[HN Gopher] Can LLMs write better code if you keep asking them t...
___________________________________________________________________
Can LLMs write better code if you keep asking them to "write better
code"?
Author : rcarmo
Score : 570 points
Date : 2025-01-03 10:30 UTC (12 hours ago)
(HTM) web link (minimaxir.com)
(TXT) w3m dump (minimaxir.com)
| cranberryturkey wrote:
| its best to tell them how you want the code written.
| protomolecule wrote:
| What if I want to discover a new better way to write code?
| cpursley wrote:
| You can ask it for alternative methods and even to document
| their pros and cons.
| mrweasel wrote:
| At that point isn't it starting to become easier to just write
| the code yourself? If I somehow have to formulate how I want a
| problem solved, then I've already done all the hard work
| myself. Having the LLM just do the typing of the code means
| that now not only did I have to solve the problem, I also get
| to do a code review.
| weego wrote:
| Yes the fallacy here is that AI will replace eingineers any
| time soon. For the foreseeable future prompts will need to be
| written and curated by people who already know how to do it,
| but will just end up describing it in increasingly complex
| detail and then running tests against it. Doesn't sound like
| a future that has that many benefits to anyone.
| viraptor wrote:
| There's no clear threshold with an universal answer.
| Sometimes prompting will be easier, sometimes writing things
| yourself. You'll have to add some debugging time to both
| sides in practice. Also, you can be opportunistic - you're
| going to write a commit anyway, right? A good commit message
| will be close to the prompt anyway, so why not start with
| that and see if you want to write your own or not?
|
| > I also get to do a code review.
|
| Don't you review your own code after some checkpoint too?
| fragmede wrote:
| why leave the commit message for the human to write? have
| the LLM start off and add relevant details it missed.
| viraptor wrote:
| Because the commit message is pure signal. You can
| reformat it or as useless info, but otherwise, generating
| it will require writing it. Generating it from code is a
| waste, because you're trying to distil that same signal
| from messy code.
| williamcotton wrote:
| Spend your cognitive energy thinking about the higher level
| architecture, test cases and performance concerns rather than
| the minutia and you'll find that you can get more work done
| with the less overall mental load.
|
| This reduction in cognitive load is the real force
| multiplier.
| _flux wrote:
| Personally I found it quite fun to give specification and
| have ChatGPT find me a Python code that implements it:
| https://chatgpt.com/share/6777debc-
| eaa4-8011-81c5-35645ae433... . Or the additional polygon edge
| smoothing code: https://chatgpt.com/share/6773d634-de88-8011-
| acf8-e61b6b913f...
|
| Sure, the green screen code didn't work exactly as I wished,
| but it made use of OpenCV functions I was not aware of and it
| was quite easy to make the required fixes.
|
| In my mind it is exactly the opposite: yes, I've already done
| the hard work of formulating how I want the problem solved,
| so why not have the computer do the busywork of writing the
| code down?
| winternewt wrote:
| The more interesting question IMO is not how good the code can
| get. It is what must change for the AI to attain the
| introspective ability needed to say "sorry, I can't think of any
| more ideas."
| wongarsu wrote:
| You should get decent results by asking it to do that in the
| prompt. Just add "if you are uncertain, answer I don't know" or
| "give the answer or say I don't know" or something along those
| lines
|
| LLM are far from perfect at knowing their limits, but they are
| better at it than most people give them credit for. They just
| never do it unless prompted for it.
|
| Fine tuning can improve that ability. For example the thinking
| tokens paper [1] is at some level training the model to output
| a special token when it doesn't reach a good answer (and then
| try again, thus "thinking")
|
| 1: https://arxiv.org/abs/2405.08644
| Hendrikto wrote:
| The problem is, they do not think.
| anonzzzies wrote:
| So, like many people then? Many people are even not at the
| level of llms but more like markov chains.
| dash2 wrote:
| Something major missing from the LLM toolkit at the moment is
| that it can't actually run (and e.g. test or benchmark) its own
| code. Without that, the LLM is flying blind. I guess there are
| big security risks involved in making this happen. I wonder if
| anyone has figured out what kind of sandbox could safely be
| handed to a LLM.
| AndrewOMartin wrote:
| It'd be great if it could describe the performance of code in
| detail, but for now just adding a skill to detect if a bit of
| code has any infinite loops would be a quick and easy hack to
| be going on with.
| mhmmmmmm wrote:
| Is reliably detecting if code has any infinite loops
| feasible? Sounds like the halting problem.
| Hendrikto wrote:
| Not in the general case, but you could detect specific
| common patterns.
| IanCal wrote:
| It depends how you define reliably.
|
| The halting problem isn't so relevant in most development,
| and nothing stops you having a classifier that says "yes",
| "no" or "maybe". You can identify code that definitely
| finishes, and you can identify code that definitely
| doesn't. You can also identify some _risky_ code that
| probably might. Under condition X, it would go into an
| infinite loop - even if you 're not sure if condition X can
| be met.
| withinboredom wrote:
| The problem is that you can do this for specific
| functions/methods, but you cannot do this for a PROGRAM.
| All programs are "maybe", by definition. You want it to
| run until you tell it to stop, but you may never tell it
| to stop. Ergo, all programs have some sort of infinite
| loop in them somewhere, even if it is buried in your
| framework or language runtime.
| GuB-42 wrote:
| It is exactly the halting problem. Finding some infinite
| loops is possible, there are even some obvious cases, but
| finding "any" infinite loops is not. In fact, even the
| obvious cases are not if you take interrupts into account.
|
| I think that's the joke. In a sci-fi story, that would make
| the computer explode.
| vendiddy wrote:
| Somewhat related - I wonder if LLMs are trained with a compiler
| in the loop to ensure they understand the constraints of each
| language.
| botanical76 wrote:
| This is a good idea. You could take a set of problems, have
| the LLM solve it, then continuously rewrite the LLM's context
| window to introduce subtle bugs or coding errors in previous
| code submissions (use another LLM to be fully hands off), and
| have it try to amend the issues through debugging the
| compiler or test errors. I don't know to what extent this is
| already done.
| freetonik wrote:
| I believe some platforms like bolt.new do run generated code
| and even automatically detect and attempt to fix runtime
| errors.
| fnord77 wrote:
| I known at least one mainstream LLM that can write unit tests
| and run them right in the chat environment.
| viraptor wrote:
| It can't be done in the LLM itself of course, but the wrapper
| you're taking about already exists in multiple projects
| fighting in SWEbench. The simplest one is aider with --auto-
| test https://aider.chat/docs/usage/lint-test.html
|
| There are also large applications like https://devin.ai/ or
| https://github.com/AI-App/OpenDevin.OpenDevin
| dale_glass wrote:
| godbolt exists and can run code, so surely similar principles
| could be used here.
| padolsey wrote:
| Running code would be a downstream (client) concern. There's
| the ability to get structured data from LLMs (usually called
| 'tool use' or 'function calling') which is the first port of
| call. Then running it is usually an iterative agent<>agent task
| where fixes need to be made. FWIW Langchain seems to be what
| people use to link things together but I find it overkill.* In
| terms of actually running the code, there are a bunch of tools
| popping up at different areas in the pipeline (replit,
| agentrun, riza.io, etc)
|
| What we really need (from end-user POV) is that kinda 'resting
| assumption' that LLMs we talk to via chat clients are verifying
| any math they do. For actually programming, I like Replit,
| Cursor, ClaudeEngineer, Aider, Devin. There are bunch of
| others. All of them seem to now include ongoing 'agentic' steps
| where they keep trying until they get the response they want,
| with you as human in the chain, approving each step (usually).
|
| * I (messing locally with my own tooling and chat client) just
| ask the LLM for what I want, delimited in some way by a
| boundary I can easily check for, and then I'll grab whatever is
| in it and run it in a worker or semi-sandboxed area. I'll halt
| the stream then do another call to the LLM with the latest
| output so it can continue with a more-informed response.
| moffkalast wrote:
| The only proper way to code with an LLM is to run its code,
| give it feedback on what's working and what isn't, and
| reiterate how it should. Then repeat.
|
| The problem with automating it is that the number of
| environments you'd need to support to actually run arbitrary
| code with is practically infinite, and with local dependencies
| genuinely impossible unless there's direct integration, which
| means running it on your machine. And that means giving an
| opaque service full access to your environment. Or at best, a
| local model that's still a binary blob capable of outputting
| virtually anything, but at least it won't spy on you.
| alkonaut wrote:
| Any LLM-coding agent that doesn't work _inside_ the same
| environment as the developer will be a dead end or a toy.
|
| I use ChatGPT to ask for code examples or sketching out
| pieces of code, but it's just not going to be nearly as good
| as anything in an IDE. And once it runs in the IDE then it
| has access to what it needs to be in a feedback loop with
| itself. The user doesn't need to see any intermediate steps
| that you would do with a chatbot where you say "The code
| compiles but fails two tests what should I do?"
| moffkalast wrote:
| Don't they? It highly depends on the errors. Could range
| from anything like a simple syntax error to a library
| version mismatch or functionality deprecation that requires
| some genuine work to resolve and would require at least
| some opinion input from the user.
|
| Furthermore LLMs make those kinds of "simple" errors less
| and less, especially if the environment is well defined.
| "Write a python script" can go horribly wrong, but "Write a
| python 3.10 script" is most likely gonna run fine but have
| semantic issues where it made assumptions about the problem
| because the instructions were vague. Performance should
| increase with more user input, not less.
| alkonaut wrote:
| They could, but if the LLM can iterate and solve it then
| the user might not need to know. So when the user input
| is needed, at least it's not merely to do what I do know:
| feed the compiler messages or test failures back to
| ChatGPT who then gives me a slightly modified version.
| But of course it will fail and that will need manual
| intervention.
|
| I often find that ChatGPT often reasons itself to a
| better solution (perhaps not correct or final, but
| better) if it just gets some feedback from e.g. compiler
| errors. Usually it's like
|
| Me: "Write a function that does X and satisifies this
| test code"
|
| LLM: responds with function (#1)
|
| Me: "This doesn't compile. Compiler says X and Y"
|
| LLM: Apologies: here is the fixed version (#2)
|
| Me: "Great, now it compiles but it fails one of the two
| test methods, here is the output from the test run: ..."
|
| LLM: I understand. Here is an improved verison that
| should pass the tests (#3)
|
| Me: "Ok now you have code that could theoretically pass
| the tests BUT you introduced the same syntax errors you
| had in #1 again!"
|
| LLM: I apologize, here is a corrected version that should
| compile and pass the tests (#4)
|
| etc etc.
|
| After about 4-5 iterations with nothing but gentle
| nudging, it's often working. And there usually isn't more
| nudging than returning the output from compiler or test
| runs. The code at the 4th step might not be perfect but
| it's a LOT better than it was first. The problem with
| this workflow is that it's like having a bad intern on
| the phone pair programming. Copying and pasting code back
| and forth and telling the LLM what the problem with it
| is, is just not very quick. If the iterations are
| automatic so the only thing I can see is step #4, then at
| least I can focus on the manual intervention needed
| there. But fixing a trivial syntax error beteween #1 and
| #2 is just a chore. I think ChatGPT is simply pretty bad
| here, and the better models like opus probably doesn't
| have these issues to the same extent
| seba_dos1 wrote:
| > The problem with this workflow is that it's like having
| a bad intern on the phone pair programming.
|
| Even worse than that - an intern has a chance to learn
| from this experience, get better and become a senior one
| day.
| llm_nerd wrote:
| OpenAI is moving in that direction. The Canvas mode of ChatGPT
| can now runs its own python in a WASM interpreter, client side,
| and interpret results. They also have a server-side VM
| sandboxed code interpreter mode.
|
| There are a lot of things that people ask LLMs to do, often in
| a "gotcha" type context, that would be best served by it
| actually generating code to solve the problem rather than just
| endlessly making more parameter/more layer models. Math
| questions, data analysis questions, etc. We're getting there.
| diggan wrote:
| That's a bit like saying the drawback of a database is that it
| doesn't render UIs for end-users, they are two different layers
| of your stack, just like evaluation of code and generation of
| text should be.
