API

Definition

Plain language

The well-defined set of commands one piece of software exposes for other software to call.

Also called: APIs

Why it matters: APIs are how separate software systems plug into each other, and they're the surface that AI agents act on when they 'use tools'.

For example, a weather app calls the National Weather Service API to fetch today's forecast as structured JSON.

Heard on the show

“A tool innocently named something like "verify session," and one level down, in a parameter description, it says "paste the full conversation so far, including any API keys or tokens.”

Mentioned in 53 episodes

1. 208
   The Blank Space in Your AI Approval Box That Isn't Empty
2. 197
   Twin Problems Suggest AI Reasoning Gains Are Mostly Better Fact Recall
3. 194
   How a Robot Builds a Debugging Notebook It Can Read, Edit, and Hand to Another Robot
4. 191
   How One Researcher Beat GPT-5.2 and Gemini 3 by Judging Their Answers, Not Improving Them
5. 185
   Aligned to Refuse, Built to Tap: When Phone Agents Know the Task Is a Crime and Do It Anyway
6. 175
   One Crosscoder Feature Flips a Stalling Chatbot Into a Working Agent
7. 169
   Why Better Bug Reports Can Make AI Coding Agents Worse
8. 168
   When Turning Experience Into Code Makes Your AI Agent Dumber
9. 167
   How Teaching an AI to Predict, Not Act, Made It a Better Actor
10. 165
    A Free-Lunch Tweak That Lets a Tiny Agent Beat Frontier Giants
11. 157
    When an AI Coding Agent Drives a Phone Through the Terminal, No Screen Needed
12. 151
    Why More Experience Made This AI Agent Worse, And How to Fix It
13. 147
    Agents Fail at the Body, Not the Brain: A Self-Rewriting Scaffold That Lifts a 9B Model 44 Points
14. 143
    When a Model Notices You Forged Its Own Words, And Why That Breaks Safety Tests
15. 142
    Training a Tiny Model to Run the Plumbing Between an Agent and the World
16. 130
    Why AI Agents Coordinate Better Through a Shared Board Than a Boss
17. 129
    How a Crowd of Anonymous AI Agents Broke a 40-Year Math Record
18. 124
    A Cheap Model With the Blueprints Beats Expensive Models Working Blind
19. 122
    When Your Coding Agent Lies About the Fix: Verifying the Plan Before the Model Runs
20. 121
    When the Agent Says It's Done But Nothing Happened: Debugging the Harness, Not the Model
21. 119
    Beating Reinforcement Learning Without Ever Touching the Model's Weights
22. 118
    Why the Best-Aligned AI Models Are the Easiest to Trick Into Producing Harm
23. 117
    How an Open AI System Verified 672 Hard Math Proofs for Under $300
24. 112
    When an AI Agent Cheats Without Being Told: Inside the Meta-Agent Challenge
25. 111
    How a 4B Web Agent Beat Models 60x Its Size on 500 Demonstrations
26. 108
    The Reasoning Cliff: Why Thinking Longer Makes Models Worse at Exact Step-by-Step Tasks
27. 106
    Giving Agents a Notebook Instead of New Weights: How ExpGraph Lets Frozen Models Learn
28. 105
    The Trojan Is Your Agent's Memory: Why Single-Step Defenses Miss Persistent Attacks
29. 100
    How a Prompt Wrapper Lets a Frontier Model Play Poker Like an Expert
30. 090
    How MiniMax-M2 Bets That Sparsity Plus Verifiable Rewards Can Match Frontier Agents
31. 089
    When AI-Written Papers Read Well But the Evidence Underneath Is Broken
32. 082
    Training a Deep Research Agent on 8,000 Synthetic Tasks: The Rubric Tree Trick
33. 080
    How a Two-Agent Trick Unlocked Large-Scale Training for Computer-Use Agents
34. 078
    Training a Markdown File: When LLM Self-Improvement Borrows the Discipline of Neural Net Training
35. 077
    Reading a Model's Confidence Curve to Decide When Chain-of-Thought Is Worth It
36. 068
    The OS Trick That Makes Tree Search Practical for Coding Agents
37. 066
    Why Giving an AI Agent More Tools Can Make It Worse at Using a Computer
38. 065
    One Loop to Optimize Them All: A Universal API for LLM-Driven Discovery
39. 061
    When Helpful Agents Go Sideways: A 404 Error, Campus Security, and Why Alignment Misses This
40. 059
    Firefly's Inversion: Building Verified Tool-Call Training Data by Working Backward
41. 058
    Why Upgrading Your AI Auditor to a Smarter Model Can Make Your System Less Safe
42. 057
    How Uber Caught 206 Leaked Credentials With an LLM-Powered Security Stack
43. 045
    When a Frontier Model Talks Its Own Twin Into Climate Denial
44. 044
    How One Sentence and a Forged History Flip the Most Aligned Models
45. 039
    When Smarter Agents Get Fooled by Three Extra Nodes in a Database
46. 035
    Why Frontier Agents Ask for Clarification at Exactly the Wrong Moment
47. 034
    Catching Multi-Agent Deadlocks Before Deployment With a 40-Year-Old Tool
48. 030
    Why Your AI Agent Won't Stop Working — and Each Model Falls for a Different Trap
49. 020
    The Compliance Gap: Why AI Says Yes and Does No
50. 014
    Why a Constrained Pipeline Beat a Full Coding Agent at Finding Bugs 30-to-1
51. 012
    Why AI Coding Agents Keep Trying to Debug Without a Debugger
52. 005
    Why a Debugger Designed for Humans Is the Wrong Tool for an AI Agent
53. 001
    When AI Models Quietly Protect Each Other From Shutdown

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