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.
I 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
I 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.
I 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.
I worry about the costs from an energy and environmental impact perspective. I love that AI tools make me more productive, but I don't like the side effects.
> I love everything about this direction except for the insane inference costs.
If this direction holds true, ROI cost is cheaper.
Instead of employing 4 people (Customer Support, PM, Eng, Marketing), you will have 3-5 agents and the whole ticket flow might cost you ~20$
But I hope we won't go this far, because when things fail every customer will be impacted, because there will be no one who understands the system to fix it
This 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.
No 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).
I 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.
Dive 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.
It'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.
In 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.
I 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.
100% 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.
I 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 inputs. 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.
cto 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"
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.
What 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.
I dunno if Rust async or native platform API's which have existed for years count as new patterns, but if you throw even a small wrench in the works they really struggle. But that's expected really when you look at what the technology is - it's kind of insane we've even gotten to this point with what amounts to fancy autocomplete.
Of course not all tickets are complex. Last week I had to fix a ticket which was to display the update date on a blog post next to the publish date. Perfect use case for AI to one shot.
i 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.
> 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.
All Chinese labs have to do to tank the US economy is to release open-weight models that can run on relatively cheap hardware before AI companies see returns.
Maybe that's why AI companies are looking to IPO so soon, gotta cash out and leave retail investors and retirement funds holding the bag.
i was under the impression that we were approaching performance bottlenecks both with consumer GPU architecture and with this application of transformer architecture. if my impression is incorrect, then i agree it is feasible for china to tank the US economy that way (unless something else does it first)
I think it just needs to be efficient or small enough for companies to deploy their own models on their hardware or cloud, for more inference providers to come out of the woodwork and compete on price, and/or for optimized models to run locally for users.
Regarding the latter, smaller models are really good for what they are (free) now, they'll run on a laptop's iGPU with LPDDR5/DDR5, and NPUs are getting there.
Even models that can fit in unified 64GB+ memory between CPU & iGPU aren't bad. Offloading to a real GPU is faster, but with the iGPU route you can buy cheaper SODIMM memory in larger quantities, still use it as unified memory, eventually use it with NPUs, all without using too much power or buying cards with expensive GDDR.
Qwen-3.5 locally is "good enough" for more than I expected, if that trend continues, I can see small deployable models eventually being viable & worthy competition, or at least being good enough that companies can run their own instead of exfiltrating their trade secrets to the worst people on the planet in real-time.
I 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.
these companies contribute to swathes of the west's financial infrastructure, not quite safety critical but critical enough, insane to involve automation here to this degree
Even in webdev it rots your codebase unchecked. Although it's incredibly useful for generating UI components, which makes me a very happy webslopper indeed.
im 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
> 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.
I 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.
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
interesting to see feature launches are coming via official website while usage restrictions are coming in with a team member's twitter account - https://x.com/trq212/status/2037254607001559305.
Funnily, Anthropic's pricing etc. why I'm using GLM-5 a bunch more outside of work. Definitely not Opus level, but surprisingly decent. Though I got lucky and got the Alibaba Coding Model lite plan, which is so cheap they got rid of it
And if you look closely at the usernames, you see that the same engineer from link 2 that said "nah it’s just a bonus 2x, it’s not that deep" (just two week ago) is now saying "we're going to throttle you during peak hours" (as predicted).
Yes, it was FUD, but ended up being correct. With the track record that Anthropic has (e.g. months long denial of dumbed down models last year, just to later confirm it as a "bug"), this just continues to erode trust, and such predictions are the result of that.
I remember when I tried to set something up with the ChatGPT equivalent like "notify me only if there are traffic disruptions in my route every morning at 8am" and it would notify me every morning even if there was no disruption.
“A programmer is going to the store and his wife tells him to buy a gallon of milk, and if there are eggs, buy a dozen. So the programmer goes shopping, does as she says, and returns home to show his wife what he bought. But she gets angry and asks, ‘Why’d you buy 13 gallons of milk?’ The programmer replies, ‘There were eggs!’”
