[brainless]

A lot of those products are from big companies who seem to be struggling the most. Software does not solve bureaucracy. As an indie engineer, I have absolutely no doubt what I am doing myself.

But that change does not mean my products will become popular. That is a lot beyond software. Also, the tooling is just barely 1.5 years old and people are already asking for world-changing results. All the while totally ignoring what indies are saying.

[etdznots]

I kind of disagree that asking for world changing results is setting the bar too high, people’s claims about their personal experience are that the world changing results are already here, productivity has been multiplied and costs have been reduced by some factor, and AFAICT everyone is using these tools, with many reporting a similar experience.

The fact that people’s personal experience using the tool don’t cohere with the impact the tool has had in the world to me doesn’t suggest a slippage between how long it takes for productivity multipliers to be felt, it suggests that these tools might actually be better at delivering the perception to the user (and where relevant, the user’s manager) of increased productivity while real productivity gains are lower, or maybe zero, or maybe negative in some cases.

[brainless]

The key word is "already". I myself absolutely expect world changing results. But that will need time. I can only say what I know. My own experiments in building nocodo, a coding agent are 12-13 years old. Pre-LLM. I used template based code generation and related ideas. Template processors and what not. nocodo.com is with me since 2013 maybe, you can verify.

I am a software engineer, most of my experiments are on GitHub. I would not have ventured into building the UI framework before LLMs.

And this is what bothers me - people are not looking at the generated software. Indies like us are experimenting like crazy. I live far outside the tech scene, in a small Himalayan village. But I resonate so much with the experiments, the methods, harness engineering and so many other topics. I see the benefits in how ambitious my projects are becoming.

I teach an online course on coding agents as a co-mentor. 600 young professionals join each month for a 2 week course. The joy of people, who did not know much technology, when they create a simple project management software by just typing English does not lie.

We used to write software in a very different manner. The entire mental paradigm has shifted. Many of my friends and acquaintances are on the fence, still! Some are internally giving up - unable to cope with this change. But the change is happening - the tooling is only going to get better.

Give it time. That is the opinion I hold.

[rafterydj]

Interesting! I am curious to ask someone who has been working on no-code tools for so long: I've been reading about no code platforms from the 1990s, and how all of those ended up failing. The reason I've seen cited most is that the tools/platforms did not allow for enough variability to do the jobs that people wanted (without becoming a full programming language themselves). What do you think about that in the context of the past ten years, pre- and post-LLMs?

And what do you think about coding agents in the next few years? Will we see a variation in agent capabilities? E.g. a company makes and distributes a specialized coding agent for CSS, or even serving up a kind of library that's language-agnostic, since they seem to be best at translation rather than creation?

[brainless]

"company makes and distributes a specialized coding agent for CSS" - weird that you think this is a path because I think this is not as appreciated as it should be.

No-code has been in a poor state for many reasons. I agree that people want more generic software to be built and the platforms did not allow for enough variability. This is what being better enabled with LLMs.

I think coding agents, particularly Claude Code, makes people think that models are the key. Some people disagree. I disagree as well. I think small models with lots of deterministic code is the way. But this will not fill Anthropic's or OpenAI's pockets.

Using an LSP, for example is recent in coding agents. But if you think about it, we should have started with that. Most agents expect LLMs to know too broadly. I would instead create 40 (random number) agents - one for each language and part of the stack. This is why your CSS example is interesting. I create just an agent for the ORM related code in a Rust/Diesel based coding agent. It worked with a 4B parameter model!

People will fight over "worked" but basically what I did was create deterministic code generator for the ORM layer - schema, model and model accessor or mutator functions and then asked the tiny model to fill in the code with lots of code example straight from the official docs. It played well for many different kinds of prompts - all focused only on model related changes.

What if we create many layers of this - a higher level agent breaks human prompts into an intermediate language and then tech-stack focused agents write the code within deterministic tooling. Agents cannot read or write any file they want - they are specific to that part of the stack, linter, compiler, etc. kick in automatically.

You get the idea.

[dare944]

> it suggests that these tools might actually be better at delivering the perception to the user of increased productivity while real productivity gains are lower, or maybe zero, or maybe negative in some cases.

Or that the gains are in niche applications (like the GP's) that don't translate at scale.