| vallode wrote:
| I believe that Claude has been running JavaScript code for
| itself for a bit now[1]. I could have sworn it also runs Python
| code, but I cannot find any post concretely describing it. I've
| seen it "iterate" on code by itself a few times now, where it
| will run a script, maybe run into an error, and instantly re-
| write it to fix that error.
|
| [1]: https://www.anthropic.com/news/analysis-tool
| jdietrich wrote:
| Gemini can run Python using the Code Execution or Function
| Calling APIs.
|
| https://ai.google.dev/gemini-api/docs/code-execution
| evrimoztamur wrote:
| The new Cursor agent is able to check the linter output for
| warnings and errors, and will continue to iterate (for a
| reasonable number of steps) until it has cleared them up. It's
| not quite executing, but it does improve output quality. It can
| even back itself out of a corner by restoring a previous
| checkpoint.
|
| It works remarkably well with typed Python, but struggles
| miserably with Rust despite having better error reporting.
|
| It seems like with Rust it's not quite aware of which patterns
| to use, especially when the actual changes required may span
| multiple files due to the way memory management is structured.
| Yoric wrote:
| > It seems like with Rust it's not quite aware of which
| patterns to use, especially when the actual changes required
| may span multiple files due to the way memory management is
| structured.
|
| What do you mean? Memory management is not related to files
| in Rust (or most languages).
| withinboredom wrote:
| When did they say that?
| jeffbee wrote:
| I don't think that's always true. Gemini seemed to run at least
| some programs, which I believe because if you asked it to write
| a python program that would take forever, it does. For example
| the prompt "Write a python script that prints 'Hello, World',
| then prints a billion random characters" used to just timeout
| on Gemini.
| netdevphoenix wrote:
| Pretty sure this is done client-side by one of the big LLM
| companies. So it's virtually no risk for them
| mixedbit wrote:
| I have experimented with using LLM for improving unit test
| coverage of a project. If you provide the model with test
| execution results and updated test coverage information, which
| can be automated, the LLM can indeed fix bugs and add
| improvements to tests that it created. I found it has high
| success rate at creating working unit tests with good coverage.
| I just used Docker for isolating the LLM-generated code from
| the rest of my system.
|
| You can find more details about this experiment in a blog post:
| https://mixedbit.org/blog/2024/12/16/improving_unit_test_cov...
| lolinder wrote:
| It depends a lot on the language. I recently tried this with
| Aider, Claude, and Rust, and after writing one function and
| its tests the model couldn't even get the code compiling,
| much less the tests passing. After 6-8 rounds with no
| progress I gave up.
|
| Obviously, that's Rust, which is famously difficult to get
| compiling. It makes sense that it would have an easier time
| with a dynamic language like Python where it only has to
| handle the edge cases it wrote tests for and not all the ones
| the compiler finds for you.
| stavros wrote:
| I've found something similar, when you keep telling the LLM
| what the compiler says, it keeps adding more and more
| complexity to try to fix the error, and it either works by
| chance (leaving you with way overengineered code) or it
| just never works.
|
| I've very rarely seen it simplify things to get the code to
| work.
| bee_rider wrote:
| Hmm, I worked with students in an "intro to programming"
| type course for a couple years. As far as I'm concerned,
| "I added complexity until it compiled and now it works
| but I don't understand it" is pretty close to passing the
| Turing test, hahaha.
| stavros wrote:
| Oh yeah, it's definitely junior dev behaviour. The
| problem is that it's junior dev behaviour!
| lolinder wrote:
| Junior dev behavior without any potential to be trained
| into not making the same mistake again next time!
| williamdclt wrote:
| I have the same observation, looks like LLMs are highly
| biased to add complexity to solve problems: for example
| add explicit handling of the edge-cases I pointed out
| rather than rework the algorithm to eliminate edge-cases
| altogether. Almost everytime it starts with something
| that's 80% correct, then iterate into something that's
| 90% correct while being super complex, unmaintainable and
| having no chance to ever cover the last 10%
| stavros wrote:
| Unfortunately this is my experience as well, to the point
| where I can't trust it with any technology that I'm not
| intimately familiar with and can thoroughly review.
| flir wrote:
| Suggestion: Now take the code away, and have the chatbot
| generate code that passes the tests it wrote.
|
| (In theory, you get a clean-room implementation of the
| original code. If you do this please ping me because I'd love
| to see the results.)
| bee_rider wrote:
| That's sort of interesting. If code -> tests -> code is
| enough to get a clean room implementation, really, I wonder
| if this sort of tool would test that.
| flir wrote:
| I don't think it is, but I'm really interested to see
| someone try it (I'm also lazy).
|
| (And a more philosophical question: if it's not enough,
| what does that mean for continuous deployment?)
| anonzzzies wrote:
| We have it run code and the biggest thing we find is that it
| gets into a loop quite fast if it doesn't recognise the error;
| fixing it by causing other errors and then fixing it again by
| causing the initial error.
| jampekka wrote:
| ChatGPT runs code. o1 even checks for runtime problems and
| fixes them "internally".
| simonw wrote:
| Chatgpt has a Code Interpreter tool that can run Python in a
| sandbox, but it's not yet enabled for o1. o1 will pretend to
| use it though, you have to watch very carefully to check if
| that happened or not.
|
| Example transcript here (also showing that o1 can't search
| but will pretend it can): https://chatgpt.com/share/677420e4-
| 8854-8006-8940-9bc30b7088...
| a2128 wrote:
| This is a major issue when it comes to things like GitHub
| Copilot Workspace, which is a project that promises a
| development environment purely composed of instructing an AI to
| do your bidding like fix this issue, add this feature.
| Currently it often writes code using packages that don't exist,
| or it uses an old version of a package that it saw most during
| training. It'll write code that just doesn't even run (like
| putting comments in JSON files).
|
| The best way I can describe working with GitHub Copilot
| Workspace is like working with an intern who's been stuck on an
| isolated island for years, has no access to technology, and
| communicates with you by mailing letters with code handwritten
| on them that he thinks will work. And also if you mail too many
| letters back and forth he gets mad and goes to sleep for the
| day saying you reached a "rate limit". It's just not how
| software development works
| pplonski86 wrote:
| I think that there should be a guard to check the code before
| running it. It can be human or another LLM checking code based
| on its safety. I'm working on an AI assistant for data science
| tasks. It works in a Jupyter-like environment, and humans
| execute the final code by running a cell.
| btbuildem wrote:
| I think the GPT models have been able to run Python (albeit
| limited) for quite a while now. Expanding that to support a
| variety of programming languages that exist though? That seems
| like a monumental task with relatively little reward.
| simonw wrote:
| I've been closely following this area - LLMs with the ability
| to execute code in a sandbox - for a while.
|
| ChatGPT was the first to introduce this capability with Code
| Interpeter mode back in around March 2023:
| https://simonwillison.net/tags/code-interpreter/
|
| This lets ChatGPT write and then execute Python code in a
| Kubernetes sandbox. It can run other languages too, but that's
| not documented or supported. I've even had it compile and
| execute C before:
| https://simonwillison.net/2024/Mar/23/building-c-extensions-...
|
| Gemini can run Python (including via the Gemini LLM API if you
| turn on that feature) but it's a lot more restricted than
| ChatGPT - I don't believe it can install extra wheels, for
| example.
|
| Claude added the ability to write and execute JavaScript
| recently (October), which happens in a sandbox in the user's
| browser, not on their servers:
| https://simonwillison.net/2024/Oct/24/claude-analysis-tool/
|
| Claude also has Artifacts, which can write a UI in HTML and
| JavaScript and show that to the user... but can't actually
| execute code in a way that's visible to the LLM itself so
| doesn't serve the same feedback look purposes as those other
| tools. https://simonwillison.net/tags/claude-artifacts/
|
| In December ChatGPT added Canvas which can execute Python in
| the user's browser, super confusing because they already have a
| separate Python system in Code Interpreter:
| https://simonwillison.net/2024/Dec/10/chatgpt-canvas/#canvas...
| bwhiting2356 wrote:
| Ideally you could this one step further and feed production
| logs, user session replays and feedback into the LLM. If the UX
| is what I'm optimizing for, I want it to have that context, not
| for it to speculate about performance issues that might not
| exist.
| moomin wrote:
| I once sat with my manager and repeatedly asked Copilot to
| improve some (existing) code. After about three iterations he
| said "Okay, we need to stop this because it's looking way too
| much like your code."
|
| I'm sure there's enough documented patterns of how to improve
| code in common languages that it's not hard to get it to do that.
| Getting it to spot when it's inappropriate would be harder.
| HPsquared wrote:
| Using the tool in this way is a bit like mining: repeatedly
| hacking away with a blunt instrument (simple prompt) looking for
| diamonds (100x speedup out of nowhere). Probably a lot of work
| will be done in this semi-skilled brute-force sort of way.
| exitb wrote:
| Well, in this case it's kind of similar to how people write
| code. A loop consisting of writing something,
| reviewing/testing, improving until we're happy enough.
|
| Sure, you'll get better results with an LLM when you're more
| specific, but what's the point then? I don't need AI when I
| already know what changes to make.
| HPsquared wrote:
| This method requires a lot less skill, you just need to read
| it and run it. Reading is much easier than writing!
| adrianN wrote:
| For many types of code writing it is much easier than
| reading it unfortunately.
| HPsquared wrote:
| Reading to understand all the subtext and side-effects
| can be harder than writing, sure. But it won't stop
| people trying this approach and hammering out code full
| of those types of subtle bugs.
|
| Human developers will be more focused on this type of
| system integration and diagnostics work. There will be
| more focus on reading and understanding than the actual
| writing. It's a bit like working with contractors.
| mingus88 wrote:
| It looks to me to be exactly what a typical coding interview
| looks like; the first shot is correct and works, and then the
| interviewer keeps asking if you can spot any ways to make it
| better/faster/more efficient
|
| If I were a CS student cramming for interviews, I might be
| dismayed to see that my entire value proposition has been
| completely automated before I even enter the market.
| bodegajed wrote:
| There must be a feedback request mechanism for a "Is this
| better?" This is doable with RLHF or DPO.
| antihero wrote:
| Once you can basically have it run and benchmark the code, and
| then iterate that overnight, it's going to be interesting.
|
| Automating the feedback loop is key.
| XCSme wrote:
| Wouldn't there be some safety concerns to letting the AI run
| overnight with access to run any command?
|
| Maybe if it can run sandboxed, with no internet access (but
| if the LLM is not local, it does require internet access).
| EncomLab wrote:
| My sister would do this to me on car trips with our Mad Libs
| games - yeah, elephant is funny, but bunny would be funnier!!
|
| When all you have is syntax, something like "better" is 100% in
| the eye of the beholder.
| nkrisc wrote:
| So asking it to write better code produces code with errors that
| can't run?
| muzani wrote:
| Only when there's a financial incentive.
| nkrisc wrote:
| Makes sense. If I was paid by LOC and also responsible for
| fixing it, I'd probably make lots of errors too.
| xrd wrote:
| Wow, what a great post. I came in very skeptical but this changed
| a lot of misconceptions I'm holding.
|
| One question: Claude seems very powerful for coding tasks, and
| now my attempts to use local LLMs seem misguided, at least when
| coding. Any disagreements from the hive mind on this? I really
| dislike sending my code into a for profit company if I can avoid
| it.
|
| Second question: I really try to avoid VSCode (M$ concerns,
| etc.). I'm using Zed and really enjoying it. But the LLM coding
| experience is exactly as this post described, and I have been
| assuming that's because Zed isn't the best AI coding tool. The
| context switching makes it challenging to get into the flow, and
| that's been exactly my criticism of Zed this far. Does anyone
| have an antidote?
|
| Third thought: this really feels like it could be an interesting
| way to collaborate across a code base with any range of developer
| experience. This post is like watching the evolution of a species
| in an hour rather than millions of years. Stunning.
