[modeless]

> When we filtered out these problematic issues, the resolution rate of SWE-Agent+GPT-4 dropped from 12.47% to 3.97%.

This matches my intuition about the coding performance of these models a lot better. I don't think any current coding benchmark accurately measures coding performance.

[OsrsNeedsf2P]

Anecdotal but I was always shocked to see Claude 3.5 perform so poorly in the benchmarks, when it generates 80% of my code in Cursor (and in cases it fails, no other model succeeds)

[TheDong]

Different people seem to get wildly different results here, and I'm not sure what percentage is down to the type of software being built vs the usage patterns.

In my case, I would guess less than 10% of the code I get out of AIs is useful.

What sort of code are you getting those results with? Is it yet-another-react-frontend-button? Is it ebpf programs? Is it a parser in rust?

For the latter two, I've found AI to have pretty low rates, and for the former I haven't had the desire to try.

[alfalfasprout]

Almost every time someone says "but most of my code nowadays is LLM generated" it's usually one of three things:

1. Very greenfield work where the LLM doesn't really have a lot of constraints to deal with and can fully control the setup + doesn't have to ingest a lot of existing context 2. Very small projects that largely follow established patterns (CRUD, frontends, etc.) 3. Well established implementation work (the kind of feature that's a simple JIRA ticket).

In my experience they're painfully bad at:

- Novel/niche work where there aren't really answers online to what you're trying to do - Complex refactoring - Architecting within existing constraints (other systems, etc.)

[cyanydeez]

I assume it's the commoditized work that made India a success at outsourced activities.

[alfalfasprout]

I suspect it's a lot of that too. Essentially pattern matching.

[darioush]

I think they are counting the autocomplete as well. Yeah when I write golang, and write: result, err := foo(thing)

it's gonna autocomplete: if err != nil { return fmt.Errorf("%w: could not foo: %v", err, thing) }

[Philip-J-Fry]

I'm pretty confident in my ability to write any code in my main language. But AI is still very useful in just filling out boiler plate, or noticing a pattern and filling out the rest of some repetitive code. Or say, I need to write wrapper around a common command-line utility. It's pretty good at generating the code for that.

What I mostly enjoy using it for is just writing bash scripts for me. I hate writing bash but Claude is excellent at writing the scripts I need.

AI isn't writing software features or anything close to that for me at the moment. But what it is great at is just being a really excellent intellisense. Knowing what you're likely to want to do in the next ~5 lines and just filling it out in one button press. Things like intellisense and automatic refactoring tools were big productivity improvements when they became ubiquitous. AI will be the same for most people, an intellisense on steroids.

Also, writing tests. Writing tests can be quite mundane and boring. But I can just type out what I want tested, give it some files as context and it can be pretty good at generating some tests.

Does AI get it right every time? No way. But, as a developer, I'd rather spend 10 minutes trying to coax an AI into generating me 90% useable code for some boring task than spend 20 minutes typing it out myself. Often, I probably could write the code faster than I could prompt an AI, but being lazy and telling something else to do the work feels pretty good and relaxing.

[ghaff]

>AI is still very useful in just filling out boiler plate

That's what I tend to find with English writing as well. It's not great. But sometimes you just need decent generic prose for an introduction or an explanation of something. If you know enough to adjust as needed, it can save time for something that readers are probably just skimming anyway. As I've written previously, about a year ago I was working on cleaning up a bunch of reference architectures and I used Google's Bard in that case to give me a rough draft of background intros for some of them which I modified as needed. Nothing miraculous but saved me a bit of time.

[ben_w]

> For the latter two, I've found AI to have pretty low rates, and for the former I haven't had the desire to try.

Similar. I've got a joke language project on the back burner, doing it properly requires going back over my 23 year old university notes on yacc etc., so I tried AI… the AI just makes a mess of it*.

For anything front end, even the original ChatGPT-3.5 model is basically magic (i.e. sufficiently advanced technology).

* I think the last time I touched it was just before o1 was announced; as o3 is now in the free tier of ChatGPT, I should try again…

[aprilthird2021]

My gut tells me the AIs will be best for small web projects that are greenfield. The kind a 1-3 person team could maintain.

