There’s something quietly impressive about getting modern AI ideas to run on old hardware (like OP's project or running LLM inference on Windows 3.1 machines). It’s easy to think all the progress is just bigger GPUs and more compute, but moments like that remind you how much of it is just more clever math and algorithms squeezing signal out of limited resources. Feels closer to the spirit of early computing than the current “throw hardware at it” narrative.
Our interest is straightforward: If coding agents scaffold a growing share of new apps, their defaults will become prevalent. Not necessarily because they’re objectively best, but because the choices are frictionless. The model becomes the gatekeeper of early architectural decisions that used to happen in a meeting room.
So we measured what those defaults actually are.
We ran structured app-building prompts through Claude Code, captured the generated repos, and extracted stack choices: auth, UI framework, database, deployment assumptions, package management, etc.
A few observations that stood out:
- Deployment assumptions are shifting toward Vercel and Railway, and away from AWS/GCP-first patterns.
- Defaults evolve as models update. For example, Opus 4.6 recommends Drizzle more frequently than Prisma.
Unlike classic search, which got worse over time due to SEO gamings, AI search might actually improve with scale. If LLMs are trained on real internet discussions (Reddit, forums, reviews), and your product consistently gets called out as bad, the model will eventually reflect that. The pressure shifts from optimizing content to improving the product itself.
A few take-aways from a study we ran (~800 consumer queries, repeated over a few days):
* AI answers shift a lot. In classic search a page-1 spot can linger for weeks; in our runs, the AI result set often changed overnight.
* Google’s new “AI Mode” and ChatGPT gave the same top recommendation only ~47 % of the time on identical queries.
* ChatGPT isn’t even consistent with itself. Results differ sharply depending on whether it falls back to live retrieval or sticks to its training data.
* When it does retrieve, ChatGPT leans heavily on publications it has relationships with (NYPost and People.com for product recs) instead of sites like rtings.com