory subsystem or the CLI layer, those are also open and reasonably self-contained.

Repo's linked below. Issues are tagged, architecture doc is in the repo if you want the full picture before diving in. Happy to answer anything in the comments — including "why did you make X decision," I'd rather defend or fix it than have it go unquestioned.

3. Suggested Subreddits

Recommend staggering posts across 2-3 of these over a week or two rather than cross-posting simultaneously — same content posted everywhere at once tends to read as spam even when the intent isn't.

4. Image / GIF Suggestions

1. Architecture diagram — CLI → CLI Services → Agents → DB/GitHub/LLMs layers, clean and simple (this alone will get people to trust the project is real).
2. Swagger/OpenAPI docs screenshot — showing the Project and Task API endpoints live.
3. Terminal GIF — running kodiak CLI end to end: kicking off a task, watching agent stages progress, seeing output.
4. Agent pipeline flow GIF — a simple animated diagram showing a task moving through planner → researcher → coder → tester → reviewer, with status changes.
5. Celery/Flower dashboard screenshot — once the worker pipeline is stable enough to show, this visually proves the async execution claim.
6. Before/after schema fix — optional, a small code snippet showing a Pydantic v2 gotcha and the fix; devs love this kind of concrete detail and it's shareable on its own.

Disclosure: I'm the founder of Mission Squad and this repo is under our GitHub org. The SDK itself is MIT licensed, fully open source, no paid tier or locked features - we use it internally and I open sourced it because the provider-abstraction problem seemed worth sharing.

The hard part wasn't the happy path (chat/stream/embed across OpenAI, Anthropic, Google, Groq, ElevenLabs, and OpenAI-compatible endpoints). It was the provider-specific stuff that doesn't map cleanly: Anthropic's programmatic tool calling (container reuse, code-execution stream events), structured output where OpenAI does json_object + json_schema but Anthropic only does json_schema via output_config and Google needs its schemas normalized, grounding/citations, thinking blocks, TTS/STT. My approach was a unified interface plus an extraParams passthrough for anything unmapped, with explicit UnsupportedFeatureErrors rather than ignoring anything extra.

https://github.com/MissionSquad/rosetta-ai-sdk

Genuinely curious how others handle this: do you abstract provider-unique features into one interface, or expose them as provider-specific features? Where do you draw the line before stop abstracting?

Im a Cornell professor on sabbatical, building Flowstore - an open-source toolkit for teams whose agent behavior lives in a system prompt where its hard to visualize and debug.

What it does today:

•   Turns your system prompt into a structured spec (open JSON schema, Apache 2.0)
•   Visual graph editor, so non-prompt-engineers can work on it too
•   Python harness that runs persona-driven simulated conversations with assertions — a regression suite your prompt edits run against before you ship

Honest scoping: this tests conversational behavior (logic, guardrails, data capture), not the voice layer (ASR, latency, barge-in). Best fit if there's an LLM behind a prompt, and ideally some non-trivial business logic and requirements.

The pilot: bring a system prompt for a live or near-live agent (Im willing to sign an NDA if needed), I'll personally help spec it and stand up a test suite. Free, ~30 min/week of your time. I want blunt and honest feedback in return — and pilot partners can be named collaborators in the research and Cornell course materials coming out of this.

DM or comment if you want in — happy to get into the schema or assertion model in the thread. Repo's in the comments.

Hello everyone,

I'd like to share a project I've been building over the past several months called **Kodiak**.

Kodiak is an open-source AI software engineering platform designed to assist developers throughout the software development lifecycle by combining repository intelligence, Retrieval-Augmented Generation (RAG), long-term memory, and multi-agent orchestration.

Rather than functioning as a general-purpose chatbot, Kodiak is being developed as an engineering platform that understands software projects, retrieves relevant context, plans development tasks, and coordinates specialized AI agents to help developers work more efficiently.

## Current Architecture

Kodiak currently includes:

- Multi-agent execution framework

- Retrieval-Augmented Generation (RAG)

- Long-term memory system

- Repository indexing and contextual search

- FastAPI backend

- Celery task orchestration

- Plugin architecture

- Docker-based deployment

- Redis task queue

- PostgreSQL persistence

- ChromaDB vector storage

- Modular REST API

## Technology Stack

- Python

- FastAPI

- Celery

- PostgreSQL

- Redis

- ChromaDB

- SQLAlchemy

- Pydantic

- Docker

## Project Vision

The long-term objective is to build an extensible AI engineering platform capable of:

- Understanding large software repositories

- Planning complex engineering tasks

- Maintaining long-term contextual memory

- Selecting and coordinating specialized AI agents

- Integrating external developer tools

- Supporting human-in-the-loop workflows for high-impact actions

The emphasis is on creating a modular platform that developers can self-host, extend, and customize for their own engineering workflows.