And my gut tells me they are the worst for the kinds of long-established software conglomerates many professionals work at, which have tons of internal services, integrated acquisitions, etc. etc.

Ultimately the AI is good at what the average developer online is good at, probably full-stack web dev of projects from scratch.

[dingnuts]

but that kind of code is so easy to write, and code is already way more terse than natural language! it's literally more typing to explain to an LLM how to write some greenfield web CRUD than it is to just type out the code, and if there's a lot of boilerplate it's faster to generate the repetitive parts with keyboard macros!

where's the value everyone on this site and on LinkedIn (but NONE in my real or professional life) seems to get?

I feel like I'm being gaslit when people say Cursor writes 80% of their code, and honestly, it's the conclusion that makes the most sense to me -- the people making these posts must be well-invested in the startups that stand to profit if AI is actually as good as they say. You know, shills.

[pdntspa]

I work on web crawlers and data mining at scale and well over 50% of my code output is written by AI. I use mostly o1 (copying and pasting isolated snippets) or Jetbrains' AI service.

I also have access to a full-service "junior developer" AI that can take in an entire git repo at once, and its code outputs are significantly less useful -- maybe 10%.

I think a lot of peoples' success rate with AI boils down to their choices in language/toolkit (AI does much better the more common it is) and how they prompt it.

Note that you still need an experienced set of eyes supervising, the thought of an LLM committing to a git repo without a human in the loop scares me.

[botanical76]

Have you tried the AI intellisense models like Copilot?

I don't understand the notion that it is faster to generate repetitive code with keyboard macros. I use Vim-mode exclusively, and while I'm not a Vim master, I don't think there's any set of macros that will do what Copilot can do.

It's not that Copilot is smart. It's that 60% of what I do doesn't require much intelligence to anticipate. It is the 40% that matters, the remainder can be trivially guessed, and this is exactly what Copilot does.

Maybe this will help: you need to imagine with an AI intellisense that with each keystroke, you are collapsing the possibility space down to a smaller, finite number of outcomes. You write exactly what code you need for the dumb AI to predict the rest of it.

There are a LOT of reasons why AI intellisense is not all there yet; it can be distracting; it can try to generate too much at once; none of the tools have LSP integrated, so it will provide bullshit suggestions of library methods that don't exist. This is all true, and yet it is still highly valuable in some domains, for some people.

That said, if you write x86 assembly for a living, you are probably out of luck.

(I write Kotlin, Java for Android apps and services, C++ that is tightly integrated with the SoC. Python and Bash for command-line tools that invoke REST APIs. Copilot is useful for these domains.)

[alfalfasprout]

Copilot is very different though. FWIW most people seem to find copilot super valuable.

The discussion is more around highly autonomous AI "coders" (cursor, cline/roocode, (open)devin, etc.)

[spamizbad]

You might not be getting gaslit.

I’ve sat through some interviews recently with candidates who started their careers in the last 6 years or so… during the boom cycle. Some were quite good but a troubling amount were clearly over-leveled at their current/previous employers.

For example, last month we interviewed someone for a Staff Engineering role (current role: L5 Senior II engineer), for Python. This person was unable to explain what a set was in Python, didn’t seem to grok the basic HTTP request/response pattern etc. This wasn’t a leetcode interview; it was an engineering conversation. It was the same questions we’d given dozens and dozens engineers in the past. It wasn’t a language barrier issue (guy was American, interviewer was American). Dude just seemed to have a very very narrow set of skills.

For people like this I imagine AI feels like a superpower.

[alfalfasprout]

I'm pretty sure that's what's going on too. The quality of junior -> midlevel engineers has plummeted and these AI tools have been a major crutch to help them appear productive/competent again.

Problem is they don't know enough to really assess if what the LLM is spitting out is any good or not so they claim amazing wins.

[lovich]

> but that kind of code is so easy to write, and code is already way more terse than natural language! it's literally more typing to explain to an LLM how to write some greenfield web CRUD than it is to just type out the code, and if there's a lot of boilerplate it's faster to generate the repetitive parts with keyboard macros!

