It seems like the author is overindexing on useful and underindexing on wonderful. He clearly had fun building these products — and in hindsight is disavowing them because they didn’t generate income? An oddly capitalist view of play.

Some really good points on how these bots are incentivized to reward mindless engagement though and the bit about voice transcription not producing useful writing landed. When the barrier to release drops the quality naturally does too.

I think the next stage of us learning to harness these tools is us building the ability to reach for excellence even when we are not required to. To accustom ourselves to going beyond minimum viable bar for functionality and to reach for qualities or standards beyond that which the AI brings to the table unaided. A new kind of engineering rigor.

I move that this was always true and is now only far more so.

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xendo 2 hours ago | parent | next [-]

In the old days, producing all those things would be tremendous learning opportunity. Today it's a pure waste, not producing income is not a problem, not producing anything is.

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elliotbnvl 2 hours ago | parent | next [-]

If it wasn’t a learning opportunity to build those things, that was the waste. You can learn from an AI far more easily than from a book — only now it’s far more easy not to and many people unconsciously choose that route.

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naasking 2 hours ago | parent | prev [-]

Learning how to use AI effectively was the learning opportunity here, what was created is completely incidental. You're effectively obsessing over programming languages obscuring the machine code that actually runs. "Imagine all the missed learning opportunity of digging into all that machine code!"

Sure, but also, who cares? The machine code is completely incidental for most purposes.

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xendo 2 hours ago | parent [-]

I work with AI everyday, despite what many people suggest there is so little to learn. After a couple of hours you are good to go. You don't even need gstack.

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elliotbnvl 2 hours ago | parent | next [-]

This is patently false. I work with and on AI every day at multiple levels of the stack, and every day I'm learning massive new swathes of information. I'm honestly shocked how deep the field goes and how much more effective you can be with time. The floor is falling and the ceiling is rising and the gap between them is widening every day.

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xendo an hour ago | parent | next [-]

Maybe it depends on the task, but the biggest productivity gains are from boiler plate generation, and there it's as easy as "generate me the boiler plate". Even if you can learn some very specific workflows today they would be model dependent and mostly obsolete within a month or two.

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skydhash an hour ago | parent | prev [-]

That would be more convincing if you put up two or more examples of what is there to learn.

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elliotbnvl an hour ago | parent | next [-]

Go off and run a comparison of Qwen 3.6 27B and GLM 5.1 GGUF (https://huggingface.co/ubergarm/GLM-5.1-GGUF) at IQ2_KL 261.988 GiB (2.985 BPW) and let me know if you learn anything.

Or maybe just compare Hermes vs OpenClaw for long-horizon personal agentic tasks. Which one performs better in offline inference personal finance analysis tasks?

Or read up on how the `/code-review` workflow works in Opus 4.8 and give me a guess as to how long it'll take Codex to implement it and which tool would be more appropriate for your engineering team (don't forget to include enterprise API token costs in workflows – it can spin up 100 agents in thirty seconds).

If you can figure out how to secure agents with simultaneous access to personal data and the internet to run unsupervised while avoiding the lethal trifecta (Willison, 2025) let me know.

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skydhash an hour ago | parent [-]

> Go off and run a comparison of Qwen 3.6 27B and GLM 5.1 GGUF

You may as well ask to run a comparison between gnu libc 2.42 and musl 1.2.5.

> Hermes vs OpenClaw for long-horizon personal agentic tasks. Which one performs better in offline inference personal finance analysis tasks

What are those tasks? This and the paragraph just after seems very much like a XY problem where all the energy is focusing on resolving the Y, not the X. It's like discussing how we can reach the moon using cannons.

> If you can figure out how to secure agents with simultaneous access to personal data and the internet to run unsupervised while avoiding the lethal trifecta (Willison, 2025) let me know.

If you can figure out how to run user submitted JavaScript inside a webpage with access to the internet and other user personal data, you will have your answer. There's a reason we escape user input before rendering it within the browser. The browser is an executing agent and it doesn't differentiate between your markup and other data you choose to embed in it. Same things happens with the processor if you choose to mix input data with executable code.

	▲	elliotbnvl 24 minutes ago \| parent [-]
		> You may as well ask to run a comparison between gnu libc 2.42 and musl 1.2.5. Telling me you wouldn't learn anything from this? > What are those tasks? This and the paragraph just after seems very much like a XY problem where all the energy is focusing on resolving the Y, not the X. It's like discussing how we can reach the moon using cannons. Or like how we can get from A to B without horses. It's a different world, one worth learning about. If these tasks don't at least arouse your interest, nothing I can say will help you.

