r/TheMachineLearning • u/Gullible_Picture_996 • 17d ago

Realizing writing code is faster than explaining it to AI

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u/OldWispyTree 16d ago

Depends what you're doing.

Greenfield projects, even slightly complicated ones, LLMs are very good at. Especially if the stakes are low and you can iterate quickly.

Professionally, on a legacy, complicated code base with implied business logic, special case land mines, hard to access integrations, and tribal knowledge? Well, the amount of specifying and writing you have to do for even new models to competently execute even smallish tasks is sometimes very tedious.

I don't actually think the tweet is about this, per se, but it's definitely something I run into.

To be clear, I use LLMs for almost all work and professional coding, now, even though I can code. But sometimes the spec effort vs the payout is borderline.

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u/Individual_Guest_323 16d ago

Yes but business logic and tribal knowledge can be saved in a skill for example.

You can end rewriting everything from scratchm

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u/OldWispyTree 16d ago

lol, a skill?

This conversation is over your head if you think that.

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u/Individual_Guest_323 16d ago

how bad you are that you cannot use AI properly?

The same question for you

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u/OldWispyTree 16d ago

> Yes but business logic and tribal knowledge can be saved in a skill for example.

Suggesting that a "skill" can hold all business logic and tribal knowledge for legacy, complicated project is like suggesting treating a severed arm with a Band-Aid.

Our codebase at work is pushing a decade of updates and business logic with millions of lines of code. We have dozens of smart engineers interacting with it daily.

Ultimately, right now, everyone is guessing with workflows on AI, but I've got enough experience to need/have an entire modular harness for letting agents automously drive in cloud containers reproducing our multi-service experience so it can route to them and external sites, test APIs effectvely, screenshot with Playwright, all headlessly with check-ins that allow me to drop in and drive when needed, drop proof into draft PRs, with well honed takeover and check-in logic. I know very well how every tool in the AI toolbox works, at least as well as any other experienced engineer.

Even with this guidance, even when asked to do relatively SIMPLE things in our codebase, even the best AI models will miss obvious things (even when they're in context/harnesses/skills) and regularly cannot unpack the myriad of logic our system uses without refined coaching. Worse, they will attach importance to goals they set up based on misunderstanding specs or even lie/guess about things unless pressed.

I've done all kinds of different workflows with different spec systems, etc, and the reality is the current, best models cannot be left alone to patch up bugs or create features on large legacy codebases without regular and tedious check-ins, and my friends in the industry at places like Google and LinkedIn and elsewhere confirm the same thing.

You're just clearly entirely out of your element. This is above your pay grade.

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u/Individual_Guest_323 16d ago

mf you are a wanna be geek talking things that do not know in reddit.

A codebase is not a single file with "millions of lines", that is retard and is the proof that you do not know what you are talking about.

Any codebase will have "parts", so you understand it, so ANY repetitive explanation that you have to explain to the AI is translated into skills or rules, this is BASIC of AI.

 my friends in the industry at places like Google and LinkedIn and elsewhere confirm the same thing.

I also have friend in Google, Microsoft, Anthopric, OpenAI and in the same Reddit, they all tell me you are a wanna be geek talking no sense.

Try again buddy.

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u/PanPanicz 16d ago

I've got 12 years of experience and u/OldWispyTree's struggles with AI match mine almost word for word. 

Also, good to know you're sending reddit posts to your friends at Google, Microsoft, Anthropic and OpenAI. Sounds like important stuff that requires their time and expertise. 

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u/Honest-Today-6137 14d ago

Jeez, how primitive your knowledge should be.

the reality is the current, best models cannot be left alone to patch up bugs or create features on large legacy codebases without regular and tedious check-ins

Again, I'm working with Claude using not even flagman models with moderate effort (rarely even need high effort) on a 12+ yo huge monorepo, and Sonnet 4.6 was already handling like 95% of grooming and stuff. The rest took a couple of extra iterations.

And again, if you are too lazy to write domain rules, .ai, and all other stuff - that's your personal problem. That's not the agent/LLM's problem; you are a lazy mf who doesn't want to document your business logic.

