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07-16

The Short Leash AI Coding Method For Beating Fable

This post distills over a year of research on using AI agents for security-critical software. The author introduces the “Short Leash” method: only expert developers can use it; never enable YOLO mode; manually review every diff in the permissions prompt to keep the AI on track; commit after each subtask to safeguard against regressions. It also details AI-assisted code review: pair human and AI, with AI catching surface errors and humans guiding direction. PR authors must self-review line-by-line and disclose AI models used. This approach beats Fable even with non‑frontier models, without sacrificing quality. Targeted at senior engineers who want productivity gains without giving up understanding.

blog.okturtles.org · 7 min · AI Agents · AI Engineering · Code

07-10

Rewriting Bun in Rust: 535K Lines, 11 Days, 64 AI Agents

Bun's creator Jarred Sumner recounts how he used Anthropic's Claude Fable 5 to rewrite Bun's 535,496 lines of Zig into Rust in 11 days. The motivation: Zig's manual memory management caused numerous use-after-free, double-free, and memory leaks when mixed with JavaScriptCore's GC. Instead of an incremental port, he orchestrated 64 Claude agents in parallel using dynamic workflows and adversarial review. 100% of Bun's test suite (over 600k assertions) passed on all 6 platforms. The rewrite fixed 128 bugs, reduced memory usage by up to 90%, shrank the binary by ~20%, and improved throughput by 2-5%. The article details the workflow, common porting mistakes (e.g., debug_assert! side effects, slice overruns, comptime format differences), and how Rust's Drop systematically prevented memory leaks. A first-hand account of using cutting-edge AI to accomplish a year-long team project in less than two weeks.

bun.com · 65 min · Agent Engineering · AI Engineering · Code

06-30

How To Make Codebases AI Agents Love

This article argues that codebase structure matters more than prompts or AGENTS.md files for AI agent output quality. The core idea is applying 'deep modules' from A Philosophy of Software Design: each module exposes a simple interface controlling lots of implementation. The author introduces 'grey box modules'—developers own and test the interface, AI owns the implementation inside. This improves feedback loops (tests are feedback), navigability (filesystem mirrors mental model), and reduces cognitive load (developers only track 7-8 module boundaries). The article notes TypeScript's difficulty enforcing boundaries and recommends the Effect library. For engineers optimizing AI coding workflows.

www.aihero.dev · 5 min · Agent Architecture · AI Engineering · Code

06-20

Stop building Foxconn factories for your agents

Garry Tan reflects on his experience building a 540,000-line Rails app, using the Foxconn factory as a metaphor for the dominant AI agent development pattern: wrapping hyper-intelligent models in mountains of code, tests, and guardrails. He argues the economics have inverted—model calls are now cheap and the models are smarter, making the old instinct to ration and control them obsolete. The new paradigm is 'just-in-time software' and 'skill packs,' where lean markdown instructions and minimal TypeScript replace bloated engineering frameworks. A concrete example shows a hackathon judge agent built in an afternoon, doing what previously required a full software project. The essay challenges engineers to abandon the 2013 mental model of measuring capability by lines of code and to embrace 'tokenmaxxing' to gain a 2-3 year competitive advantage. It is aimed at engineers who are coding with AI but still trapped by traditional software metrics and mistrustful architectures.

x.com · 14 min · Agents · Ai Tooling · Code

06-19

Imagine Naked People Were Stupider. Naked Models Are.

YC partner Garry Tan responds to Kyle Kingsbury's anti-AI essay, arguing that Kingsbury's tests of naked models are like testing an engine on a bench and concluding cars are unsafe. The article details the 'thin harness, fat skills' architecture: skill files (reusable Markdown procedures) constrain model input, resolvers (routing tables) dispatch tasks, deterministic code handles precision operations, and testing covers the full pipeline. Using Kingsbury's own bathroom rendering and stock data hallucination examples, Tan shows how architecture can turn unreliable models into reliable systems, and shares a personal resolver that reduced file misfilings from 10/13 to zero. The automotive metaphor concludes: seatbelts, traffic lights, and crumple zones made cars safe, not skepticism of engines. Targeted at engineers building or evaluating AI systems.

x.com · 18 min · Agent Architecture · Agents · Code

06-19

On the LOC controversy: doing the math on a 810x developer output increase

Garry Tan, CEO of Y Combinator, responds to criticism of his claim of shipping 600,000 lines of production code in 60 days. He concedes LOC is a flawed metric but provides a rigorous before-and-after comparison: in 2013, as a part-time coder, he averaged 14 logical lines per day; in 2026, with the same day job, he now averages 11,417. Even after deflation for logical SLOC and an aggressive 2x AI-verbosity factor, the daily rate is 5,708 lines – a 408x increase. Quality data is provided: 2.0% revert rate, 6.3% fix commits, and a test suite that grew from 100 to over 2,000 tests. He details his testing infrastructure (Playwright-based browser CLI, slop-scan), product traction (75k GitHub stars, ~7k WAU), and argues the real shift is the collapse of the “idea to shipping" cycle from weeks to hours. The core argument is that the productivity ground has shifted for all engineers, not just him.

x.com · 12 min · AI · Ai Tooling · Claude Code

06-19

Resolvers: The Routing Table for Intelligence

Garry Tan argues that resolvers—lightweight context routers—are the missing governance layer in agent systems, more crucial than models or skills. Using a mis-filed article as a trigger, he demonstrates how a 200-line resolver replaced 20,000 lines of crammed context, fixing model attention degradation and knowledge base drift. He details a production audit revealing that 10 of 13 skills ignored the resolver, and how he built trigger evals, a "check-resolvable" meta-skill to detect dark capabilities, and a self-healing loop against context rot. The piece reframes resolvers as the organizational chart and management of an agent system, and announces the open-sourcing of his personal architecture (GBrain/GStack) that embodies these patterns. Key evidence: real agent managing 25,000 files and 200 daily inputs, with concrete metrics on skill reachability defects.

x.com · 18 min · Agent Architecture · Agents · Ai-Memory