7 ms·
I feel like we are just inching closer and closer to a world where rapid iteration of software will be by default. Like for example a trusted user makes feedbac
by nickandbro 7mo ago
I feel like we are just inching closer and closer to a world where rapid iteration of software will be by default. Like for example a trusted user makes feedback -> feedback gets curated into a ticket by an AI agent, then turned into a PR by an Agent, then reviewed by an Agent, before being deployed by an Agent. We are maybe one or two steps from the flywheel being completed. Or maybe we are already there.
- theredbeard 7mo agoWe haven’t been inching closer to users writing a half-decent ticket in decades though.
- edf13 7mo agoOr perhaps we end up where all software is self evolving via agents… adjusting dynamically to meet the users needs.
- PeterStuer 7mo agoThe "user" being the one that's in charge of the AI, not the person on the receiving end.
- charcircuit 7mo agoThen sets up telemetry and experiments with the change. Then if data looks good an agent ramps it up to more users or removes it.
- jvuygbbkuurx 7mo agoTusted user like Jia Tan.
- tossandthrow 7mo agoI think the Ai agent will directly make a PR - tickets are for humans with limited mental capacity. At least in my company we are close to that flywheel.
- Gigachad 7mo agoThe agents have even more limited capacity
- _puk 7mo agoTickets need to exist purely from a governance perspective. Tickets may well not look like they do now, but some semblance of them will exist. I'm sure someone is building that right now. No. It's not Jira.
- tossandthrow 7mo agoYes, so my point is that PRs act as that governance layer - with preview environments, you can see the complexity and risk of the change etc.
- yieldcrv 7mo agoWe do feedback to ticket automatically We dont have product managers or technical ticket writers of any sort But us devs are still choosing how to tackle the ticket, we def don't have to as I’m solving the tickets with AI. I could automate my job away if I wanted, but I wouldn't trust the result as I give a degree of input and steering, and there’s bigger picture considerations its not good at juggling, for now
- MattGaiser 7mo agoI am already there with a project/startup with a friend. He writes up an issue in GitHub and there is a job that automatically triggers Claude to take a crack at it and throw up a PR. He can see the change in an ephemeral environment. He hasn't merged one yet, but it will get there one day for smaller items. I am already at the point where because it is just the two of us, the limiting factor is his own needs, not my ability to ship features.
- jondwillis 7mo agoWhy doesn’t he merge them?
- MattGaiser 7mo agoHe is not technical but a product guy, so he still wants me to check it over.
- m00x 7mo agoMust be nice working on simple stuff.
- chatmasta 7mo agoI love everything about this direction except for the insane inference costs. I don’t mind the training costs, since models are commoditized as soon as they’re released. Although I do worry that if inference costs drop, the companies training the models will have no incentive to publish their weights because inference revenue is where they recuperate the training cost. Either way… we badly need more innovation in inference price per performance, on both the software and hardware side. It would be great if software innovation unlocked inference on commodity hardware. That’s unlikely to happen, but today’s bleeding edge hardware is tomorrow’s commodity hardware so maybe it will happen in some sense. If Taalas can pull off burning models into hardware with a two month lead time, that will be huge progress, but still wasteful because then we’ve just shifted the problem to a hardware bottleneck. I expect we’ll see something akin to gameboy cartridges that are cheap to produce and can plug into base models to augment specialization. But I also wonder if anyone is pursuing some more insanely radical ideas, like reverting back to analog computing and leveraging voltage differentials in clever ways. It’s too big brain for me, but intuitively it feels like wasting entropy to reduce a voltage spike to 0 or 1.
- eksu 7mo agoThis is the wrong way to see it. If a technology gets cheaper, people will use more and more and more of it. If inference costs drop, you can throw way more reasoning tokens and a combination of many many agents to increase accuracy or creativity and such.
- gf000 7mo ago> throw way more reasoning tokens and a combination of many many agents to increase accuracy or creativity and such. But this is just not true, otherwise companies that can already afford such high prices would have already outpaced their competitors.
- spacebanana7 7mo agoNo company at the moment has enough money operate with 10x the reasoning tokens of their competitors because they're bottlenecked by GPU capacity (or other physical constraints). Maybe in lab experiments but not for generally available products. And I sense you would have to throw orders of magnitude more tokens to get meaningfully better results (If anyone has access to experiments with GPT 5 class models geared up to use marginally more tokens with good results please call me out though).
- bredren 7mo agoWhat you're describing is absolutely where we're headed. But the entire SWE apparatus can be handled. Automated A/B testing of the feature. Progressive exposure deployment of changes, you name it.
- slopinthebag 7mo agoWhat kind of software are people building where AI can just one shot tickets? Opus 4.6 and GPT 5.4 regularly fail when dealing with complicated issues for me.
- thin_carapace 7mo agoi dont see anyone sane trusting ai to this degree any time soon, outside of web dev. the chances of this strategy failing are still well above acceptable margins for most software, and in safety critical instances it will be decades before standards allow for such adoption. anyway we are paying pennies on the dollar for compute at the moment - as soon as the gravy train stops rolling, all this intelligence will be out of access for most humans. unless some more efficient generalizable architecture is identified.