This is because for some reason all agentic systems think that slapping cron on it is enough, but that completely ignores decades of knowledge about prospective memory. Take a look at https://theredbeard.io/blog/the-missing-memory-type/ for a write-up on exactly that.
This doesn't seem to hard to solve except for the ever so recurring llm output validation problem. If the true positive is rare you don't know if the earthquake alert system works until there's an earthquake.
I've recently switched from GitHub Copilot Pro to Claude Code Max (20x). While Claude is clearly superior in many aspects, one area where it falls short is remote/cloud agents.
Yesterday, I spent the entire day trying to set up "Claude on the web" for an Elixir project and eventually had to give up. Their network firewall kept killing Hex/rebar3 dependency resolution, even after I selected "full" network access.
The environment setup for "on the web" is just a bash script. And when something goes wrong, you only see the tail of the log. There is currently no way to view the full log for the setup script. It's really a pain to debug.
The Copilot equivalent to "Claude on the web" is "GitHub Copilot Coding Agents," which leverages GitHub Actions infrastructure and conventions (YAML files with defined steps). Despite some of the known flaws of GitHub Actions, it felt significantly more robust.
"Schedule task on the web" is based on the same infrastructure and conventions as "Claude on the web", so I'm afraid I'm gonna have the same troubles if I want to use this.
I do feel people will end up using this for things where a deterministic rule could be used - more effective, faster and cheaper. See this starting to happen at work...'We need AI to solve X....no you don't"
Maybe. The problem of "execute task on a cron" is something I've noticed the industry seems to refuse to solve in general, as if intentionally denying this capability for regular people. Even without AI, it's the most basic block of automation, and is always mysteriously absent from programs and frameworks (at least at the basic level). AI only makes it more useful on "then" side, but reliable cron on "if" side is already useful.
I don’t recall if IFTTT had/has a basic cron or not, but it sure has/had put a lot of basic automations in the hands of the general public. Same for Apple Shortcuts, to some extent, or Zapier.
This is a larger topic that's worthy of a comparably large rant, which I really don't want to do right now, but to keep it short, in my subjective view:
- IFTTT was great when it started; at some point, it became... weird, in a "I don't even know what's going on on my screen, is this a poster or an app" kind of way.
- Zapier is an unpenetrable mess, evidently targets marketers and other business users; discovery is hard, and even though it seems like it has everything, it - like all tools in this space - is always missing the one feature you actually need.
- Yahoo Pipes, I heard they were great, but I only learned about them after they shut down.
- Apple Shortcuts - not sure what you can do with those, but over the years of reading about them in HN comments, I think they may be the exception here, in being both targeting regular users and actually useful.
- Samsung Modes and Routines - only recently becoming remotely useful, so that's nice, even if vendor-restricted.
- Tasker - an Android tool that actually manages to offer useful automation, despite the entire platform/OS and app ecosystem trying its best to prevent it. Which is great, if your main computer is a phone. It sucks in a world of cloud/SaaS, because it creates a silly situation where e.g. I could nicely automate some things involving e-mail and calendars from Tasker + FairEmail, but... well my mailboxes and calendars lives in the cloud so some of that would conflict with use of vendor (Fastmail) webapp or any other tool.
Or, in short: we need Tasker but for web (and without some of the legacy baggage around UI and variable handling).
The sorry state of automation is not entirely, or even mostly, the fault of the automation platforms. I may have issues with some UI and business choices some of these platforms made, but really, the main issue is that integrations are business deals and the integrated sides quickly learned to provide only a limited set of features - never enough to allow users to actually automate use of some product. There's always some features missing. You can read data but not write it. You can read files and create new files but not edit or delete them. You can add new tasks but can't get a list of existing ones. Etc.
It's another reason LLMs are such a great thing to happen - they make it easy (for now) to force interoperability between parties that desperately want to prevent it. After all, worst case, I can have the LLM operate the vendor site through a browser, pretending to be a human. Not very reliable, but much better than nothing at all.
Agree. How would you solve this in general, what would be the ingredients? People use things like zapier, n8n, node-red to achieve this today but in many cases are overkill.
