Articles.
Notes from the work: product thinking, engineering quality, and what AI tools do well and what they leave to you.
- articles
- 59
- series
- 3
- topics
- 5
All articles
Page 3 of 7
AI engineering
AI Writes Tests. The Tests Pass. Nobody Can Tell You What They're Testing.
AI can generate a complete test suite — descriptive names, 94% coverage, green CI — in the same session it writes the implementation. The gap is behavioral: tests written after code verify what the code does, not what it should. Mutation testing makes this visible.
Shivani Sutreja8 min read
AI engineering
AI Is Producing Senior-Looking Code Written by Developers Who Don't Know Why
59% of developers ship AI-generated code they don't fully understand. The output looks professional. The reasoning underneath is absent. Here's what broke, why it matters, and how engineering discipline rebuilds the apprenticeship AI bypassed.
Shivani Sutreja8 min read
How we build
Example Mapping for AI: How We Turn Specs into Executable Tests
A PM and an engineer sit down with four colors of sticky notes. Sixty minutes later they have surfaced every business rule, every edge case, and every unanswered question — and produced acceptance tests an AI agent can build against. No prose. No translation layer. No "the AI guessed wrong" post-mortem.
Vishvjitsinh Vanar11 min read
How we build
AI Readiness Score: Should You Deploy Agents on This Codebase?
Most teams deploy agents at stage five speed on stage two or three foundations. AI amplifies whatever it's applied to — strong process gets faster, broken process gets more broken, faster. The question before expanding agent use is not whether. It's where your codebase actually sits.
Shivani Sutreja8 min read
Metrics and code health
From 12.4 Days to 7.2 Days Lead Time: An Incremental Approach (No Rewrite Required)
Cutting software delivery lead time in half does not require a rewrite, a new platform, or a green-field project. Here is the four-step sequence that takes teams from around twelve days to under eight days on the system they already have.
Devraj12 min read
AI engineering
From Prototype to Production: The Engineering Gap That Kills AI Projects
The demo works. The prototype impresses. Then it hits production and quietly fails — wrong answers at scale, cascading LLM timeouts, no way to know when the prompt regressed. This is not an AI problem. It is an engineering problem with a known solution.
Vishvjitsinh Vanar11 min read
How we build
Your Tests Are the Only Spec AI Reads
When an AI agent works on your codebase, your test suite is the only artifact of intent it consistently sees. Tickets, Notion pages, and Slack threads do not enter the loop. If your tests describe what the system should do, the agent is constrained. If they only describe what the code already does, the agent ships whatever it generated.
Harsh Parmar11 min read
AI engineering
Don't Outsource the Thinking
AI agents and offshore teams break in exactly the same way. They are execution accelerators, not thinking substitutes. The work that doesn't compress — specs, architecture, test design, critique — is the work you have to keep in-house. Outsource the typing. Keep the thinking.
Harsh Parmar11 min read
How we build
Quality Gates That Actually Work: Why 'Best Practices' Documents Don't Scale
Best practices documents fail because compliance requires humans to consistently remember and apply them. Quality gates work because they remove humans from the enforcement loop entirely.
Shivani Sutreja10 min read
Working through one of these problems?