Is it all just vapourware?

EA-3167

The tech is real, the use cases are increasingly forced and fabricated to justify a ludicrous valuation that will never come to pass. For research and military applications ML is great, but it isn’t going to print trillions for anyone.

mariocesar

The product is being launched before the value is clear. We've seen similar waves with new technologies before: overexposing the pros, dismissing the cons, hyper-optimism, and people using a lot of jargon without saying much of substance. The difference this time is the scale of the impact and the volume around it.

robertclaus

The trouble is that the loudest voices will dominate, so the little measurable wins adding up in most organizations don't get attention.

gwilikz

Was ready for some interesting music theory or analysis on genres of music that somehow map to the sound of vaporwave... but of course it's just about LLMs.

simonw

When this article talks about ONA it means https://ona.com/ - a cloud agent service that was acquired by OpenAI a couple of months ago. (I wouldn't suggest basing my evaluation of the entire field of coding agents around that particular product.)

Multiplayer

YMMV but I think with the SOTA models building a token efficient engineering loop is absolutely doable with (imho) solid development and testing practices. I see no reason to buy a 3rd party system for this either. I'm not in enterprise so don't come at me here - I'm a small shop and I'm not trying to corral 100 devs or whatever. We've got linear boards getting cards pushed and pulled, debated over by multiple models, coded, debated, tested, accepted, etc etc. True software factories now exist and they don't screw up the login.

wewewedxfgdf

"The hard part of software engineering was never writing the code." Again, are we still on this ridiculous concept. I stopped reading at that sentence. Is that going to be the revisionist future that the industry tells itself - ""one day, LLMs came along and suddenly computers could program themselves, and it was an absolute revolution, but it made no difference at all to anything because "The hard part of software engineering was never writing the code." so people stopped using LLMs because of that. And that's how LLM's came to an end after being a short trend.""

zatkin

I find it hard to buy this generalization about _all_ "AI"/agent software when the author has only provided _one_ example. A couple alternatives could've been explored, like Cursor Cloud Agents or even just running Claude Code with remote control on another machine. I'd also personally never spend a dime on any product unless I've read _some_ positive sentiment from online commentary, like Hacker News. To my point, Ona appears to have a pretty low amount of feedback that makes it difficult to justify any $20 bills: https://hn.algolia.com/?q=ona.com

grebc

It’s starting to resemble a pyramid scheme with Nvidia at the top, OpenAI & Anthropic beneath. That Nvidia finances a lot of this should give anyone with a clue on finance pause, that it doesn’t tells you you’re in the middle of a bubble.

firasd

I wonder if there is cargo culting afoot There are definitely guys in some companies just pointing an agent swarm at 50 Github tickets and saying "go get 'em boys" and hence need a lot of orchestration tools but for the rest of us do we really need the AI to do all the build steps and the testing steps... or do we just need the new feature that runs when the button is clicked and then we can check it out in the browser ourselves. The code gen is what I need the AI for, not the 'smoke tests' and the tsc finagling. I'm willing to spend some mental calories myself in figuring this feature out step by step Actually this makes me wonder if some people are just not very into building step by step like "okay first let's check the JSON shape the endpoint returns", "okay now let's make a simple form", etc

protimewaster

> I was optimistic, but once I had it wired up to one of my projects, instead of making magical hands-off progress on my todo list it spent nearly my entire $20 worth of “ona compute units”, whatever those are, thrashing and trying to get a hold of the todos from linear just so it could pick one to start. This is one of the reasons that I've simply not bothered with a lot of these types of AI products. It feels like gambling. Maybe I'll spend $20 on tokens and end up with something awesome. Or maybe I'll spend $20 on tokens and end up with nothing useful and then I'll be glad it was only $20 I lost.

trencedamp

> But so far most of what they do is make more annoying work for me. More integrations to debug, more auth tokens to refresh, more bills to keep track of, more meandering and drawn out descriptions of non-bugs with bogus fix suggestions and misinformed “root cause” analyses. It’s tiresome. God this times 1000. I'm so sick of having AI chase a bug and having to lead it by the hand like a toddler to try and help me solve bugs. The difference is, a toddler learns when you explain. An LLM holds your responses in context and uses them to generate more authentic sounding garbage, but next session, foomf , the lesson is unlearned again. I had a tiny problem with prerendering not working on a react site last night, I must have spent close to an hour running commands it asked me to run like a moron, verifying dumb things I had already checked like "did the file actually upload to ftp" or had I restarted Apache or was there some magic htaccess file somewhere. Eventually I just gave up on Claude and tried some different paths in Apache virtual host settings and it worked. Of course if I went back to Claude and reported that, as would be my instinct with a human, it would confidently explain why that was the bug, why it couldn't spot the issue, and how smart I am for finding it. And then it would forget everything tomorrow

badlibrarian

It's August 9, 2026 and if you're a software engineer who hasn't had multiple "holy shit, I can't believe it just did that" moments, it's time to consider a new trade.