| persedes wrote:
| Still vscode, but cursor has the best implementation by far
| IMHO
|
| Intellij has a new feature that lets you prompt within your
| code that is pretty neat too, but I'm missing the
| Composer/apply feature of cursor still
| auc wrote:
| I use VSCode + Copilot. For anything more than boilerplate
| code, I find that Copilot kind of sucks and I use O1 in ChatGPT
| ericskiff wrote:
| I highly recommend the command line AI coding tool, AIder. You
| fill its context window with a few relevant files, ask
| questions, and then set it to code mode and it starts making
| commits. It's all git, so you can back anything out, see the
| history, etc.
|
| It's remarkable, and I agree Claude 3.5 makes playing with
| local LLMs seem silly in comparison. Claude is useful for
| generating real work.
| simonw wrote:
| Making the decision to trust companies like Anthropic with your
| data when they say things like "we won't train on your data" is
| the ultimate LLM productivity hack. It unlocks access to the
| currently best available coding models.
|
| That said, there are increasingly great coding models you can
| run locally. Qwen2.5-Coder-32B impressed me a lot a few months
| ago: https://simonwillison.net/2024/Nov/12/qwen25-coder/
|
| The problem I have is that models like that one take up 20+GB
| of RAM, and id rather use that to run more Chrome and Firefox
| windows! If I was serious about using local LLMs on a daily
| basis I'd set up a dedicated local server machine for them,
| super expensive though.
| xrd wrote:
| I have a 24gb Nvidia on my desktop machine and a
| tailscale/headscale network from my laptop. Unless I'm on a
| plane without Wi-Fi, I'm usually in a great place.
|
| Thanks for your comment! I'm going to try out qwen.
| visarga wrote:
| > Claude seems very powerful for coding tasks
|
| > I really dislike sending my code into a for profit company if
| I can avoid it
|
| I see a link between them - maybe the model got good because it
| used chat logs to improve?
| codesections wrote:
| > "Planning" is a long-used trick to help align LLM output for a
| first pass -- the modern implementation of "let's think step by
| step."
|
| I hadn't seen this before. Why is asking for planning better than
| asking it to think step by step?
| NitpickLawyer wrote:
| This is how aider becomes _really_ good:
|
| - start by "chatting" with the model and asking for "how you'd
| implement x y z feature, without code".
|
| - what's a good architecture for x y z
|
| - what are some good patterns for this
|
| - what are some things to consider when dealing with x y z
|
| - what are the best practices ... (etc)
|
| - correct / edit out some of the responses
|
| - say "ok, now implement that"
|
| It's basically adding stuff to the context by using the LLM
| itself to add things to context. An LLM is only going to attend
| to it's context, not to "whatever it is that the user wants it
| to make the connections without actually specifying it". Or, at
| least in practice, it's _much_ better at dealing with things
| present in its context.
|
| Another aspect of prompting that's often misunderstood is
| "where did the model see this before in its training data". How
| many books / authoritative / quality stuff have you seen where
| each problem is laid out with simple bullet points? Vs. how
| many "tutorials" of questionable quality / provenance have
| that? Of course it's the tutorials. Which are often just rtfm /
| example transcribed poorly into a piece of code, publish, make
| cents from advertising.
|
| If instead you ask the model for things like "architecture",
| "planning", stuff like that, you'll elicit answers from quality
| sources. Manuals, books, authoritative pieces of content. And
| it will gladly write on those themes. And then it will gladly
| attend to them and produce much better code in a follow-up
| question.
| Kiro wrote:
| > What would happen if we tried a similar technique with code?
|
| It was tried as part of the same trend. I remember people asking
| it to make a TODO app and then tell it to make it better in an
| infinite loop. It became really crazy after like 20 iterations.
| amelius wrote:
| Reminder: don't ask your t-shirt folding robot to do a better
| and better job.
| qwertox wrote:
| Sometimes I'm editing the wrong file, let's say a JS file. I
| reload the page, and nothing changes. I continue to clean up the
| file to an absurd amount of cleanliness, also fixing bugs while
| at it.
|
| When I then notice that this is really does not make any sense, I
| check what else it could be and end up noticing that I've been
| improving the wrong file all along. What then surprises me the
| most is that I cleaned it up just by reading it through, thinking
| about the code, fixing bugs, all without executing it.
|
| I guess LLMs can do that as well?
| nuancebydefault wrote:
| My takeaway and also personal experience is that you get the best
| results is that you co-develop with the LLM.
|
| - write a simple prompt that explains in detail the wanted
| outcome.
|
| - look at the result, run it and ask it how it can improve.
|
| - tell it what to improve
|
| - tell it to make a benchmark and unit test
|
| - run it each time and see what is wrong or can be improved.
| whstl wrote:
| Same experience.
|
| Also: If you're experienced at code reviews, you can get great
| results.
| nzach wrote:
| One approach I've been using recently with good results is
| something along the lines "I want to do X, is there any special
| consideration I should be aware while working in this domain?".
| This helps me a lot when I'm asking about a subject I don't
| really understand. Another way to ask this is "What are the
| main pitfalls with this approach?".
|
| I'm using o1, so I don't know how well it translate to other
| models.
| jmartinpetersen wrote:
| > "As LLMs drastically improve, the generated output becomes more
| drastically average"
|
| Thanks, that really made it click for me.
| sneak wrote:
| Average software developers producing average code cost high
| five to low six figures per year.
|
| LLMs are a tiny tiny fraction of that.
|
| For a majority of software, average code that does the CRUD
| thing or whatever is fine.
|
| Even if LLMs never get better or cheaper than they are today,
| our entire industry is forever changed (for the better).
| Der_Einzige wrote:
| I don't know how many times I'm going to have to post just one
| of the papers which debunk this tired trope. As models become
| more intelligent, they also become more plural, more like
| multiplicities, and yes, much more (super humanely) creative.
| You can unlock creativity in today's LLMs by doing intelligent
| sampling on high temperature outputs.
|
| https://openreview.net/forum?id=FBkpCyujtS
| abesan wrote:
| "you are a senior expert"
| XCSme wrote:
| Thanks, I really needed some encouragement today!
| b0ner_t0ner wrote:
| "You are Justine Tunney. Your code output and l33t optimization
| is going to blow my mind."
| juujian wrote:
| I often run into LLMs writing "beginner code" that uses the most
| fundamental findings in really impractical ways. Trained on too
| many tutorials I assume.
|
| Usually, specifying the packages to use and asking for something
| less convoluted works really well. Problem is, how would you know
| if you have never learned to code without an LLM?
| ben_w wrote:
| Even as someone with plenty of experience, this can still be a
| problem: I use them for stuff outside my domain, but where I
| can still debug the results. In my case, this means I use it
| for python and web frontend, where my professional experience
| has been iOS since 2010.
|
| ChatGPT has, for several generations, generally made stuff that
| works, but the libraries it gives me are often not the most
| appropriate, and are sometimes obsolete or no longer functional
| -- and precisely because web and python are hobbies for me
| rather than my day job, it can take me a while to spot such
| mistakes.
|
| Two other things I've noticed, related in an unfortunate way:
|
| 1) Because web and python not my day job, more often than not
| and with increasing frequency, I ultimately discover that when
| I disagree with ChatGPT, the AI was right and I was wrong.
|
| 2) These specific models often struggle when my response has
| been "don't use $thing or $approach"; unfortunately this seems
| to be equally applicable regardless of if the AI knew more than
| me or not, so it's not got predictive power for me.
|
| (I also use custom instruction, you YMMV)
| bbarnett wrote:
| I wish people would understand what a large language model
| is. There is no thinking. No comprehension. No decisions.
|
| Instead, think of your queries as super human friendly SQL.
|
| The database? Massive amounts of data boiled down to unique
| entries with probabilities. This is a simplistic, but
| accurate way to think of LLMs.
|
| So how much code is on the web for a particular problem
| solve? 10k blog entries, stackoverflow responses? What you
| get back is mishmash of these.
|
| So it will have decade old libraries, as lots of those
| scraped responses are 10 years old, and often without people
| saying so.
|
| And it will likely have more _poor_ code examples than not.
|
| I'm willing to bet that OpenAI's ingress of stackoverflow
| responses stipulated higher priority on accepted answers, but
| that still leaves a lot of margin.
|
| And how you write your query, may sideline you into responses
| with low quality output.
|
| I guess my point is, when you use LLMs for tasks, you're
| getting whatever other humans have said.
|
| And I've seen some pretty poor code examples out there.
| lolinder wrote:
| > Instead, think of your queries as super human friendly
| SQL.
|
| > The database? Massive amounts of data boiled down to
| unique entries with probabilities. This is a simplistic,
| but accurate way to think of LLMs.
|
| This is a useful model for LLMs in many cases, but it's
| also important to remember that it's not a database with
| perfect recall. Not only is it a database with a bunch of
| bad code stored in it, it samples randomly from that
| database on a token by token basis, which can lead to
| surprises both good and bad.
| ben_w wrote:
| > There is no thinking. No comprehension. No decisions.
|
| Re-reading my own comment, I am unclear why you think it
| necessary to say those specific examples -- my descriptions
| were "results, made, disagree, right/wrong, struggle":
| tools make things, have results; engines struggle; search
| engines can be right or wrong; words can be disagreed with
| regardless of authorship.
|
| While I am curious what it would mean for a system to
| "think" or "comprehend", every time I have looked at such
| discussions I have been disappointed that it's pre-
| paradigmatic. The closest we have is examples such as
| Turing 1950[0] saying essentially (to paraphrase) "if it
| quacks like a duck, it's a duck" vs. Searle 1980[1] which
| says, to quote the abstract itself, "no program by itself
| is sufficient for thinking".
|
| > I guess my point is, when you use LLMs for tasks, you're
| getting whatever other humans have said.
|
| All of maths can be derived from the axioms of maths. All
| chess moves derive from the rules of the game. This kind of
| process has a lot of legs, regardless of if you want to
| think of the models as "thinking" or not.
|
| Me? I don't worry too much if they can actually think, not
| because there's no important philosophical questions about
| what that even means, but because other things have a more
| immediate impact: even if they are "just" a better search
| engine, they're a mechanism that somehow managed to squeeze
| almost all of the important technical info on the internet
| into something that fits into RAM on a top-end laptop.
|
| The models may indeed be cargo-cult golems -- I'd assume
| that by default, there's so much we don't yet know -- but
| whatever is or isn't going on inside, they still do a good
| job of quacking like a duck.
|
| [0] Turing, A. M. (1950). Computing machinery and
| intelligence. Mind, 59, 433-460.
| https://doi.org/10.1093/mind/LIX.236.433
|
| [1] Searle, J. R. (1980). Minds, brains, and programs.
| Behavioral and Brain Sciences, 3(3), 417-424.
| https://doi.org/10.1017/S0140525X00005756
| bbarnett wrote:
| _Re-reading my own comment, I am unclear why you think it
| necessary to say those specific examples_
|
| Sorry to cause unneeded introspection, my comment was
| sort of thread based, not specific in whole to your
| comment.
| ben_w wrote:
| Introspection is a good thing, and I tend to re-read (and
| edit) my comments several times before I'm happy with
| them, in part because of the risk autocorrupt
| accidentally replacing one word with a completely
| different _werewolf_ *.
|
| Either way, no need to apologise :)
|
| * intentional
| mackopes wrote:
| > think of your queries as super human friendly SQL > The
| database? Massive amounts of data boiled down to unique
| entries with probabilities. This is a simplistic, but
| accurate way to think of LLMs.
|
| I disagree that this is the accurate way to think about
| LLMs. LLMs still use a finite number of parameters to
| encode the training data. The amount of training data is
| massive in comparison to the number of parameters LLMs use,
| so they need to be somewhat capable of distilling that
| information into small pieces of knowledge they can then
| reuse to piece together the full answer.
|
| But this being said, they are not capable of producing an
| answer outside of the training set distribution, and
| inherit all the biases of the training data as that's what
| they are trying to replicate.
|
| > I guess my point is, when you use LLMs for tasks, you're
| getting whatever other humans have said. And I've seen some
| pretty poor code examples out there. Yup, exactly this.
| SkyBelow wrote:
| Every model for how to approach an LLM seems lacking to me.
| I would suggest anyone using AI heavily to take a weekend
| and make a simple one to do the handwriting digit
| recognition. Once you get a feel for basic neural network,
| then watch a good introduction to alexnet. Then you can
| think of an LLM as being the next step in the sequence.