- slopinthebag 7mo agoEven in webdev it rots your codebase unchecked. Although it's incredibly useful for generating UI components, which makes me a very happy webslopper indeed.
- thin_carapace 7mo agoim grateful to have never bothered learning web dev properly, it was enlightening witnessing chat gpt transform my ten second ms paint job into a functional user interface
- m00x 7mo agoSeveral fintechs like Block and Stripe are boasting thousands of AI-generated PRs with little to no human reviews. Of course it's in the areas where it doesn't matter as much, like experiments, internal tooling, etc, but the CTOs will get greedy.
- slopinthebag 7mo agoI don't think anybody is doubting its ability to generate thousands of PR's though. And yes, it's usually in the stuff that should have been automated already regardless of AI or not.
- eranation 7mo agoUm, we are already there...
- eranation 7mo agoNot sure why the downvote, I'm seeing this happening...
- Leptonmaniac 7mo agoI think that as a user I'm so far removed from the actual (human) creation of software that if I think about it, I don't really care either way. Take for example this article on Hacker News: I am reading it in a custom app someone programmed, which pulls articles hosted on Hacker News which themselves are on some server somewhere and everything gets transported across wires according to a specification. For me, this isn't some impressionist painting or heartbreaking poem - the entity that created those things is so far removed from me that it might be artificial already. And that's coming from a kid of the 90s with some knowledge in cyber security, so potentially I could look up the documentation and maybe even the source code for the things I mentioned; if I were interested.
- slopinthebag 7mo agoArt is and has always been about the creator.
- vntok 7mo agoTake a walk in any museum, I'm pretty sure you'll react to some of the art displayed there and find it cool before you read the name of the artist.
- rjknight 7mo agoIt's not that you know the artist first and then say "this art is cool because I like the artist". The art is the means by which you know the artist. The more of their works you encounter, the closer you get to understanding the artist and what they are trying to communicate.
- egeozcan 7mo agoDive into a forest, you'll find a couple of cool trees. Art isn't about being cool. Art is about context. When I tell people that art cannot be unpolitical, they react strongly, because they think about the left/right divide and how divided people are, where art is supposed to be unifying. But art is like movement, you need an origin and a destination. Without that context, it will be just another... thing. Context makes it something.
- eru 7mo agoInstead of having a trusted user, you can also do statistics on many users. (That's basically what A/B testing is about.)
- tuo-lei 7mo agoThe missing piece for me is post-hoc review. A PR tells me what changed, but not how an AI coding session got there: which prompts changed direction, which files churned repeatedly, where context started bloating, what tools were used, and where the human intervened. I ended up building a local replay/inspection tool for Claude Code / Cursor sessions mostly because I wanted something more reviewable than screenshots or raw logs.
- hyperionultra 7mo ago"Trusted user" also can be an Agent.
- heavyset_go 7mo agoFeedback loops like that would be an exercise in raising garbage-in->garbage-out to exponential terms. It's the "robots will just build/repair themselves" trope but the robots are agents
- TeMPOraL 7mo agoYes. Next they'll want nanobots that build/repair themselves. Oh wait. That's already here and is working fine.
- heavyset_go 7mo agoTurns out we were the nanobots all along
- mindwok 7mo agoI think Anthropic will launch backend hosting off the back of their Bun acquisition very soon. It makes sense to basically run your entire business out of Claude, and share bespoke apps built by Claude code for whatever your software needs are.
- pxtail 7mo ago100% its going to happen - also OpenAI will do same, there were already rumors about them building internal "github" which is stepping stone for that Also it is requirement for completing lock-in - the dream for these companies.
- overfeed 7mo ago> I feel like we are just inching closer and closer to a world where rapid iteration of software will be by default. There's a lots of experimentation right now, but one thing that's guaranteed is that the data gatekeepers will slam the door shut[1] - or install a toll-booth when there's less money sloshing about, and the winners and losers are clear. At some point in the future, Atlassian and Github may not grant Anthropic access to your tickets unless you're on the relevant tier with the appropriate "NIH AI" surcharge. 1. AI does not suspend or supplant good old capitalism and the cult of profit maximization.
- shafyy 7mo agoHaha sure, let's just let every user add their feedback to the software.
- andy_ppp 7mo agoUsers are often incorrect about what the software should actually be doing and don’t see the bigger picture.
- backscratches 7mo agoIn the past three weeks a couple of projects I follow have implemented AI tools with their own github accounts which have been doing exactly this. And they appear to be doing good work! Dozens of open issues iterated, tested and closed. At one point i had almost 50 notification for one projects backlog being eradicated in 24 hours. The maintainer reviewed all of it and some were not merged.
