Harnesses aren’t the differentiator. I’m arguing the integration between harness and models are
CODE
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Verify AI Answers with Working Code Evidence
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The trick here is that you don't believe ChatGPT's answers to this kind of question until you've seen the code running yourself – the thing that's worth collecting is evidence that stuff works, not unproven answers from the bots
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Code as Specification: Understanding Software Through Technical Specs
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I guess we can reduce it all to “specs “ 🙂 Code is also a spec
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Spec-Heavy vs Validation-Heavy Approaches in Development
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Consider an example: "add a stop button that will cancel runs". Spec-heavy approach:
Plan -> make sure the button is placed correctly, check APIs it plans to make, check concurrency, check… etc make sure spec is correct. Human manually validates. Validation-heavy approach: -
Claude Code Tool Improvements Signal Better Development Experience
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The 'it just works' feeling after switching tools is the best signal that something was actually improved under the hood 🙂 Got that feeling when first switching to claude code too.
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Kaggle Book: Master Data Science with ML and LLMs
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The Kaggle Book — Master Data Analysis and #DataScience Competitions with #MachineLearning, GenAI, and LLMs [2nd Edition]: http://
amzn.to/4pxJpTC v/ @PacktDataML Table of Contents:
Introducing Data Science Competition
Organizing Data with Datasets
Work & Learn with -
Extracting Agent Skills from Open-Source Code Repositories
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GitHub already has millions of repos full of procedural knowledge. The work introduces a framework for extracting agent skills directly from open-source repos. The pipeline analyzes repo structure, identifies procedural knowledge through dense retrieval, and translates it into
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Agentic Engineering Patterns: Understanding Coding Agents Mechanics
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New chapter for Agentic Engineering Patterns: I tried to distill key details of how coding agents work under the hood that are most useful to understand in order to use them effectively
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Deep Learning with PyTorch: Three-Volume Beginner’s Guide Series
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Deep Learning with PyTorch — Step-by-Step Beginner's Guides (3 volumes) Vol.1 (Fundamentals): http://
amzn.to/4aRmIUv Vol.2 (Computer Vision): http://
amzn.to/4s88BkD Vol.3 (Sequences and NLP): http://
amzn.to/49bh5h2
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#MachineLearning #ML #AI #DataScience #DataScientist -
Graph Machine Learning 2nd Edition with PyTorch Geometric Advancements
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2nd Edition — now at http://
amzn.to/45Y3LyI v/ @PacktDataML Graph Machine Learning — Latest advancements in Graph Data to build robust #MachineLearning algorithms 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
Master new graph ML techniques through updated examples using PyTorch Geometric and