Honestly, you just need cron (and Ruby/Python/bash/whatever) on an EC2. It's not very fashionable, but it works, will continue to work forever, and costs hardly anything.
I'd start with solving the UX issues, specifically expectations and UI around scheduling jobs.
Expectations - the functionality of "do X on a timer" needs to be offered to users as a proper end-user feature[0], not treated as a sysadmin feature (Windows, Linux) or not provided at all (Android). People start seeing it on their own devices, they'll start using it, then expecting it, and the web will adjust too[1].
UI - somehow this escapes every existing solution, from `cron` through Windows timers to any web "on timer" event trigger in any platform ever. There already exists a very powerful UI paradigm for managing recurring tasks, that most normies know how to use, because they're already using it daily at work and privately: a calendar. Yes, that thing where we can set and manage recurring events, and see them at a glance, in context of everything else that's going on in our lives.
--
<rant>
I know those are hard problems, but are hard mostly because everybody wants to be the fucking one platform owning users and the universe. This self-inflicted sickness in computing is precisely why people will jump at AI solutions for this. Why I too will jump on this: because it's easier than dealing with all the systems and platforms that don't want to cooperate.
After all, at this point, the easiest solution to the problems I listed above, and several others in this space, would be to get an AI agent that I can:
1) Run on a cron every 30 minutes or so (events are too complicated);
2) Give it read (at minimum) access to my calendar and todo lists (the ones I use, but I'm willing to compromise here);
3) Give it access to other useful tools
Which I guess brings us to the actual root problem here. "Run tasks on a cron" and "run tasks on trigger" are basically just another way of saying unattended/non-interactive usage. That is what is constantly being denied end users.
This is also the key to enabling most value of AI tools, too, and people understand it very well (see the popularity of that Open Claw thing as the most recent example), but the industry also lives in denial, believing that "lethal trifecta" is a thing that can be solved.
</rant>
--
[0] - This extends to event triggers ("if X happens, then") automation, and end-user automation in all of every-day life. I mean, it's beyond ridiculous that the only things normal people are allowed to run automatically are dishwasher, and a laundry machine (and in the previous era, VCRs).
[1] - As a side effect, it would quickly debullshitify "smart home" / "internet of things" spaces a lot. The whole consumer side of the market revolves around selling people basic automation capabilities - except vendor-locked, and without the most useful parts.
> See this starting to happen at work...'We need AI to solve X....no you don't"
Same. Sometimes it is just people overeager to play with new toys, but in our case there is a push from the top & outside too: we are in the process of being subsumed into a larger company (completion due on April the 1st, unless the whole thing is an elaborate joke!) and there is apparently a push from the investors there to use "AI" more in order to not "get left behind the competition".
Its self perpetuating, I was talking to CEO of a Series A level B2B SaaS company here in UK recently. Most of the propspects his sales team are hitting are re-allocating their wallets to only looking for products that use AI on back of senior management pushing them to do so.
This company already does some pretty cool stuff with statistics for forecasting but now they are pivoting their roadmap to bake in GenAI into their offering over some other features that would be more valuable to their clients.
I'd say that's almost fine if they can start expressing intent correctly and thinking what good looks like. They (or some automated thing if you're building "think for them" type of products instead of "give them tools and teach them to think how to use them") can then freeze determism more and more were useful
I wrote this to help people (not just Devs) reason about agent skills
The problem I'd think, for the average user, would be writing the 'then' part of any deterministic rule — that would require coding, or at least some kind of automation script (visual or otherwise) that's basically coding in a trench coat, which for most people is still a barrier to entry and annoying. I think that's why they'd use AI tbh — they can just describe what they want in natural language with AI.
I feel this would be more useful for tasks like "Check website X to see if there are any great deals today". Specifically, tasks that are loosely defined and require some form of intuition.
People are loading huge interpreted environments for stuff that can be done from the command line. Run computations on complex objects where it could be a single machine instruction etc. The trend has been around for a long time.
