Articles.
Notes from the work: product thinking, engineering quality, and what AI tools do well and what they leave to you.
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Page 4 of 7
Metrics and code health
Senior Engineers Are the Bottleneck: How to Democratise Engineering Excellence
In most teams, every architecture decision, every risky PR, and every onboarding conversation routes through three or four senior engineers. That is not seniority — it is a single point of failure. Here is how to move that judgment out of their heads and into the workflow.
Vishvjitsinh Vanar10 min read
AI engineering
Your Job Was Never Writing Code: It's Translating Intent Into Value
AI didn't take your job. It exposed it. The work was never typing characters into an editor — it was translating ambiguous business intent into something that adds value to users. Code was always the byproduct. The translation was always the work.
Harsh Parmar11 min read
AI engineering
AI Wrote the Bug. You Shipped It.
When AI generates code that breaks production, ownership doesn't transfer to the model — the post-mortem still names the human who clicked merge. Vibe engineering is the craftsmanship answer: you delegate the typing, but never the understanding, the design intent, or the accountability.
Harsh Parmar10 min read
AI engineering
Why AI Agents Only Work on Clean Codebases (And What That Means for Your Outsourced Code)
AI coding agents are multipliers. Multiply zero, get zero. Multiply a typical outsourced codebase, and you don't accelerate delivery — you accelerate rework. Here's the founder math on why your AI ROI is landing on the wrong substrate.
Harsh Parmar10 min read
Engineering practices
Continuous Delivery Is Not CI/CD: The Practices That Actually Matter
Most teams say they "do CI/CD" because they have a pipeline that builds, tests, and deploys. That pipeline is automation — Continuous Delivery is a discipline. Here are the practices that turn one into the other.
Harsh Parmar9 min read
Engineering practices
The Mutation Testing Playbook: Finding the Tests That Are Lying to You
Most teams have heard of mutation testing. The reason 90% of them never run it: nobody told them how. This is the operating manual — pick the tool, read the report, kill the surviving mutants, run it fast enough to gate every pull request.
Harsh Parmar15 min read
Product discovery
AI Won't Do This by Default: User Story Mapping & Shape
Even a good hypothesis needs a map before it needs code. AI agents build whatever ticket lands in front of them — they can't see the journey the ticket sits inside. User story mapping is the practice that turns a backlog of features into a walking skeleton of the user's journey. It's the second unlock.
Chirag11 min read
AI engineering
The Push and Pull of AI Adoption: Why Teams Resist Better Tools
Engineering resistance to AI tools is not a change management problem. Every complaint maps to a specific missing prerequisite. Here is how to read the resistance as a diagnostic — and the five-stage sequence that resolves it.
Shivani Sutreja10 min read
Metrics and code health
The Hidden Cost of Code Reviews: 22 Hours to First Review
Median time from PR opened to first review is 22 hours. That is not review effort — it is system latency. Here is what it costs, why it happens, and what elite teams do differently.
Shivani Sutreja8 min read
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