jasim

If tokens are going to get cheaper and faster - then we would stop caring about token efficiency, like how we're all using bloated and slow software, and which keep growing to take up all available hardware improvements.

I think this is somewhere in the middle east - Dubai likely - and it is hot there. It is quite predictable that there would be mass opposition with institutional support against turning off air conditioning in a common space, despite having a couple of hours of exclusive rental. That said, it is a good reminder that we have a choice in what we are willing to accept, and can fall back on a BATNA, even if it is not the most ideal outcome.

I've been paying JetBrains in a personal capacity for about 15 years. Almost from the first year of my professional career. I didn't renew this year. I feel so sad and disheartened that a company that produced such brilliant tools have completely lost the plot on AI. They had started work on Fleet, a lightweight VSCode like IDE, before LLMs became big, but abandoned the effort without seeing it through. It could've been a great agentic IDE (all I want is whole file+project view for every conversation, instead of just diff so I can have a better perspective of the codebase). Instead they built multiple confusing tools (might be internal org structure and competition leaking out), and took far too long to adopt SOTA CLI coding agents, and kept charging for it as a separate subscription. I wish they do a whole company rethink on agentic development, but it is going to be tough because the existing enterprise licenses will continue to be the main revenue driver for a few more years, until it all dries up all at once.

We might be moving towards putting more intent - in text - into the code itself, for agents. We should've been doing that for other humans in the past, but it wasn't always encouraged - "code should be self-explanatory, and comments are a sign of poor naming and abstraction", and all that.

These days I run a prompt of this shape to ensure that my code contains as much tacit knowledge as I have -- "I want to make sure all the implicit invariants and structures and concepts described in these plans, are present in the code comments, namings, comments around major types and modules. So even if the original plan file is not present, we should still be able to understand it (Peter Naur - Programming is Theory Building)"

However - I still need the type system as another preserver of invariants and intents. Coding agents still make subtle mistakes and miss very obvious things as the code gets more intricate and conceptually large.

A type error where an expected case is not handled - that is a feedback that can ground it back and force it to think through whether windspeed matters to is_dangerous or not. And the choice of is_dangerous in the article is a bit misleading, because anyone can at first read make the connection from windspeed to is_dangerous. It is a pretty bold variable name and attention grabbing situation.

However codebases are made of far more mundane but widespread and braided sets of tiny invariants that wouldn't catch either a human's or a coding agent's attention without needing a more firmer hand, and so I still do love my type systems despite my best laid comments.

But couple that with a deterministic system - the type checker with exhaustive destructing (and pattern matching, while we are it it) - that would become a fool-proof combination.

I hadn't considered that, you are right. Though I would still prefer TS because the language is all about types; the entire ecosystem is well-typed and the type system is quite powerful (I do enjoy an occasional Omit and Pick). But it is a personal choice; as long as LLMs can generate well-typed Python, then for people who like the language it makes sense. However from the article, I got the sense that they went completely dynamic.

I'm curious about the choice of Python, rather than TypeScript.

I find Ruby a very beautiful language, and Rails is an excellent web framework, but I need typed functions, record types and sum types.

They help not just with correctness, but also as living documentation that lets me understand AI generated code. TypeScript provides discriminated union, but not exhaustive pattern-matching, and its syntax is a bit verbose, but since I'm no longer writing most of the code myself, I can live with it.

However I can't imagine using Python or any other dynamic language going forward. There is likely good reason for you to choose it, and I'm curious to know what that is.

There is no other stack in the last 15 years or so that I look back with the same fond affection as building business applications in Clipper. One important aspect was that software was delivered physically, and I could watch my users using them. If I made a report that helped the operator, or made a workflow easier, their happiness was immediate and nurturing. And there was an immediacy to programming in xBase - if we're building a database backed business application, which was their strong suit, there was very little standing in between us and the problem space.

"There was no exploit. No vulnerability disclosure. No CVE for me to write. The attacker filled out my signup form 942 times, made 942 workspaces, sent 942 batches of about a hundred invitations each, and stopped. They used my tool exactly as designed. The design was just bad enough that the tool was good for phishing."