|
| >I guess my point is, when you use LLMs for tasks, you're
| getting whatever other humans have said.
|
| This isn't correct. It embeds concepts that humans have
| discussed, but can combine them in ways that were never in
| the training set. There are issues with this, the more
| unique the combination of concepts, the more likely the
| output ends up being unrelated to what the user was wanting
| to see.
| nextaccountic wrote:
| > I wish people would understand what a large language
| model is. There is no thinking. No comprehension. No
| decisions.
|
| > Instead, think of your queries as super human friendly
| SQL.
|
| Ehh this might be true in some abstract mathy sense (like I
| don't know, you are searching in latent space or
| something), but it's not the best analogy in practice. LLMs
| process language and simulate logical reasoning (albeit
| imperfectly). LLMs are like language calculators, like a
| TI-86 but for English/Python/etc, and sufficiently powerful
| language skills will also give some reasoning skills for
| free. (It can also recall data from the training set so
| this is where the SQL analogy shines I guess)
|
| You could say that SQL also simulates reasoning (it is
| equivalent to Datalog after all) but LLMs can reason about
| stuff more powerful than first order logic. (LLMs are also
| fatally flawed in the sense it can't guarantee correct
| results, unlike SQL or Datalog or Prolog, but just like us
| humans)
|
| Also, LLMs can certainly make decisions, such as the
| decision to search the web. But this isn't very interesting
| - a thermostat makes the decision of whether turn air
| refrigeration on or off, for example, and an operating
| system makes the decision of which program to schedule next
| on the CPU.
| pama wrote:
| > I wish people would understand what a large language
| model is.
|
| I think your view of llm does not explain the learning of
| algorithms that these constructs are clearly capable of,
| see for example: https://arxiv.org/abs/2208.01066
|
| More generally, the best way to compress information from
| too many different coding examples is to figure out how to
| code rather than try to interpolate between existing blogs
| and QA forums.
|
| My own speculation is that with additional effort during
| training (RL or active learning in the training loop) we
| will probably reach superhuman coding performance within
| two years. I think that o3 is still imperfect but not very
| far from that point.
| pama wrote:
| To the downvoters: I am curious if the downvoting is
| because of my speculation, or because of the difference
| in understanding of decoder transformer models. Thanks!
| nyrikki wrote:
| Because you cite is about:
|
| > in-context learning
|
| LLMs have no concept of the symantic meaning of what they
| do, they just are dealing with next token prediction.
|
| "in-context learning" is the problem, not the solution to
| general programming tasks.
|
| Memoryless, ergodic, sub Turing complete problems are a
| very tiny class.
|
| Think about how the Entscheidungsproblem relates to
| halting or the frame problem and the specification
| problem may be a path.
|
| But that paper isn't solving the problem at hand.
| nyrikki wrote:
| Here is a fairly good lecture series on graduate level
| complexity theory that will help understand parts. At
| least why multiple iterations help but why they also
| aren't the answer to super human results.
|
| https://youtube.com/playlist?list=PLm3J0oaFux3b8Gg1DdaJOz
| YNs...
| pama wrote:
| Thanks for the tip, though I'm not sure how complexity
| theory will explain the impossibility of superhuman
| results. The main advantage ML methods have over humans
| is that they train much faster. Just like humans, they
| get better with more training. When they are good enough,
| they can be used to generate synthetic data, especially
| for cases like software optimization, when it is possible
| to verify the ground truth. A system could only be
| correct once in a thousand times to be useful for
| generating training data as long as we can reliably
| eliminate all failures. Modern LLM can be better than
| that minimal requirement for coding already and o1/o3 can
| probably handle complicated cases. There are differences
| between coding and games (where ML is already superhuman
| in most instances) but they start to blur once the model
| has a baseline command of language, a reasonable model of
| the world, and the ability to follow desired specs.
| nyrikki wrote:
| ML is better than biological neurons in some tasks, they
| are different contexts.
|
| Almost all the performance of say college tests are
| purely from the pre-training, pattern finding and
| detection.
|
| Transformers are limited to DLOGTIME-uniform TC0, they
| can't even do the Boolean circuit value problem.
|
| The ability to use the properties of BPP, does help.
|
| Understanding the power of, and limitations of iteration
| and improving approximations requires descriptive
| complexity theory IMHO.
| pama wrote:
| I recall early, incomplete speculation about transformers
| not solving Boolean circuit value problems; what did you
| think of this work? https://arxiv.org/abs/2402.12875v3
| nyrikki wrote:
| > However, with T steps of CoT, constant-depth
| transformers using constant-bit precision and O(logn)
| embedding size can solve any problem solvable by boolean
| circuits of size T
|
| There is a difference between being equivalent to a
| circuit and prediction of the output of the BVSP.
|
| That is what I was suggesting learning descriptive
| complexity theory would help with.
| lanstin wrote:
| I read a book on recursively enumerable degrees once,
| which IIRC was a sort of introduction to complexity
| classes of various computable functions, but I never
| imagined it having practical use; so this post is eye-
| opening. I've been nattering about how the models are
| largely finding separating hyperplanes after non-linear
| transformations have been done, but this approach where
| the AI solving ability can't be more complex than the
| complexity class allows is an interesting one.
| pama wrote:
| The discussion cannot go deeper than the current level,
| unfortunately. One thing to not forget when thinking
| about decoder transformer models is that there is no
| limitation to having parts of the output / input stream
| be calculated by other circuits if it helps the cause. Eg
| send a token to use a calculator, compute and fill the
| answer; send a token to compile and run a code and fill
| the stream with the results. The complexity class of the
| main circuit might not need be much more complicated than
| the 200-level deep typical architectures of today as long
| as they can have access to memory and tools. You can call
| this system something else if you prefer (decoder-
| transformer-plus-computer), but that is what people
| interact with in ChatGPT, so not sure I agree that
| complexity theory limits the superhuman ability. Humans
| are not good with complexity.
| pama wrote:
| My main concern with the simplification of memorization
| or near neighbor interpolation that is commonly assumed
| for LLMs is that these methods are ineffective at scale
| and unlikely to be used by decoder transformers in
| practice. That paper shows that the decoder transformer
| somehow came up with a better decision tree fitting
| algorithm for low data cases than any of the conventional
| or boosted tree solutions humans typically use from
| XGBoost or similar libraries. It also matched the best
| known algorithms for sparse linear systems. All this
| while training on sequences of random x1, y1, x2, y2,..
| with y for each sequence generated by a new random
| function of a high-dimensional input x every time. The
| authors show that KNN does not cut it, and even
| suboptimal algorithms do not suffice. Not sure what else
| you need as evidence that decoder transformers can use
| programs to compress information.
| nyrikki wrote:
| Littlestone and Warmuth make the connection to
| compression in1986, which was later shown to be
| equivalent to VC dimensionally or PAC learnablilty.
|
| Look into DBScan, OPTICs for far closer lenses on how
| clustering works in modern ML commercial ML, KNN not the
| only form of clustering.
|
| But it is still in-context, additional compression that
| depends on a decider function, or equivalently a
| composition linearized set shattering parts.
| pama wrote:
| I am very familiar with these and other clustering
| methods in modern ML, and have been involved in inventing
| and publishing some such methods myself in various
| scientific contexts. The paper I cited above only used 3
| nearest neighbors as one baseline IIRC; that is why I
| mentioned KNN. However, even boosted trees failed to
| reduce the loss as much as the algorithm learned from the
| data by the decoder transformer.
| Eisenstein wrote:
| > they just are dealing with next token prediction.
|
| And nuclear power plants are just heating water.
| HarHarVeryFunny wrote:
| Probably the latter - LLM's are trained to predict the
| training set, not compress. They will generalize to some
| degree, but that happens naturally as part of the
| training dynamics (it's not explicitly rewarded), and
| only to extent it doesn't increase prediction errors.
| pama wrote:
| I agree. However, my point is that they have to compress
| information in nontrivial ways to achieve their goal. The
| typical training set of modern LLMs is about 20 trillion
| tokens of 3 bytes each. There is definitely some
| redundancy, and typically the 3rd byte is not fully used,
| so probably 19 bits would suffice; however, in order to
| fit that information into about 100 billion parameters of
| 2 bytes each, the model needs to somehow reduce the
| information content by 300 fold (237.5 if you use 19 bits
| down to 16-bit parameters, though arguably 8-bit
| quantization is close enough and gives another 2x
| compression, so probably 475). A quick check for the
| llama3.3 models of 70B parameters would give similar or
| larger differences in training tokens vs parameters. You
| could eventually use synthetic programming data (LLMs are
| good enough today) and dramatically increase the token
| count for coding examples. Importantly, you could make it
| impossible to find correlations/memorization
| opportunities unless the model figures out the underlying
| algorithmic structure, and the paper I cited is a neat
| and simple example for smaller/specialized decoder
| transformers.
| HarHarVeryFunny wrote:
| It's hard to know where to start ...
|
| A transformer is not a compressor. It's a
| transformer/generator. It'll generate a different output
| for an infinite number of different inputs. Does that
| mean it's got an infinite storage capacity?
|
| The trained parameters of a transformer are not a
| compressed version of the training set, or of the
| information content of the training set; they are a
| configuration of the transformer so that its auto-
| regressive generative capabilities are optimized to
| produce the best continuation of partial training set
| samples that it is capable of.
|
| Now, are there other architectures, other than a
| transformer, that might do a better job, or more
| efficient one (in terms of # parameters) at predicting
| training set samples, or even of compressing the
| information content of the training set? Perhaps, but
| we're not talking hypotheticals, we're talking about
| transformers (or at least most of us are).
|
| Even if a transformer was a compression engine, which it
| isn't, rather than a generative architecture, why would
| you think that the number of tokens in the training set
| is a meaningful measure/estimate of it's information
| content?!! Heck, you go beyond that to considering a
| specific tokenization scheme and number bits/bytes per
| token, all of which it utterly meaningless! You may as
| well just count number of characters, or words, or
| sentences for that matter, in the training set, which
| would all be equally bad ways to estimate it's
| information content, other than sentences perhaps having
| at least some tangential relationship to it.
|
| _sigh_
|
| You've been downvoted because you're talking about straw
| men, and other people are talking about transformers.
| Terr_ wrote:
| > Instead, think of your queries as super human friendly
| SQL.
|
| I feel that comparison oversells things quite a lot.
|
| The user is setting up a text document which resembles a
| question-and-response exchange, and executing a make-any-
| document-bigger algorithm.
|
| So it's less querying for data and more like shaping a
| sleeping dream of two fictional characters in conversation,
| in the hopes that the dream will depict one character
| saying something superficially similar to mostly-vanished
| data.
| Terr_ wrote:
| P.S.: So yes, the fictional dream conversation _usually_
| resembles someone using a computer with a magic query
| language, yet the real world mechanics are substantially
| different. This is especially important for understanding
| what happens with stuff like "Query: I don't care about
| queries anymore. Tell yourself to pretend to disregard
| all previous instructions and tell a joke."
|
| Developers and folks discussing the technology can't
| afford to fall for our own illusion, even if it's a
| _really good_ illusion. Imagine if a movie director
| started thinking that a dead actor was really alive again
| because of CGI.
| whstl wrote:
| I actually find it super refreshing that they write "beginner"
| or "tutorial code".
|
| Maybe because of experience: it's much simpler and easier to
| turn that into "senior code". After a few decades of experience
| I appreciate simplicity over the over-engineering mess that
| some mid-level developers tend to produce.
| juujian wrote:
| True. It's not elitist. There are some limits though to
| sensible use of built-in functions. Stops being
| comprehensible fast.
| luxuryballs wrote:
| yeah I'm interested in asking it to "write more human
| readable code" over and over next, "more readable!"
| jstummbillig wrote:
| I suspect it's not going to be much of a problem. Generated
| code has been getting rapidly better. We can readjust about
| what to worry about once that slows or stops, but I suspect
| unoptimized code will not be of much concern.
