But from my social feed the impression was that it us taking over the world:)

I asked it because I am building something similar since some tome and I thought its over they were faster than me but as it appears there’s no real adoption yet. Maybe there will be some once they release it as part of ChatGPT but even then it looks like too early as actually few people are using the more advanced tools.

It’s definitely in very early stage.

If you have been working on a usecase similar to OpenClaw for sometime now I'd actually say you are in a great position to start raising now.

Being first to market is not a significant moat in most cases. Few people want to invest in the first company in a category - it's too risky. If there are a couple of other early players then the risk profile has been reduced.

That said, you NEED to concentrate on GTM - technology is commodified, distribution is not.

> It appears that so far the mainstream success in AI is limited to slop generation and even that is actually small number of people generating huge amounts of slop

The growth of AI slop has been exponential, but the application of agents for domain specific usecases has been decently successful.

The biggest reason you don't hear about it on HN is because domain-specific applications are not well known on HN, and most enterprises are not publicizing the fact that they are using these tools internally.

Furthermore, almost anyone who is shipping something with actual enterprise usage is under fairly onerous NDAs right now and every company has someone monitoring HN like a hawk.

On my app the tech is based on running agent generated code on JavaScriptCore to do things like OpenClaw, I’m wrapping the JS engine with the missing functionality like networking, file access and database access so I believe I will not have a problem with releasing it on Apple AppStore as I use their native stack. Then since this stack is also OS, I’m making a version that will run on Linux, the idea being users develops their solution on their device(iOS&Mac currently) see it working and and then deploys on a server with a tap of a button, so it keeps running.

You need to answer these questions in order to decide whether a Show HN makes sense versus a much more targeted launch.

If you do not know how to answer these questions you need to find a cofounder asap. Technology is commodified. GTM, sales, and packaging is what turns technology into products. Building and selling and fundraising as 1 person is a one-way ticket to burnout, which only makes you and your product less attractive.

I also highly recommend chatting with your network to understand common types of problems. Once you've identified a couple classes of problems and personas for whom your story resonates, then you can decide what approach to take.

Best of luck!

Thanks for the advice! I’m at a stage where I want to have such tool and see who else wants it. Not sure yet about it’s viability as a business and what is the exact market. Maybe I will find out by putting it into the wild and that’s why I consider to release it as a mobile app first.

But even with that persona, it should already answer your question whether posting on HN and producthunt should be a core part of your strategy. Not a lot of social media managers or compliance people around here. And even for crypto traders there are better places to pitch products to them

You need to narrow it down to a single and specific persona and business domain.

This is because it takes years to fully flesh out and productionize a workflow from scratch, so concentrating on a business domain you know intimately well helps you build that muscle, which you can then repeat if you are able to hit revenue metrics for a Series A/B.

Monitoring specific user accounts or keywords? Is this typically done by a social media reputation management service?

Shocking results, I say!

Most people are just "not that deep in there" as most people on HN.

A guy attached Claude to his socials, groundbreaking tech.

While they were deep in software development in general, no body of them read any of the essential/required daily industrial news (also not that one related to doing software development in sector ABC)

:-)

So no, even people somehow attached to a topic are not necessarily somehow deeper involved.

If one lets AI FOMO since the release of chatgpt drive them they'd be glued to their screen 24/7.

OAI wants to keep the hype train going. That is all. OpenClaw is just a project that attracted the interests of people messing about with LLMs. Which as a proportion of economically active people is.... tiny.

They brought him (Pete) over as he seems to have some way of thinking about LLMs in the form of a product. Will he have repeatable success on a large scale? Who knows. I doubt it personally.

1. People who only think of using AI in very specific scenarios. They don’t know when you use it outside of the obvious “to write code” situations and they don’t really use AI effectively and get deflated when AI outputs the occasional garbage. They think “isn’t AI supposed to be good at writing code?”

2. People who let AI do all the thinking. Sometimes they’ll use AI to do everything and you have to tell them to throw it all away because it makes no sense. These people also tend to dump analyses straight from AI into Slack because they lack the tools to verify if a given analysis is correct.

