Collection summary Coding
/wayfinder: Nothing is too big to plan anymore
From Matt Pocock: Agent Skills and Engineering Workflows
Matt Pocock · Source dated 30 Jul 2026
Recently added
New to this library, not necessarily new to the world.
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video Coding
Every Repo Is a Software Factory Now | Don Syme, GitHub
GitHub Principal Researcher Don Syme defines continuous AI as recurring, shared agent work that runs beside deterministic CI/CD. He explains GitHub Agentic Workflows: bounded repository context, permission and security controls, scheduled cost management, concurrent model evaluation, and quality gates that give human reviewers evidence. The claims are first-party reports about a public-preview product, without an independent security assessment or comparative data.
AI Native Dev · Source dated 18 Aug 2026
video Coding
Creator of TypeScript: 10x Faster TypeScript, Why AI Won't Replace SWEs | Anders Hejlsberg
Ryan Peterman interviews Anders Hejlsberg about TypeScript’s compatibility-preserving port to Go, the role of shared-memory concurrency in its reported 10x speedup, and why faster compilers matter for coding-agent feedback loops. Hejlsberg recommends using AI to build deterministic transformation tools rather than trusting one-off answers, argues that typed languages benefit agents, and keeps responsibility with engineers who review what they ship. The performance and capability claims are first-party reports and practitioner judgments.
Ryan Peterman · Source dated 17 Aug 2026
video Coding
Beyond Code Coverage: Functionality Testing with Playwright MCP
Marlene Mhangami presents a behavior-first testing loop for agent-generated web changes: derive feature-level Playwright tests from expected user behavior, let the agent implement against them, inspect the browser evidence, then spend human attention on refactoring. A prepared GitHub Copilot CLI and Playwright MCP demo exercises search and filtering in a sample app. It illustrates the workflow without comparing defect detection or maintenance cost with other approaches.
AI Engineer · Source dated 16 May 2026
video CodingAI
Software Development Now Costs Less Than Minimum Wage
Geoffrey Huntley argues that cheap inference and persistent agent loops are lowering the cost of implementation and will favor lean, model-first companies. He recommends deliberate practice, building a small agent to understand the loop, modernizing workflows before adoption, and shifting judgment toward what should be built. The headline $10.42-per-hour figure omits workload, quality, and supervision details; the productivity, staffing, and market claims are first-party anecdotes or forecasts.
Geoffrey Huntley · Source dated 5 May 2026
Collection additions Coding
The REWORK Podcast — selected coding-agent conversations
A manually reviewed selection from The REWORK Podcast following 37signals’ move from cautious LLM assistance to terminal agents, AI-accelerated Basecamp development, agent-accessible product interfaces, and agent-built competition. Jason Fried and David Heinemeier Hansson emphasize disposable prototyping, senior review, architecture, product scope, and consequence-aware use. Their examples are first-party experience and forecasts, not controlled measurements.
37signals
7 summaries on this page
- AI Challenges in Software Development →
Source dated 1 Jul 2026
- AI Revisited – Part 2 →
Source dated 4 Mar 2026
- AI Revisited →
Source dated 21 Jan 2026
- Bring your AI agents to Basecamp →
Source dated 25 Mar 2026
- Building with LLMs, finding a co-founder & other listener questions →
Source dated 20 Aug 2025
- Competing with giants, the end of SaaS & other listener questions →
Source dated 19 Aug 2026
- Pencils down →
Source dated 2 Apr 2026
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video CodingAI
How to Build a Self-Improving Company with AI
YC General Partner Tom Blomfield argues that AI-native companies should be built as recursive loops of signals, policies, tools, quality gates, and feedback rather than by adding copilots to existing hierarchies. He describes YC agents that watch failed database queries and propose and deploy fixes, recommends AI-legible organizational knowledge and disposable internal software, and predicts flatter teams. The examples and five-times revenue-per-employee claim are first-party reports; recording all communications and autonomous deployment raise consent, privacy, and governance questions the talk leaves open.
Y Combinator · Source dated 21 May 2026
video Coding
The Era of Compound Engineering
Kieran Klaassen presents compound engineering: keep human judgment at brainstorming and polish, automate the repeatable middle, and store corrections and rationale so future work starts with more context. He demonstrates the loop on Cora, where he is the primary engineer with design and database support. Claims that one engineer beats teams and that implementation is ‘mostly solved’ are practitioner judgments; the talk offers no comparative delivery, quality, or token-use measurements.
AI Engineer · Source dated 20 Aug 2026
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video Coding
Think Harder: How I Prompt — Thorsten Ball
In this Laracon US 2026 talk, Thorsten Ball explains prompting as an information-design problem: show coding agents where authoritative context lives, externalize hidden constraints, use screenshots and diagnostic reports as compact evidence, and move recurring instructions into the codebase so shorter prompts can still work.
Laracon US · Source dated 30 Jul 2026
video Coding
Pi Coding Agent - Deep Dive
0xSero gives a live source-level tour of Pi: provider normalization, the streamed agent loop, built-in tools, extensions, session storage, and the SDK. He then shows how those pieces can support local models, delegated agents, and long-running goal loops. The cache-hit, latency, cost, and benchmark comparisons are demonstrations without enough linked configuration or methodology to count as controlled evaluations.
0xSero · Source dated 21 Aug 2026
video Coding
Coding Agents Don't Scale Themselves. Neither Do Your Teams.
Patrick Debois argues that coding-agent advantage will come less from individual prompting than from organizations improving the systems around agents. He recommends shifting retros from code failures to system failures, separating agent-ready work from decisions that need conversation, centralizing reusable context and tools behind a few paved roads, and tracking human interventions. His dark-factory forecast, hiring model, and metrics are practitioner guidance, not validated research.
AI Engineer · Source dated 22 Aug 2026
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video Coding
Meet Pi: The Minimalist, Self-Modifying Coding Agent
CultRepo profiles Mario Zechner’s path from open-source game tooling to Pi, a deliberately small coding-agent harness. Zechner explains Pi’s nested tool loop, four-package TypeScript architecture, and self-extension model; he and Armin Ronacher connect minimalism to predictable tools, human responsibility, and model neutrality. The film’s adoption figures and leaderboard placement are first-party reports without defined scope, and its open-foundation plans describe an ambition.
CultRepo · Source dated 20 Aug 2026
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Field guide Coding
What Is AI Slop? When Generated Output Only Looks Finished
AI slop looks finished even though something important is missing: a clear purpose, evidence that it works, or someone who owns the result.
With Agents · Post published 19 Aug 2026
video Coding
So I tried Matt's skills...
After a week using skills from Matt Pocock and Lauren Tan’s pstack, Theo Browne shows how he audits skill collections against his own agent history, treats descriptions as routing triggers, tests text-only skills before installing them, and keeps only the workflows that fit. Examples cover clearer agent writing, design interviews, blast-radius checks, and decision trails. The results are personal demonstrations, not controlled evaluations.
Theo - t3.gg · Source dated 18 Aug 2026