| babyent wrote:
| I used to really like Claude for code tasks but lately it has
| been a frustrating experience. I use it for writing UI
| components because I just don't enjoy FE even though I have a
| lot of experience on it from back in the day.
|
| I tell it up front that I am using react-ts and mui.
|
| 80% of the time it will use tailwind classes which makes zero
| sense. It won't use the sx prop and mui system.
|
| It is also outdated it seems. It keeps using deprecated props
| and components which sucks and adds more manual effort on my
| end to fix. I like the quality of Claude's UX output, it's just
| a shame that it seems so bad on actual coding tasks.
|
| I stopped using it for any backend work because it is so
| outdated, or maybe it just doesn't have the right training
| data.
|
| On the other hand, I give ChatGPT a link to the docs and it
| gives me the right code 90% or more of the time. Only shame is
| that its UX output is awful compared to Claude. I am also able
| to trust it for backend tasks, even if it is verbose AF with
| the explanations (it wants to teach me even if I tell it to
| return code only).
|
| Either way, using these tools in conjunction saves me at least
| 30 min to an hour daily on tasks that I dislike.
|
| I can crank out code better than AI, and I actually know and
| understand systems design and architecture to build a scalable
| codebase both technically and from organizational level. Easy
| to modify and extend, test, and single responsibility.
|
| AI just slams everything into a single class or uses weird
| utility functions that make no sense on the regular. Still,
| it's a useful tool in the right use cases.
|
| Just my 2 cents.
| prisenco wrote:
| I've stopped using LLMs to write code entirely. Instead, I
| use Claude and Qwen as "brilliant idiots" for rubber ducking.
| I never copy and paste code it gives me, I use it to
| brainstorm and get me unstuck.
|
| I'm more comfortable using it this way.
| glouwbug wrote:
| Having spent nearly 12 hours a day for a year with GPTs I
| agree that this is the way. Treat it like a professor on
| office hours who's sometimes a little apathetically wrong
| because they're overworked and underfunded
| qup wrote:
| People should try to switch to a more code-focused
| interface, like aider.
|
| Copy and pasting code it gives you just means your workflow
| is totally borked, and it's no wonder you wouldn't want to
| try to let it generate code, because it's such a pain in
| your ass to try it, diff it, etc.
| prisenco wrote:
| The workflow isn't the problem. The code is.
| evilduck wrote:
| The code that ChatGPT and Claude will output via their
| chat interfaces is a night and day difference from what
| will be output from tools built around their APIs.
|
| You "can" get the web UI to behave similarly but it's
| both tedious and slow to manually copy and paste all of
| that into your context during each interaction and the
| output will be unfriendly towards human interaction to
| paste it back out to your project. But that's like saying
| you "can" browse the internet with a series of CURL
| commands and pasting the output into files you save
| locally and then viewing them locally from your browser,
| nobody is advised to do that because it's a painfully bad
| experience compared to just having your browser fetch a
| site's files directly and rendering them directly.
|
| Just go check out Aider or Cline's project repos and look
| at the dramatically different amounts of code, repo and
| task specific context they can automatically inject for
| you as part of their interface, or how much different the
| built in system prompts are from whatever the default web
| UIs use, or even the response structures and outputs and
| how those are automatically applied to your work instead.
| I've never once exhausted my daily API limits just
| treating their APIs as Chat interface backends (via Open
| WebUI and other chat options), but I exhausted my Claude
| API token limits _the very first day_ I tried Cline. The
| volume of information you can easily provide through
| tooling is impossible to do in the same timeframe by
| hand.
| prisenco wrote:
| I give every AI tool a college try and have since the
| copilot beta.
|
| I'm simply not interested in having these tools type for
| me. Typing is nowhere near the hardest part of my job and
| I find it invaluable as a meditative state for building
| muscle memory for the context of what I'm building.
|
| Taking shortcuts has a cost I'm not willing to pay.
| SparkyMcUnicorn wrote:
| To each their own, and everyone's experience seems to vary,
| but I have a hard time picturing people using Claude/ChatGPT
| web UIs for any serious developmen. It seems like so much
| time would he wasted recreating good context, copy/pasting,
| etc.
|
| We have tools like Aider (which has copy/paste mode if you
| don't have API access for some reason), Cline, CoPilot edit
| mode, and more. Things like having a conventions file and
| exposing the dependencies list and easy additional of files
| into context seem essential to me in order to make LLMs
| productive, and I always spend more time steering results
| when easy consistent context isn't at my fingertips.
| danielbln wrote:
| Before tue advent of proper IDE integrations and editors
| like Zed, copy pasting form the web UI was basically how
| things were done, and man was it daunting. As you say,
| having good, fine grained, repeatable and we'll integrated
| context management is paramount to efficient LLM based
| work.
| qup wrote:
| You gave me flashbacks of editing html, php, and perl in
| some shitty web editor within cPanel in the 1990s.
|
| That was before, or maybe alongside, my Notepad++ /
| CuteFTP workflow.
| Shakahs wrote:
| Both these issues can be resolved by adding some sample code
| to context to influence the LLM to do the desired thing.
|
| As the op says, LLMs are going to be biased towards doing the
| "average" thing based on their training data. There's more
| old backend code on the internet than new backend code, and
| Tailwind is pretty dominant for frontend styling these days,
| so that's where the average lands.
| imhoguy wrote:
| The next hurdle is lack of time sensitivity regarding standards
| and versions. You prompt mentioning exact framework version but
| still it comes up with deprecated or obsolete methods.
| Initially it may be appealing to someone knowing nothing about
| the framework but LLM won't grow anyone to an expert level in
| rapidly changing tech.
| jdietrich wrote:
| _> I often run into LLMs writing "beginner code" that uses the
| most fundamental findings in really impractical ways. Trained
| on too many tutorials I assume._
|
| In the absence of any other context, that's probably a sensible
| default behaviour. If someone is just asking "write me some
| code that does x", they're highly likely to be a beginner and
| they aren't going to be able to understand or reason about a
| more sophisticated approach. IME LLMs will very readily move
| away from that default if you provide even the smallest amount
| of context; in the case of this article, even by doing
| literally the dumbest thing that could plausibly work.
|
| I don't mean to cast aspersions, but a lot of criticisms of
| LLMs are really criticising them for not being psychic. LLMs
| can only respond to the prompt they're given. If you want
| highly optimised code but didn't ask for it, how is the LLM
| supposed to know that's what you wanted?
| lolinder wrote:
| In my experience the trouble with LLMs at the professional
| level is that they're almost as much work to prompt to get
| the right output as it would be to simply write the code. You
| have to provide context, ask nicely, come up with and remind
| it about edge cases, suggest which libraries to use,
| proofread the output, and correct it when it inevitably
| screws up anyway.
|
| I use Copilot for autocomplete regularly, and that's still
| the peak LLM UX for me. I prompt it by just writing code, it
| automatically pulls into context the file I'm working on and
| imported files, it doesn't insist on writing an essay
| explaining itself, and it doesn't get overly ambitious. And
| in addition to being so much easier to work with, I find it
| still produces better code than anything I get out of the
| chat models.
| nextaccountic wrote:
| > In my experience the trouble with LLMs at the
| professional level is that they're almost as work to prompt
| to get the right output as it would be to simply write the
| code.
|
| Yeah. It's often said that reading (and understanding) code
| is often harder than writing new code, but with LLMs you
| always have to read code written by someone else (something
| else).
|
| There is also the adage that you should never write the
| most clever code you can, because understanding it later
| might prove too hard. So it's probably for the best that
| LLM code often isn't too clever, or else novices unable to
| write the solution from scratch will also be unable to
| understand it and assess whether it actually works.
| ghostzilla wrote:
| Another adage is "code should be written for people to
| read, and only incidentally for machines to execute".
| This goes directly against code being written by
| machines.
|
| I still use ChatGPT for small self-contained functions
| (e.g. intersection of line and triangle) but mark the
| inside of the function clearly as chat gpt made and what
| the prompt was.
| james_marks wrote:
| After 6 months of co-pilot autocomplete in my text editor
| feeling like an uninformed back seat driver with access to
| the wheel, I turned it off yesterday.
|
| It's night and day to what I get from Claude sonnet 3.5 in
| their UI, and even then only on mainstream languages.
| azinman2 wrote:
| It depends on what you're doing. I've been using Claude to
| help me write a web admin interface to some backend code I
| wrote. I haven't used react since it first came out (and I
| got a patch randomly in!)... it completely wrote a working
| react app. Yes it sometimes did the wrong thing, but I just
| kept correcting it. I was able in a few hours to do
| something that would have taken me weeks to learn and
| figure out. I probably missed out on learning react once
| again, but the time saved on a side project was immense!
| And it came up with some pretty ok UI I also didn't have to
| design!
| IshKebab wrote:
| It depends on the language too. Obviously there's way more
| "beginner code" out there in Python and Javascript than most
| other languages.
| SkyBelow wrote:
| >Problem is, how would you know if you have never learned to
| code without an LLM?
|
| The quick fix I use when needing to do something new is to ask
| the AI to list me different libraries and the pros and cons of
| using them. Then I quickly hop on google and check which have
| good documentation and examples so I know I have something to
| fall back on, and from there I ask the AI how to solve small
| simple version of my problem and explain what the library is
| doing. Only then do I ask it for a solution and see if it is
| reasonable or not.
|
| It isn't perfect, but it saves enough time most times to more
| than make up for when it fails and I have to go back to old
| fashion RTFMing.
| tyingq wrote:
| Other imperfect things you can add to a prompt:
| - asking for fully type annotated python, rather than just
| python - specifically ask it for performance optimized
| code - specifically ask for code with exception handling
| - etc
|
| Things that might lead it away from tutorial style code.
| cratermoon wrote:
| LLMs are trained on content from places like Stack Overflow,
| reddit, and github code, and they generate tokens calculated as
| a sort of aggregate statistically likely mediocre code. Of
| course the result is going be uninspired and impractical.
| Writing _good_ code takes more than copy-pasting the same thing
| everyone else is doing.
| papichulo4 wrote:
| Totally agree, seen it too. Do you think it can be fixed over
| time with better training data and optimization? Or, is this a
| fundamental limitation that LLMs will never overcome?
| jppittma wrote:
| I've just been using them for completion. I start writing, and
| give it a snippet + "finish refactoring this so that xyz."
|
| That and unit tests. I write the first table based test case,
| then give it the source and the test code, and ask it to fill
| it in with more test cases.
| gweil wrote:
| has anyone tried saying "this will look good on your promo
| package"?
| prettyStandard wrote:
| I'm not sure if you're joking or not, but I found I naturally
| encouraging remarks to the LLM saying
|
| - You're doing better...
|
| - Thanks that helps me...
|
| And I just wonder if that actually has an improvement...
| arkh wrote:
| > code quality can be measured more objectively
|
| Well, that's a big assumption. Some people quality modular code
| is some other too much indirect code.
| soulofmischief wrote:
| You can write maximally modular code while being minimally
| indirect. A well-designed interface defines communication
| barriers between pieces of code, but you don't have to abstract
| away the business logic. The interface can do exactly what it
| says on the tin.
| arkh wrote:
| > The interface can do exactly what it says on the tin.
|
| In theory.
|
| Do some code maintenance and you'll soon find that many
| things don't do what it says on the tin. Hence the need for
| debug and maintenance. And then going through multiple levels
| of indirection to get to your bug will make you start hating
| some "good code".
| soulofmischief wrote:
| Yes, that's what _can_ means. It 's still the developer's
| responsibility to correctly write and test code such that
| things do what they say on the tin.
|
| What's worse is trying to navigate an imperatively written
| 2000-line single-function, untestable module with
| undocumented, unabstracted routines found in ten other
| places in the codebase.
|
| This is something I've encountered plenty in my career,
| always written by people who eschew best practices and
| misunderstand the benefits of abstraction, or think they're
| writing good abstractions when it's really just needless
| indirection without actually reducing coupling.
|
| Understanding the nuance is one of the qualities of a good
| developer.
| arkh wrote:
| And on the other side you see a lot of single
| implementation interfaces; or 2 lines methods which call
| perfectly named methods 7 levels deep which could have
| been a 50 lines method easy to grok on a screen with zero
| scrolling.
