So frequently beginners in linux command lines complain about the irregularity or redundance in command line tool conventions (sometimes actual command parameters -h --help or /h ? other times: man vs info; etc...)

When the first transformers that did more than poetry or rough translation appeared everybody noticed their flaws, but I observed that a dumb enough (or smart enough to be dangerous?) LLM could be useful in regularizing parameter conventions. I would ask an LLM how to do this or that, and it would "helpfully" generate non-functional command invocations that otherwise appeared very 'conformant' to the point that sometimes my opinion was that -even though the invocation was wrong given the current calling convention for a specific tool- it would actually improve the tool if it accepted that human-machine ABI or calling convention.

Now let us take the example of man vs info, I am not proposing to let AI decide we should all settle on man; nor do I propose to let AI decide we should all use info instead, but with AI we could have the documentation made whole in the missing half, and then it's up to the user if they prefer man or info to fetch the documentation of that tool.

Similarily for calling conventions, we could ask LLM's to assemble parameter styles and analyze command calling conventions / parameters and then find one or more canonical ways to communicate this, perhaps consulting an environment variable to figure out what calling convention the user declares to use.

Something related to this article, but not related to AI:

As someone who loves coding pet projects but is not a software engineer by profession, I find the paradigm of maintaining all these config files and environment variables exhausting, and there seem to be more and more of them for any non-trivial projects.

Not only do I find it hard to remember which is which or to locate any specific setting, their mechanisms often feel mysterious too: I often have to manually test them to see if they actually work or how exactly. This is not the case for actual code, where I can understand the logic just by reading it, since it has a clearer flow.

And I just can’t make myself blindly copy other people's config/env files without knowing what each switch is doing. This makes building projects, and especially copying or imitating other people's projects, a frustrating experience.

How do you deal with this better, my fellow professionals?

> Then a brick hits you in the face when it dawns on you that all of our tools are dumping crazy amounts of non-relevant context into stdout thereby polluting your context windows.

Not just context windows. Lots of that crap is completely useless for humans too. It's not a rare occurrence for warnings to be hidden in so much irrelevant output that they're there for years before someone notices.

I think about what I do in these verbose situations; I learn to ignore most of the output and only take forward the important piece. That may be a success message or error. I've removed most of the output from my context window / memory.

I see some good research being done on how to allow LLMs to manage their own context. Most importantly, to remove things from their context but still allow subsequent search/retrieval.

Huh. I've noticed CC running build or test steps piped into greps, to cull useless chatter. It did this all by itself, without my explicit instructions.

Also, I just restart when the context window starts filling up. Small focused changes work better anyway IMO than single god-prompts that try do do everything but eventually exceed context and capability...

Rather than an LLM=true, this is better handled with standardizing quiet/verbose settings, as this is a question of verbosity, where an LLM is one instance where you usually want it to be quieter, but not always.

Secondly, a helper to capture output and cache it, and frankly a tool or just options to the regular shell/bash tools to cache output and allow filtered retrieval of the cached output, as more so than context and tokens the frustration I have with the patterns shown is that often the agent will re-execute time-consuming tasks to retrieve a different set of lines from the output.

A lot of the time it might even be best to run the tool with verbose output, but it'd be nice if tools had a more uniform way of giving output that was easier to systematically filter to essentials on first run (while caching the rest).

On a lot of linux distros there is the `moreutils` package, which contains a command called `chronic`. Originally intended to be used in crontabs, it executes a command and only outputs its output if it fails. I think this could find another use case here.

This is merely scratching the surface.

LLMs (Claude Code in particular) will explicitly create token intensive steps, plans and responses - "just to be sure" - "need to check" - "verify no leftovers", will do git diff even tho not asked for, create python scripts for simple tasks, etc. Absolutely no cache (except the memory which is meh) nor indexing whatsoever.

Pro plan for 20 bucks per month is essentially worthless and, because of this and we are entering new era - the era of $100+ monthly single subscription being something normal and natural.

could we not instruct the LLM to run build commands in a sub agents which could then just return a summary of what happened?

this avoids having to update everything to support LLM=true and keep your current context window free of noise.

Interesting idea but bad tspec. A better approach would be a single env var (DEV_MODE perhaps) with “agent” and “human” as values (and maybe “ci”).

I noticed this on my spring boot side project. Successful test runs produce thousands of log lines in default mode because I like to e.g. log every executed SQL statement during development. It gives me visibility into what my orm is actually doing (yeh yeh I know I should just write SQL myself). For me it's just a bit of scrolling and cmd+f if I need to find something specific but Claude actually struggles a lot with this massive output. Especially when it then tries to debug things finding the actual error message in the haystack of logs is suddenly very hard for the LLM. So I spent some time cleaning up my logs locally to improve the "agentic ergonomics" so to say.

In general I think good DevEx needs to be dialed to 11 for successful agentic coding. Clean software architecture and interfaces, good docs, etc. are all extremely valuable for LLMs because any bit of confusion, weird patterns or inconsistency can be learned by a human over time as a "quirk" of the code base. But for LLMs that don't have memory they are utterly confusing and will lead the agent down the wrong path eventually.

I never considered the volume of output tokens from dev tools, but yeah, I like this idea a lot.

great idea. thought about the waste of tokens dozens of times when I saw claude code increase the token count in the CLI after a build. I was wondering if there's a way to stop that, but not enough to actually look into it. I'd love for popular build tools to implement something along those lines!

I think I noticed LLMs doing >/dev/null on routine operations.

`Humans=True`

The best friend isn't a dog, but the family that you build. Wife/Husband/kids. Those are going to be your best friends for life.

Speaking of obvious questions. Why are you counting pennies instead of getting the LLM to do it? (Unless the idea was from an LLM and the executive decision was left to the operator, as well as posting the article)

So much content about furnishing the Markdown and the whatnot for your bots. But content is content?

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This all seems like a lot of effort so that an agent can run `npm run build` for you.

I get the article's overall point, but if we're looking to optimise processing and reduce costs, then 'only using agents for things that benefit from using agents' seems like an immediate win.

You don't need an agent for simple, well-understood commands. Use them for things where the complexity/cost is worth it.