Muthukrishnan Bommiah · muthub.org
AI has made writing code cheap. Verification, trusted context and human judgment are now the scarce resources, and teams that redesign their lifecycle around them will pull away from teams that only add assistants.
AI-native SDLC6 min readShipping AI-generated code without recording where it came from is unmanaged risk. Regulated teams need deterministic provenance for non-deterministic models: line-level attribution, Git-native session history and AI bills of materials.
Governed AI5 min readPlatform-embedded, sandboxed and evolutionary coding agents each earn trust in a different way. Then the editor became a fleet manager. Choose and govern agents by how their work will be verified.
AI-native SDLC6 min readFor complex work in regulated sectors, prompt-to-code cannot pass an audit. Making the specification the contract an AI must fulfil can, provided the spec stays anchored to the code and machines check conformance.
AI-native SDLC6 min readEditors now run local, background and cloud agents side by side. The leadership job shifts from writing code to designing the system that writes it.
Note1 min readAd-hoc prompting yields code that looks right but misses intent. Making the spec the gated source of truth is how AI-assisted delivery becomes repeatable.
Note1 min readTechnical debt is a strategic risk rather than messy code. Coding agents make continuous paydown affordable inside everyday work.
Note1 min read