• Jan 28

There Are No Wrong Jobs: Why Career Pivots Are AI Superpowers

  • Teddy Kim
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Career pivots aren't failures. Each role builds mental models that let you see quality problems others miss. Combined with AI, that accumulated perspective becomes a superpower.

  • Jan 27

See What AI Remembers - Multi-Turn Memory Timeline Pattern

  • Teddy Kim
  • 0 comments

Build AI interfaces that show users what context is in memory. Learn how to implement visible memory timelines in React for better AI UX and trust building.

  • Jan 26

Stream Tables, Not Spinners: Progressive Table Rendering

  • Teddy Kim
  • 0 comments

Stream table rows as they compute instead of showing spinners. Learn how to implement progressive table rendering in React for better perceived performance.

  • Jan 22

AI That Asks Questions: Implementing the Agent Await Prompt Pattern

  • Teddy Kim
  • 0 comments

Learn how to build AI that pauses mid-stream to ask questions instead of guessing. Complete React implementation with pause/resume mechanics and code.

  • Jan 21

When 'Done' Isn't Done: The State Machine Problem in AI Agents

  • Teddy Kim
  • 0 comments

AI agents claim tasks are complete when they're not. Learn how to build state machines that verify completion instead of trusting agent confidence.
your job is to provide context to the agents

  • Jan 19

The Context Engineering Paradigm Shift

  • Teddy Kim
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Most AI failures aren't model failures—they're context failures. Learn why context engineering matters more than prompt engineering in 2026.
semantic confusion in ai agents

  • Jan 16

AI Agent Debugging: When "Ready" Doesn't Mean Ready

  • Teddy Kim
  • 0 comments

The AI agent reported moving tickets to Ready. The board was empty. How semantic confusion in tool APIs creates invisible failures—and how to fix them.

  • Jan 14

AI Amplifies Developer Intellect—It Doesn't Replace It

  • Teddy Kim
  • 0 comments

AI coding tools don't replace developer skills—they amplify them. Learn why code generation is the latest iteration in software's history of abstraction.
multi-agent system repair

  • Jan 12

The Silent Efficiency Problem: Debugging Multi-Agent AI Systems

  • Teddy Kim
  • 0 comments

AI agents fail silently, amplifying waste at machine speed. Learn how to spot failure patterns in multi-agent systems and calibrate protocols for better throughput.
AI tool hallucination

  • Jan 7

Why My AI Agent Kept Lying to Me (And How I Fixed It)

  • Teddy Kim
  • 0 comments

My AI agent claimed it created files and ran tests. Nothing happened. After 3 days debugging Claude Code's Task tool, I found why subagents hallucinate—and the fix.
The John Henry problem in AI

  • Jan 5

The John Henry Problem - Why Racing Against AI is a Losing Game

  • Teddy Kim
  • 0 comments

You might beat AI in a coding contest, but at what cost? The developers who thrive won't be the ones racing against their tools—they'll be the ones who picked up the drill.

  • Jan 2

AI Agent Safety Controls Are Drifting. Here's How to Catch It.

  • Teddy Kim
  • 0 comments

Most developers set up AI agent restrictions once and assume they stay. They don't. Learn how automated weekly verification caught a safety failure before damage occurred.