These are all designed for the closed source APIs not for open models unfortunately
OPEN SOURCE
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Open Models vs Closed APIs: Comparing Apples and Oranges
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That’s why I usually say that comparing open models with closed-source APIs or products is like comparing apples and oranges. Or comparing an engine with a full car. Or comparing an ingredient with a Michelin dinner (missing ingredients, prep and chef). There’s a lot of
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Codex excels at porting model architectures for async work
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Fun writeup: porting entire model architectures from scratch, and how the Codex app shines for async work! https://t.co/eIsROU3As9
— Alexander Embiricos (@embirico) 1 avril 2026Fun writeup: porting entire model architectures from scratch, and how the Codex app shines for async work! Niels Rogge (@NielsRogge) New blog post: how I used Codex to contribute VidEoMT, a SOTA model for video segmentation, to the Transformers library In December 2025, a shift occurred, and coding agents suddenly succeeded at a task they previously failed at: porting entire models. I list some of my best practices for using coding agents. Link: huggingface.co/blog/nielsr/c… — https://nitter.net/NielsRogge/status/2038654071054426595#m
→ View original post on X — @romainhuet, 2026-04-01 03:11 UTC
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MSA Paper Published, Inference Code Open Sourced This Week
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A little spoiler: 1. @EverMind's MSA paper has been selected for publication by AlphaXiv, and it's getting great traffic 2. MSA's Inference code will be open sourced this week github.com/EverMind-AI/MSA — alphaXiv (@askalphaxiv) Scaling Attention to 100M context!? Memory Sparse Attention introduces an idea where instead of rereading an entire 100M-token entry, it learns to jump straight into the relevant memories and reason from them end-to-end. More specifically, it first encodes documents into compressed memory slots, then for each question it uses a learned router to score which chunks are actually relevant, pulls only the top few, and runs normal attention over that tiny assembled context. So the model's compute grows with "how much it needs to look at" not "how much memory exists". This retrieval step is trained jointly with answer generation, so memory lookup is part of the model itself, and can decouple memory capacity from reasoning cost. [Translated from EN to English]
→ View original post on X — @elliotchen100, 2026-04-01 03:05 UTC
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GSK Transforms Open Source Adoption With Internal R Expertise
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GSK didn't just adopt open source. They rebuilt how an organization relates to them. At Rev, Ben Arancibia shares how @GSK built internal R expertise, dismantled constraints, and created governance that made it stick. Rev PHL, May 12 RSVP: https://
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TRL v1 Released: 75+ Methods for Model Post-Training
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Today we’re releasing TRL v1. 75+ methods. SFT, DPO, GRPO, async RL to take advantage of the latest and greatest open-source. 6 years from first commit to the library that post-trains most open models in the world. Built to be future proof. pip install trl
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Why Open Source OpenClaw Matters for AI Development
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Why @openclaw being open source is so important, from @clairevo pic.twitter.com/42xVKreKdy
— Lenny Rachitsky (@lennysan) 31 mars 2026Why @openclaw being open source is so important, from @clairevo
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LFM2.5-350M: Tiny Agentic AI Model Running in Browser
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This is an always-on model living in your browser.
— Maxime Labonne (@maximelabonne) 31 mars 2026
Sub-500 MB QA, data extraction, tool use 🪄 https://t.co/6ttaJBTxAcThis is an always-on model living in your browser. Sub-500 MB QA, data extraction, tool use 🪄 Xenova (@xenovacom) NEW: LiquidAI just released LFM2.5-350M, a tiny model that brings agentic AI and tool-calling capabilities to resource-constrained environments. 🤯 It can even run locally in your browser via WebGPU, serving as a powerful companion while you browse the web. Try the demo! 👇 — https://nitter.net/xenovacom/status/2039043406823833964#m
→ View original post on X — @maximelabonne, 2026-03-31 21:46 UTC
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Open Source Projects Need More Maintainers
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Yeah, give us more maintainers. Reason is too much to do