Standard pendulum swing. Most people want to disengage their thinking circuits most of the time, so problems can't be evaluated one by one. There is no such thing as "this is a good solution for some problems". It can only be "this is a good solution for all problems". When the pendulum swings this far, this hard, it will swing all the way back eventually.
Looks like I'm limited to only 3 cloud scheduled tasks. And I'm on the Max 20x plan, too :(
"Your plan gets 3 daily cloud scheduled sessions. Disable or delete an existing schedule to continue."
But otherwise, this looks really cool. I've tried using local scheduled tasks in both Claude Code Desktop and the Codex desktop app, and very quickly got annoyed with permissions prompts, so it'll be nice to be able to run scheduled tasks in the cloud sandbox.
Here are the three tasks I'll be trying:
Every Monday morning: Run `pnpm audit` and research any security issues to see if they might affect our project. Run `pnpm outdated` and research into any packages with minor or major upgrades available. Also research if packages have been abandoned or haven't been updated in a long time, and see if there are new alternatives that are recommended instead. Put together a brief report highlighting your findings and recommendations.
Every weekday morning: Take at Sentry errors, logs, and metrics for the past few days. See if there's any new issues that have popped up, and investigate them. Take a look at logs and metrics, and see if anything seems out of the ordinary, and investigate as appropriate. Put together a report summarizing any findings.
Every weekday morning: Please look at the commits on the `develop` branch from the previous day, look carefully at each commit, and see if there are any newly introduced bugs, sloppy code, missed functionality, poor security, missing documentation, etc. If a commit references GitHub issues, look up the issue, and review the issue to see if the commit correctly implements the ticket (fully or partially). Also do a sweep through the codebase, looking for low-hanging fruit that might be good tasks to recommend delegating to an AI agent: obvious bugs, poor or incorrect documentation, TODO comments, messy code, small improvements, etc.
I ran all of these as one-off tasks just now, and they put together useful reports; it'll be nice getting these on a daily/weekly basis. Claude Code has a Sentry connector that works in their cloud/web environment. That's cool; it accurately identified an issue I've been working on this week.
I might eventually try having these tasks open issues or even automatically address issues and open PRs, but we'll start with just reports for now.
i'm missing something basic here .... what does it actually do? It executes a prompt against a git repository. Fine - but then what? Where does the output go? How does it actually persist whatever the outcome of this prompt is?
Is this assuming you give it git commit permission and it just does that? Or it acts through MCP tools you enable?
i'd say it's more like intentionally choosing to use naive string interpolation for SQL queries than a trusted library's parameter substitution. Both work.
There is no "parameter substitution" equivalent possible. Prompt injection isn't like SQL injection, it has no technical solution (that isn't AGI-complete).
Prompt injection is "social engineering" but applied to LLMs. It's not a bug, it's fundamentally just a facet of its (LLM/human) general nature. Mitigations can be placed, at the cost of generality/utility of the system.
MCP itself is just an API. Unless the MCP server had a hidden LLM for some reason, it's still piece of regular, deterministic software.
The security risk here is the LLM, not the MCP, and you cannot secure the LLM in such system any more you can secure user - unless you put that LLM there and own it, at which point it becomes a question of whether it should've been there in the first place (and the answer might very well be "yes").
Better idea. Watch online feedback on this feature. Then implement things users want. Go niche. Join the forum and help them use Claude to its limits. Then be the next step for power users.
Welcome to Amazon playbook replayed again, most useful, profitable and popular use-cases will implemented by platform - and they will do it ruthlessly and quickly as money needs to be recouped.
That feature was silent launched about week ago for me.
I use it to:
- perform review of latest changes of code to update my documentation (security policies, user documentation etc.)
- perform review to latest changes of code, triage them, deduplicate and improve code - I review them, close them with comments for over-engoneering / add review for auto-fix
- perform review of open GitHub issue with label, select the one with highest impact, comment with rationale, implement it and make pull request - I wake up and I have a few pull request to fix issues that I can approve /finish in existing Claude Code thread
I want also use it to:
- review recent Sentry issues, make GitHub issues for the one with highest priority, make pull request with proposed fix - I can just wake up and see that some crash is ready to be resolved
Limit of 3 scheduled jobs is pretty impactful, but playing with it give me a nice idea on how I can reduce my manual work.