> where's the value everyone on this site and on LinkedIn (but NONE in my real or professional life) seems to get?

I can remember how to describe that every time I need to make a button. I can’t remember the new flavor of the months special snowflake way of expressing that. I’ve had decent traction just listing the pieces in my stack and then subbing those out whenever it changes

[aprilthird2021]

> it's literally more typing to explain to an LLM how to write some greenfield web CRUD than it is to just type out the code, and if there's a lot of boilerplate it's faster to generate the repetitive parts with keyboard macros!

I mostly agree with you, but I do think it's faster than searching for and finding the boilerplate you need. I also think AI code completions and the ability to use it to generate the small blocks you will put together into the main app are helpful. Idk, it's not a nothing burger. It's not going to start working at AWS either.

[doug_durham]

Your intuition runs counter to most folks experience. I work on complex machine learning training loops and loss functions. LLMs work great on that.

[pitpatagain]

I work in machine learning research: training loops and loss functions are incredibly repetitive and pattern filled, highly represented in the code the LLMs are trained on, and typically short. They are exactly my intuition of simple code that LLMs would work well on.

[alfalfasprout]

With respect, having trialed these tools on pretty large ML codebases it's very much most folks' experiences that they're not very good across the board.

Training loops, sure... those are pretty much straight pattern recognition w/ well-represented APIs. But more broadly? Not so much.

[aprilthird2021]

I didn't say it cannot work well on anything other than greenfield web projects. I said it would probably be best at those as those have the most training data available. It can work well for your use case and still fit the pattern I laid out

[duped]

I think it's frontend javascript versus everything else.

There's a few languages/tools I use often but am not an expert in and have been using Claude 3.5 to help me work with existing code. On paper this is a perfect use case. In practice it's like working with an intern that has google in front of them and enough jargon to convince me what they're saying isn't bullshit. Eventually, I'll be able to coax the answers I need out of it.

I'll say though the fact AI can't say "I don't know" and closely related "that is not possible in the context you've given me" combined with the inability to reason is what gives you results that look OK but are subtly trash.

[cyanydeez]

the reason AI is a giant f'n bubble is because doing an exhaustive review of results means _doing the actual work of millions of manhours_.

Instead, people squint their eyes at scrolling matrix text and convince themselves it must be true.

[dgunay]

I've been using LLMs for tab autocomplete for a while and just recently started trying out agentic coding AI (Copilot Edits and Cline). I think the disappointing shortfall of agentic AIs (at least for me) comes from the feedback loop being so much looser than the autocomplete style. With autocomplete, I don't have to actively think about what context to feed it, and I can gently correct it if it goes in the wrong direction on a line-by-line basis. With AI agents, they have a lot more leeway to generate a ton of code and reason themselves off the rails before you're able to step in and correct them. Now granted, I am also not very good yet at managing context and crafting prompts, but it feels a lot harder to get good at than simply dropping an AI autocompleter into an existing programming workflow. It's a new paradigm.

[modeless]

When I use Cursor I ask for specific small tasks that I know it should be able to handle. Larger, open-ended tasks fail almost universally for me.

[jerpint]

I think the big thing overlooked is how much the human steering the models matters. If you know what you’re doing and what changes you need, cursor and other tools make you so productive.

If you don’t know what you’re doing, these things can sometimes produce good code, and sometimes produce things that don’t work at all

[rco8786]

That's been my experience too, but I would guess the problem of "here is a ton of context, produce a small amount of code" is significantly better suited for LLMs than "here is a problem, produce a ton of code".

[serjester]

I write a lot of Python and personally I find Claude significantly worse than OpenAI’s reasoning models. I really feel like this varies a ton language to language.

[gcr]

I've been quite underwhelmed at Copilot's suggestions. Is Claude all that better?

[theturtletalks]

I personally use Aider's Polyglot Benchmark [0] which is a bit low-key and not gamed just yet. It matches my experience too where Claude Sonnet 3.5 is the best and still beats the new reasoning models like o3-mini, DeepSeek, etc.

