Another conclusion is that we could benefit from benchmarks for architectural quality.

Instant answers, correct or not.

Cheaper per answer by magnitudes.

Solutions provided with extensive documentation.

I'm still learning though

It's important to have this self awareness. Don't let AI trick you into thinking it can build anything good. When starting a project like in the article, your time is probably better spent taking a step back, learning the finer points of the new language (like, from a book or proper training course) and going from there. Otherwise, you're going to be spending even more time debugging code you don't understand.

It's the same thing with a crappy consultant. It seems great to have someone build something for you, but you need to make preparations for when something breaks after their contract is terminated.

Overall, it makes you think, what is the point? We can usually find useful crowd-sourced code snippets online, on stack exchange, etc. We have to treat them the same way, but, it's basically free compared to AI, and keeping the crowd-sourced aspect alive makes sure there's always documentation for future devs.

Seriously, though, within the context of software development, these are all issues I've encountered as well, and I don't know how to program: sweeping solutions, inability to resolve errors, breaking down all components to base levels to isolate problems.

But, again, I don't know how to program. For me, any consultant is better than no consultant. And like the author, I've learned a ton on how to ask for what I want out of Cursor.

Just this morning my CTO was crowing about how he was able to use Claude to modify the UI of one of our internal dev tools. He absolutely cannot wait to start replacing devs with AI.

Nobody wanted to hear it back when software development was easy street, but maybe we should have unionized after all...

The amount of time I save just by not having to write tests or jsdocs anymore is amazing. Refactoring is amazing.

And that's just the code - I also use AI for video production, 3d model production, generating art and more.

More reports of 'vibe-coding' causing chaos because one trusted what the LLM did and it 'checked' that the code was correct. [0] As always with vibe-coding:

Zero tests whatsoever. It's no wonder you see LLMs not being able to understand their own code that they wrote! (LLM cannot reason)

Vibe coding is not software engineering.

[0] https://twitter.com/levelsio/status/1921974501257912563

My own anecdote with a codebase I'm familiar with is indeed, as the article mentions, it's a terrible architect. The problem I was solving ultimately called for a different data structure, but it never had that realization, instead trying to fit the problem shape into an existing, suboptimal way to represent the data.

When I mentioned that this part of the code was memory-sensitive, it indeed wrote good code! ...for the bad data structure. It even included some nice tests that I decided to keep, including memory benchmarks. But the code was ultimately really bad for the problem.

This is related to the sycophancy problem. AI coding assistants bias towards assuming the code they're working with is correct, and that the person using them is also correct. But often neither is ideal! And you can absolutely have a model second-guess your own code and assumptions, but it takes a lot of persistent work because these damn things just want to be "helpful" all the time.

I say all of this as a believer in this paradigm and one who uses these tools every day.

When you hire a team of consultants, it is typically the case that you are doing so because you have an incomplete view of the problem and are expecting them to fill in the gaps for you.

The problem arises due to the fact that the human consultants can be made to suffer certain penalties if they don't provide reasonable advice. A transformer model ran in-house cannot experience this. You cannot sue yourself for fucking up your own codebase.

It would be interesting to see a LLM trained in a completely different way. There's got to be some tradeoff between how coherent the generations are and how interesting they are.

I also use it as a cognitive assistant, I've always found that talking about a design with a colleague helped me to think more clearly and better organize my ideas, even with very little insights from the other side. In this case the assistant is often a bit lacking on the skepticism side but it does not matter too much.

But back on point, I found AI works best when given a full set of guardrails around what it should do. The other day I put it to work generating copy for my website. Typically it will go off the deep end if you try to make it generate entire paragraphs but for small pieces of text (id say up to 3 sentences) it does surprisingly well and because it's outputting such small amounts of text you can quickly make edits to remove places where it made a bad word choice or didn't describe something quite right.

But I would say I only got ChatGPT to do this after uploading 3-4 large documents that outline my product in excruciating detail.

As for coding tasks again it works great when given max guardrails. I had several pages that had strings from an object and I wanted those strings to be put back in the code and taken out of the object. This object has ~500 lines in it so it would have taken all day but I ended up doing it in about an hour by having AI do most of the work and just going in after the fact and verifying. This worked really well but I would caution folks that this was a very very specific use case. I've tried vibe coding once for shits and giggles and I got annoyed and stopped after about 10 minutes, IMHO if you're a developer at the "Senior" level, dealing with AI output is more crumbsome than just writing the damn code yourself.

They emerge from the simple assumptions that:

- LLMs fundamentally pattern match bytes. It's stored bytes + user query = generated bytes.

- We have common biases and instinctively use heuristics. And we are aware of some of them. Like confirmation bias or anthropomorphism.

Some tricks:

1. Ask for alternate solutions or let them reword their answers. Make them generate lists of options.

2. When getting an answer that seems right, query for a counterexample or ask it to make the opposite case. This can sometimes help one to remember that we're really just dealing with clever text generation. In other cases it can create tension (I need to research this more deeply or ask an actual expert). Sometimes it will solidify one of the two, answers.

3. Write in a consistent and simple style when using code assistants. They are the most productive and reliable when used as super-auto-complete. They only see the bytes, they can't reason about what you're trying to achieve and they certainly can't read your mind.

4. Let them summarize previous conversations or a code module from time to time. Correct them and add direction whenever they are "off", either with prompts or by adding comments. They simply needed more bytes to look at to produce the right ones at the end.

5. Try to get wrong solutions. Make them fail from time to time, or ask too much of them. This develops a intuition for when these tools work well and when they don't.

6. This is the most important and reflected in the article: Never ask them to make decisions, for the simple fact that they can't do it. They are fundamentally about _generating information_. Prompt them to provide information in the form of text and code so you can make the decisions. Always use them with this mindset.

Also, there's a lot of value already in a crappy but fast and cheap consultant.

If I understand the problem well enough, and have a really good description of what I want, like I'm explaining it to a junior engineer, then they do an OK job at it.

At my last job, we had a coding "challenge" as part of the interview process, and there was a really good readme that described the problem, the task, and the goal, which we gave the candidate at the start of the session. I copy/pasted that readme into copilot, and it did as good a job as any candidate we'd ever interviewed, and it only took a few minutes.

But whenever there are any unknowns or vagaries in the task, or I'm exploring a new concept, I find the AIs to be more of a hindrance. They can sometimes get something working, but not very well, or the code they generate is misleading or takes me down a blind path.

The thing for me, though, is I find writing a task for a junior engineer to understand to be harder than just doing the task myself. That's not the point of that exercise, though, since my goal is to onboard and teach the engineer how to do it, so they can accomplish it with less hand-holding in the future, and eventually become a productive member of the team. That temporary increase in my work is worth it for the future.

With the AI, though, its not going to learn to be better, I'm not teaching it anything. Every time I want to leverage it, I have to go through the harder tasks of clearly defining the problem and the goal for it.