Fuck AI

It's situation specific. For tabulating data, yes. For everything else, probably not. But the thing is, you have to ask LLM if they can read the raw data to confirm they are reading it right, before ordering it to execute more complex commands and tass. You have to define the parameters one by one, one query everytime.

For every post I see of people complaining, I have to imagine there are 100 other people that get value out of LLMs quietly.

There are right tools and wrong tools depending on the application.

There are right ways to use said tools and wrong ways...like you wouldn't use a phillips head screwdriver on a flat head.

I guarantee your company's provided tool is Copilot or OpenAI based, which is already bottom of the barrel for usefulness.

Generally I equate positivity about LLMs with people’s technical ability. I find the more they say AI is good the worse programmer they are.

Claude and super powers / planning have changed my mind more on AI feature development. Iterating on the spec and making it as unambiguous as possible gives good results when you clear context and have it implement the plan. Even if it starts to stray you can just do a git reset and start a new session with the spec, adjusting it a bit, because time wise you probably haven't invested much.

It also depends on the code base, if the code base has very clear separation of concerns, good documentation, and good contracts between layers then claude can handle it pretty well. If the code base is full of spaghetti code with multiple ways to do the same thing then AI will struggle with it. In our large legacy monolith repo it doesn't do well, in our micro service repos it does great.

Also time wise it may not seem like a benefit if you just set it and wait for it to complete, the productivity advantage comes from running a couple sessions in parallel.

Also context is key, having a good claude.md file in the repo to explain patterns helps it to avoid pitfalls. If it's only context is the prompt you gave it and you tell it to implement a feature without a plan / spec outlined it will generate shit code.

Id say you dont know how to use the tool. 'Write tests here, develop.feature X' is not a good way to use llms. Using 80k tokens and keep using same a Session is context rotting. There are a lot of boring, everyday tasks in my job that got faster. Many others that meh. Use AI, dont be driven by AI.

every time I use it, i waste hours

Yes, exactly this. It looks good, I ask for it to tweak something. It tweaks, but now something else needs adjustment. Then it comes back unusable.

It ends up taking the same time as doing it myself. There’s some value perhaps in either the novelty or engagement that keeps me focused but it’s not more efficient.

When it does work, I’m always worried it is an illusion I’ve missed something. Like how you send an email and immediately see the typo.

People who love it, love it because they don’t need to or care about having accuracy and precision in their work. Sales and marketing, management, etc. Business idiots.

you're obviously prompting it wrong, and/or not using the latest models ~/s~

My experience is that it can work reasonabky well, but you have to waste absurd amount of tokens and have the 1m token context window.

I only do gamedev, which means a little bit more simple scriots, but it could handle even more involved systems.

If i force it to first document and explain the wholw architecture and data flow.

Did it help? Yes, but it still did take only a slightly faster. I could do it in a day more, probably.

It also cost like 50$ in tokens, in today's prices - where every AI company is loosing trillions of money, so the costs will get a lot worse. And if I tried to conserve tokens, it's shit. You have to feed it 10$ of data to be useful.

Add to that the fact that it also causes skill attrition, so once the expensive-cost future arrives, you probsbly won't be able to afford it, and good luck getting your skills back after that.

Our company wants us to use it, and the average token consumption is like 100$ per day in consumer prices. How is that even considered for such a minor gain?

So, I'll pass.

I recently used it to install Nvidia l40s drivers on redhat 9 and pass it through to my Frigate instance. Took me a few minutes. Would have been a lot of reading to find the exact answers manually.

Fwiw, when I limit it to creating outlines based on given source docs or summarizing transcripts it does fine.

Definitely not worth what it cost to get there, but useful enough in those strict scenarios.

Is your work paying for dumb robots? Like Atlassian's shit? Or something built-in to your industry software (I guess some kinda CAD)? These are next to useless. Or is work only paying for basic model access? Also pretty useless for detailed work. The only models that give me consistent, detailed-ish work are the state of the art models. And even then you have to watch them, or have very strong verification/validation so they can bash their head against to eventually get the right result.

I'll say that I am not so much a booster, but more of a pragmatist. After the step change in quality this past fall/winter, I gave the SOTA models a try with hard earned cash. And it was worth it.

My ADHD makes it difficult to really finish personal projects once they get past the fun and interesting learning portion and neck deep into tedium of actually molding the code into the right shape or shaving the various yaks that came up. All motivation ceases. Unfortunately, my job is also my hobby. I don't wanna work after work, yo.

That game I always wanted to finish writing but got stuck at needing to grind out code? Done in an afternoon of carefully directing it. That programming language I spent significant amounts of time thinking and designing and getting the shitty PoC running but now needed to actually make it work? A week to the first version. Another to my first significant application written in that language which revealed flaws in my design for real use cases. Another to the next version with a conformance test suite which was then used along with the spec to do a complete reimplementation in another language. Another project was trying to "grow" a sorting algorithm expressed in a niche esoteric programming language using a genetic algorithm. Stuck at the point of needing to build the tools for analysis, needing a refactor to fix the poor persistence choices, just nothing but yaks to shave. Got it unstuck over a weekend and actually started to DO the damn experiment after spending so much time writing the esoteric lang interpreter and all of the experiment harness.

It is not perfect. It fucks up frequently. I have to really watch it and steer it. It loves mediocrity and shortcuts. All that said...

Like, holy shit. The amount of work I've finished or moved forward in two months is nothing short of miraculous given how many projects like these I have in various states of finished.

All I can say is that my experience aligned with your experience any time I needed to use a bolted-on AI to some product (Atlassian, Lucid, etc) but that does not reflect my experience when using SOTA models for real work.

It really depends on the task and the tool. Current MOE models that have agentic hooks can actually be really useful for doing automated tasks. Generally, you don't want to be using AI to create things. What you want to do is hand it a very clear set of instructions along with source material. And then tell it to either iterate build on summarize or in some cases create from that.

I created a simple script with the help of AI to automate scanning files in from an automatic document reader. Convert them to OCRD PDFs. Scan through the document properly. Title the file based on the contents, then create a separate executive summary and then add an index to a master index file of a growing json.

Doing this allowed me to automate several steps that would have taken time and in the end I'm able to just search through my folders and my PDFs and very quickly find any information I need.

And this is only scratching the surface. I wouldn't have AI write me a resume or write me an email or a book. I might use it to generate an image that I then give to a real artist saying this is kind of what was in my head.

But boring stuff repetitive stuff. Things that really benefit from automation with a little bit of reasoning and thinking behind it. That's where we are right now with AI.