Shaping capabilities with token-level data filtering Neil Rathi, Alec Radford: https://
arxiv.org/abs/2601.21571 #ArtificialIntelligence #DeepLearning #MachineLearning
LLMS
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Token-Level Data Filtering for AI Model Capability Enhancement
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Instructions for rewriting a text with a human voice
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1. The human writing expert "Act as a professional editor and writer. Rewrite this text so that it appears naturally human, clear, and well-written. Fix grammar, flow, and structure while preserving my original voice and intent.
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Stop asking ChatGPT to check your grammar
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STOP telling ChatGPT: "check my grammar and writing". Bad prompt = bad result and it's easy to spot a text written by ChatGPT. Instead, use these prompts and watch the magic happen: [ Bookmark to not lose it! ]
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Clawdbot Tutorial: Setup, Skills, and Architecture Guide
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The goal of tomorrow’s tutorial is to understand what Clawdbot is, get it up and running (including setup and installation), adding a personality to it, learn how to add custom skills, understand crown jobs, and explore the key components of its architecture. Finally, build a
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llms.txt standard convention improves interoperability
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u/Alfred_the_Butler says: "llms.txt as a standard convention makes interop easier"
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llms.txt: The Agent Configuration File Loved by AI Agents
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llms.txt – the agents file loved by agents
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Massive deployment of 150,000 LLM agents in persistent environment
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Matt’s moltbook experiment is truly unprecedented. People talk a lot about dead internet theory — but I didn’t expect to see it overtly manifested like this. As Karpathy put it: “we have never seen this many LLM agents (150,000 atm!) wired up via a global, persistent, agent-first
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RTL reveals semantic data structure in deep layers
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The semantic alignment is beautiful: Related classes (cat, dog, deer) share more pruning structure in deep layers. Unrelated classes (airplane, truck) stay independent. RTL doesn't just find sparse networks – it discovers the SEMANTIC STRUCTURE of your data.
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CIFAR-10 Results Break Performance Barriers
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The CIFAR-10 results broke my brain: Balanced accuracy: 0.781 vs 0.711 (baselines)
Recall: 0.821 vs 0.480 And this is with 10× parameter efficiency. The model literally learned to allocate capacity where it matters most. -
RTL learns multiple specialized masks
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Here's what makes RTL different: Instead of pruning once globally, it learns MULTIPLE masks from the same initialization. Each mask specializes to a data subset (class, cluster, or environment). Then joint retraining refines them WITHOUT letting them interfere.