Had a great time at PyCon & PyData DE. Highly recommend it. Great open-source, community-focused conference with lots of builders in the Python AI, LLM and agent space. Taking a short family break, my first "vacation" in years (hopefully, I won't miss the DeepSeek V4 drop )
OPEN SOURCE
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Open Research and AI: Choosing Values Over Closed Systems
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Adithya is putting his work where his heart is. If you believe in open research and open AI, join a company that actually lives those values, not a closed-source, revenue-maximizing one!
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Pi Agent Traces Now Visualizable on Hugging Face
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You can now visualize Pi traces that you upload on @huggingface
! Let's make sharing agent traces 10x more common to make agent AI more open and collaborative! Also, because it's fun to analyze @badlogicgames
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Mythos monitors your open source repositories automatically
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Mythos is watching your open source repos while you sleep
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Local Qwen 35B Outperforms Claude Opus 4.7 on Benchmark
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Shocking result on my pelican benchmark this morning, I got a better pelican from a 21GB local Qwen3.6-35B-A3B running on my laptop than I did from the new Opus 4.7! Qwen on the left, Opus on the right
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Opus 4.7 Released: More Powerful, More Expensive or Run Local
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opus 4.7 slightly more dangerous, slightly more expensive OR: run local models!
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Obliteratus Tool Removes Safety Refusals From Open-Weight LLMs
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Someone built a tool that deletes censorship from any open-weight LLM with a single click.
— AlphaSignal AI (@AlphaSignalAI) 16 avril 2026
Most open-source models ship with built-in refusal.
They reject prompts even when the use case is legitimate.
Research, red-teaming, creative writing.
Obliteratus is a toolkit that… pic.twitter.com/KnNohJirS1Someone built a tool that deletes censorship from any open-weight LLM with a single click. Most open-source models ship with built-in refusal. They reject prompts even when the use case is legitimate. Research, red-teaming, creative writing. Obliteratus is a toolkit that
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Alibaba Qwen3.6-35B-A3B Sparse MoE Model Released
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Alibaba released Qwen3.6-35B-A3B today. Big jump compared to Qwen 3.5-35B model. It's a sparse MoE, 35B total params, only 3B active. Natively multimodal, thinking and non-thinking modes. Hardfacts:
SWE-bench Verified: 73.4, near dense Qwen3.5-27B (75.0), way ahead of -
Gemma 4 Colab: Reinforcement Learning Sudoku Game Guide
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Gemma 4 Colab: https://
colab.research.google.com/github/unsloth
ai/notebooks/blob/main/nb/Gemma4_(E2B)_Reinforcement_Learning_Sudoku_Game.ipynb
… RL Guide:
