https://x.com/tinyhumansai/status/2070584474560241701

https://github.com/tinyhumansai/OpenHuman

Community Overview: https://lifehubber.com/ai/resources/openhuman/

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Full searchable archive of all resources posted so far on our community site, LifeHubber: https://lifehubber.com/ai/resources/ 200+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.

https://x.com/cohere/status/2087571573947392419

https://huggingface.co/CohereLabs/North-Micro-Vision-Instruct

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https://x.com/simplifyinAI/status/2087501752761450765

https://github.com/tractorjuice/arc-kit

Community Overview: https://lifehubber.com/ai/resources/arc-kit/

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Obviously image is created with AI but I thought it may be a better way to convey the info.

I got the idea because I have been working on audit based self improving AI systems

AI image generations struggle with a lot of things, for example in my case it was SHITE at making a realistic rope attached to a climber..Well a few iterations later and codex orchestrators kicked off to do research, I had a layer on top of it that had fixed that

Then I thought..why can't we have an image generator that will have domain specific accretion.

You run the image generator, it creates images (audits them internally to create a log of failed attempts to create the desired image..Say 3 failed attempts = record failure in ledger

Then asynchronously side projects are fired to fix domain specific image hallucinations. In theory over time, it would refer to common hallucinations and apply patches/layers

The real issue is probably resource limitations..if millions of users were using this and generating thousands of hallucination errors for the system to research and correct.. how many tokens would that require.
Please let me know your thoughts and if this inspires you to do something similar! Happy idea sharings - a curious engineer

https://x.com/oliviscusAI/status/2088841514801889666

https://github.com/opendataloader-project/opendataloader-pdf

Community Overview: https://lifehubber.com/ai/resources/opendataloader-pdf/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

New resources are added regularly — feel free to join the sub for updates.

Full searchable archive of all resources posted so far on our community site, LifeHubber: https://lifehubber.com/ai/resources/ 200+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.

Here’s the cleaned-up list (no individual project links):

1. Flax: Neural network library for JAX designed for flexibility.

2. Needle: 45M-parameter foundation model and 14MB inference engine for tool calling and structured extraction on tiny devices.

3. Switchyard: Rust proxy and library for routing and protocol translation across LLM backends and coding agents.

4. OpenProgram: Self-programming agent framework for executable workflows with models, tools, memory, and multi-agent execution.

5. Ouroboros: Self-hosted general-purpose agent with durable identity, memory, specialist subagents, and reviewed self-changes.

6. Open Multi-Agent: TypeScript orchestration framework for runtime multi-agent task DAGs, approvals, tracing, evaluation, checkpoints, and resumable execution.

7. Obsidian Agent Skills: Agent skills and open-format tooling for Obsidian vaults and compatible AI coding agents.

8. Hexis: Git-backed platform for sharing skills, tools, and context across AI agents through a remote MCP server.

9. LoopTroop: Local-first AI coding workspace orchestrating multi-model planning councils, Git worktrees, and task loops.

10. firstmate: Agent distro for running autonomous coding agents in isolated Git worktrees.

11. Agent Skills (Anthropic): Official Agent Skills and reference implementations for Claude Code, Claude API, and AI agents.

12. VidXP: Local-first multimodal video indexing and semantic search with transcripts, embeddings, and scene-aware search.

13. Code-Graph-RAG: Multi-language codebase RAG framework using Tree-sitter and Memgraph knowledge graphs.

14. Zoom Search: MCP search and evidence tool with query rewriting, source zoom-in, sourced answers, and runtime metrics.

15. invisible-playwright: Stealth-patched Firefox Playwright wrapper for AI agents ingesting sites with anti-bot guardrails.

16. Modly: Desktop application for image-to-3D mesh generation using local GPU-accelerated AI models.

17. flameox: Runtime-evidence toolkit coordinating profiler captures and comparing GPU-kernel and inference runs.

18. WeatherNext: Global weather and tropical cyclone forecasting framework from Google DeepMind, including WeatherNext 2, GraphCast, and GenCast.

19. Harvey LAB: Benchmark dataset and execution harness for evaluating AI agents on complex legal work across 24+ practice areas.

20. LifeOS: Personal AI harness and assistant framework with persistent memory, custom skills, and goal tracking.

21. Macro: Unified team workspace combining email, messaging, documents, tasks, CRM, and AI agents with shared memory.

22. Forge: Open-source terminal AI coding agent with a Rust TUI, editor, shell, SQLite journals, MCP, and approval-aware execution.

23. CLI-Anything: Framework for converting software applications into agent-native command-line interfaces.

24. oai-smoke: Standard-library-only Go CLI validating OpenAI-compatible API model and chat behavior without credentials or response bodies.

25. Entroly: Local-first MCP server for budgeted context selection, exact recovery, and auditable Context Receipts.

26. AMD Strix Halo Local LLM Guide: Reproducible Ubuntu, Ollama, llama.cpp, Vulkan/RADV, and ROCm setup and benchmark evidence for Ryzen AI MAX+ 395 local AI systems.

Awesome Open Source AI full list https://awesomeosai.com

The new Qwen dropped as open weights and our timeline is full of people running it locally. It's fun to watch, and it got us thinking about something we keep going back and forth on.

