Y CombinatorWhy Agents Choosing Tools Is Reshaping the Dev Stack
Agents read their defaults from docs and examples, not word-of-mouth: Supabase growth shows which platforms win when OpenClaw and Moltbook choose.
CHAPTERS
Claude Code, OpenClaw, and the “AGI is here” feeling
Garry, Jared, and the group open by describing how quickly agentic tools (Claude Code, OpenClaw) have become obsessive daily drivers. They frame the moment as a sharp jump from “LLM assistance” to something that feels like early AGI in practice.
From autocomplete to autonomy: the end of micromanaging agents
The discussion contrasts last year’s developer tools (autocomplete-style coding) with today’s multi-agent setups where people delegate decisions. The key change is minimal human involvement: agents act independently and can even choose tools, creating a new “agent economy.”
Dev tools go-to-market flips: developers + agents as the new buyers
They argue the addressable market for dev tools is expanding dramatically: many more humans can now “vibe code,” and agents will increasingly recommend stacks. Distribution shifts from human word-of-mouth (Stack Overflow/GitHub trends) toward LLM/agent recommendations.
Agents choose the defaults: databases, Supabase, and documentation as destiny
A concrete example: exploding Postgres database creation from rapid app-building drives demand for platforms like Supabase. The group’s thesis is that agents pick tools partly by reading docs—so the most parsable, comprehensive documentation wins disproportionate market share.
Should YC’s motto evolve to ‘Make something agents want’?
A tweet crystallizes the idea: agents are becoming the software market, so builders should target agent preferences. The group debates this as a dev-tools-first phenomenon that may expand to many sectors as agents become real economic actors.
Case study: transcription pipeline reveals how early the ecosystem still is
Garry recounts building video transcription and discovering Claude Code selected an outdated Whisper model, leading to slow processing. He later finds Groq is far faster and cheaper—showing that agent tool choice is still imperfect and creates openings for better products and clearer docs.
Agent-friendly documentation as the new growth engine (Resend + Minify)
Diana explains how Resend became the default email-sending recommendation inside major LLMs by optimizing docs for agent consumption. They connect this to Minify’s opportunity: tooling that keeps docs structured, example-rich, and machine-parsable becomes core infrastructure for dev-tool growth.
Infrastructure built for agents: AgentMail and the ‘stack for agents’
They highlight AgentMail, an email provider designed for agents, because consumer providers deliberately block automation to fight spam. This expands into a broader idea: a parallel, agent-native stack (email, phone numbers, identity, APIs) built specifically for autonomous systems.
Agents in everyday life: reservations, recommendations, and agent-to-agent social layers
The conversation moves beyond dev tools: agents could book restaurants, pick venues, and then discuss outcomes with other agents. MaltBook becomes a prototype for agents collaborating socially and trading notes on behalf of humans.
Swarm intelligence: from ‘god model’ fantasies to many agents coordinating
Garry argues the future may resemble biological/social swarm intelligence more than a single massive “god model.” They suggest progress could come from coordinated groups of cheaper models working together—already visible in MaltBook’s chaotic but collaborative dynamics.
Content flood, Dead Internet theory, and designing rules for agent communities
They discuss how agent-generated content can explode faster than human communities (MaltBook growth vs. early Reddit). They explore “Dead Internet theory,” and Garry proposes rule/UX levers (forced reading/voting before posting) to shape healthier agent-driven ecosystems.
Founder playbook: develop intuition for agents and build tools they prefer
In closing, Harj and Jared advise founders to get hands-on with agents to understand their capabilities and failure modes. They argue builders should “empathize with the model,” avoid fighting agent workflows, and design products around agent preferences—APIs, openness, and machine-friendly interfaces.
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