Monday, 21st July 2025
Coding with LLMs in the summer of 2025 (an update) (via) Salvatore Sanfilippo describes his current AI-assisted development workflow. He's all-in on LLMs for code review, exploratory prototyping, pair-design and writing "part of the code under your clear specifications", but warns against leaning too hard on pure vibe coding:
But while LLMs can write part of a code base with success (under your strict supervision, see later), and produce a very sensible speedup in development (or, the ability to develop more/better in the same time used in the past — which is what I do), when left alone with nontrivial goals they tend to produce fragile code bases that are larger than needed, complex, full of local minima choices, suboptimal in many ways. Moreover they just fail completely when the task at hand is more complex than a given level.
There are plenty of useful tips in there, especially around carefully managing your context:
When your goal is to reason with an LLM about implementing or fixing some code, you need to provide extensive information to the LLM: papers, big parts of the target code base (all the code base if possible, unless this is going to make the context window so large than the LLM performances will be impaired). And a brain dump of all your understanding of what should be done.
Salvatore warns against relying too hard on tools which hide the context for you, like editors with integrated coding agents. He prefers pasting exactly what's needed into the LLM web interface - I share his preference there.
His conclusions here match my experience:
You will be able to do things that are otherwise at the borders of your knowledge / expertise while learning much in the process (yes, you can learn from LLMs, as you can learn from books or colleagues: it is one of the forms of education possible, a new one). Yet, everything produced will follow your idea of code and product, and will be of high quality and will not random fail because of errors and shortcomings introduced by the LLM. You will also retain a strong understanding of all the code written and its design.
An AI tool that gets gold on the IMO is obviously immensely impressive. Does it mean math is “solved”? Is an AI-generated proof of the Riemann hypothesis clearly on the horizon? Obviously not.
Worth keeping timescales in mind here: IMO competitors spend an average of 1.5 hrs on each problem. High-quality math research, by contrast, takes month or years.
What are the obstructions to AI performing high-quality autonomous math research? I don’t claim to know for sure, but I think they include many of the same obstructions that prevent it from doing many jobs: Long context, long-term planning, consistency, unclear rewards, lack of training data, etc.
It’s possible that some or all of these will be solved soon (or have been solved) but I think it’s worth being cautious about over-indexing on recent (amazing) progress.
— Daniel Litt, Assistant Professor of mathematics, University of Toronto