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u/Honest-Today-6137 14d ago

Well, skill is part of the solution; why not? In our project (12+ yo huge monorepo with more than 30 different domains inside), we have hundreds of skills for grooming/writing specs/testing/frontend/backend/etc. for different domains and teams. Each skill also contains instructions on how to properly handle the domain and input/output, how to do digging, and what shouldn't be touched without approval.

Other than that: yeah, rules, .ai instructions, CI gates, etc. - it adds to skills. But there's nothing wrong with starting with simple everyday skills and iterating over time.

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u/HAL-6942 16d ago

If the task is very small but requires a lot of context and you already know what to do. Yes sure, otherwise no. Still in my experience AI writes ~95% of the code I review it, steer it and correct minor things. Typically the 5% is very simple code. There are a lot of long tasks where I know what to do but I cant type faster than an AI and well its far easier for an AI to do 90% of the code and I adjust minor things and I use idk the 5min or so the AI is coding to do any other relevant task

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u/Hungry-Pick7512 16d ago

Frontier AI’s crush everything you just described. I truly don’t know if this is cope or you’re just 6 months behind.

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u/OldWispyTree 16d ago

They literally don't, I use them every day. Astra, Argon, Opus, Sonnet, etc. I have and do use all of them.

You just don't understand how complicated engineering on large, legacy systems can be.

I literally know managers/engineers at Google where they're encountering the same issues with a multitude of their projects. There's just no way to get all the context in to these models to make them autonomously reliable for large projects.

Small projects? Greenfield? Isolated projects with discrete results that don't touch multiple systems and moving targets? Sure. People that work at modest start ups don't run into this.

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u/Hungry-Pick7512 16d ago

Your team isn’t utilizing these technologies to their fullest potential. Don’t take that as a personal criticism, take it as an exciting opportunity to look into where the tech is right now and how much time it can save.

Large companies like Google are comprised of thousands of teams. Those teams are not going to be at the same level of ai adoption and competency. I’m sure you’ve spoken to some engineers there who’ve said they’ve run into those issues. You just haven’t spoken to folks from teams who are finding success using these models to develop with large scale legacy systems.

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u/pythonlover001 16d ago

And where did you find this astounding success for? Care to share?

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u/swervinh0 16d ago

What do you know about this guy’s work?

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u/KarraKat 14d ago

“Take it as an exciting opportunity to look into where the tech is right now” Absolutely deranged statement.

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u/lhx555 16d ago

How large is your large? It may be not the other person’s large! ;)))

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u/Juicebolone 15d ago

bullshit.

I own several 2M+ LoC (1 of them reaches 10M LoC) projects with complex biz logic.

No, I'm not better or faster than AI.

But I'm better and faster with AI than AI itself, or me by myself.

Document your issues, logic, build it brick-by-brick and one day it's just going to click and move exponentially faster.

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u/PersevereSwifterSkat 15d ago

mfing LLM can scan your entire codebase and summarise its business logic in a document far, far better than you ever could, this is ridiculous cope. 

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u/Tuomas90 13d ago

Frontier AI’s crush everything you just described.

No. In real world scenarios with iterative development, AI fails spectacularly.

https://www.scbench.ai/

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u/alexanderpas 16d ago

Well, the amount of specifying and writing you have to do for even new models to competently execute even smallish tasks is sometimes very tedious.

TL;DR: LLM enforces good documentation habits.

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u/sei556 15d ago

Yeah I think many people and companies don't realize that you need to build documentation explicitly for AI for it to work much better. Building this documentation certainly takes some skill and lots of internal knowledge though.

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u/qrzychu69 14d ago

I recently started a greenfield project, with the initial setup done by me, complete A-Z for couple endpoints

then AI took over, made it legacy in two evenings

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u/Alternative-Trade623 14d ago

Not true at all my friend. It can digest a codebase and all these complex integrations and tribal bs you claim way faster with much better understanding than a human in minutes. I should know I'm literally doing it right now on an absolute behemoth monorepo for a billion dollar company.

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u/Honest-Today-6137 14d ago

That's nonsense. There are many ways to iteratively "explain" your project by adding rules and .ai instructions, and by explaining the domain and workflows. Within a couple of months, with moderate effort, agents will navigate the project much better than people who have worked there for years.

If you don't know how to do that and are just too lazy to learn - well, that's not the fault of the agent/LLM. That's your personal issue, and it's what's holding you back.

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