If You Want Taste, You're Gonna Have to Eat
A reflection on taste, style, attention, and why AI makes the ability to notice matter more than ever.
Jason Liu
Applied AI, RAG, and personal notes.
Latest posts
Two kinds of scheduled work in Codex
A simple rule for choosing between Scheduled Tasks and Scheduled Messages in Codex.
Three Ways Codex Can Use a Computer
When to use Computer Use, Chrome, or the in-app browser in Codex.
Six levels of complexity in a Codex morning brief
A simple way to teach Codex through an AI-powered morning brief that grows into automations, project threads, drafted work, and memory vaults.
Codex-maxxing
How I use Codex as a place where long-running work can live.
What Music Do You Listen To?
My top albums and artists from Spotify — and what the mix actually says about taste.
Sunsetting 567 Labs and Open Sourcing the Course Content
Sunsetting 567 Labs, closing the courses today, and open sourcing the written content from transcripts as ebooks.
Things
A list of things I've been using and plan to use.
Do Your Engineers Know How to Leverage AI?
A letter to the readers of the AI Coding Accelerator.
Coding Agents Speaker Series: Lessons from Industry Leaders
Deep insights from the teams behind Devin, Amp, Cline, and Augment on building effective coding agents. Learn why simple approaches are winning over complex architectures in autonomous coding systems.
RAG Master Series: Complete Guide to Retrieval-Augmented Generation
Comprehensive guide to building, improving, and scaling RAG systems. From fundamentals to advanced enterprise implementations with real-world examples and proven strategies.
Domain Experts: The Lever for Vertical AI
How to successfully apply LLMs in specialized industries by building domain‑expert review loops, augmenting prompts with expert knowledge, and earning customer trust.
Text Chunking Strategies for RAG Applications
Technical session with Anton from ChromaDB on text chunking fundamentals, evaluation methods, and practical tips for improving retrieval performance
Why Grep Beat Embeddings in Our SWE-Bench Agent (Lessons from Augment)
Insights from Colin Flaherty on building autonomous coding agents and how agentic approaches reshape retrieval-augmented generation systems.
Why Cognition does not use multi-agent systems
A deep dive into why multi-agent systems might not be the optimal approach for coding contexts, exploring context engineering, challenges of context passing between agents, and how single agents with proper context management can outperform multi-agent setups.
Stop Trusting MTEB Rankings (Kelly Hong, Chroma)
A deep dive into generative benchmarking - creating custom evaluation sets from your own data to better assess embedding model performance.
How Extend Achieves 95%+ Document Automation (Lessons from Eli Badgio)
Insights from Eli Badgio, CTO of Extend, on mapping document workflows, building task-specific evaluations, and implementing partial automation with human-in-the-loop approaches for 95%+ extraction accuracy.
The 12% RAG Performance Boost You're Missing (Ayush, LanceDB)
Practical approaches to enhancing retrieval quality through fine-tuning, re-ranking, and understanding trade-offs in RAG systems
Why Glean Builds Custom Embedding Models for Every Customer
How Glean achieves 20% search performance improvements through customer-specific embedding models, unified data architecture, and smart feedback loops that most enterprise AI companies are missing.
Lexical Search in RAG Applications
Guest lecture with John Berryman on traditional search techniques, their application in RAG systems, and how lexical search complements semantic search