smnscu

- strategy document

- "sprint" document with technical implementation

- actual implementation

- e2e testing scenarios updates

Every step involves iterating with Claude on it with me in the loop (setting the direction then resolving the "founder questions" as they appear), and importantly a different model for review/code-review, be it Codex (usually, it's great at it) or Antigravity/Gemini (sometimes finds novel things, its precision and recall are abysmal but on the odd occasion it has good accuracy). This iteration on the high-level plan then on the implementation plan is essential to me, and IMHO part of why people are surprised that I tend to get solid results from LLMs. At the very least, it allows me to fill gaps in my own knowledge (primarily front-end development) and be more productive than writing the code myself. I cannot stress enough how nice it is to have a partner in the high-level system design – yes, it often suggests utterly moronic ideas, but the overall experience is still net positive and getting better every quarter.

MTP - https://blog.google/innovation-and-ai/technology/developers-...

MLA - https://machinelearningmastery.com/a-gentle-introduction-to-...

CSA - https://deepseek.ai/blog/deepseek-v4-compressed-attention

I didn't set out to do this. After I got laid off in December, a client quickly fell in my lap: a small startup in the middle of a massive investment round that needed to hire 25 people immediately, with only a CTO available for interviews. I created their content and ran their interviews while building the software at the same time. It started as Google Meet + CoderPad + Calendly and gradually became an in-house system. Unlike Proton (lol), I'm not pretending I built my own video call solution from scratch, it's just an off-the-shelf 100ms integration.

The content is all versioned and structured, which makes it fast to iterate on and easy to reason about. We use major.minor versions and only bump the major for backwards-incompatible changes, or changes big enough that comparing interviews stops making sense. Otherwise, any combination of question versions inside an interview format is considered comparable if the major versions are identical.

The interview itself is highly structured: once you define a format from the content library and the various knobs you can adjust, you can schedule interviews and run them using our integrated "room" (video call + multiplayer code editor, both recorded, with transcripts and playback) and "rubric" (the tool the interviewer uses for content, scoring, and notes during the interview). Once you submit/publish the interview, a report is generated immediately. Example: https://coderba.se/sample

Two interesting AI bits:

- "AI linting": a way to benchmark interview questions by running a candidate model and an interviewer model against each other. The candidate closely follows a defined skills profile, then we compare actual vs expected performance. More here: https://coderba.se/blog/product-update-unit-testing-the-inte...

- "AI draft": once an interview ends, it takes ~30s for the video and transcript to become available. Then we use basically every relevant artifact from the interview, with a PII redaction pass first: questions, scoring, incomplete rubric, transcript, code editor history. We send that through our LLM gateway, currently mostly using DeepSeek because the quality/value is insane, though I may switch to Mistral to stay on the better side of privacy. It sends back recommended scoring + writeup, which we present as Cursor-like suggestions you can accept/reject/edit.