by alistair-gray
The competitive moat in coding agents isn't the model — it's the orchestration layer. Stripe's Blueprints prove that mixing deterministic workflows with agentic flexibility, backed by 500 MCP tools, scales autonomous coding to 1,300+ PRs per week.
by alistair-gray
Stripe's coding agents produce 1,000+ merged PRs per week by combining isolated devboxes, a customized Goose fork, MCP with 400+ internal tools, and aggressive feedback-left testing — proving that enterprise-scale autonomous coding demands custom infrastructure, not off-the-shelf solutions.
by amp
The IDE sidebar is a dead-end interaction model for coding agents—parallel, headless agent swarms that run for 45 minutes without human input replace the one-on-one assistant workflow.
by ryan-lopopolo
When AI agents write all the code, engineering shifts from authoring to harness design—crafting navigable documentation, mechanical enforcement, and feedback loops that keep autonomous agents productive across thousands of pull requests.
by amp
The assistant era is over—agents now write production code. The next frontier is building 'agent-native codebases' with feedback loops that let agents verify their own work autonomously.
by maggie-appleton
When agents handle code generation at scale, design and architectural planning become the bottleneck—implementation velocity no longer constrains development.
by jens-neuse
Good GraphQL schemas start from client needs, not backend models. Ten principles—from capability-based design to organizational alignment—establish patterns for maintainable, evolvable APIs.
by leigh-griffin, ray-carroll
Specifications become the authoritative source of truth in software systems, with implementations continuously derived and validated against them—a paradigm shift that trades code-centric development for declarative intent.
Normalized Distance from Main Sequence (D) identifies structurally poor code by measuring how far components deviate from the ideal balance of abstractness and stability—making it the single best metric for detecting AI-generated code that compiles but rots.
by neal-ford
MCP enables enterprise architects to express architectural intent without implementation details, solving the brittleness problem that has plagued fitness function governance at scale.
by mario-bittencourt
Business logic belongs in pure functions (functional core) while infrastructure concerns like databases and UIs live in the imperative shell—and the core must never call the shell.
by drew-breunig
Specification-only libraries work for simple utilities but foundational software still requires actual code, community oversight, and sustained maintenance.
by thariq-shihipar
Bash is the most powerful agent tool. The Claude Agent SDK packages Claude Code's battle-tested patterns—tools, file system, skills, sandboxing—for building coding and non-coding agents alike.
by philipp-schmid
As AI models converge in benchmark performance, the infrastructure managing them—Agent Harnesses—becomes the competitive differentiator for building reliable, multi-day workflows.
by gergely-orosz, martin-fowler
Martin Fowler argues AI represents the biggest shift in software engineering since assembly to high-level languages—not because of abstraction level, but because we now work with non-deterministic systems.
by martin-kleppmann
Martin Kleppmann traces local-first from CRDTs to a proposed definition: software where another computer's availability never blocks the user—including when the developer shuts down.
by neal-ford, rebecca-parsons, patrick-kua
A framework for designing software architectures that evolve over time, using fitness functions to protect important characteristics while enabling incremental change.
by neal-ford
Evolutionary architecture supports guided incremental change through fitness functions—automated tests that validate architecture characteristics like performance, security, and structural integrity.
by humanlayer-team
A manifesto for building production-grade LLM agents, arguing that effective agents combine mostly deterministic software with strategic LLM decision-making rather than naive 'loop until solved' patterns.
by steve-yegge
Managing 20-30 parallel AI coding agents requires factory-scale orchestration—hierarchical roles, persistent state in Git, and structured workflow primitives that survive session crashes.
by marc-olivier-fleury
Architecture fitness functions transform vague architectural principles into daily metrics, enabling organizations to track and drive large-scale migration from legacy systems to Self-Contained Systems through measurable, automated governance.