Vibecoding #2

If anything in my small circle the promise is waning a bit, in that even the best models on the planet are still kinda shitty for big project work. I work as a game dev and have found agents to only be mildly useful to do more of what I've already laid out, I only pay for the $100 annual plan with jetbrains and that's plenty. I haven't worked at a big business in a while, but my ex-coworkers are basically the same. a friend only uses chat now because the agents were "entirely useless" for what he was doing.

I'm sure someone is getting use out of them making the 10 billionth node.js express API, but not anyone I know.

Especially considering that these 200$ subscriptions are just the start because those companies are still mostly operating at a loss.

It's either going to be higher fees or Ads pushed into the responses. Last I need is my code sprinkled with Ads.

I had ChatGPT Codex GPT5.2 high reasoning running on my side project for multiple hours the last nights. It created a server deployment for QA and PROD + client builds. It waited for the builds to complete, got the logs from Github Actions and fixed problems. Only after 4 days of this (around 2-4 hours) active coding I reached the weekly limit for the ChatGPT Plus Plan (23€). Far better value so far.

To be fully honest, it fucked up one flyway script. I have to fix this now my self :D. Will write a note in the Agent.md to never alter existing scripts. But the work otherwise was quite solid and now my server is properly deployed. If I would switch between High reasoning for Planing and Middle reasoning for coding, I would get even more usage.

I think for me it's a case of fear of being left behind rather than missing out.

I've been a developer for over 20 years, and the last six months has blown me away with how different everything feels.

This isn't like JQuery hitting the scene, PHP going OO or one of the many "this is a game changer" experiences if I've had in my career before.

This is something else entirely.

No, most programmers I know outside of my own work (friends, family, and old college pals) don't use AI at all. They just don't care.

I personally use Cursor at work and enjoy it quite a bit, but I think the author is maybe at the tail end of _their circle's_ adoption, but not the industry's.

(“Scrape kindle highlights from the kindle webpage, store it in a database, and serve it daily through an email digest”).

No success so far in getting it to do so without a lot of handholding and manually updating the web scraping logic.

It’s become something of a litmus test for me.

So, maybe there is some FOMO but in my experience it’s a lot of snake oil. Also at work, I manage a team of engineers and like 2 out of 12 clearly submit AI generated code. Others stopped using it, or just do a lot more wrangling of the output.

If you rephrase the question as "Are most engineers already using AI?" -- because it transcends the specific modality (agents vs chat vs autocomplete) and $200 subscriptions (because so many tools are available for free) -- signs point to "yes."

Adoption seems to be all the way upto 85% - 90% in 2025, but there is a lot of variance in the frequency of use:

https://dora.dev/research/2025/

https://survey.stackoverflow.co/2025/

https://newsletter.pragmaticengineer.com/p/the-pragmatic-eng...

If there is FOMO, I'm not sure it's "weird."

The $20/month subscriptions go a long way if you're using the LLM as an assistant. Having a developer in the loop to direct, review, and write some of the code is much more token efficient than trying to brute force it by having the LLM try things and rewrite until it looks like what you want.

If you jump to the other end of the spectrum and want to be in the loop as little as possible, the $100/$200 subscriptions start to become necessary.

My primary LLM use case is as a hyper-advanced search. I send the agent off to find specific parts of a big codebase I'm looking for and summarize how it's connected. I can hit the $20/month windowed limits from time to time on big codebases, but usually it's sufficient.

In those days already the attitude with regard to professional work was that if you aren't constantly advancing in your industry, you are falling behind.

(I'm not the author) The easiest way to charge for this kind of software is to make it SaaS, and I think that's pretty gross, especially for a CLI tool.

> I'm skeptical and I may just release it all as OSS

It doesn't have to be one or the other: you could sell the software under libre[1] terms, for example.

[1]: https://en.wikipedia.org/wiki/Free_software

sudo shutdown +15 (or other amount of minutes)

when I need a compute instance and don’t want to forget to turn it off. It’s a simple trick that will save you in some cases.

Lots of people are using PRD files for this. https://www.atlassian.com/agile/product-management/requireme...

I've been using checklists and asking it to check off items as it works.

Another nice feature of using these specs is that you can give the AI tools multiple kicks at the can and see which one you like the most, or have multiple tools work on competing implementations, or have better tools rebuild them a few months down the line.

So I might have a spec that starts off:

    #### Project Setup

    - [ ] Create new module structure (`client.py`, `config.py`, `output.py`, `errors.py`)
    - [ ] Move `ApiClient` class to `client.py`
    - [ ] Add PyYAML dependency to `pyproject.toml`
    - [ ] Update package metadata in `pyproject.toml`

    Please continue implementing the software described in the file "dashcli.md".  Please implement the software one phase at a time, using the checkboxes to keep track of what has already been implemented.  Checkboxes ("[ ]") that are checked ("[X]") are done.  When complete, please do a git commit of the changes.  Then run the skill "codex-review" to review the changes and address any findings or questions/concerns it raises.  When complete, please commit that review changeset.  Please make sure to implement tests, and use tests of both the backend and frontend to ensure correctness after making code changes.

Ollama with qwen3 and starcoder2 are ok.

I'd recomment to experiment with the following models atm. (eg. with "open-webui"): - gpt-oss:20b ( fast) - nemotron-3-nano:30b ( good general purpose)

It doesn't compare to the large LLM's atm. though.

Using those as hint I bet CC would have one-shotted it pretty easily