Alessandro

Tech & Sports Enthusiast // Chief AI Officer @

[Qwen @Alibaba_Qwen]

⚡ Meet Qwen3.6-35B-A3B：Now Open-Source！🚀🚀

A sparse MoE model, 35B total params, 3B active. Apache 2.0 license.

🔥 Agentic coding on par with models 10x its active size
📷 Strong multimodal perception and reasoning ability
🧠 Multimodal thinking + non-thinking modes

Efficient. Powerful. Versatile. Try it now👇

Blog：https://t.co/EXx5y466su
Qwen Studio：https://t.co/bg4tAU1p74
HuggingFace：https://t.co/w4pDX14DZS
ModelScope：https://t.co/SuRyLzdQiO
API（‘Qwen3.6-Flash’ on Model Studio）：Coming soon～ Stay tuned

[clandestine.eth 🦇🔊 @0xClandestine]

Heterogeneous acceleration on Apple Silicon achieved.

ANE + GPU running in parallel.

Mirror SD with DFlash, ported to MLX — targeting ANE + GPU simultaneously.

The M-series was designed for this. We just hadn't unlocked it yet.

[Alessandro @alew3]

@ivanzugec @ernielm @felixrieseberg It appeared a few hours later. Thanks.

[Alessandro @alew3]

@ernielm @felixrieseberg @ernielm it now appears on my mobile app as "Dispatch" under the burger menu

[Alessandro @alew3]

@Tibbzzee @ChanaMessinger @felixrieseberg don't have it, just updated it.

[Alessandro @alew3]

@felixrieseberg I have Max 20 , how do I get this working? Already update the desktop and mobile.

[Andrej Karpathy @karpathy]

I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then:

- the human iterates on the prompt (.md)
- the AI agent iterates on the training code (.py)

The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc.

https://t.co/YCvOwwjOzF
Part code, part sci-fi, and a pinch of psychosis :)

[Alessandro @alew3]

@trq212 @trq212 just watched it, can you share the slides?

[Alessandro @alew3]

@awnihannun @angeloskath thanks!

[Rachel Thomas @math_rachel]

"People who go all in on AI agents now are guaranteeing their obsolescence. If you outsource all your thinking to computers, you stop upskilling, learning, and becoming more competent. AI is great at helping you learn." @jeremyphoward @NVIDIAAI

https://t.co/s2ZIeHK3sq 2/

[Alessandro @alew3]

@awnihannun Looking good! BTW, any reason MLX can't leverage the Neural Engine?

[Alessandro @alew3]

@awnihannun wow! whats the syntax to get this going?

[OpenAI @OpenAI]

ChatGPT agent is ready to introduce itself.

https://t.co/EOvjGJsf0R

[Alessandro @alew3]

@seeedstudio @huggingface any promotional coupon for this amazing kit?

[Charlie Marsh @charliermarsh]

You can set `UV_TORCH_BACKEND=auto` and uv will automatically install the right CUDA-enabled PyTorch for your machine, zero configuration

[Alessandro @alew3]

@Prince_Canuma @arcee_ai good luck at Apple 😜

[Alessandro @alew3]

@Prince_Canuma holy cow! that was fast!

[Alessandro @alew3]

@scottsLockedIn @maximelabonne this is to prevent other big tech from using it.

[Alessandro @alew3]

Meta has just released their Llama 4 models. The "Llama 4 Scout" variant features an impressive 10 million token context window. https://t.co/JIN22jqpmG #llama4 #llama