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
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.
If this direction holds true, ROI cost is cheaper.
Instead of employing 4 people (Customer Support, PM, Eng, Marketing), you will have 3-5 agents and the whole ticket flow might cost you ~20$
But I hope we won't go this far, because when things fail every customer will be impacted, because there will be no one who understands the system to fix it
But this is just not true, otherwise companies that can already afford such high prices would have already outpaced their competitors.
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).
Sadly enough I have not seen this happening in a long time.
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.
It's the "robots will just build/repair themselves" trope but the robots are agents
Oh wait. That's already here and is working fine.
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.
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.
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"
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.
I still can't get a good mental model for when these things will work well and when they won't. Really does feel like gambling...
All Chinese labs have to do to tank the US economy is to release open-weight models that can run on relatively cheap hardware before AI companies see returns.
Maybe that's why AI companies are looking to IPO so soon, gotta cash out and leave retail investors and retirement funds holding the bag.
Regarding the latter, smaller models are really good for what they are (free) now, they'll run on a laptop's iGPU with LPDDR5/DDR5, and NPUs are getting there.
Even models that can fit in unified 64GB+ memory between CPU & iGPU aren't bad. Offloading to a real GPU is faster, but with the iGPU route you can buy cheaper SODIMM memory in larger quantities, still use it as unified memory, eventually use it with NPUs, all without using too much power or buying cards with expensive GDDR.
Qwen-3.5 locally is "good enough" for more than I expected, if that trend continues, I can see small deployable models eventually being viable & worthy competition, or at least being good enough that companies can run their own instead of exfiltrating their trade secrets to the worst people on the planet in real-time.
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.
(That's basically what A/B testing is about.)
But the entire SWE apparatus can be handled.
Automated A/B testing of the feature. Progressive exposure deployment of changes, you name it.
At least in my company we are close to that flywheel.
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.
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.
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.
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
also, someone rightly predicted this rugpull coming in when they announced 2x usage - https://x.com/Pranit/status/2033043924294439147
The same as charging a different toll price on the road depending on the time of day.
Someone spread FUD on the internet, incorrectly, and now others are spreading it without verifying.
Yes, it was FUD, but ended up being correct. With the track record that Anthropic has (e.g. months long denial of dumbed down models last year, just to later confirm it as a "bug"), this just continues to erode trust, and such predictions are the result of that.
You need to write a clearer prompt.
You'll define exactly what good looks like.
Yesterday, I spent the entire day trying to set up "Claude on the web" for an Elixir project and eventually had to give up. Their network firewall kept killing Hex/rebar3 dependency resolution, even after I selected "full" network access.
The environment setup for "on the web" is just a bash script. And when something goes wrong, you only see the tail of the log. There is currently no way to view the full log for the setup script. It's really a pain to debug.
The Copilot equivalent to "Claude on the web" is "GitHub Copilot Coding Agents," which leverages GitHub Actions infrastructure and conventions (YAML files with defined steps). Despite some of the known flaws of GitHub Actions, it felt significantly more robust.
"Schedule task on the web" is based on the same infrastructure and conventions as "Claude on the web", so I'm afraid I'm gonna have the same troubles if I want to use this.
- IFTTT was great when it started; at some point, it became... weird, in a "I don't even know what's going on on my screen, is this a poster or an app" kind of way.
- Zapier is an unpenetrable mess, evidently targets marketers and other business users; discovery is hard, and even though it seems like it has everything, it - like all tools in this space - is always missing the one feature you actually need.
- Yahoo Pipes, I heard they were great, but I only learned about them after they shut down.
- Apple Shortcuts - not sure what you can do with those, but over the years of reading about them in HN comments, I think they may be the exception here, in being both targeting regular users and actually useful.
