Theory 3: Audience Analysis (Aristotle to today) The oldest communication principle: know who you're talking to. In prompting, your "audience" is the model. And every model processes differently. Claude reads system prompts differently than ChatGPT. Gemini handles long context
LLMS
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Anthropic releases open-source Claude plugin suite for legal professionals
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/4 Anthropic ships free open-source legal tool that drafts contracts in minutes. Anthropic just shipped claude-for-legal, a free, open-source plugin suite that turns Claude into a legal assistant. It covers 10+ practice areas, from contracts to litigation to AI governance.
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NVIDIA-backed sparsity optimization for faster LLM training and inference
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/2 NVIDIA-backed sparsity trick makes LLM training and inference 20% faster on H100s. AI models already do less math than you'd think. Over 95% of neurons stay silent for any given word processed. That's free efficiency, right? Not quite. The problem: GPUs hate irregular work.
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Running 284B Parameter Models Locally on MacBook Pro
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/1 Garry Tan tests a 1M token coding model running locally on a MacBook Pro. Antirez, the person who built Redis, just shipped ds4: a custom engine that runs DeepSeek V4 Flash, a massive 284B parameter AI model, fully on your MacBook. No cloud. No API costs. How is that even
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Top AI Repositories and Tools of the Week
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Top Repo of the Week (May 11 – 17) 1. DeepSeek 4 Flash local inference engine for Metal and CUDA
2. Sparser, Faster, Lighter Transformer Language Models
3. RuView: Turn commodity WiFi signals into real-time spatial intelligence
4. Claude for Legal: A suite of plugins for legal -
Glean Platform Integrates Enterprise Systems with AI Agents for Unified Search
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Glean Prompt Library! @glean Glean’s platform seamlessly integrates with a wide array of enterprise systems such as email, intranet, cloud storage, and database by providing a powerful, unified search interface with AI Agents that effortlessly retrieves information from all
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RAG with LLM: Creating an AI-Powered File Reader
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RAG with LLM: Creating an AI-Powered File Reader! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding
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Training AI on Amazon Reviews with Python for Natural Language Processing
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Training AI on Amazon Electronic Reviews Using #Python for Natural Language! – by – @gp_pulipaka
! JupyterLab/Jupyter Notebook WordNet, Lexical Semantic Relation Analyzer
Thesaurus, 155,000 Words
Synset 115,000, 205,000 word-Sense Pair. NLTK Library, spaCy, TextBlob -
Technical Discussion on LLM Reasoning and Architectural Iteration
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literally i didn’t say that. adding “reasoning” already borrows tools like iteration and evaluation from classical AI and isn’t a pure LLM. and the reasoning has all kinds of problem. and i didn’t say “just”; i was careful to say “basically”, suggesting an approximation.
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Debating LLM scaling versus symbolic integration for AI progress
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this is a such a muddle. (at least relative to my views) LLMs are more or less just autcomplete, but (as I have always said) they have their uses. And the real progress now is coming from adding new (symbolic) techniques to the mix, not from pure scaling.