I have been sitting with this question for a long time. The more I fold language models into my daily work as a software developer, the less sure I am about a very basic thing: which model do I actually need for the task in front of me?
Take a concrete case. I want to build a chat app. What is the breakdown of...
The last post covered the quality chain. This one is about visibility. An AI can push three features forward in an afternoon. If you can't tell where the project stands, what a change will touch, or how the last stretch went, the automation is running in a black box. pdlc has three tools for this: the statusline together with
/pdlc-status,/pdlc-...
Most advice about context windows is about fitting more in. Bigger windows, more retrieved documents, the whole file instead of the function. In practice the thing that has improved my results the most is the opposite move: getting the junk out. A window packed with marginally relevant material makes the model answer worse, not better, and you pay for every token of it on every turn.
In the modern Next.js ecosystem, the allure of "Edge Everything" is strong. It promises lightning-fast global latency and a seamless developer experience. However, this architectural shift has introduced a common, silent performance killer: running database-backed authentication checks directly inside Next.js Middleware.
The question this whole series exists to answer is whether language-model-like behaviour can come from something other than a large number of trained parameters. An early set of experiments turned that into a bar that a non-neural method has to clear, and the bar is a slope rather than a score: keep converting extra data into accuracy after exact-context statistics have saturated.
...Originally published at linkdigest.dev, where I build this.
Paste a Xiaohongshu, Douyin, TikTok, YouTube or X link into an AI agent and it fetches the URL, gets an app-download shell or a login wall, and tells you ther...