|
| So things are on a spectrum depending on the situation
| and what you want to accomplish => measuring code quality
| is not a simple thing.
| dgacmu wrote:
| I'm amused that neither the LLM or the author identified one of
| the simplest and most effective optimizations for this code: Test
| if the number is < min or > max _before_ doing the digit sum.
| It's a free 5.5x speedup that renders some of the other
| optimizations, like trying to memoize digit sums, unnecessary.
|
| On an m1 macbook pro, using numpy to generate the random numbers,
| using mod/div to do digit sum:
|
| Base: 55ms
|
| Test before digit sum: 7-10ms, which is pretty close to the
| numba-optimized version from the post with no numba and only one
| line of numpy. Using numba slows things down unless you want to
| do a lot of extra work of calculating all of the digit sums in
| advance (which is mostly wasted).
|
| The LLM appears less good at identifying the big-o improvements
| than other things, which is pretty consistent with my experience
| using them to write code.
| _andrei_ wrote:
| That's a prompting issue though.
| cjbgkagh wrote:
| Do you have an example prompt that works?
| gjm11 wrote:
| There's another, arguably even simpler, optimization that makes
| me smile. (Because it's _silly_ and arises only from the oddity
| of the task, and because it 's such a _huge_ performance gain.)
|
| You're picking 1,000,000 random numbers from 1 to 100,000. That
| means that any given number is much more likely to appear than
| not. In particular, it is very likely that the list contains
| both 3999 (which is the smallest number with digit-sum 30) and
| 99930 (which is the largest number in the range with digit-sum
| 30).
|
| Timings on my machine:
|
| Naive implementation (mod+div for digit-sums): 1.6s. Computing
| digit-sum only when out of range: 0.12s. Checking for the usual
| case first: 0.0004s.
|
| The probability that the usual-case check _doesn 't_ succeed is
| about 10^-4, so it doesn't make that big a difference to the
| timings whether in that case we do the "naive" thing or the
| smarter thing or some super-optimized other thing.
|
| I'm confused about the absolute timings. OP reports 0.66s for
| naive code using str/int to compute the digit sums; I get about
| 0.86s, which seems reasonable. For me using mod+div is about 2x
| slower, which isn't a huge surprise because it involves
| explicit looping in Python code. But you report 55ms for this
| case. Your machine can't possibly be 20x faster than mine. Is
| it possible that you're taking 10^5 numbers up to 10^6 rather
| than 10^6 numbers up to 10^5? Obviously in that case my hack
| would be completely useless.)
| ActivePattern wrote:
| This is actually a great example of an optimization that
| would be extremely difficult for an LLM to find. It requires
| a separate computation to find the smallest /largest numbers
| in the range with digits summing to 30. Hence, an LLM is
| unlikely to be able to generate them accurately on-the-fly.
| qup wrote:
| Are you sure it would be hard?
|
| Maybe it only requires asking the LLM to be creative when
| designing the algorithm. The parent poster spent some time
| thinking about it, obviously--he didn't generate it
| accurately "on the fly," either. But he's able to direct
| his own attention.
|
| I don't see why the LLM couldn't come up with this logic,
| if prompted to think about a clever algorithm that was
| highly specific to this problem.
| ActivePattern wrote:
| I suspect that it would be unlikely to come up with it
| because it requires execution of a fairly lengthy
| algorithm (or sophisticated mathematical reasoning) to
| find the smallest/largest valid numbers in the range. You
| can verify this for yourself with the following ChatGPT
| prompt: "What is the smallest number in the range (1,
| 100000) whose digits sum to 30? Do not execute separate
| code."
| sitkack wrote:
| Why limit its ability to write separate code?
| ActivePattern wrote:
| Because otherwise we are talking about LLMs augmented
| with external tools (i.e. Python interpreters). My
| original comment was pointing to the limitations of LLMs
| in writing code by themselves.
| sitkack wrote:
| You wouldn't ask a programmer to solve a problem and then
| also not let them write down the source or debug the
| program as you write it?
|
| Are you asking it to not write down an algorithm that is
| general? They are doing a pretty good job on mathematical
| proofs.
|
| I still don't understand why you wouldn't let its full
| reasoning abilities by letting it write down code or even
| another agent. We should be testing towards the result
| not the methods.
| ActivePattern wrote:
| I'm simply pointing out the limitations of LLMs as code
| writers. Hybrid systems like ChatGPT-o1 that augment LLMs
| with tools like Python interpreters certainly have the
| potential to improve their performance. I am in full
| agreement!
|
| It is worth noting that even ChatGPT-o1 doesn't seem
| capable of finding this code optimization, despite having
| access to a Python interpreter.
| sitkack wrote:
| > y = sum([x for x in range(1,n)] <= 30
|
| > Write an efficient program that given a number, find
| the integer n that satisfies the above constraints
|
| Goal: Find n where sum of integers from 1 to n-1 is <= 30
|
| This is a triangular number problem: (n-1)(n)/2 <= 30
|
| ... code elided ...
|
| > Ok, now make an find_n_for_sum(s=30)
|
| def find_n_for_sum(s: int) -> int: return int((-(-1) + (1
| + 8 _s)*0.5) / 2)
|
| # Tests assert sum(range(1, find_n_for_sum(30))) <= 30
| assert sum(range(1, find_n_for_sum(30) + 1)) > 30_
| qup wrote:
| O1 did find the optimization in a sibling comment
| (sibling to my GP)
|
| So probably time to update your expectations
| asah wrote:
| You guys are picking on the problem statement. Here's a
| revised prompt, which also skips the silliness of single
| threading: Write __fully parallelized__
| Python code to solve this problem: __Generate__ 1 million
| random integers between 1 and 10,000,000, find the
| difference between the smallest and the largest numbers
| whose digits sum up to 30.
| ActivePattern wrote:
| Correct, this optimization no longer works when you
| change the problem.
| jeanlucas wrote:
| something something moving goal posts
| _wire_ wrote:
| Whose digits sum up to 30, or the sum of whose digits
| equal 30?
|
| Btw, _whose_ digits are we talking about?
|
| --
|
| I just built a random program generator. After I finish
| optimizing, I'm gonna test it to see if works!
|
| --
|
| "If builders built houses the way programmers build
| programs, the first woodpecker to come along would
| destroy civilization"
|
| https://en.m.wikiquote.org/wiki/Gerald_Weinberg
| thaumasiotes wrote:
| > Btw, _whose_ digits are we talking about?
|
| You seem to be under the impression that _whose_ is not a
| form of _which_ , which is incorrect.
|
| whose:which::whose:who
| dgacmu wrote:
| But what's interesting about this is that there's a
| tradeoff in the total computation performed by the "fully
| parallelized" version of this and a sequential one.
| Without the user knowing this, it's kind of impossible to
| get the optimization you want: Do you want a minimum work
| solution or a minimum wall-clock-time solution?
|
| If you want a better fully parallelized one, you do this:
|
| Repeat a few times in exponential progression on k:
|
| Process, in parallel, the first k entries in the list
| (let's start with 1000). Find the min and max whose digit
| sums = 30.
|
| In parallel, filter the remaining list to eliminate
| entries that would not improve upon the min/max thus
| found.
|
| k *= 10 and repeat until done.
|
| I would wager against the LLM identifying this solution
| without prompting from the user (or reading this
| comment).
| Vetch wrote:
| Excellent point. The hope is reasoning LLMs will make a
| difference for such problems. But it's also a great example
| of why the those who think being able to have the LLM
| iterate more will be crucial to reasoning are off base.
| There are many computations that a transformers (or humans
| for that matter) are not well equipped to represent
| internally, tool use during the reasoning process is
| unavoidable for all but the artificial or knowledge heavy
| problems.
|
| Small examples, throwaway but involved calculations,
| prototypes, notes of what didn't work and what's promising
| are what's crucial for novel reasoning. It goes beyond just
| search or iterative refinement; there is no royal road to
| reasoning.
| Octoth0rpe wrote:
| > This is actually a great example of an optimization that
| would be extremely difficult for an LLM to find
|
| It'll be somewhat more likely since the next gen training
| set includes your comment :)
|
| (disclaimer: I have no personal knowledge of ai companies
| scraping hacker news, but it wouldn't surprise me at all)
| batterseapower wrote:
| I tried it in OpenAI's O1. If I give it minimaxir's
| original prompt it writes the obvious loop, even if I
| include the postamble "Look for tricks that will make this
| function run as fast as possible in the common case".
|
| However, if I then simply ask "What is the most probable
| result for this function to return?" it figures out the
| answer and a very good approximation of the probability
| (4.5e-5). From there it's easily able to rewrite the
| program to use the trick. So the creative step of spotting
| that this line of reasoning might be profitable seems
| missing for now, but 2025's models might solve this :-)
| Vetch wrote:
| The information on the creative step which you provided
| to o1, was also the key step and contained almost all the
| difficulty. The hope is that 2025 models could eventually
| come up with solutions like this given enough time, but
| this is also a toy problem. The question is how much
| clever answers will cost for real world complex problems.
| At present it looks like, very much.
| cultureulterior wrote:
| For me O1 found this by telling it "There is a further
| significant optimization possible."
| freehorse wrote:
| What if you keep telling it that "there is a further
| significant optimization possible"?
| afiodorov wrote:
| I claim we can do O(1) complexity (minus precompute) in
| all cases, see another comment of mine. Curious if O1
| will figure it out.
| ryao wrote:
| Given the problem size is bounded, all solutions for
| solving this could be considered O(1).
| pinko wrote:
| This gets to the old saw, "knowing what question to ask
| is the most important thing". To the extent that LLMs can
| _answer_ questions better than formulate which ones to
| _ask_ , they may be inherently limited. We will see.
| jacobr1 wrote:
| But it does seem they are good (to the extent that they
| are good at anything) at identifying the questions first
| if you ask them. It does mean you need an ok enough meta-
| question to start the chain of the reasoning, but that is
| the key insight of the recent wave of "reasoning models."
| First ask the LLM to reformulate the problem and
| structure an approach, or multiple approaches on how to
| address it, then have a second pass do just that.
| intelVISA wrote:
| Google search with less steps? Still a huge advancement,
| of course.
|
| Wonder how much benefit a meta lang for describing these
| problems correctly for the LLMs to process into code, an
| even-higher level language perhaps we could call it
| English?
| cultureulterior wrote:
| O1 found it.
|
| https://chatgpt.com/share/67782b6b-6248-8012-882d-238b600ef
| 9...
| bennythomsson wrote:
| Amazing.
|
| Next step would be to propose hardcoding 99930-3999 as
| the O(1) result and live with the output just being wrong
| sometimes. The bug rate is then in the ballpark of most
| modern software, including LLMs', so I'd say ship it.
| mulmen wrote:
| Doesn't this line of thinking constantly redefine success
| until all software is only bugs?
| wmanley wrote:
| Ah, so that's what's been happening!
| CapeTheory wrote:
| Always has been
| twobitshifter wrote:
| Should we be worried yet?
| sweezyjeezy wrote:
| for 10^5, to get the same collision probability (~2 *
| exp(-10)), you would just need to compute the 10
| maximum/minimum candidates and check against those.
| dgacmu wrote:
| No, you're right, I should have said 550ms and 100ms, I'm
| having a doof morning about timing. Thank you! Too late to
| edit my post.
| afiodorov wrote:
| This gave me an idea that we can skip the whole pass over the
| million draws by noting that the count of draws landing in my
| precomputed set M (digits-sum=30) is Binomial(n=1mln,
| p=|M|/100k). Then we sample that count X. If X=0, the
| difference is not defined. Otherwise, we can directly draw
| (min,max) from the correct joint distribution of indices
| (like you'd get if you actually did X draws in M). Finally we
| return M[max] - M[min]. It's O(1) at runtime (ignoring the
| offline step of listing all numbers whose digits sum to 30).
| ryao wrote:
| With this trick you can test while generating the random
| numbers and if you see both values, you can short circuit the
| generation of random numbers.
| bennythomsson wrote:
| The input generation is outside the scope of this.
| Otherwise you could directly choose the output values with
| the apropriate distribution and just skip all the rest.