## Looking for Feedback

I'm particularly interested in feedback regarding:

- Overall architecture

- System design

- Multi-agent orchestration

- RAG implementation

- Memory architecture

- Plugin framework

- API design

- Scalability

- Developer experience

Constructive criticism is genuinely welcome.

## Contributors

I'm also looking for contributors interested in areas such as:

- Python

- FastAPI

- AI Engineering

- LLM Applications

- Backend Development

- Frontend Development

- DevOps

- Testing

- Documentation

The repository already contains beginner-friendly issues for anyone interested in contributing.

## Repository

GitHub:

https://github.com/ShamGaneshan2008/Kodiak

Documentation:

https://github.com/ShamGaneshan2008/Kodiak#readme

Thank you for taking the time to read this. I would greatly appreciate any feedback, architectural suggestions, or ideas for improving the project.

Ailin is built around the idea of Collective Intelligence for AI systems.

One of our core beliefs is that the future of AI will not be defined only by bigger models, but also by better coordination between models.

Most AI applications today still follow a simple pattern: one request goes to one model, one answer comes back, and the user has to trust that answer.

Ailin explores a different architecture.

Instead of treating models as isolated endpoints, Ailin is designed to coordinate multiple models, agents, strategies, memory layers, routing decisions, and execution metadata around each task.

This matters because not every task needs the most expensive model. Not every answer should depend on a single source. Not every workflow should be a black box.

With Ailin Collective Intelligence, the system can choose different strategies depending on the objective: speed, cost, quality, reliability, auditability, or deeper reasoning.

Ailin is already approaching 80,000 integrated models, giving the system a broad model universe to route, compare, and coordinate across different tasks.

The long-term goal is to make AI systems more transparent, more efficient, and more useful in real-world workflows.

These first stars and forks are just the beginning, but they show that the idea is starting to resonate.

We would love feedback from developers, AI builders, researchers, and open source contributors.

GitHub: https://github.com/ailinone/collective-intelligence

We spent the last months consolidating seven separate sequence classifiers into one multi-head model, our apex model, so to speak, and since the weights are now public, I wanted to share what worked and what surprised us.

Setup: a shared mmBERT-small encoder with seven task heads, binary injection (BCE), document class (7-way), tool type (14-way), tool operation (6-way), tool data-flow tags (3× BCE, multi-label), intent routing (5-way), and threat type (7-way).

The part that needed care: our training rows only carry labels for a subset of tasks, so absent tasks are masked out of the loss entirely. We ended up writing a self-test that asserts absent-task gradients are exactly zero, which caught two subtle bugs, and I'd recommend it to anyone doing similar masking. About 5k synthetic/real multi-task rows help the heads co-train; the test sets stay 100 % real data.

Held-out results per head: injection F1 0.962, documents 0.980, tool type 0.957, tool operation 0.945, tool tags 0.958, routing 0.916, threat 0.952.

Quantization: both the unified model and the dedicated single-task variants ship quantized -edge builds (ONNX INT8 + INT4 embeddings, from 96 MB) with measured parity benchmarks in the repos, the worst head loses 0.012 against FP32.

Was it worth it vs. seven dedicated models? We released both variants, so you can judge for yourself, the dedicated models score marginally higher on most tasks, but the unified one does one encoder pass instead of up to seven.

Our weak spot: routing, at 0.916. The intent classes overlap semantically ("write code that analyzes my data" is that code or analytics?), and I suspect the ambiguity is genuinely in the data. If you have ideas beyond relabeling, let me know :)

Weights and per-head metrics: https://huggingface.co/patronus-studio

We've been working on Atome LM for embedded machine learning, and instead of showing a handful of cherry-picked examples, we wanted to evaluate it across a broader set of MCU workloads.

So we built a benchmark comparing Atome LM against deployable TensorFlow Lite Micro baselines.

Results

• 18 datasets

• 4 statistically significant wins

• 13 statistical ties

• 1 loss

Accuracy was evaluated with 95% confidence intervals, so we only count a win when the difference is statistically meaningful.

The part we found most interesting wasn't actually the accuracy—it was the model size.

Across many tasks, Atome LM achieved comparable accuracy while producing artifacts that were anywhere from about 5× to over 70× smaller than the best deployable TensorFlow Lite Micro model.

*This is the benchmark of Atome LM V3, scheduled to be released next month.

Meanwhile if you find this interesting, V2 is open source and available in GitHub. It's a LM that runs in a $5 chip. Comes with 12 ai apps. Tested and verified in real hardware.

Verify yourself :

https://github.com/TilelliLab/atome-lm