taosx

"When someone shows you who they are, believe them the first time.".

I dropped them when they first started forcing you to have an account and from them only used it when work made it mandatory, in some cases we even dropped them for other alternatives (http with source in git, scripts..etc)

You are right, I didn't look too much through it and just went ahead and installed/configured it to figure out if it fits.

The vram usage is: niri = ~163mb

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noctalia = ~221mb ashell = ~2mb ironbar = ~10mb

I just wish I had a polished shell experience that was not a vram hog. Quickshell (multiple big monitors) seems to hog a lot of it, ashell and ironbar are better on resource management but require a lot of customization.

On my linux machine chrome was crashing almost daily before activating rebar by patching bios.

I have no idea where most people writing code have worked at but in all product and platform teams I worked at the code quality has been much higher than the latest slop SOTA llms can output.

TLDR: coding is not solved.

I have 2 projects, one it's a distributed platform, the other one is a general processing engine with an inner workflow engine; Since gpt 5.2 I've tried new models to work in these codebases where the code is of good quality and every time I gave the model a slice of work instead of a single step from that slice the code, the tests, the comments, the docs and everything else has been suboptimal, unmaintainable, complex, bloated and just slop, unless I micro-manage and do many passes.

As a dev when you make a change you consider the broad picture, you consider the user, the codebase, future requirements, maintainability, performance, your team's understanding and some of these you do unconsciously. We are slow but that's for multiple good reasons, you push the organization/understanding forward not just loc of that specific project. I can't count how many PR notes or comments I've added considering teammates or just for a specific team member.

I don't see any way forward for an LLM to reach that unless it reaches general problem solving, my definition of GAI that could tackle software development or "coding" would be a model that doesn't require additional pretraining to solve new tasks or improve how it solves tasks in the future, it would just learn as it's going.

Can everything I mentioned be solved with current generation of LLMs and lot's of markdown and gates? Maybe... but the amount of effort required would be similar to the effort an expert system (pre-llm AI) would require to embed the rules, evolve them, check them everytime... which would require billions or trillions of tokens.

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off: I really like the discussions around how to prevent slop and bloated code as it's something it would benefit coding even without LLMs and can fit as another piece of automated infra for checking and ensuring code quality, I hope something materializes.

In the past I managed to get measureable performance optimizing a harness by looking at few traces to see if the traces contained surprised, a lot of text in order to figure out how to use my custom tool, then renamed the tool, changed some parameters and it was already great across around 20 eval tasks in rust/typescript, I repeated the same more recently but I used an llm to look at the traces... didn't achieve the desired result, mostly due to how cost-prohibitive it's for me to run expensive models.

Does any of them support more advanced functionality that can be provided with plugins? Similar to how, let's say, Emacs has slime for lisp?

From what I see helix has stopped being developed, probably ppl think its done but i feel its half baked without a plugin system and tons of basic functionality missing.

Fully agree with this. I did that for pi for a while, maintained it and brought merges from upstream while having my own patches on top but then went on holidays and there was that refactor where the agent pipeline failed... now I'm stuck on the version from april/may (works great but I can't use extensions; and I'm too lazy to debug/fix while everything works great).

off: I'm working on my spare time on a code mode lisp alternative (great opportunity to learn lisp) and might switch to it fully as long as I built some simple evals (I'm concerned about token usage, which is why i forked pi the first time)

So I can take that as ~infinite amount of tokens don't don't even get you 2x on any novel tasks? No auto-researcher, no rsi..

Is that correct?

Done, I'll take any other suggestions and apply them later, I will also split it a bit for different usecases as this was initially a throwaway prototype but found it useful. Basically it needs a bit more human touch.

I've been extracting the asar and using it for a while on linux, I rarely start it but it's nice that I can start a session in chatgpt and then move on to codex in the same UI.

It's packed with features, voice mode, browser. I'm not really the target audience..