solomonb

The churn in this space puts javascript to shame. As an example, its only been a few months and AFAICT no one is even talking about openclaw anymore.

fhub

> Is it all just vapourware? No. Anyone making this claim is being disingenuous.

jeffreyrogers

I was fairly skeptical of agentic coding before I used it for a real product. Although I still have to be heavily involved in planning the code that LLMs write for me, they can write code much faster than I can, and they know more about edge cases than I do, so they can handle edge cases/subtle bugs that I would have missed. I have been paid to write code at every level of the stack from assembly to frontend javascript, but I'm not equally good at all those areas. In some areas I can still outperform LLMs, but for areas I'm weak they do a much better job than I would have. I still think of what I'm doing as software engineering, and I'm glad that I had many years of professional and hobby development before using agents since I think that's given me the ability to make good architectural decisions (and helps me resteer the LLMs when they want to do something suboptimal), but my involvement in actually writing code is quickly going to zero. That said, they aren't perfect and they still introduce bugs, but I believe the quality of my current product is higher than what I would have created pre-agentic coding. Things I've found helpful in keeping quality high: - Visual regression tests (detect UI bugs before you commit them) - Fuzz testing of interfaces and app behavior - Automatically add regression tests for any bug that I/the LLM fixes - Logging/alerting that tracks an errors/invariant violations triggered in the app - Performance metrics that are surfaced in a dashboard. All of these are very easy to add since the LLM can create this infrastructure for you. The fuzz testing in particular is something very few products I've previously worked on have since most people don't know how to implement it. I ran the fuzzers for a few minutes and they quickly caught multiple subtle bugs that I was not aware of. This is a real product that helps a real, non-VC funded service business, and although I could have made something similar myself it would have taken me a lot longer, be harder to use, and probably be less reliable. Edit: while it's true that you can quickly blow through the $20/month plan, the $200/month plan allows you to get a lot done and is basically sufficient for my needs. It's also very cheap when you consider what it would cost to pay someone to do similar work.

lordnacho

I don't know how you can claim it's all vapour ware. Two years ago, I couldn't just roughly describe my backlog and then have the code fixed. I had to type it out myself, run it, look at logs, fix toolchain issues, and so on. It was tedious. Or I could get a junior to do it. Now can get these things done quite fast, without concentrating nearly as hard. Clearly, it isn't vapour. It delivers something. That something we have yet to figure out the best way to use, but there's definitely something there that works. I get the feeling a lot of people are frustrated because the little gains are lost in organisational chaos, rather than the tools not working.

20k

>If agentic development actually worked the way any of them say it does I think its fascinating just how much of a gap there is between what's being claimed, and the verifiable observable data of the open source world. Major open source projects are by and large starting to ban LLMs now, because the contributions made by LLM users have been universally terrible and unhelpful. There doesn't appear to be a single major project that's found generating code to lead to major productivity speedups, and the consensus appears to be that its just lead to a lot of crappy contributions that are harder to spot immediately as being obvious crap I regularly see people claim that they are now 10x more productive with LLM code generation, and I just wonder where all the code is. Is it somehow true that these gains are only being realised in proprietary projects, and not a single one of them has put even a small fraction of their new found engineering powers into eg Godot? Why do only the poor quality LLM code generation users make PRs to open source projects, and never the engineers that know how to really use it correctly? If you look in the open source major project space, you can find almost no evidence that AI code generation exists at all. Go browse your favourite critical tool and look for AI generated PRs that have landed in the codebase, its probably a tiny handful of them in comparison to the human written PRs prior to an LLM ban. It turns out that once you have a verifiable, open quality review bar, for some reason almost no LLM commits really meet the level of quality necessary I strongly suspect that what we're seeing is that much of the tech code-writing economy had already become completely performative prior to AI turning up. It no longer matters in the current age if your code is good, or works, because your job is to give the illusion of product development while the stock market price gets pumped, until you all cash out your share value, get bought, or hop jobs in 2 years. For many companies it literally does not matter if you produce anything that generates value (or works), because the illusion of progress is all that matters. AI is absolutely incredible at creating the illusion of progress, because it looks a whole lot like real code, it just appears to have failed the bar of making actual projects that work . If that was never the goal in the first place, it probably really is a 10x productivity boost

storus

Writing meaningful and correct code was always difficult. That we know how to generate pointless CRUD or half-baked apps using agents doesn't mean we can do them well. It's probably sufficient for selling them as a business but far from being great.

gryfft

It's strange to me that the voices naysaying agentic coding capabilities seem to be getting louder in recent weeks. I almost wonder if there's a campaign to start suppressing public awareness of where SOTA capabilities really are.