Theo’s Low-Cost Experiment Auditing T3 Code Pull Requests

Theo describes using an inexpensive model to review recently updated pull requests and proposes a recurring readiness check.

ThTheo연사
채널
Theo - t3․gg
게시일
2026년 9월 3일
영상
0:24
읽기
1분
슬라이드
2
원문
영어
YouTube에서 열기
스크롤하면 영상이 오른쪽 아래로 작아져 계속 재생됩니다. 자막은 읽기 언어를 따릅니다.
읽기 언어2개 언어로 읽을 수 있음
이 글은 아직 한국어 번역이 없어 영어 원문으로 표시합니다.
요약

30초 요약

Theo says an AI-assisted audit of recently updated T3 Code pull requests produced useful insight for 12 cents. He proposes running a bot every three hours to flag PRs it considers ready to merge. The spoken token totals differ from the figures visible in the on-screen snapshot, so they should not be assumed to describe the same measurement.

  1. 1A low-cost auditTheo describes getting useful insight for 12 cents from a recent PR audit.
  2. 2Scope and usageThe request covered recently updated T3 Code PRs. The spoken token counts and the displayed usage snapshot do not match.
  3. 3A proposed recurring botHe plans a bot that checks periodically and alerts him to PRs it considers ready to merge.

핵심 정리

  1. Theo asked an agent to audit T3 Code pull requests updated in the previous five days.
  2. He describes the audit as costing 12 cents and proposes repeating it every three hours.
  3. The spoken token figures differ from the visible snapshot, and the clip does not explain the discrepancy.
전문

챕터별로 다듬은 전문 읽기

원본 자막만 보기
01

Auditing PRs and planning a recurring check

Theo describes a low-cost audit of recently updated pull requests. He proposes automating a readiness check every three hours.

The PR audit request
슬라이드 1 / 2The PR audit requestThe on-screen prompt asks for recently updated open PRs and reports on overlap, issues and merge risk.
Usage snapshot
슬라이드 2 / 2Usage snapshotThe visible token totals differ from the spoken figures; the screen also shows a request to revise cache pricing.

I got a useful insight for 12 cents. I did not just ask the agent to audit my own pull requests: I asked it to examine the open T3 Code PRs updated in the last five days.

I say the audit used six billion input tokens, mostly cached, and 300,000 output tokens. The usage snapshot shown in the clip instead displays about 6.23 million input tokens and 30,642 output tokens, with a pricing calculation being revised; the clip does not explain whether these are the same measurement.

I plan to set up a bot to repeat this every three hours and notify me when it thinks a pull request is ready to merge. I expect that to help us ship faster.

같은 채널의 다른 글

슬라이드 5장38:47EN
Theo - t3․gg· 2026년 10월 28일Theo’s Long-Running Agent Experiments: Verification, Compute and CostTheo Browne · Ping LabsA skateboard game and two compiler projects show how repeated checks and flexible compute can sustain agent work—and how expensive those loops can become.코딩 에이전트에이전트 엔지니어링AI cost & performance+2
슬라이드 5장36:07EN
Theo - t3․gg· 2026년 10월 27일Theo on Token Costs, Coding Agents, and the Price of AI CapabilityTheo Browne · Ping LabsA viral estimate of Lauren's AI spending prompts a closer look at cache pricing, benchmarks, and the changing work of coding agents.AI cost & performance코딩 에이전트Inference+1
슬라이드 5장30:37EN
Theo - t3․gg· 2026년 10월 26일Theo’s Three Skills for Reviewing and Shipping with Coding AgentsTheo Browne · Ping LabsHow sol-loop, full-send and full-stop encode review, verification and different levels of merge authority.Agent skills코딩 에이전트Code review & verification+2
슬라이드 2장0:43EN
Theo - t3․gg· 2026년 10월 23일Theo Distinguishes Security Vulnerabilities from Active AttacksTheo Browne · Ping LabsA short explanation of why disclosed software flaws and reported attacks are different kinds of security news.CybersecuritySoftware engineering
슬라이드 6장29:22EN
Theo - t3․gg· 2026년 10월 22일Theo on Security Vulnerabilities, Stolen API Keys and AITheo Browne · Ping LabsA look at the gap between vulnerability disclosure and patching, and a speculative account of why stolen inference keys may be valuable.CybersecurityAI securitySoftware engineering+1
슬라이드 4장31:13EN
Theo - t3․gg· 2026년 10월 20일Theo on Why Smarter Models Help More Than DevelopersTheo Browne · Ping LabsFrom game modifications to compiler experiments, Theo revisits his assumptions about who benefits from more capable AI.코딩 에이전트Developer productivityHuman-AI Collaboration+1