0. https://aider.chat/docs/leaderboards/

[KaoruAoiShiho]

Sonnet is literally lower on the aider benchmark you just linked. It's only the top with Deepseek as architect, otherwise it's lower than many others.

[refulgentis]

Let's steelman a bit: once you multiply out the edit accuracy versus completion accuracy, Sonnet, on its own, is within 5% of the very top one not using sonnet.

[theturtletalks]

Yes, but I use Cursor Composer Agent mode with Sonnet which is like Aider's architect mode where 1 LLM is instructing another one. Not to mention the new reasoning models can't use tool calling (except o3-mini which is not multi-modal).

[KaoruAoiShiho]

Me too, cursor+sonnet is also my go to, I just didn't really understand what you were getting at by pointing out this benchmark. I guess it is significant that Sonnet is the actual line by line coder here. It is the best at that, and it's better than DeepSeek+any other combination and better than Any other reasoner+Sonnet.

[theturtletalks]

Yes I've followed this benchmark for a while and before Deepseek + Sonnet Architect took the top spot, Sonnet was there alone followed by o1 and Gemini EXP. This is one of the few benchmarks where Sonnet is actually on top like my experience shows, other popular ones have 03-mini and DeepSeek r1 which fall short in my opinion.

[nyrikki]

Quite the corpus for Exercism tasks that were almost certainly trained on, which could lead this to doing what we know LLM/LRM's are good at...approximate retrieval.

https://github.com/search?q=Exercism&type=repositories

[yunwal]

Are Exercism coding exercises really low key? I thought it was like the standard free platform for learning a new language now

[theturtletalks]

Low-key as in many people don't check this leaderboard as much as the other high profile ones.

[azinman2]

Would love if they put latency in this too.

[delusional]

> where the resolution rates of the models drop significantly, which are 0.73%, 0.55%, and 3.83%, respectively.

Matches my experience pretty well as too. It'll usually output something that a novice would assume is correct but an expert can clearly identify as "know it all teenager forum post" level stuff.

[alfalfasprout]

Yep anecdotally that's basically spot-on. It's also one of the reasons that I still find copilot vastly more useful than highly autonomous AI tooling (cursor, roocode, avante, etc.)

[siva7]

o3-mini and gpt-4o are so piss poor in agent coding compared to claude that you don't even need a benchmark

[jbellis]

o3-mini-medium is slower than claude but comparable in quality. o3-mini-high is even slower, but better.

[danielbln]

Claude really is a step above the rest when it comes to agentic coding.

[dr_kiszonka]

When I used it with Open Hands it was great but also quite expensive (~$8/hr). In Trea, it was pretty bad, but free. Maybe it depends on how the agents use it? (I was writing the same piece of software, a simple web crawler for a hobby RAG project.)

[avs733]

It is worth reflecting, as much as HN seems to hate the social sciences, on this point. But the difficulty of measuring intelligence is a challenge that several fields have struggled with for decades. It is inherently hard because defining intelligence and building intelligence are very closely coupled. This both makes it hard to make unbiased measures as well making measures that don't affect the phenomenon basically NP hard, or known as the Flynn effect[0].

It also goes to how a lot of people misunderstand the replication crisis. 'Hard science' really should replicate - we should be able filter out sources fo error and variance because the phenomena (generally) isn't affected by our attempts to measure it. Making social science replicate often requires so much control that it is deabstracted from reality, meaning the effort at replication reduces the value and usefulness of the knowledge. Generalizable claims are hard because the sources of variance are so much larger adn more complex. Speaking as someone who went through a transition from engineering to social sciences, it is the concept that made it hard. I started my time in social sciences with a cool idea of a whole carrer based on just doing replication studies, because science. That was...useful and stupid at the same time.

[0] https://en.wikipedia.org/wiki/Flynn_effect

[0x20cowboy]

It matches my experience as well.

I find the models very useful to chat about library documentation or high level algorithm concepts, but I find the code it generates to be… I don’t know how else to say it… really bad and often out of context.

I know developers who blindly follow the hype and use them to generate production code. That scares the poop emoji out of me, and the code reads like an asset flipped 3D game.