In an epoch where lies rule, we ought to ourselves and future generation to seek the truth. it will not find you because your algorithm feeds on 90% lies.
This is a super exciting release - Claude Fable 5 is the same underlying model as Mythos but with added safeguards. The benchmarks are great and it's SOTA on everything by a margin but I'll add that *qualitatively* also, this is a major-version-bump-deserving step change forward (imo of the same order as Claude 4.5 was in November), peaking especially for long problem-solving sessions on very difficult problems. You can give it a lot more ambitious tasks than what you're used to, the model "gets it" and it will just go, and it's never felt this tempting to stop looking at the code at all (but don't do this in prod!). The model still has quirks that people will run into and the safeguards are configured to be a little too trigger happy for launch, which can hopefully be tuned over time.
I feel a lot of things changing as working software increasingly comes out on a tap. The Jevon's paradox kicks in and I feel my own demand for software growing substantially. You can ask for anything - explainers, visualizers, dashboards, bespoke single-use apps (e.g. a full wandb that is hyper-specific just for your project), you can 10X your test suite, auto-optimize code, run giant research projects with custom HTML for the results, anything! "Free your mind" (Matrix ref). Really looking forward to all the things people build!
HERMES AGENT IS LEARNING LIKE A REAL WORKER
• Hermes crossed 140K GitHub stars by turning past tasks into reusable self-improving skills
• Paired with local Qwen models, it can run private agent workflows with no monthly AI bill
@Kurrco This post about to get Kurrco more X interactions than any of the iceman posts this month. Why?
“The hate get realer, the love gets fake, but when you're this great, thats how you should like it”- DOT
STOP PAYING FULL PRICE FOR CLAUDE CODE. THERE IS A FREE SETUP THAT PLUGS OPENROUTER, OLLAMA, DEEPSEEK AND KIMI IN UNDER 5 MINUTES.
Same workflow, different model behind the scenes, free APIs or local models and one dashboard to manage everything.
A GUY FROM SINGAPORE GOT FIRED FOR RUNNING UNAUTHORIZED MODELS. 11 DAYS LATER HE TURNED $400 INTO $479,401 ON POLYMARKET.
The firm that fired him now watches as his bot runs their model 24/7. It doesn't need a desk, references or a risk committee.
What if you could take three completely different model families… and distill them into one tiny model? 🤯
📜 Paper: https://t.co/K2iKD4xFvp
MOPD (Multi-Teacher On-Policy Distillation) has become a standard procedure in post-training. We already distill multiple specialized variants of the same model into a single set of weights.
But what if we could go further - and distill models from entirely different families? Turns out, it is possible.
Today we’re releasing a paper on cross-tokenizer distillation - our first steps in this exciting direction. 📄
We distilled Qwen3-4B, Phi-4-Mini, and Llama-3B into Llama-3.2-1B.
MMLU jumped from 32.05 → 46.32 when using multiple teachers. 📈
The team is now working on Nemo-RL integration so the community can try this method in their own settings. Plus, we are scaling experiments up. 🚀
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