CHAPTERS
DevTools founders roll call: what each company builds
A rapid-fire introduction to multiple YC-backed devtool companies and what they’re shipping, with an emphasis on infrastructure and APIs that power modern developer workflows. The founders briefly position their products in the AI era—often as “picks and shovels” for agentic software.
From developer-first to agent-first: redefining the primary user
Founders describe a shift in audience: developers remain buyers and operators, but agents increasingly become the “users” executing workflows. This reframes product design around machine-consumable interfaces and compatibility with coding assistants.
How agents are changing internal operations and shipping velocity
Beyond product changes, teams are reorganizing work around internal agents across functions (marketing, sales, DevOps). Parallelization becomes the new norm—many features shipped simultaneously—with humans moving toward orchestration and review.
Agent experience as a first-class product surface (CLI, context, docs)
Founders argue that making products usable by agents requires explicit UX work: stable APIs, CLI-first access, and documentation designed for model context limits. Documentation becomes an input to models, not just humans, changing information architecture.
Biggest early mistakes: drifting from developer values and over-designing
Several founders reflect on missteps: deviating from the “spirit of developers,” over-focusing on UI polish instead of time-to-value, and hesitating to be opinionated. Another theme is learning to trust founder instincts while still listening to users.
Building in a world that invalidates features quickly: delete ruthlessly
In fast-moving AI markets, founders emphasize emotional detachment from shipped work. The capability curve can make parts of a product obsolete in months, so teams need the discipline to prune aggressively and refocus on what remains durable.
Do founders still write code? The new split: prompting, coding, and review
Responses range from “I don’t write code” to “daily PRs,” but a consistent pattern emerges: most new code is produced by agents, while founders shift to review, integration, and direction-setting. Some founders return to hands-on coding to rebuild intuition about rapidly evolving tools.
Most unexpected discoveries: agents as a growth channel and planning critic
Founders share surprises: models recommending products directly drives inbound demand, and agents can critique roadmaps when given broad context like support tickets and PR history. Another thread is custom internal agents outperforming generic tools in specialized domains like security.
What’s underrated right now: strategy, open models, quality—and a long-tail agent market
Despite execution acceleration, founders argue strategy and taste become more important, especially when agents can push back and expose gaps. Open models are seen as rapidly improving and more efficient, and “quality” becomes a major differentiator. The coding-agent ecosystem may mirror IDEs: many winners for many personas.
Predictions: verification bottlenecks, agent-chosen tools, and “idea-to-code” workflows
Looking forward, founders expect bigger parallel development but recognize verification/review as the constraint. Tool selection will increasingly be made by agents’ recommendations, pushing companies to optimize “agent experience.” Many anticipate a shift toward humans expressing intent and taste while agents produce industrial-grade software.
What’s next for the world of software work: identity shifts, autonomy, and more engineers
Founders predict continued turbulence and faster cycles—long-range forecasts become unreliable. They argue code itself matters less than context, problem understanding, and customer relationships. Rather than reducing engineering demand, agentic development may increase it as software expands into every industry.
Hardware and infrastructure horizon: local assistants and incident response autonomy
A key bet is that today’s data-center-grade capabilities will become local, personal assistants as hardware improves. In operations, incident resolution is highlighted as a near-term, verifiable domain where AI can take meaningful autonomous action, accelerating reliability work.
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