We have integrated @huggingface as a first-class inference provider in Hermes Agent. When you select Hugging Face in the model picker it now shows 28 curated models organized by use case, with a custom option for the 100+ other models they serve. clem 🤗 (@ClementDelangue) Been really cool to see the traction of @NousResearch Hermes Agent, the open source agent that grows with you! Hermes Agent is open-source and remembers what it learns and gets more capable over time, with a multi-level memory system and persistent dedicated machine access. Starting today, you can use a bunch of @huggingface open-source models thanks to our inference provider partners. Let's go open agents! — https://nitter.net/ClementDelangue/status/2037634211973140898#m
Google Turbo Quant running Locally in Atomic Chat MacBook Air M4 16 GB
Model: QWEN3.5-9B
Context window: 100000 Summarising 50000 words in just seconds..
You can do 3x larger context window, processing 3x faster than before! They are first that have integrated Google
Buy a GPU was always going to win All I wanted was for smart individuals & researchers to have access to the compute they need so opensource progress doesn’t stall, I wasn’t making anything up There’s still time to secure your compute before prices go wild btw
Scikit-learn Cookbook — 80+ recipes for #MachineLearning in Python with scikit-learn [3rd Edition]: http://
amzn.to/4oDGOq7 v/ @PacktDataML 𝓒𝓸𝓷𝓽𝓮𝓷𝓽𝓼:
Common Conventions & API Elements of Scikit-Learn
Pre-Model Workflow and Data Preprocessing
Dimensionality
One way to see the advancement of AI is to see how much further you can get with new models on the same hardware
Here is "an otter using a laptop on an airplane" generated on my home computer using the open weights Wan 2.1, first try. We have come pretty far in 18 months. https://t.co/loxNM2PRybpic.twitter.com/c4iz9UcmTB
One way to see the advancement of AI is to see how much further you can get with new models on the same hardware Here is "an otter using a laptop on an airplane" generated on my home computer using the open weights Wan 2.1, first try. We have come pretty far in 18 months.
Here's why I shill Droid 24/7 ———- Today Droid single-handedly: 1. Published a REAP of GLM-5 in FP8, there's a reason no one else has done it DSA is still very new: huggingface.co/0xSero/GLM-5-… 2. Found and Fixed an upstream issue with VLLM + DSA + Hopper where GLM-5's kv-cache would need to recompute and spend 20x the time needed, fixed. 3. Created multiple working quantisations on it's own, it tried exl3 and autoround but both failed so resorted to GGUF (autoround 3 bits doesn't work on ampere) huggingface.co/0xSero/GLM-5-… 4. Implemented github.com/0xSero/turboquant within 24 hours of the research paper coming out, tested it across 5090s, 3090s, H100s, and B200s 5. Has been distilling larger models into LoRA to help me test arxiv.org/abs/2505.21835 and it got an 80% prune to be semi-coherent again. 6. Helped my find research papers, clean up slop with the human-writing skill. 7. Got BYOK working with Anthropic, ZAI, Kimi, MiniMax, OpenAI working in Cursor github.com/0xSero/factory-cu… 8. Helped me Implement blog.comfy.org/p/dynamic-vra… 's dynamic loading, only works on a tiny model, but still. ——- I only have to check in on it every 30-45 minutes (I am talking all 8 of my sessions) the thing will run for 16 hours with like 0 prep All this while I am mostly focused on my actual job and tweeting 24/7 Keep in mind each one of these experiments is running on a different server, with different constraints, like I don't understand how I can get such good results here. ——— I love novelty. Which is why I jump around and talking about all these different tools. I have used all of these harnesses and messed around with every feature. I keep coming back to this, and I keep shilling it because I sincerely wish others get to experience this.
BREAKING: Claude has a secret mode called "Aristotle First Principles Deconstructor." It strips any complex problem down to its fundamental truths, eliminates every assumption you didn't know you were making, and rebuilds the solution from zero. Aristotle invented this
Open source vs proprietary race: the key point here, is that gap between open source and proprietary models was fairly large (100-150 points), then proprietary models fumbled around so the gap narrowed to c.50 pts, then the 50 point gap remained for over a year, so no clear… https://t.co/v0MknSZuEJ
Open source vs proprietary race: the key point here, is that gap between open source and proprietary models was fairly large (100-150 points), then proprietary models fumbled around so the gap narrowed to c.50 pts, then the 50 point gap remained for over a year, so no clear