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xendo an hour ago | parent | prev | next [-]

Even with examples it's still not convincing. I'm working on real products so I don't have time to waste comparing models that won't be relevant next month.

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naasking an hour ago | parent | prev [-]

Using AI effectively for long horizon tasks, like maintaining a large codebase, is a wide open field. No single AI is good at it autonomously. That means achieving the right balance of testing, formal specification of pre/post-conditions and invariants and manual review.

It's like having a naive but super knowledgeable junior developer starting under you. It's obvious you'd learn a lot in how to communicate, framing, specifications, and what kind of follow-up you'd need to do to ensure good results.

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naasking an hour ago | parent | prev [-]

Unless you just happen to work in a domain where the code you generate every day is very common in the AI training data, this isn't true.

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cardanome 2 hours ago | parent | prev | next [-]

> He clearly had fun building these products

The author did not build those products. AI did.

And I don't read anything indicated they had fun.

There is pleasure in making something yourself. There is learning. There is pride.

With generative AI you are just stealing other people's work. You are learning nothing. Anything could have generated the same projects. There was no skill involved, just enough disposable income to pay for tokens.

And yes some people develop some weird psychosis and think that they did the thing and not the AI. Everyone else is vibe coding but they got the special sauce, the perfect prompts. They are delusional.

	▲	elliotbnvl 2 hours ago \| parent [-]
		> And I don't read anything indicated they had fun. Maybe I'm just projecting. I enjoy making things. Maybe they do, maybe they don't. Sounds like you don't. > There is pleasure in making something yourself. There is learning. There is pride. You're speaking second person, when you should really be speaking first person. You enjoy making everything yourself, by hand. That is fine. It's also your personal perspective. > You are learning nothing. If you really aren't learning anything, you're doing AI wrong. > Everyone else is vibe coding but they got the special sauce, the perfect prompts. They are delusional. The delusion here is constructing a strawman out of the worst qualities you can imagine and berating that instead of actually looking at what other people are doing and trying to work out what they're thinking / how they feel. I can guarantee you that virtually nobody thinks they are the only person that can prompt a particular piece of software into existence. I know this post probably won't land with you, because I'm a little annoyed while I write it (if only because your post comes off emotional and annoyed as well) (and, sorry in advance), but I do encourage you to consider that perhaps there are other worldviews than the clearly embittered and deeply entrenched one you've espoused. And perhaps those other worldviews are more suited to surviving the oncoming storm.

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GuB-42 an hour ago | parent | prev [-]

It is not just about not generating income, it is also about learning very little.

I like to compare AI to GPS navigation. At least my experience of it. With GPS, I enter my destination, follow the direction and get to it. Problem is, I have no idea how I got there, I didn't pay attention to the landmarks, time and orientation, only to the arrow on the screen telling me where I should go, I learned nothing and should I go back, I will need the GPS again. And if the GPS is wrong, maybe because some road closed and it didn't get the update, too bad.

One may argue that using AI is a skill, yeah, sure, as much as following an arrow on a navigation screen is. It is nothing like actual development/navigation.

Personally, I have a terrible sense of direction, so I fully embrace GPS, and importantly, it isn't my job, no one pays me to navigate (they would want their money back anyways :)). But programming is my job, and I believe that if I want to keep it, I have to offer more than mindless vibe coding, that is a part that anyone can do, and practicing is the way to go. And even without the capitalist view, passion is about doing things the hard way because it is more rewarding, the easy way is wonderful at first, but it gets boring quickly.

Now, more specifically for AI, I think it has its uses. It can be a good rapid prototyping tool. I used to write some quick and dirty scripts, but rewrote them completely in a different language, with proper design, once I realized it would grow in complexity and have to be maintained. The first part can be vibe coded, before scrapping everything and doing it over by hand before it starts to grow. It is not an AI problem, it is more like a language problem, plain english simply isn't great for telling computers what to do exactly, in fact it is not good enough for telling other people what to do precisely, that's why many professions evolved their own language, math, chemical diagrams, blueprints, music scores, etc... In fact, that why porting is what AI does best: it already has a precise description of what to do in a programming language, human programmers already did the hard work, the AI just has to translate into another programming language. In the best case scenario, someone even wrote unit test so the AI can go over if it screwed up.