Este usuario ha hecho que Claude le hable como un cavernicola para ahorrar tokens Lo peor: ha funcionado 75% menos uso de tokens por sesión El prompt más tonto a veces es el más eficiente
LLMS
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Google DeepMind Announced as Presenting Sponsor of AIE Europe
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always wanted to do one of those “is it coachella” announcements – here is our designer’s take on it! AI Engineer (@aiDotEngineer) 🇬🇧 London is the birthplace of @GoogleDeepMind, and we're so honored to have them back as: Presenting Sponsors of this week's AIE Europe! DeepMind has pushed the AI frontier on every modality — from Gemini 3.1, to Embeddings 2, to Veo 3, to @NanoBanana Pro, and last far from least Gemma 4, byte for byte the most capable multimodal models in the world! Meet the team to catch up on everything GDM has shipped for AI Engineers in the past few months – from our keynoters Dr @RaiaHadsell (VP of Research and UK AI Ambassador) and @osanseviero (Lead AI DX), to returning speakers @DynamicWebPaige and @thorwebdev and @_philschmid, to researchers and engineers working on those exact products @sedielem, @tara_ojo, @FMuntenescu and more! — https://nitter.net/aiDotEngineer/status/2040806678849884422#m
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Collection of Free AI Mastery and Prompt Engineering Guides
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I should charge $99 for each of these.
But every single guide on this page is free. → Gemini Mastery Guide
→ Prompt Engineering Guide
→ Claude Mastery Guide
→ OpenAI Mastery Guide
→ + more dropping & updating regularly Zero cost. Zero catch. Just open and learn -
Coding agents outperform long-context models
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BREAKING: Duke researchers just proved that coding agents are better at processing long documents than models with million-token context windows. > Not because of longer context. Because grep and sed are better retrieval tools than attention. > +17.3% average improvement
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Huawei’s Smart Noise Scheduler Improves Diffusion LLM Logic Learning
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What if Diffusion LLMs learned logic more efficiently? Huawei's Noah's Ark Lab proposes a breakthrough: their "smart noise scheduler" uses priority masking to focus training on only information-dense data, making DLLMs master core reasoning and structure. This boosts average accuracy by 4% on Code & Math reasoning, beating uniform baselines and unlocking DLLM potential. Mask Is What DLLM Needs: A Masked Data Training Paradigm for Diffusion LLMs Paper: arxiv.org/abs/2603.15803 Dataset: huggingface.co/datasets/malr… Our report: mp.weixin.qq.com/s/1yTd36hev… 📬 #PapersAccepted by Jiqizhixin
→ View original post on X — @jiqizhixin, 2026-04-05 14:05 UTC
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Learning Path: LLM Architecture, Reasoning Models, and Production Systems
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I would probably start with 1. my Build A Large Language Model (From Scratch) book to understand the basic architecture and basic pipeline. Then maybe 2. Build A Reasoning Model (From Scratch) for inference scaling and reinforcement learning
3. Maybe one of the "production" -
Progressive Disclosure Pattern Optimizes AI Token Efficiency
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We've been building skills at Towards AI for months now and the progressive disclosure pattern is key. Loading full context upfront wastes tokens and confuses the model. The lightweight trigger + on-demand loading is how you actually scale this. Super cool to see Anthropic
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Python Rewrite and Architecture Leak Analysis Revealed
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The Python rewrite angle is clever haha. Honestly the leak was more interesting for what it revealed about the architecture than anything else. Skills, hooks, the whole execution model… not that surprising if you use it daily but nice to see confirmed.
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Intermediate Reasoning Steps in AI Models and Error Tolerance
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This is a good reframe. The intermediate reasoning steps being "wrong" doesn't matter if the final output is correct. It's similar to how humans think through problems, lots of wrong turns before the right answer. The error compounding argument assumes each token is a final