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.
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.
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 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.
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.
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.
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.
> 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 Microsoft may not grant Anthropic access to your tickets unless you're on the relevant tier with the appropriate "NIH AI" surcharge, or Anthropic signs a deal with them.
1. AI does not suspend or supplant good old capitalism and the cult of profit maximization.
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 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.
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.
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.
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'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.
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.
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 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.
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 mean theoretically if there are many competitiors the costs of the product should generally drop because competition.
Sadly enough I have not seen this happening in a long time.
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.
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.
Art is and has always been about the creator.
We haven’t been inching closer to users writing a half-decent ticket in decades though.
Maybe the agent can ask the user clarifying questions. Even better if it could do it at the point of submission.
Tusted user like Jia Tan.
> 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 Microsoft may not grant Anthropic access to your tickets unless you're on the relevant tier with the appropriate "NIH AI" surcharge, or Anthropic signs a deal with them.
1. AI does not suspend or supplant good old capitalism and the cult of profit maximization.
Feedback 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
Yes. Next they'll want nanobots that build/repair themselves.
Oh wait. That's already here and is working fine.
Or perhaps we end up where all software is self evolving via agents… adjusting dynamically to meet the users needs.
The "user" being the one that's in charge of the AI, not the person on the receiving end.
Instead of having a trusted user, you can also do statistics on many users.
(That's basically what A/B testing is about.)
The 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.
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.
Not just complicated, but even simple ones if the current software is too “new” of a pattern they’ve never seen before or trained on.
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.
They could still eliminate relatively cheap hardware.
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.
Several 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.
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
"Trusted user" also can be an Agent.
What 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.
I 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.
Tickets 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.
Yes, 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.
The agents have even more limited capacity
At the moment, maybe. But it's growing.
Even so they would probably still benefit from intermediate organisational steps.
For a while, sure.
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.
Why doesn’t he merge them?
Must be nice working on simple stuff.
We 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
Then sets up telemetry and experiments with the change. Then if data looks good an agent ramps it up to more users or removes it.
Um, we are already there...
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.
also, someone rightly predicted this rugpull coming in when they announced 2x usage - https://x.com/Pranit/status/2033043924294439147
If you read the replies to the second, you’ll see an engineer on Claude Code at Anthropic saying that it is false.
Someone spread FUD on the internet, incorrectly, and now others are spreading it without verifying.
To me it makes perfect sense for them to encourage people to do this, rather than eg making things more expensive for everyone.
The same as charging a different toll price on the road depending on the time of day.
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.
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.
Me too. It doesn't have ability to alert only on true positive. I has to also alert on true negative. So dumb
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.
Claude is moving fast.
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.
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.
0 7 * * 1-5 ANTHROPIC_API_KEY=sk-... /path/to/claude-cron.sh /path/to/repo >> ~/claude-reports.md 2>&1
Seems trivial.
A trivial way to rack up hundreds of dollars in API costs, sure.
But you can set up a claude -p call via a cronjob without too much hassle and that can use subscriptions.
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?
MCP tools. We're doing some MCP bundling and giving it here, pretty cool stuff.
We use to do do automated sec audits weekly on the code base and post the result on slack
so is slack posting an MCP tool it has? or a skill it just knows?
In Claude it is a "connector" which is essentially an mcp tool.
This is a bit restrictive, doesn't take screenshots. So you can't "say take screenshots of my homepage and send it to me via email"
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...
What's the per-unit-time compute cost (independent of tokens)? Compute deadline etc.? They don't charge for the Cloud Environment https://code.claude.com/docs/en/claude-code-on-the-web#cloud... currently running?
Is only Github supported as a repository?
Is this free? I don’t see pricing info. I guess just a way to make you forget that you’re spending money on tokens?
You don't spend money on tokens. It is a subscription.
Here goes my project.
What were you working on?
This is powerful. Combined with MCPs, you can pretty much automate a ton of work.
Can you give some examples?