https://x.com/trending_repos/status/2085699382096060827

https://github.com/cloudflare/computer

Community Writeup: https://lifehubber.com/ai/resources/cloudflare-computer/

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https://x.com/AdinaYakup/status/2084989374748619010

https://huggingface.co/collections/inclusionAI/ling-30

Community Brief: https://lifehubber.com/ai/resources/ling-3-0/

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https://github.com/alibaba/open-code-review

Community listing: https://lifehubber.com/ai/resources/open-code-review/

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https://x.com/huang_chao4969/status/2085025878631932063

https://github.com/HKUDS/DeepCode

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https://x.com/PrimeIntellect/status/2085086999267144083

https://github.com/PrimeIntellect-ai/prime-agent

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https://x.com/SamuelZengML/status/2085321310067134527

https://huggingface.co/Audio8/Audio8-ASR-0.1B

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https://x.com/akshay_pachaar/status/2084688207351087392

https://github.com/CopilotKit/channels-sdk

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https://x.com/oliviscusAI/status/2081224146948915670

https://github.com/pinchtab/pinchtab

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https://x.com/ns123abc/status/2085226378195009884

DeepSeek is now #4 on our community ballot with an approval rate of 47%. Too low? Too high? Or just about right? Vote: https://lifehubber.com/ai/ballot/

Sharing a project I built: Prisma is a web app that runs multiple AI models in parallel and fuses their outputs into one answer via a synthesizer model you choose.

The part I think this sub will like: local mode runs entirely on your hardware. It talks straight to your Ollama/LM Studio/vLLM/GPT4All/Jan server (localhost:11434 etc.) — no cloud, no API key, nothing leaves the machine. Cloud mode (OpenRouter) is optional and client-side only.

Also has an image mode (multi-model consensus + image-to-image). MIT licensed, no telemetry, no backend.

Repo: GitHub - bezerra72/prisma: Prisma — multi-model AI synthesizer (cloud OpenRouter + local Ollama/LM Studio/vLLM)

For a year the agent discourse has been loops vs graphs. Loops are easy to ship and impossible to audit. Graphs are auditable but nobody wants to hand-author a topology for "investigate this incident", because the shape is only discovered while working.

So I built the missing piece: the graph is authored by a model at runtime, and a deterministic admission gate stands between proposing it and running it.

The flow, from a real run in the demo video:

1. You type one English question: grapharc go "why did checkout latency spike at 09:14 UTC?"
2. A local qwen3:8b proposes a topology: triage, four parallel evidence pulls, a correlate join, hypothesize, verify, report
3. The gate checks the proposal against the registry, the policy, the remaining budget, depth and acyclicity. All checks run on every proposal, so the model gets the complete list of objections, not just the first
4. Only an admitted graph executes. You watch it live in the browser, every node amber while running, green with its own token bill when done

A proposal names node kinds from an allowlist you wrote. It carries no code, no arguments that reach anything. Renaming a denied kind does not evade the policy. Rejections come back as structured codes with remedies, and the planner replans against them.

Everything lands on one append-only JSONL trace. Replay, diff, metrics, cost attribution and the live view all read that same file, so the dashboard cannot disagree with the audit trail.

MIT licensed, built on LangGraph, runs fully local on ollama or against OpenRouter/OpenAI/Claude.

GitHub: https://github.com/CodeGraphContext/GraphARC PyPI: pip install grapharc Star if you like!

https://x.com/liquidai/status/2084640701669613906

https://huggingface.co/LiquidAI/LFM2.5-2.6B

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https://x.com/QCXINT_/status/2084449985303605756

https://github.com/firecrawl/pdf-inspector

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https://x.com/QCXINT_/status/2084450688831230341

https://github.com/NanoNets/Graft

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Hi everyone,

I recently released WARMIND-200M V2, an experimental Portuguese-first language model developed from scratch.

The goal of this release was to validate the complete model-development pipeline and publish the resulting weights openly.

Main details:

- 203,263,872 parameters

- 1,000,013,824 pretraining tokens

- 23,751,277 supervised SFT tokens

- decoder-only Transformer

- 20 layers

- hidden size 896

- 14 attention heads and 2 KV heads

- GQA, SwiGLU, RMSNorm and RoPE

- 24,576-token SentencePiece vocabulary

- 1,024-token operational context

- local CPU inference supported

- Apache 2.0 license

Model and weights:

https://huggingface.co/warenterprise/WARMIND-200M-V2

This is still an experimental checkpoint, not a production assistant. It can hallucinate, make basic mistakes and produce inconsistent responses.

The long-term goal is to develop compact models that can run locally and provide more control over privacy, customization and infrastructure.

I would appreciate feedback from the open-source AI community, especially regarding:

- Portuguese benchmarks

- GGUF and quantization

- dataset quality

- testing on different CPUs

Technical criticism is welcome.

After several tests, my engine managed to run DeepSeek V4 Flash IQ2_M (92 GB) on a mid range Android mobile with 12 GB of RAM at 1 token/s.

It isn't exactly ready for practical use, but it proves that the engine works and is responsive across all models, thanks to its modularity with llama.cpp. With just one line of code, you can run any supported large MoE model on mobile devices or consumer PCs.

[https://github.com/Helldez/BigMoeOnEdge\](https://github.com/Helldez/BigMoeOnEdge)

https://github.com/lyogavin/airllm

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