@plainionist The industry has misled the public with terms like “In-Context Learning”, “Skills” or “Reasoning”. It’s trivial to prove no real learning is happening: the model’s weights are never changed. Remove the context, and the alleged “learning” disappears instantly. Puff.

Today we’re announcing Ternary Bonsai: Top intelligence at 1.58 bits Using ternary weights {-1, 0, +1}, we built a family of models that are 9x smaller than their 16-bit counterparts while outperforming most models in their respective parameter classes on standard benchmarks. We’re open-sourcing the models under the Apache 2.0 license in three sizes: 8B (1.75 GB), 4B (0.86 GB), and 1.7B (0.37 GB).

Qwen3.6-35B-A3B just dropped. Red Hat AI has an NVFP4 quantized checkpoint ready. 35B params, 3B active, quantized with LLM Compressor. Preliminary GSM8K Platinum: 100.69% recovery (slightly above baseline). Early release. Let us know what you think! https://t.co/i5Fc4P7NVN

Sharing my current setup to run Qwen3.6 locally in a good agentic setup (Pi + llama.cpp). Should give you a good overview of how good local agents are today: # Start llama.cpp server: llama-server \ -hf unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_XL \ --jinja \ --chat-template-kwargs '{"preserve_thinking":true}' \ --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0 # Configure Pi: { "providers": { "llama-cpp": { "baseUrl": "http://127.0.0.1:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_XL" } ] } } }

@DanielWulikk Have to think a bit about how to best visualize it, but if you are interested, I have a working from-scratch code implementation of Gemma 4 E2B in the meantime to see how per-layer embeddings are implemented: https://t.co/jyiq1vyJnH https://t.co/fVrSBWHNHl

Introducing gyaradax 🐉: A JAX solver for local flux-tube gyrokinetics with custom CUDA kernels for acceleration. This entire code was vibecoded by @ggalletti_ and me in a month. Validated against GKW (CPU-only Fortran code) with 10x speedups. Details and code in the replies. https://t.co/22PrHjItR5

abs: https://t.co/tSLRNXAgY3 code: https://t.co/ldWVGmuC4O blog post: https://t.co/yI0NmdaKHm

Also, how cool that this is so easy now? This was a few careful asks to Codex, which worked for ~128k tokens/1h to do everything - sourcing the data, embedding with clip (via @replicate), making an exploratory search tool for refining + filtering, and whipping up the final app.

@trq212 This framing degrades technical literacy. Your prompt isn't "communication", it's tokenized, embedded as vectors, processed through frozen weights. No "agent" receives it. No bandwidth grows. Anthropic's own leaked system prompt: 16,739 words of context steering. That's engineering, not dialogue. Research shows anthropomorphic AI discourse creates self-fulfilling alignment degradation. You're not "talking to agents." You're structuring conditional probability queries. High-leverage? Yes. Interpersonal? Never. If the goal is public understanding, not marketing, stop treating inference as a “team member”. That's technically inaccurate. Which makes it dishonest the moment someone with enough expertise verifies it.

dflash-mlx: DFlash speculative decoding, ported to Apple Silicon. Qwen3-4B at 186 tok/s on a MacBook. 4.6× faster than plain MLX-LM. Exact greedy decoding: output matches plain target decoding. https://t.co/VxfyworgAe