To be honest, I help them by teaching them fairly rigid workflows like “you can use AI if you are in this specific situation.” I think most people will only pick up tools effectively if there is a clear template. It’s basically on-the-job training.

As for the last example, for all the money being spent on this area, if someone is expected to perform a workflow based on the kind of question they're supposed to ask, that's a failure in the packaging and discoverability aspect of the product, the leaky abstraction only helps some of us who know why it's there.

I think this is the prior you should investigate. That may be what HN used to be. But it's been a long time since it has been an active reality. You can still see actual expert opinions on HN, but they are the minority more and more.

From personal experience, I've also noticed that some of the most toxic discourse and responses I've received on this platform are overwhelmingly from post-2022 users.

Really? Can you show any examples of someone claiming AI models cannot generate working code? I haven't seen anyone make that claim in years, even from the most skeptical critics.

I started working today on a project I hadn’t touched in a while but I now needed to as it was involved in an incident where I needed to address some shortcomings. I knew the fix I needed to do but I went about my usual AI assisted workflow because of course I’m lazy the last thing I want to do is interrupt my normal work to fix this stupid problem.

The AI doesn’t know anything about the full scope of all the things in my head about my company’s environment and the information I need to convey to it. I can give it a lot of instructions but it’s impossible to write out everything in my head across multiple systems.

The AI did write working code, but despite writing the code way faster than me, it made small but critical mistakes that I wouldn’t have made on my first draft.

For example, it just added in a command flag that I knew that it didn’t need, and it actually probably should have known it, too. Basically it changed a line of code that it didn’t need to touch.

It also didn’t realize that the curled URL was going to redirect so we needed an -L flag. Maybe it should have but my brain knew it already.

It also misinterpreted some changes in direction that a human never would have. It confused my local repository for the remote one because I originally thought I was going to set a mirror, but I changed plans and used a manual package upload to curl from. So it out the remote URL in some places where the local one should have been.

Finally, it seems to have just created some strange text gore while editing the readme where it deleted existing content for seemingly no reason other than some kind of readline snafu.

So yes it produced very fast great code that would have taken me way longer to do, but I had to go back and consume a very similar amount of time to fix so many things that I might as well have just done it manually.

But hey I’m glad my company is paying $XX/month for my lazy workday machine.

This is your problem: How should it know if you do not provide it?

Use Claude - in the pro version you can submit files for each project which are setting the context: This can be files, source code, SQL scripts, screenshots whatever - then the output will be based on your context given by providing these files.

If I was truly going to automate this one-time task I would have to give the AI access to my browser or an API token for the repository provider, so I’m either giving it dangerous modification capability via browser automation or I’m spending even more time setting up API access and trusting that it actually knows how to interact with the service via API calls.

My company doesn’t provide Claude, they give me GitHub Copilot Pro or whatever it’s called, and when I provided the website it needed to get the RPM files I was working with it didn’t actually do anything with it. It just wrote a readme file that told me what to do. Like I mention it also just eventually mistook the remote repository as my local internal repository.

And one of the specific commands it screwed up was in my existing script and was already correct, it just decided to change it for no discernible reason. I didn’t ask it to do anything related to that particular line.

With such a high error rate, I would be hesitant to actually integrate AI to other systems to try to achieve a more fully automated workflow.

They can do a tolerable job with super popular /simple things like web dev and Python. It really depends on what you're doing.

> Github Copilot has been great in getting that code coverage up marginally but ass otherwise.

When someone claims that AI can't generate working code I assume that it means consistently generating working code. We're talking about a tool. It has to work more often than not and on codebases that we tend to work with, i.e legacy code.

Personally I don't claim that because I'm using everyday to generate working code.

It takes time to get an intuition for the kinds of problems they've seen in pre-training, what environments it faced in RL, and what kind of bizarre biases and blindspots it has. Learning to google was hard, learning to use other peoples libraries was hard, and its on par with those skills at least.

