#Gary Tan
36 related articles
·4 min
12 Open-Source AI Agent Projects Worth Exploring: Frameworks, Skill Packs, and Toolchains
A roundup of 12 trending open-source AI agent projects on GitHub, covering video generation, agent frameworks, skill packs, code engines, security scanning, and voice processing.
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·3 min
6 Practical Prompt Techniques to 10x the Quality of AI Responses
6 proven prompt techniques — role-playing, deep questioning, adversarial critique, failure pre-mortem, reverse engineering, and dual-version explanation — to dramatically improve AI output quality.
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·3 min
Erste Group's AI Transformation in Practice: How a European Bank Balances Compliance and Innovation
Erste Group's CPO shares real-world AI transformation lessons: connecting customer data from day one, dual-track strategy, serving the neglected 80%, and rapid iteration under European banking regulations.
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·2 min
A Complete Nutrition Plan Centered on Steak: A Guide to Combining High Nutrient-Dense Foods
A science-based guide to building a complete nutrition plan around steak, eggs, liver, sardines, dark vegetables, and dairy—achieving full nutrient coverage with minimal whole foods.
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Product Reviews·3 min
Superpowers vs GStack In-Depth Comparison: How to Choose Your Claude Code Plugin
In-depth comparison of Claude Code's top open-source plugins Superpowers and GStack — their skills, workflows, and use cases to help developers choose the best AI coding assistant setup.
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Tutorials·3 min
GStack Tutorial: 23 Commands That Give One Person an Entire AI Development Team
Deep dive into GStack, the open-source toolkit by YC President Gary Tan. 23 slash commands turn Claude Code into a full AI dev team covering product decisions to deployment.
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Expert Opinions·2 min
Token Maxing: YC CEO's Methodology for Achieving 400x Programming Efficiency with AI
YC CEO Gary Tan returned to coding after 13 years using Token Maxing with Claude Code, completing a $4M project in 5 days. Detailed breakdown of his 15 parallel agents, Plan-Eng-Review skills framework, and G-Stack open-source workflow.
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Deep Dives·2 min
LLM 0.32 Refactor: An Architecture Upgrade from Text Q&A to Multimodal Streaming Interaction
Simon Willison releases LLM 0.32a0 alpha with a major refactor introducing message sequence API, typed streaming output, and flexible serialization while maintaining backward compatibility.
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Industry Insights·3 min
Personalized Health Tech: The Promises and Pitfalls of Wearables and the Quantified Self
A deep analysis of personalized health tech: from smartwatches and genetic testing to CGMs, revealing the data anxiety, lack of scientific validation, and commercial conflicts behind consumer health products.
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Tech Frontiers·2 min
LLM 0.32 Refactoring Deep Dive: Message Sequence Input and Streaming Multi-Type Response Design
Deep dive into LLM 0.32 alpha's two core refactorings: message sequence API aligned with OpenAI format, stream_events for typed streaming responses, serialization design, and backward compatibility.
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Deep Dives·3 min
LLM 0.32 Refactor: How Message Sequences and Streaming Chunks Are Reshaping Python LLM Interactions
Simon Willison releases LLM 0.32a0 alpha with message sequences and typed streaming chunks, redefining how Python developers interact with LLMs while maintaining full backward compatibility.
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Expert Opinions·3 min
Surviving the AI Layoff Wave: The Workplace Hype Truths Exposed by a Satirical Video
Mo Bitar's satirical TikTok exposes AI workplace absurdities: fake jargon to secure budgets, automating colleagues for promotions. A deep dive into overestimated AI capabilities, fear-driven decisions, and the moral cost of tech hype.
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Tech Frontiers·2 min
LLM 0.32a0 Major Refactor: A Deep Dive into Message Sequences and Typed Streaming Responses
Simon Willison releases LLM Python library 0.32a0 alpha with message sequence prompts and typed streaming responses, redesigning model interaction abstractions for multimodal output, tool calls, and modern LLM features.
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Tech Frontiers·3 min
LLM 0.32 Refactoring Explained: Message Sequence API and Typed Streaming Responses
Deep dive into Simon Willison's LLM Python library 0.32 refactoring: new message sequence API, typed streaming responses, and universal serialization for multimodal AI and tool calling.
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Deep Dives·3 min
LLM 0.32 Major Refactor: Message Sequences and Streaming Multi-Type Responses Explained
Simon Willison releases LLM 0.32a0 alpha with message sequences API and streaming multi-type parts, redesigning how the Python LLM library handles model interactions for complex inputs and outputs.
Read more →
Deep Dives·3 min
LLM 0.32 Refactoring Deep Dive: Message Sequence Input and Typed Streaming Response Design
Deep dive into Simon Willison's LLM 0.32a0 refactoring: message sequence inputs, typed streaming events, SQLite-decoupled serialization, and backward-compatible API evolution strategies.
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