The Bitter Lesson: why compute always beats hand-coded knowledge
Richard Sutton's 2019 essay summarised: general methods that leverage compute win, four examples, the link to LLMs and lessons for products.
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Weekly summaries of the best AI reads. · 6 articles
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Karpathy's Software 2.0: when the program is neural network weights
Andrej Karpathy's 2017 essay summarised: Software 1.0 vs 2.0, benefits, limits, and why data, evals and prompts are source code in the LLM era.
Chip Huyen's GenAI platform architecture: five expansion steps
A summary of Building A Generative AI Platform: context, guardrails, router and gateway, caching, write actions, plus observability and orchestration.
Your AI Product Needs Evals: Hamel Husain's three-level framework
Hamel Husain's post summarised: the Lucy assistant story, three eval levels (assertions, human and LLM judges, A/B tests) and reading real data.
Stanford CS146S “The Modern Software Developer”: a course on building software with AI
A course on how AI is changing the whole software lifecycle — IDEs, terminals, review, testing — and when AI can be trusted.
Motion graphics with Claude: sort the repos before you install them
“20 motion graphics repos” lists are easy to misread: some are skills, some are video engines, some are just web animation libraries.
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