- Samsung Modes and Routines - only recently becoming remotely useful, so that's nice, even if vendor-restricted.
- Tasker - an Android tool that actually manages to offer useful automation, despite the entire platform/OS and app ecosystem trying its best to prevent it. Which is great, if your main computer is a phone. It sucks in a world of cloud/SaaS, because it creates a silly situation where e.g. I could nicely automate some things involving e-mail and calendars from Tasker + FairEmail, but... well my mailboxes and calendars lives in the cloud so some of that would conflict with use of vendor (Fastmail) webapp or any other tool.
Or, in short: we need Tasker but for web (and without some of the legacy baggage around UI and variable handling).
The sorry state of automation is not entirely, or even mostly, the fault of the automation platforms. I may have issues with some UI and business choices some of these platforms made, but really, the main issue is that integrations are business deals and the integrated sides quickly learned to provide only a limited set of features - never enough to allow users to actually automate use of some product. There's always some features missing. You can read data but not write it. You can read files and create new files but not edit or delete them. You can add new tasks but can't get a list of existing ones. Etc.
It's another reason LLMs are such a great thing to happen - they make it easy (for now) to force interoperability between parties that desperately want to prevent it. After all, worst case, I can have the LLM operate the vendor site through a browser, pretending to be a human. Not very reliable, but much better than nothing at all.
Expectations - the functionality of "do X on a timer" needs to be offered to users as a proper end-user feature[0], not treated as a sysadmin feature (Windows, Linux) or not provided at all (Android). People start seeing it on their own devices, they'll start using it, then expecting it, and the web will adjust too[1].
UI - somehow this escapes every existing solution, from `cron` through Windows timers to any web "on timer" event trigger in any platform ever. There already exists a very powerful UI paradigm for managing recurring tasks, that most normies know how to use, because they're already using it daily at work and privately: a calendar. Yes, that thing where we can set and manage recurring events, and see them at a glance, in context of everything else that's going on in our lives.
--
<rant>
I know those are hard problems, but are hard mostly because everybody wants to be the fucking one platform owning users and the universe. This self-inflicted sickness in computing is precisely why people will jump at AI solutions for this. Why I too will jump on this: because it's easier than dealing with all the systems and platforms that don't want to cooperate.
After all, at this point, the easiest solution to the problems I listed above, and several others in this space, would be to get an AI agent that I can:
1) Run on a cron every 30 minutes or so (events are too complicated);
2) Give it read (at minimum) access to my calendar and todo lists (the ones I use, but I'm willing to compromise here);
3) Give it access to other useful tools
Which I guess brings us to the actual root problem here. "Run tasks on a cron" and "run tasks on trigger" are basically just another way of saying unattended/non-interactive usage. That is what is constantly being denied end users.
This is also the key to enabling most value of AI tools, too, and people understand it very well (see the popularity of that Open Claw thing as the most recent example), but the industry also lives in denial, believing that "lethal trifecta" is a thing that can be solved.
</rant>
--
[0] - This extends to event triggers ("if X happens, then") automation, and end-user automation in all of every-day life. I mean, it's beyond ridiculous that the only things normal people are allowed to run automatically are dishwasher, and a laundry machine (and in the previous era, VCRs).
[1] - As a side effect, it would quickly debullshitify "smart home" / "internet of things" spaces a lot. The whole consumer side of the market revolves around selling people basic automation capabilities - except vendor-locked, and without the most useful parts.
Same. Sometimes it is just people overeager to play with new toys, but in our case there is a push from the top & outside too: we are in the process of being subsumed into a larger company (completion due on April the 1st, unless the whole thing is an elaborate joke!) and there is apparently a push from the investors there to use "AI" more in order to not "get left behind the competition".
This company already does some pretty cool stuff with statistics for forecasting but now they are pivoting their roadmap to bake in GenAI into their offering over some other features that would be more valuable to their clients.
I wrote this to help people (not just Devs) reason about agent skills
https://alexhans.github.io/posts/series/evals/building-agent...
And this one to address the drift of non determism (but depending on the audience it might not resonate as much)
https://alexhans.github.io/posts/series/evals/error-compound...
