241–250 of 384 posts
Right now I'm working two AI-jobs. I build agents for enterprises and I teach agent development at a university. So I'm probably too deep to see straight. But I think the future of programming is english. Agent frameworks are converging on a small set of core concepts: prompts, tools, RAG, agent-as-tool, agent handoff, and state/runcontext (an LLM-invisible KV store for sharing state across tools, sub-agents, and pro…
This essay somehow sounds worse than AI slop, like ChatGPT did a line of coke before writing this out. I use AI everyday for coding. But if someone so obviously puts this little effort into their work that they put out into the world, I don’t think I trust them to do it properly when they’re writing code.
I wrote it myself. But the irony isn't lost on me. "Who did what" is kind of the whole point of the article. Appreciate the feedback.
Earlier quoted context omitted.
The AI doesn't know what good or bad code is. It doesn't know what surpassing someone means. It's been trained to generate text similar to its training data, and that's what it does. If you feed it only good code, we'd expect a better result, but currently we're feeding it average code. The cost to evaluate code quality for the huge data set is too high.
The training data includes plenty of examples of labelled good and bad code. And comparisons between two implementations plus trade-offs and costs and benefits. I think it absolutely does "know" good code, in the sense that it can know anything at all.
You can do some basic checks like "does it actually compile", but for the most part you'd really need to go out and do manual categorization, which would be brutally expensive.
This essay somehow sounds worse than AI slop, like ChatGPT did a line of coke before writing this out. I use AI everyday for coding. But if someone so obviously puts this little effort into their work that they put out into the world, I don’t think I trust them to do it properly when they’re writing code.
I wrote it myself. But the irony isn't lost on me. "Who did what" is kind of the whole point of the article. Appreciate the feedback.
I've been shifting from being the know-it-all coder who fixes all of the problems to a middle manager of AIs over the past few months. I'm realizing that most of what I've been doing for the last 25 years of my career has largely been a waste of time, due to how the web went from being an academic pursuit to a profit-driven one. We stopped caring about how the sausage was made, and just rewarded profit under a results-driven economic model. And those results have been self-evidently disastrous for anyone who cares about process or leverage IMHO. So I ended up being a custodian solving other people's mistakes which I would never make, rather than architecting elegant greenfield solutions.
For example, we went from HTML being a declarative markup language to something imperative. Now rather than designing websites like we were writing them in Microsoft Word and exporting them to HTML, we write C-like code directly in the build product and pretend that's as easy as WYSIWYG. We have React where we once had content management systems (CMSs). We have service-oriented architectures rather than solving scalability issues at the runtime level. I could go.. forever. And I have in countless comments on HN.
None of that matters now, because AI handles the implementation details. Now it's about executive function to orchestrate the work. An area I'm finding that I'm exceptionally weak in, due to a lifetime of skirting burnout as I endlessly put out fires without the option to rest.
So I think the challenge now is to unlearn everything we've learned. Somehow, we must remember why we started down this road in the first place. I'm hopeful that AI will facilitate that.
Anyway, I'm sure there was a point I was making somewhere in this, but I forgot what it was. So this is more of a "you're not alone in this" comment I guess.
Edit: I remembered my point. For kids these days immersed in this tech matrix we let consume our psyche, it's hard to realize that other paradigms exist. Much easier to label thinking outside the box as slop. In the age of tweets, I mean x's or whatever the heck they are now, long-form writing looks sus! Man I feel old.
Earlier quoted context omitted.
A market has to exist for this expanded range and for the expanded ranges of every other bakery. Otherwise the bakery's just wasting flour. Where is this expanded demand coming from?
Two loaves of bread off the same line are perfect substitutes for each other, and compete to be sold. Lines of code within the same code base aren't competing to be sold. They either complement each other by adding new features, making the actual product sold more valuable, or one replaces another to make a feature more desirable- look better, work faster, etc. The market grows if you add new features- your bread now…
But on your point (which seems to hinge on wish thinking), this infinity of new features you propose for every product still needs those new markets you take for granted to justify their inclusion in the product. However cornering a new market isn't as straightforward as deploying a new feature - we all wish it was. The tech that makes it trivial for one firm to develop these features, makes it trivial for everyone else to build them. This means any new market will be immediately saturated.
Even if the leap of finding new markets was as easy as you think, you still need to explain why this hypothetical company would keep paying millions in avoidable salaries. Because whatever jobs you assign to AI, it won't be any less available to do the work of the human labor.
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And those mediocre engineers put their work online, as do top-tier developers. In fact, I would say that the scale is likely tilted towards mediocre engineers putting more stuff online than really good ones. So statistically speaking, when the "AI" consumes all of that as its training data and returns the most likely answer when prompted, what percentage of developers will it be better than?
That's not how modern LLMs are built. The days of dumping everything on the internet into the training data and crossing your fingers are long past. Anthropic and OpenAI spent most of 2025 focusing almost expensively on improving the coding abilities of their models, through reinforcement learning combined with additional expert curation of training data.
Thank you for teaching me, AI understander. You're definitely not detached from reality one bit. It's me, obviously.
Earlier quoted context omitted.
I am going to try to put this kindly: it is very glib, and people will find it offensive and obnoxious, to implicitly round off all resistance or skepticism to incuriosity. Perhaps to alienate AI critics even further is the goal, in which case - carry on. But if you are genuinely confused by the attitudes of your peers, try asking not "what do I have that they lack" ("curiosity"?) but "what do they see that I don't"…
I don't think that people who don't want to use these tools or clean old ways are incurious. But I think these developers should face the fact that those skills and those ways they are reticent to give up are more or less obviated at this point. Not in the future, but now. It's just that the adoption of these tools isn't evenly distributed yet. I think there's a place for thoughtful dialogue around what this means fo…
Of course, there’s nothing wiser than tying the entirety of your career to a $20/month subscription (that will jump 10x in price as soon as the market is captured).
Is writing solved because LLMs can make something decently readable? Why say anything at all when LLMs can glob your ideas into a glitzy article in a couple of seconds?
I swear, some people in this field see no value in their programming work — like they’ve been dying to be product managers their entire lives. It is honestly baffling to me. All I see is a future full of horrifying security holes, heisenbugs, and performance regressions that absolutely no one understands. The Idiocracy of software. Fuck!
Not a day goes by that a fellow engineer doesn't text me a screenshot of something stupid an AI did in their codebase. But no one ever mentions the hundreds of times it quietly wrote code that is better than most engineers can write. The catch about the "guided" piece is that it requires an already-good engineer. I work with engineers around the world and the skill level varies a lot - AI has not been able to bridge…
I am going to try to put this kindly: it is very glib, and people will find it offensive and obnoxious, to implicitly round off all resistance or skepticism to incuriosity. Perhaps to alienate AI critics even further is the goal, in which case - carry on. But if you are genuinely confused by the attitudes of your peers, try asking not "what do I have that they lack" ("curiosity"?) but "what do they see that I don't"…
Earlier quoted context omitted.
That's not how modern LLMs are built. The days of dumping everything on the internet into the training data and crossing your fingers are long past. Anthropic and OpenAI spent most of 2025 focusing almost expensively on improving the coding abilities of their models, through reinforcement learning combined with additional expert curation of training data.
Silly old me, how could've I forgotten about such drastic improvements between say Sonnet 3.7 and Sonnet 4.6. It's 500x better now! Thank you for teaching me, AI understander. You're definitely not detached from reality one bit. It's me, obviously.
All your incantations can't protect you