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- Hacker News
- Coding has been solved for 20 years at least.
90% of problems are easy once you know what you actually want well enough for you to be able to ask it from an LLM.
90% of code before LLMs was badly copied from StackOverflow anyway.
That 10% that's remaining, I've see 0, ZERO, nil progress. Windows is still awful. Spotify still doesn't work correctly offline. Youtube search is trash. Jira takes 20 seconds sometimes to load a task. LLMs haven't created a new database or a new game engine or a new renderer or anything like that.
The maths breakthroughs are really more of a testament to the efforts of the last 150 years for maths to be an organised verifiable principle. If LLMs had to practice math they way Euler did, they wouldn't be able to find shit.
(sorry if I sound incoherent, just some thoughts while I'm commuting)
by antoni4040 - This is once again RLHF loops.
the AI labs are and have been 100% focused on correctness because it is easy to setup and validate.
Adding one more function that almost does the same thing as another will not break anything.
I think this is just a matter of time. At some point there'll be less value to squeeze out of correctness and then the AI labs will start focusing on maintainability. It's probably a lot harder to set up environment to Train for this behavior though.
by mikkelam - I started Valknut (https://github.com/sibyllinesoft/valknut) when I saw the writing on the wall regarding Agent code structure/abstractions/etc being a limiting factor in the ability to autonomously build projects. My experience was that good linters helped, but it wasn't enough, you needed to be able to enforce information-theoretic related organizing principles in addition to file/function LOC and local complexity metrics to guide agents on how to structure code.
Originally I tried to walk the line between improved agent performance and human readability, but current models are so good I don't think human readability matters much, though at a high level, being able to grok the overall folder structure still matters. I've got my hands full polishing a demo for my game, but I intend to revisit Valknut by crafting an eval set that lets me calculate the difference in agent token consumption and task failure rate between ~isomorphic codebase structures. This will let me loop agents to discover organizing policies that improve them.
Truthfully though, with today's models I don't think this sort of codebase optimization is likely to have much impact below 250k-300k LoC projects, and it probably won't be a decisive win till you're near 1M. Also, the shelf life of a product like this isn't infinite as each generation of models pushes those numbers up while also having new policy preferences that require re-evaluating existing policies.
by CuriouslyC - Coding is solved, perhaps, with unlimited token spend on a frontier model. It remains to be seen if it that is prohibitively expensive forever. At my company, we token maxed while the getting was good. But when we had to switch to Anthropic's enterprise plan, and start paying per token, the shit really hit the fan. Now we're retreating back to sane cost levels and finding that - guess what? - people power might just be more cost effective. AI of course is an immense tool to leverage, but still too expensive to create loops and let it run. This will change over time of course, but assuming it is a solved problem is nonsense. Maybe if we solve cold fusion, yes. Until then, evolution is winning the war on entropy.by toddwprice
- Having reached the same conclusions as the author led me to create my first agent to do architecture review, and that's how I learned about the metrics behind good practices that I'd been following for years. LCOM, cyclomatic complexity, that kind of stuff...
It's so easy to ship a lot of code, more effort should be put into ensuring the code is correct, with self-improving feedback loops that involve developers, and dedicated tooling...
But again, a while ago, everything was about prompt engineering, and now you can express you idea vaguely and get a somewhat working result, so this likely will evolve fast as well...
by justinmarsan - Coding is not just the program running in memory, its also the process of distributing the mental model of understanding among the team.
If humans increasingly are kept out of coding, then who holds the mental model?
If AI holds the mental model, by definition human prompts will be over lossy channel. This is true without AI too. Software quality is directly dependent on good devs that translate from business/PM speak to technical decisions.
So is coding solved now? it was already solved decades ago.
by conqrr - Really glad to see folks looking into quantitative approaches to give agents feedback on code quality. This post looks like a good start!
My main feedback for the authors would be, the most important problems for sloppiness are global properties, not local ones. In my experience an agent, like a human, has finite capacity for its attention, but if it runs into local sloppiness that gets in its way, it can fix it on a by-need basis. The technical debt issues that matter are usually global issues that aren't so easy to fix: they require global analysis and global refactoring.
I don't know the answer, but I think we're going to need ways to measure architectural properties, like separation of concerns, clear architectural layering, well-defined interfaces, etc.
by dherman - All: please don't post generic reflexive reactions to titles. That's covered by this guideline, among others, in https://news.ycombinator.com/newsguidelines.html:
"Please don't pick the most provocative thing in an article or post to complain about in the thread. Find something interesting to respond to instead."
I've taken the provocative bit out of the title above, but please remember that we want reflective comments, not reflexive ones, in HN threads.
https://hn.algolia.com/?dateRange=all&page=0&prefix=true&sor....
by dang