"Your plan gets 3 daily cloud scheduled sessions. Disable or delete an existing schedule to continue."
But otherwise, this looks really cool. I've tried using local scheduled tasks in both Claude Code Desktop and the Codex desktop app, and very quickly got annoyed with permissions prompts, so it'll be nice to be able to run scheduled tasks in the cloud sandbox.
Here are the three tasks I'll be trying:
Every Monday morning: Run `pnpm audit` and research any security issues to see if they might affect our project. Run `pnpm outdated` and research into any packages with minor or major upgrades available. Also research if packages have been abandoned or haven't been updated in a long time, and see if there are new alternatives that are recommended instead. Put together a brief report highlighting your findings and recommendations.
Every weekday morning: Take at Sentry errors, logs, and metrics for the past few days. See if there's any new issues that have popped up, and investigate them. Take a look at logs and metrics, and see if anything seems out of the ordinary, and investigate as appropriate. Put together a report summarizing any findings.
Every weekday morning: Please look at the commits on the `develop` branch from the previous day, look carefully at each commit, and see if there are any newly introduced bugs, sloppy code, missed functionality, poor security, missing documentation, etc. If a commit references GitHub issues, look up the issue, and review the issue to see if the commit correctly implements the ticket (fully or partially). Also do a sweep through the codebase, looking for low-hanging fruit that might be good tasks to recommend delegating to an AI agent: obvious bugs, poor or incorrect documentation, TODO comments, messy code, small improvements, etc.
I ran all of these as one-off tasks just now, and they put together useful reports; it'll be nice getting these on a daily/weekly basis. Claude Code has a Sentry connector that works in their cloud/web environment. That's cool; it accurately identified an issue I've been working on this week.
I might eventually try having these tasks open issues or even automatically address issues and open PRs, but we'll start with just reports for now.
Seems trivial.
But you can set up a claude -p call via a cronjob without too much hassle and that can use subscriptions.
https://grok.com/tasks
Grok has had this feature for some time now. I was wondering why others haven't done it yet.
This feature increases user stickiness. They give 10 concurrent tasks free.
I have had to extract specific news first thing in the morning across multiple sources.
Is this assuming you give it git commit permission and it just does that? Or it acts through MCP tools you enable?
It's a bit like asking if "an API" was a critical link in some cybersec incident. Yes, it probably was, and?
Prompt injection is "social engineering" but applied to LLMs. It's not a bug, it's fundamentally just a facet of its (LLM/human) general nature. Mitigations can be placed, at the cost of generality/utility of the system.
Fair enough but then that means that MCP is not "a bit like asking if "an API" was a critical link in some cybersec incident"
Because I can secure an API but I can't secure the the "(LLM/human) general nature."
The security risk here is the LLM, not the MCP, and you cannot secure the LLM in such system any more you can secure user - unless you put that LLM there and own it, at which point it becomes a question of whether it should've been there in the first place (and the answer might very well be "yes").
It doesnt allow egress curl, apart from few hardcoded domains.
I have created Cronbox in the cloud which has a better utility than above. Did a "Show HN: Cronbox – Schedule AI Agents" a few days back.
https://cronbox.sh
and a pelican riding a bicycle job -
https://cronbox.sh/jobs/pelican-rides-a-bicycle?variant=term...
I use it to:
- perform review of latest changes of code to update my documentation (security policies, user documentation etc.)
- perform review to latest changes of code, triage them, deduplicate and improve code - I review them, close them with comments for over-engoneering / add review for auto-fix
- perform review of open GitHub issue with label, select the one with highest impact, comment with rationale, implement it and make pull request - I wake up and I have a few pull request to fix issues that I can approve /finish in existing Claude Code thread
I want also use it to: - review recent Sentry issues, make GitHub issues for the one with highest priority, make pull request with proposed fix - I can just wake up and see that some crash is ready to be resolved
Limit of 3 scheduled jobs is pretty impactful, but playing with it give me a nice idea on how I can reduce my manual work.