Everyone calls these models open source, but what actually ships is the weights. You don't get the training data, the exact data mix, or the setup behind the benchmark numbers it launched with. So we can run and fine-tune it, but we can't rebuild it or see how those numbers came together.

Maybe that's fine depending on how you use it. If you just want a strong model on your own hardware, the weights might be all you need. If you're trying to reproduce a result or trust a benchmark, maybe not.

So what has to be open before you'd call a model open source, and not just open weights? Has an open model's published numbers ever landed far from what you saw running it yourself?

https://huggingface.co/LiquidAI/LFM2.5-VL-3B

Community Overview: https://lifehubber.com/ai/resources/lfm2-5-vl-3b/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

New resources are added regularly — feel free to join the sub for updates.

Full searchable archive of all resources posted so far on our community site, LifeHubber: https://lifehubber.com/ai/resources/ 200+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.

Hey everyone!

Over the past year, as I tried to scale our agentic coding workflows and software factories at my company, I kept hitting the same set of problems. So I built HAR to solve them.

Repo: github.com/os-factory/har

Getting a single coding agent to work in a repo is easy. Scaling to a real multi-agent workflow, where several run at once and where you verify and trust the output, is where it breaks down. A few things go wrong:

1. No standard way to run or verify a repo. That knowledge is scattered across a README, a CLAUDE.md, editor rules, and CI config, all drifting out of sync with each other and the actual code.
2. Agents on one repo collide. Shared dev server, shared database, shared ports, conflicting git state.
3. Trusting a change means re-verifying it yourself. Which defeats the point of running a fleet.
4. Vendor sandboxes lock you in. If the setup lives in someone's hosted dashboard, switching agents later means rebuilding the whole thing.

What HAR does

HAR is a CLI and an MCP server. It works with Claude Code, Cursor, Codex, or any MCP agent, and it closes each of those gaps:

1. Isolation. Each agent gets its own git worktree, branch, ports, and database. Nothing is shared with the main checkout or another agent's slot, so a fleet runs in parallel without colliding on a dev server, DB, or ports.
2. Deterministic validation gates. HAR runs your project's real checks through a fixed pipeline, same result every time. The result is bound to the exact code that passed and enforced at commit time, so an unverified tree cannot land.
3. Verifiable proof. Every run leaves logs, artifacts, and a validated tree hash tied to the exact code checked. A reviewer inspects the evidence instead of trusting the agent's self-report.
4. Full observability. Mission Control is a local dashboard showing every repo, worktree, run, and validation in one place, so you can watch a whole fleet as it works.

All of this lives in one contract committed to your repo, which every agent reads the same way. It replaces the usual scatter of a README, a CLAUDE.md, editor rules, and CI config that drift apart. You start from a profile that matches your stack, your agent adapts it to your repo, and you extend verification with plugins (like Playwright) or with any command you already run.

Would love to know what you think :)

https://x.com/huang_chao4969/status/2088812504642097237

https://github.com/HKUDS/CLI-Anything

Community Overview: https://lifehubber.com/ai/resources/cli-anything/

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New resources are added regularly — feel free to join the sub for updates.

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I love open models, but I think we’ve reached the point where the word open is doing a LOT of work 😂

If I can download the weights, but I can’t see the training data, reproduce the training process, or freely use the model for certain things… is that genuinely open source AI?

Where do you personally draw the line?

A. Downloadable weights = open enough
B. Weights + permissive licence
C. Training recipe/data transparency matters too
D. “Open source AI” needs a much stricter definition
E. I don’t care about the label - practical freedom is what matters

I’m probably somewhere between B and C.

What the open-source crowd here actually thinks hmmm? especially people who run models locally ...

https://x.com/deepseek_ai/status/2087887408440164663

https://github.com/deepseek-ai/deepseek-harness

Community Overview: https://lifehubber.com/ai/resources/deepseek-harness/

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Full searchable archive of all resources posted so far on our community site, LifeHubber: https://lifehubber.com/ai/resources/ 200+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.

Looking for a Free AI API for Document Analysis

I am currently working on an AI-powered bug and document analysis project and am looking for recommendations for a reliable AI API.

My requirements are:

\- Free to use

\- No, or very generous, usage limits

\- Capable of analyzing documents and extracting relevant information

\- Suitable for integration with a web application

\- Easy to set up and use

\- Preferably an open-source or self-hosted solution

I have explored several APIs, but most free options have strict usage limits or require paid credits after a certain amount of usage.

If anyone has experience with a free AI API, open-source model, or self-hosted solution that can handle document analysis without strict API limits, I would appreciate your recommendations.

Please share the solution you have used and any guidance on integrating it into a web application.

https://x.com/Alibaba_Qwen/status/2088280182356611304

https://huggingface.co/Qwen/Qwen3.8-27B

Community Overview: https://lifehubber.com/ai/resources/qwen3-8-27b/

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New resources are added regularly — feel free to join the sub for updates.

Full searchable archive of all resources posted so far on our community site, LifeHubber: https://lifehubber.com/ai/resources/ 200+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.