- jwpapi 7mo agoI just don’t see it coming. I was full on that camp 3 months ago, but I just realize every step makes more mistakes. It leads into a deadlock and when no human has the mental model anymore. Don’t you guys have hard business problems where AI just cant solve it or just very slowly and it’s presenting you 17 ideas till it found the right one. I’m using the most expensive models. I think the nature of AI might block that progress and I think some companies woke up and other will wake up later. The mistake rate is just too high. And every system you implement to reduce that rate has a mistake rate as well and increases complexity and the necessary exploration time. I think a big bulk of people is of where the early adaptors where in December. AI can implement functional functionality on a good maintained codebase. But it can’t write maintable code itself. It actually makes you slower, compared to assisted-writing the code, because assisted you are way more on the loop and you can stop a lot of small issues right away. And you fast iterate everything• I’ve not opened my idea for 1 months and it became hell at a point. I’ve now deleted 30k lines and the amount of issues I’m seeing has been an eye-opening experience. Unscalable performance issues, verbosity, straight up bugs, escape hatches against my verification layers, quindrupled types. Now I could monitor the ai output closer, but then again I’m faster writing it myself. Because it’s one task. Ai-assisted typing isn’t slower than my brain is. Also thinking more about it FAANG pays 300$ per line in production, so what do we really trying to achieve here, speed was never the issue.A great coder writes 10 production lines per day. Accuracy, architecture etc is the issue. You do that by building good solid fundamental blocks that make features additions easier over time and not slower
- EdgeNRoots 7mo ago[dead]
- aspenmartin 7mo agoI think this sounds like a true yet short sighted take. Keep in mind these features are immature but they exist to obtain a flywheel and corner the market. I don’t know why but people seem to consistently miss two points and their implications - performance is continuing to increase incredibly quickly, even if you rightfully don’t trust a particular evaluation. Scaling laws like chinchilla and RL scaling laws (both training and test time) - coding is a verifiable domain The second one is most important. Agent quality is NOT limited by human code in the training set, this code is simply used for efficiency: it gets you to a good starting point for RL. Claiming that things will not reach superhuman performance, INCLUDING all end to end tasks: understanding a vague business objective poorly articulated, architecting a system, building it out, testing it, maintaining it, fixing bugs, adding features, refactoring, etc. is what requires the burden of proof because we literally can predict performance (albeit it has a complicated relationship with benchmarks and real world performance). Yes definitely, error rates are too high so far for this to be totally trusted end to end but the error rates are improving consistently, and this is what explains the METR time horizon benchmark.
- dominotw 7mo agoI dont mean this as a shade but ppl who are not coders now seem to think "coding is now solved" and seem to be pushing absurd ideas like shipping software with slack messages. These ppl are often high up in the chain and have never done serious coding. Stripe is apparently pushing gazzaliion prs now from slack but their feature velocity has not changed. so what gives? how is that number of pr is now the primary metric of productivity and no one cares about what is being shipped or if we are shipping product faster. Its total madness right now. Everyone has lost their collective minds.
- rkomorn 7mo agoI ask myself the same question. I'm not seeing the apps, SaaS, and other tools I use getting better, with either more features or fewer bugs. Whatever is being shipped, as an end user, I'm just not seeing it.
- dominotw 7mo agocto and ceo are now feeling insane pressure to show how they are using ai but its not evident in output. So now they've resorted to blabbering publicly about prs, lines of code ect to save face. And ofcourse ppl giving them voice and platform have their own agendas that prevent them from asking "so what exactly have you shipped stripe from million pr/day". Its baffling to see these comments on hacknernews though. I guess you have to prove that you are not a luddite by making "ai forward" predictions and show that you "get it"
- duped 7mo agoI think a lot of SWE roles are really bullshit jobs (1) and these have been particularly susceptible to getting sniped with AI tools. (1) https://en.wikipedia.org/wiki/Bullshit_Jobs https://en.wikipedia.org/wiki/Bullshit_Jobs
- obastani 7mo agoI don't know if this is the future, but if it is, why bother building one version of the software for everyone? We can have agents build the website for each user exactly the way they want. That would be the most exciting possibility to come out of AI-generated software.
- bwestergard 7mo ago"why bother building one version of the software for everyone?" So one user's experience is relevant to another, so they can learn from one another?
- lancekey 7mo agoHa I just SPECed out a version of this. I have a simple static website that I want a few people to be able to update. So, we will give these 3 or 4 trusted users access to an on-site chat interface to request updates. Next, a dev environment is spun up, agent makes the changes, creates PR and sends branch preview link back to user. Sort of an agent driven CMS for non-technical stakeholders. Let’s see if it works.
- EastLondonCoder 7mo agoI think some type of tickets can be done like this but your trusted user assumption does a lot of work here. Now I don't see this getting better than that with the current architecture of LLMs, you can do all sorts of feedback mechanisms which helps but since LLMs are not conscious drift is unavoidable unless there is a human in the loop that understands and steers what's going on. But I do think even now with certain types of crud apps, things can be largely automated. And that's a fairly large part of our profession.
- fatata123 7mo ago[dead]
- eerikkivistik 7mo agoI know a company already operating like this in the fintech space. I foresee a front page headline about their demise in their future.