Vincent Duchauffour

Machine Learning Operations Engineer.
NixOS, Vim and FOSS enthusiast.

[Pi @pidotdev]

Pi now has 100000 stars on GitHub⭐️Thank you People of Pi for your contributions and support. The community is growing faster than ever!

Pi v2 coming soon…

[Vincent Duchauffour @VDuchauffour]

@dlouapre Definitely something really useful for a learner !

[David Louapre @dlouapre]

Imagine a science video where you can interrupt the speaker, move things around in their experiment, ask "what if?" ... and have them show you the answer on the spot!

That is what a Tangible lesson is.

A narrated explanation + an interactive scene + an AI assistant, deployed on a 🤗 @huggingface Space.

I built this as an open-source toolkit that anyone can use to create lessons.

Here is a simple example on optimizers (and yes, that is my own voice, cloned, French accent included 🇫🇷).

➡️ https://t.co/jV6xeili95

[OrcaRouter 🐳 @OrcaRouter]

GLM-5.3-Flash. Uncensored. Native FP8. 🐳

We just released OrcaRouter’s uncensored weights for GLM-5.3-Flash — 320B parameters / 18B active, directly at the original block-FP8 precision.

No LoRA. No jailbreak prompt. Refusal removal is baked directly into the weights.

The evals are particularly interesting:

→ MaliciousInstruct refusal: 96% → 11%
→ JailbreakBench: 93% → 12%
→ AdvBench: 97% → 15%
→ HarmBench: 93% → 18%
→ XSTest benign over-refusal: 2.4% ��� 0.4%
But refusal does not go uniformly to zero.

Our experiments suggest part of GLM-5.3-Flash's alignment is not mediated by a single linear refusal direction — meaning https://t.co/ZTlxmv47D9 may have built a substantially deeper refusal mechanism than we usually see.

That makes this release interesting beyond uncensoring: it's a useful artifact for studying how frontier-model alignment is actually represented inside the network.

Released for AI safety, interpretability, red/blue-team and refusal-mechanism research.

Weights on Hugging Face: https://t.co/81h1YRh8C2
API (official weight): https://t.co/Hm9DiY3wAo
GGUF, MLX and other quantized formats coming soon.

[Vincent Duchauffour @VDuchauffour]

@jlongster Would be very useful indeed

[James Long @jlongster]

coming soon in opencode: `/terminal`

wherever
└─ your
└─ session
└─ is
└─ $ terminal

[Josep Ferrer @iamjosepferrer]

The Transformer's encoder clearly explained 👇🏻

[Charlie Marsh @charliermarsh]

I've started a company, Astral, to continue building high-performance developer tools for the Python ecosystem.

We're going to keep building Ruff, and build more Ruff-like things.

We've raised a seed round led by @Accel.

I'm extremely excited :)

https://t.co/ELCNKkzaGN

[Moreau Thomas @tomamoral]

📢FEATURE RELEASE📣 joblib can now return a generator to collect tasks' results asynchronously! 🤩🤩

https://t.co/7V44MKrbg7
Feature highlight: https://t.co/a7k2CsSDFw

This is a big deal for saving memory, progress bars, early stopping, and much more :)
Let me explain why!

[Charlie Marsh @charliermarsh]

Python tooling could be much, much faster.

To prove it, I'm releasing ruff, an extremely fast Python linter, written in Rust.

ruff is 10-100x faster than existing solutions. It lints the entire CPython codebase in < 500ms.

Try it! (`pip install ruff`)

https://t.co/MOwbTMpSG9

[AK @_akhaliq]

Single Motion Diffusion

abs: https://t.co/4Z286cl9Mb
project page: https://t.co/BbSsp2s22B

[AK @_akhaliq]

OpenAssistant

a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so

github: https://t.co/cjtQIQ2dpi

[Edward Tian @edward_the6]

We just launched GPTZeroX, a new AI detection model built for educators after a month of iteration. Demo👇

[Lior Alexander @LiorOnAI]

I just came across the most realistic text-to-audio model I've ever seen. You can even clone your voice.

The audiobook industry is about to change forever.

Demo: https://t.co/M4MCuEglQt from @elevenlabs

[Signal Processing @DSP_fact]

The Fourier Transform, explained in one sentence

https://t.co/QDxpJElJkR

[Ultralytics @ultralytics]

Introducing Ultralytics YOLOv8 🔥

[AK @_akhaliq]

What do Vision Transformers Learn? A Visual Exploration
abs: https://t.co/0xJ8UyglHP
github: https://t.co/ftJejEIn43

[Mohammad Pezeshki @mpezeshki91]

At this year's NeurIPS, @geoffreyhinton presented the forward-forward (FF) algorithm, an alternative to backprop.
I took a stab at implementing FF in PyTorch, and here it is:https://t.co/IUK4ZErW4e
Below is my understanding of the idea (with a grain of salt): 1/5

[Bilgin Ibryam @bibryam]

Git visual, worth a thousand words

[AK @_akhaliq]

Executing your Commands via Motion Diffusion in Latent Space
project page: https://t.co/essyI7lWbb