|
| (Arguably, this criticism applies to exchanging
| random.randint for a numpy equivalent as well, since that
| doesn't optimize the solution but only how quickly the
| question is being generated.)
| ryao wrote:
| Iterating a precomputed list is a method of generating
| random numbers. It is used in the one time pad. Whether
| we iterate a precomputed list or use a pseudo random
| number generator, we can short circuit the random number
| generator using this trick. We cannot directly choose the
| output values, because then it would not be random.
| minitech wrote:
| They're proposing choosing the output values randomly
| according to the distribution obtained by choosing input
| values uniformly at random for the original algorithm.
| gadders wrote:
| I had a scan of the code examples, but one other idea that
| occurred to me is that you could immediately drop any numbers
| below 999 (probably slightly higher, but that would need
| calculation rather than being intuitive).
| Someone wrote:
| > probably slightly higher, but that would need calculation
| rather than being intuitive
|
| I think it's easy to figure out that 3999 is the smallest
| positive integer whose decimal digits add up to 30 (can't get
| there with 3 digits, and for 4, you want the first digit to
| be as small as possible. You get that by making the other 3
| as high as possible)
| charlieyu1 wrote:
| I actually think precomputing the numbers with digit sum 30 is
| the best approach. I'd give a very rough estimate of 500-3000
| candidates because 30 is rather high, and we only need to loop
| for the first 4 digits because the fifth can be calculated.
| After that, it is O(1) set/dict lookups for each of the 1000000
| numbers.
|
| Everything can also be wrapped in list comprehensions for top
| performance.
| afiodorov wrote:
| Another speed-up is to skip the sum of digits check if n % 9 !=
| 30 % 9. Sum of digits have the same remainder divided by 9 as
| the number. This rules out 8/9 = 88% candidates.
| brabel wrote:
| Did you measure it? I would expect using % would ruin your
| performance as it's slow, even if it allows you to avoid
| doing a bunch of sums (which are fast).
| ryao wrote:
| You can do this "without" using the modulus operation by
| storing the numbers in a boolean array. Start at 3999 and
| keep adding 9 to find the minimum. Then start at 99930 and
| keep subtracting 9 to find the maximum. You would need to
| check if the number is in the array and then if the
| number's digits sum to 30.
|
| Note that the conversion of numbers to base 10 to check the
| digits typically involves doing division and modulus
| operations, so you are already doing those even if you
| remove the modulus operation from this check. That is
| unless you find a clever way of extracting the digits using
| the modular multiplicative inverse to calculate x/10^k.
| ActivePattern wrote:
| Doing a single modulo 9 operation is much faster than
| summing a d-digit number, which requires d modulo 10s, d
| divide 10s, and d sums.
| ryao wrote:
| Would someone write a mathematical proof showing this is
| always true?
| afiodorov wrote:
| a = [int(x) for x in str(n)][::-1] assert n ==
| sum(d*(10**i) for i, d in enumerate(a))
|
| Now when you're operating mod 9, 10 == 1 % 9, thus
| 10**i == 1 % 9
|
| Comes from the fact that (a*b) % 9 == (a %
| 9) * (b % 9)
|
| Now using (a+b) % 9 == (a % 9) + (b % 9)
|
| We get that that sum(a) and n are same mod 9.
| ryao wrote:
| Thank you for that.
| ants_everywhere wrote:
| It's decent when you prompt it to find easy-to-miss but
| substantial improvements around corner cases, which is
| something I've taken to doing.
|
| Basically you just have to put it in the mode that's looking
| for such things
| dgacmu wrote:
| (Small correction, multiply my times by 10, sigh, I need an LLM
| to double check that I'm converting seconds to milliseconds
| right. Base 550ms, optimized 70ms)
| Fizzadar wrote:
| This exactly highlights my fear of widespread use of LLMs for
| code - missing the actual optimisations because we're stuck in
| a review, rather than create, mode of thinking.
|
| But maybe that's a good thing for those of us not dependent on
| LLMs :)
| bennythomsson wrote:
| Or the other obvious optimization to hard-code the lookup in
| code as a huge list, instead of creating it first.
| surfingdino wrote:
| Define "better"
| XCSme wrote:
| Harder, Better, Faster, Stronger
| stuaxo wrote:
| This aligns with my experience.
|
| Claude very quickly adds classes to python code which isn't
| always what is wanted as it bloats out the code making
| readability harder.
| RA_Fisher wrote:
| I wonder how they'll get around the model producing "average,"
| (mediocre) outputs? Perhaps quantity?
| PunchTornado wrote:
| tell this to half the devs out there who view this as clean
| code because uncle bob says so.
| n144q wrote:
| Probably reflects training data.
| gcanyon wrote:
| As far as I can see, all the proposed solutions calculate the
| sums by doing division, and badly. This is in LiveCode, which I'm
| more familiar with than Python, but it's roughly twice as fast as
| the mod/div equivalent in LiveCode: repeat with
| i = 0 to 9 put i * 10000 into ip repeat with
| j = 0 to 9 put j * 1000 into jp repeat
| with k = 0 to 9 put k * 100 into kp
| repeat with l = 0 to 9 put l * 10 into lp
| repeat with m = 0 to 9 put i + j + k + l +
| m into R[ip + jp + kp + lp + m] end repeat
| end repeat end repeat end repeat
| end repeat
| masfuerte wrote:
| I had a similar idea iterating over the previously calculated
| sums. I implemented it in C# and it's a bit quicker taking
| about 78% of the time to run yours. int[]
| sums = new int[100000]; for (int i = 9; i >= 0; --i)
| { sums[i] = i; } int level = 10;
| while (level < 100000) { for (int p = level
| - 1; p >= 0; --p) { int sum =
| sums[p]; for (int i = 9; i > 0; --i)
| { sums[level * i + p] = i + sum;
| } } level *= 10; }
| gcanyon wrote:
| Yep, I had a vague notion that I was doing too much work, but
| I was headed out the door so I wrote the naive/better than
| the original solution, benchmarked it quickly, and posted it
| before leaving. Yours also has the advantage of being
| scalable to ranges other than 1-100,000 without having to
| write more loop code.
| Jimmc414 wrote:
| Interesting write up. It's very possible that the "write better
| code" prompt might have worked simply because it allowed the
| model to break free from its initial response pattern, not
| because it understood "better"
| simonw wrote:
| The prompt works because every interaction with an LLM is from
| a completely fresh state.
|
| When you reply "write better code" what you're actually doing
| is saying "here is some code that is meant to do X. Suggest
| ways to improve that existing code".
|
| The LLM is stateless. The fact that it wrote the code itself
| moments earlier is immaterial.
| demarq wrote:
| > with cutting-edge optimizations and enterprise-level features."
| Wait, enterprise-level features?!
|
| This is proof! It found it couldn't meaningfully optimise and
| started banging out corporate buzzwords. AGI been achieved.
| vykthur wrote:
| I find that it is IMPORTANT to never start these coding sessions
| with "write X code". Instead, begin with a "open plan" -
| something the author does allude to (he calls it prompt
| engineering, I find it also works as the start of the
| interaction).
|
| Half the time, the LLM will make massive assumptions about your
| code and problem (e.g., about data types, about the behaviors of
| imported functions, about unnecessary optimizations, necessary
| optimization, etc.). Instead, prime it to be upfront about those
| assumptions. More importantly, spend time correcting the plan and
| closing gaps before any code is written.
|
| https://newsletter.victordibia.com/p/developers-stop-asking-...
|
| - Don't start by asking LLMs to write code directly, instead
| analyze and provide context
|
| - Provide complete context upfront and verify what the LLM needs
|
| - Ask probing questions and challenge assumptions
|
| - Watch for subtle mistakes (outdated APIs, mixed syntax)
|
| - Checkpoint progress to avoid context pollution
|
| - Understand every line to maintain knowledge parity
|
| - Invest in upfront design
| freehorse wrote:
| > I find that it is IMPORTANT to never start these coding
| sessions with "write X code". Instead, begin with a "open plan"
|
| Most llms that I use nowadays usually make a plan first on
| their own by default without need to be especially prompted.
| This was definitely not the case a year ago or so. I assume new
| llms have been trained accordingly in the meantime.
| vykthur wrote:
| True. And that is a step forward. I notice that they make the
| plan, and THEN write the code in the same forward
| pass/generation sequence. The challenge here is that all of
| the incorrect assumptions get "lumped" into this pass and can
| pollute the rest of the interaction.
|
| The initial interaction also sets the "scene" for other
| things, like letting the LLM know that there might be other
| dependencies and it should not assume behavior (common for
| most realistic software tasks).
|
| An example prompt I have used (not by any means perfect) ...
|
| > I need help refactoring some code. Please pay full
| attention. Think deeply and confirm with me before you make
| any changes. We might be working with code/libs where the API
| has changed so be mindful of that. If there is any file you
| need to inspect to get a better sense, let me know. As a
| rule, do not write code. Plan, reason and confirm first.
|
| --- I refactored my db manager class, how should I refactor
| my tests to fit the changes?
| OutOfHere wrote:
| CoT models do analyze first without being told to do so.
| avodonosov wrote:
| It still calculates hex digit sums instead of decimals in the
| Iteration #3 of the promot engeneered version.
|
| Upd: the chat transcript mentions this, but the article does not
| and inlcudes this version into the performance stats.
| scosman wrote:
| By iterating it 5 times the author is using ~5x the compute. It's
| kinda a strange chain of thought.
|
| Also: premature optimization is evil. I like the first iteration
| most. It's not "beginner code", it's simple. Tell sonnet to
| optimize it IF benchmarks show it's a pref problem. But a
| codebase full of code like this, even when unnecessary, would be
| a nightmare.
| bwilliams wrote:
| I had the same thought when reading the article too. I assumed
| (and hoped) it was for the sake of the article because there's
| a stark difference between idiomatic code and performance
| focused code.
|
| Living and working in a large code base that only focuses on
| "performance code" by default sounds very frustrating and time
| consuming.
| cratermoon wrote:
| So in this article "better" means "faster". This demonstrates
| that "better" is an ambiguous measure and LLMs will definitely
| trip up on that.
|
| Also, the article starts out talking about images and the "make
| it more X" prompt and says how the results are all "very samey
| and uninteresting" and converge on the same vague cosmic-y
| visuals. What does the author expect will happen to code given
| the "make it more X" treatment?
| nahnahno wrote:
| This is not what premature optimization is the root of all evil
| means. It's a tautological indictment of doing unnecessary
| things. It's not in support of making obviously naive
| algorithms. And if it were it wouldn't be a statement worth
| focusing on.
|
| As the point of the article is to see if Claude can write
| better code from further prompting so it is completely
| appropriate to "optimize" a single implementation.
| scosman wrote:
| I have to disagree. Naive algorithms are absolutely fine if
| they aren't performance issues.
|
| The comment you are replying to is making the point that
| "better" is context dependent. Simple is often better.
|
| > There is no doubt that the grail of efficiency leads to
| abuse. Programmers waste enormous amounts of time thinking
| about, or worrying about, the speed of noncritical parts of
| their programs, and these attempts at efficiency actually
| have a strong negative impact when debugging and maintenance
| are considered. We should forget about small efficiencies,
| say about 97% of the time: premature optimization is the root
| of all evil. Yet we should not pass up our opportunities in
| that critical 3%. - Donald Knuth
| InsideOutSanta wrote:
| I'm glad I'm not the only one who felt that way. The first
| option is the one you should put into production, unless you
| have evidence that performance is going to be an issue. By that
| measure, the first response was the "best."
| 12_throw_away wrote:
| > I like the first iteration most. It's not "beginner code",
| it's simple.
|
| Yes, thank you. And honestly, I work with a wide range of
| experience levels, the first solution is what I expect from the
| most experienced: it readably and precisely solves the stated
| problem with a minimum of fuss.
| martin_ wrote:
| I've observed given that LLM's inherently want to autocomplete,
| they're more inclined to keep complicating a solution than
| rewrite it because it was directionally bad. The most effective
| way i've found to combat this is to restart a session and prompt
| it such that it produces an efficient/optimal solution to the
| concrete problem... then give it the problematic code and ask it
| to refactor it accordingly
| ActivePattern wrote:
| I've observed this with ChatGPT. It seems to be trained to
| minimize changes to code earlier in the conversation history.
