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Enterprise AI - AI agents harness - Local LLMs

UK
Joined June 2009
The most bullish thing for the AI sector is to actually cause measurable deflation.
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Generative AI is great at writing code. It’s terrible at deleting it. The next frontier is Subtractive AI: systems rewarded for what they remove and simplify — not what they add.
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1/8 Looped transformers are coming. OpenMythos, recurrent-depth architectures, weight recycling — the frontier labs are all exploring this. But here's what nobody's talking about: the hardware implications are massive. Let me break it down. 🧵
Introducing OpenMythos An open-source, first-principles theoretical reconstruction of Claude Mythos, implemented in PyTorch. The architecture instantiates a looped transformer with a Mixture-of-Experts (MoE) routing mechanism, enabling iterative depth via weight sharing and conditional computation across experts. My implementation explores the hypothesis that recursive application of a fixed parameterized block, coupled with sparse expert activation, can yield improved efficiency–performance tradeoffs and emergent multi-step reasoning. Learn more ⬇️🧵
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2/8 Traditional transformers: 100+ unique layers, loaded once per token. Looped transformers: 10-20 layers × 5-10 loops = same "effective depth" with fewer unique weights. Result? 30-50% smaller model files. But you pay for it elsewhere.
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Such a big deal. Alternatives are @UnslothAI and @axolotl_ai
Introducing Trellis for Kimi K2 Thinking. It's post-training code that's 50x faster than the best single-node open-source version and 2x cheaper than training APIs. After safety testing, we're open-sourcing it, giving builders the best tools to customize a frontier model. 🧵
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Curious to see whether they will release medium-size models to compete with Alibaba Qwen3.5 series
Finally, V4 is coming * Set to release next week ahead of the Two Sessions * Optimised for Chinese chips led by Huawei and Cambricon * Multi-modal and early tests results are “impressive” Exclusive: DeepSeek to release long-awaited AI model as.ft.com/r/cd6577d9-0633-49…
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This is a fantastic playbook for autoscaling inference of multiple LLMs with Ray and vLLM. vLLM by itself can only serve one model per process.
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