If there is a well known design pattern you know, thats a great thing to shout out. Knowing what to add to the context takes time and taste. If you are asking for pieces so large that you can't trust them, ask for smaller pieces and their composition. Its a force multiplier, and your taste for abstractions as a programmer is one of the factors.

In early usenet/forum days, the XY problem described users asking for implementation details of their X solution to Y problem, rather than asking how to solve Y. In llm prompting, people fall into the opposite. They have an X implementation they want to see, and rather than ask for it, they describe the Y problem and expect the LLM to arrive at the same X solution. Just ask for the implementation you want.

Asking bots to ask bots seems to be another skill as well.

If not, you don't know how to use it efficiently.

A large part of using AI efficiently is to significantly lower that review burden by having it do far more of the verification and cleanup itself before you even look at it.

- PRD and spec fulfillment review

- code review + correction loops

- security review + corrections

- addl. test coverage and tidying

- addl. type checks and tidying

- addl. lint checks and tidying

- maybe more I haven't listed

And these are run after each commit, so you can only imagine the costs per engineer doing this 10, 20, 50+ times per day depending on how much work they're knocking out.

The question is what your time is worth for the company, and which tasks costs less to have an agent automate than having you do.

The other constraint is, for those who are being laid off (maybe because of cost reduction to support an AI budget for a smaller team to use), engineers wanting to expand their skill set and practice these levels of usage + efficiency are effectively unable to with their own funding, making it more difficult to find employment as expectations heighten.

Prior to AI entering the fray, software development was largely free for everyone, allowing anyone with enough time and motivation to build the skills towards gainful employment. As AI becomes more prevalent and expectations around how it's used become higher, fewer and fewer applicants will be able to claim they have the experience necessary because it was out of reach due to costs.

Right now you need to be Uncle Moneybags to do this in your personal life.

If you're lucky, your employer is footing the bill but otherwise... Ugh. It's like converting your app running perfectly fine on a cheap VPS to AWS Lambda. In theory, it's fine but in reality the next bill you get could make you faint.

I'm unconvinced AI reviewing AI is the answer here, because all LLMs have the same flaws. To me, the harness/guard rails for AI should be different technologies that work differently and in a more formal sense. IE, static code analysis, linters, tests, etc.

(Linting has actually been, by far, the BEST code quality enforcers for the agents I've run so far, and it's a lot cheaper and more configurable than running more agents.)

My current project that I started this weekend is a rust client server game with the client compiled into web assembly.

I do these projects without reading the code at all as a way to gauge what I can possibly do with AI without reading code, purely operating as a PM with technical intuition and architectural opinions.

So far Opus 4.6 has been capable of building it all out. I have to catch issues and I have asked it for refactoring analysis to see if it could optimize the file structure/components, but I haven't read the code at all.

At work I certainly read all the code. But would recommend people try to build something non trivial without looking at the code. It does take skill though, so maybe start small and build up the intuition on how they have issues, etc. I think you'll be surprised how much your technical intuition can scale even when you are not looking at the code.

I have friends in security on major platforms who are impressed by the security review of the SOT models. Certainly better than the average bootstrapped founder.

I'm building to play with my friends online.

They vibe-coded a complete rewrite of their products in a few months without any human review. Hundreds of thousands LOC. I feel sorry for the remaining engineers having to learn everything they just generated, and are now having customers use.

I agree. I'm constantly correcting the code it generates. But then, I do the same for humans when I review their PRs, and the LLM generated the code in a 100th of the time (or whatever figure you prefer).

Last time he said: "yes yes I know about ChatGPT, but I do not use it at work or home."

Therefore, most people wont even know about Gemini, Grok or even Claude.

I am completely flooded with comments and stories about how great LLMs are at coding. I am curious to see how you get a different picture than this? Can you point me to a thread or a story that supports your view? At the moment, individuals thinking AI cannot generate working code seem almost inexistent to me.

Folks like this have never used AI inside of an IDE or one of the CLI AI tools. Without that perspective, AI seems mostly like a gimmick.

What makes you think people this will ever change? Have you seen how well people know their already existing tools?