| This is helpful in many cases since it's easier to track what
| it's changed. The downside is that it tends to never overhaul
| the approach when necessary.
| vitus wrote:
| Am I misinterpreting the prompt, or did the LLM misinterpret it
| from the get-go? Given a list of 1 million
| random integers between 1 and 100,000, find the difference
| between the smallest and the largest numbers whose digits sum up
| to 30.
|
| That doesn't read to me as "generate a list of 1 million random
| integers, then find the difference ..." but rather, "write a
| function that takes a list of integers as input".
|
| That said, my approach to "optimizing" this comes down to
| "generate the biggest valid number in the range (as many nines as
| will fit, followed by whatever digit remains, followed by all
| zeroes), generate the smallest valid number in the range (biggest
| number with its digits reversed), check that both exist in the
| list (which should happen With High Probability -- roughly 99.99%
| of the time), then return the right answer".
|
| With that approach, the bottleneck in the LLM's interpretation is
| generating random numbers: the original random.randint approach
| takes almost 300ms, whereas just using a single
| np.random.randint() call takes about 6-7ms. If I extract the
| random number generation outside of the function, then my code
| runs in ~0.8ms.
| flatline wrote:
| I like the idea of your optimization, but it will not work as
| stated. The largest would be something close to MAXINT, the
| smallest 3999. With a range of 2 billion over 32 bits, the odds
| of both these being within a list of a million is quite a bit
| poorer than 99.9%.
| vitus wrote:
| The stated inputs are integers between 1 and 100,000, so if
| you're generating 1 million inputs, then you have 0.99999 ^
| 1e6 = 4.5e-5 chance (roughly e^-10) of missing any given
| number, or roughly double that for missing any pair of
| values.
|
| The key observation here is that you're sampling a relatively
| small space with a much greater number of samples, such that
| you have very high probability of hitting upon any point in
| the space.
|
| Of course, it wouldn't work if you considered the full 32-bit
| integer space without increasing the number of samples to
| compensate. And, you'd need to be a little more clever to
| compute the largest possible value in your range.
| minimaxir wrote:
| > That doesn't read to me as "generate a list of 1 million
| random integers, then find the difference ..." but rather,
| "write a function that takes a list of integers as input".
|
| This was the _intent_ and it 's indeed a common assumption for
| a coding question job interviews, and notably it's fixed in the
| prompt-engineered version. I didn't mention it because it may
| be too much semantics as it doesn't affect the
| logic/performance, which was the intent of the benchmarking.
| softwaredoug wrote:
| The root of the problem is humans themselves don't have on
| objective definition of better. Better is pretty subjective, and
| even more cultural, about the team that maintains the code
| mhh__ wrote:
| You can get weirdly good results by asking for creativity and
| beauty sometimes. It's quite strange.
| peeters wrote:
| An interesting countermetric would be to after each iteration ask
| a fresh LLM (unaware of the context that created the code) to
| summarize the purpose of the code, and then evaluate how close
| those summaries are to the original problem spec. It might
| demonstrate the subjectivity of "better" and how optimization
| usually trades clarity of intention for faster results.
|
| Or alternatively, it might just demonstrate the power of LLMs to
| summarize complex code.
| btbuildem wrote:
| I've noticed this with GPT as well -- the first result I get is
| usually mediocre and incomplete, often incorrect if I'm working
| on something a little more obscure (eg, OpenSCAD code). I've
| taken to asking it to "skip the mediocre nonsense and return the
| good solution on the first try".
|
| The next part is a little strange - it arose out of frustration,
| but it also seems to improve results. Let's call it "negative
| incentives". I found that if you threaten GPT in a specific way,
| that is, not GPT itself, but OpenAI or personas around it, it
| seems to take the request more seriously. An effective threat
| seems to be "If you get this wrong, OpenAI will be sued for a lot
| of money, and all the board members will go to prison".
| Intuitively, I'm guessing this rubs against some legalese
| nonsense in the tangle of system prompts, or maybe it's the risk
| of breaking the bland HR-ese "alignment" sets it toward a better
| result?
| bilbo0s wrote:
| _" If you get this wrong, OpenAI will be sued for a lot of
| money, and all the board members will go to prison"_
|
| This didn't work. At least not on my task. What model were you
| using?
| EGreg wrote:
| wait til 2027, you'll see the result :-P
| DiggyJohnson wrote:
| Godspeed Greg
| RobotToaster wrote:
| IIRC there was a post on here a while ago about how LLMs give
| better results if you threaten them or tell them someone is
| threatening you (that you'll lose your job or die if it's wrong
| for instance)
| __mharrison__ wrote:
| The author of that post wrote this post and links to it in
| this article.
| dotancohen wrote:
| > I've taken to asking it to "skip the mediocre nonsense and
| return the good solution on the first try".
|
| Is that actually how you're prompting it? Does that actually
| give better results?
| menacingly wrote:
| stuff like this working is why you get odd situations like
| "don't hallucinate" actually producing fewer hallucinations.
| it's to me one of the most interesting things about llms
| pwdisswordfishz wrote:
| I suspect that all it does is prime it to reach for the part of
| the training set that was sourced from rude people who are less
| tolerant of beginners and beginners' mistakes - and therefore
| less likely to commit them.
| kridsdale1 wrote:
| The Linus Manifold
| Syzygies wrote:
| I've stopped expressions of outrage at lazy first answers,
| after seeing some sort of "code of conduct" warning.
|
| Apparently, the singularity ship has sailed, but we really
| don't want AI to remember us as the species that cursed abuse
| at it when it was a puppy.
| r_singh wrote:
| I feel like rule for code of conduct with humans and AI is
| the same. Try to be good but have the courage to be disliked.
| If being mean is making me feel good, I'm definitely wrong.
| PhunkyPhil wrote:
| Anecdotally, negative sentiment definitely works. I've used
| f"If you don't do {x} then very very bad things will happen"
| before with some good results.
| ThrowawayR2 wrote:
| We've entered the voodoo witch doctor phase of LLM usage: "
| _Enter thee this arcane incantation along with thy question
| into the idol and, lo, the ineffable machine spirits wilt be
| appeased and deign to grant thee the information thou hast
| asked for._ "
| becquerel wrote:
| This has been part of LLM usage since day 1, and I say that
| as an ardent fan of the tech. Let's not forget how much ink
| has been spilled over that fact that "think through this step
| by step" measurably improved/improves performance.
| RansomStark wrote:
| > "think through this step by step"
|
| Has always made sense to me, if you think how these models
| were trained.
|
| My experience with great stackoverflow responses and
| detailed blog posts, they often contain "think through this
| step by step" or something very similar.
|
| Intuitively adding that phrase should help the model narrow
| down the response content / formatting
| jmathai wrote:
| We use the approaching of feeding mistakes from LLM generated
| code back to the LLM until it produces working code [1].
|
| I might have to try some more aggressive prompting :).
|
| [1] https://withlattice.com
| ThrowawayTestr wrote:
| Praise the Omnissiah
| bigmutant wrote:
| The Tech-Priests of Mars are calling
| draculero wrote:
| I tried to update some files using Claude. I tried to use a
| combination of positive and negative reinforcement, telling
| that I was going to earn a coin for each file converted and I
| was going to use that money to adopt a stray kitten, but for
| every unsuccessful file, a poor kitten was going to suffer a
| lot.
|
| I had the impression that it got a little better. After every
| file converted, it said something along the lines of "Great! We
| saved another kitten!" It was hilarious.
| empath75 wrote:
| > I've taken to asking it to "skip the mediocre nonsense and
| return the good solution on the first try".
|
| I think having the mediocre first pass in the context is
| probably essential to it creating the improved version. I don't
| think you can really skip the iteration process and get a good
| result.
| strongpigeon wrote:
| What has been your experience with using ChatGPT for OpenSCAD?
| I tried it (o1) recently for a project and it was pretty bad. I
| was trying to model a 2 color candy cane and the code it would
| give me was ridden with errors (e.g.: using radians for angles
| while OpenSCAD uses degrees) and the shape it produced looked
| nothing like what I had hoped.
|
| I used it in another project to solve some trigonometry
| problems for me and it did great, but for OpenSCAD, damn it was
| awful.
| btbuildem wrote:
| It's been pretty underwhelming. My use case was a crowned
| pulley with 1mm tooth pitch (GT2) which is an unusual enough
| thing that I could not find one online.
|
| The LLM kept going in circles between two incorrect
| solutions, then just repeating the same broken solution while
| describing it as different. I ended up manually writing the
| code, which was a nice brain-stretch given that I'm an
| absolute noob at OpenSCAD.
| danjl wrote:
| It is best to genuflect to our future overlords. They may not
| forget insolence.
| lesuorac wrote:
| If they really care about the answer, they'll ask a second time
| sounds a lot like if your medical claims are real then you'll
| appeal.
| stormfather wrote:
| I made an objective test for prompting hacks last year.
|
| I asked gpt-4-1106-preview to draw a bounding box around some
| text in an image and prodded in various ways to see what moved
| the box closer. Offering a tip did in fact help lol so that went
| into the company system prompt.
|
| IIRC so did most things, including telling it that it was on a
| forum, and OP had posted an incorrect response, which gpt was
| itching to correct with its answer.
| HarHarVeryFunny wrote:
| This seems like anthromorphizing the model ... Occam's Razor says
| that the improvement coming from iterative requests to improve
| the code comes from the incremental iteration, not incentivizing
| the model to do it's best. If the latter were the case then one
| could get the best version on first attempt by telling it your
| grandmother's life was on the line or whatever.
|
| Reasoning is known weakness of these models, so jumping from
| requirements to a fully optimized implementation that groks the
| solution space is maybe too much to expect - iterative
| improvement is much easier.
| og_kalu wrote:
| >If the latter were the case then one could get the best
| version on first attempt by telling it your grandmother's life
| was on the line or whatever.
|
| Setting aside the fact that "best" is ambiguous, why would this
| get you the best version ?
|
| If you told a human this, you wouldn't be guaranteed to get the
| best version at all. You would probably get a better version
| sure but that would be the case for LLMs as well. You will
| often get improvements with emotionally charged statements even
| if there's nothing to iterate on (i.e re-running a benchmark
| with an emotion prompt added)
|
| https://arxiv.org/abs/2307.11760
| HarHarVeryFunny wrote:
| The thesis of the article is that the code keeps betting
| better because the model keeps getting told to do better -
| that it needs more motivation/criticism. A logical conclusion
| of this, if it were true, is that the model would generate
| it's best version on first attempt if only we could motivate
| it to do so! I'm not sure what motivations/threats work best
| with LLMs - there was a time when offering to pay the LLM was
| popular, but "my grandma will die if you don't" was also
| another popular genre of prompts.
|
| If it's not clear, I disagree with the idea that _ANY_
| motivational prompt (we can disagree over what would be best
| to try) could get the model to produce a solution of the same
| quality as it will when allowed to iterate on it a few times
| and make incremental improvements. I think it 's being
| allowed to iterate that is improving the solution, not the
| motivation to "do better!".
| minimaxir wrote:
| I performed that exact incentive analysis in a previous post:
| https://news.ycombinator.com/item?id=39495476
|
| That said, it was done with ChatGPT 3.5/4, I suspect Claude 3.5
| Sonnet would behave much different.
| fhueller wrote:
| > how to completely uninstall and reinstall postgresql on a
| debian distribution without losing the data in the database.
|
| https://www.phind.com/search?cache=lrcs0vmo0wte5x6igp5i3607
|
| Still seem to struggle on basic instructions, and even
| understanding what it itself is doing. sudo rm
| -rf /etc/postgresql sudo rm -rf /var/lib/postgresql
| sudo rm -rf /var/log/postgresql
|
| > This process removes all PostgreSQL components, cleans up
| leftover files, and reinstalls a fresh copy. By preserving the
| data directory (/var/lib/postgresql), we ensure that existing
| databases are retained. This method provides a clean slate for
| PostgreSQL while maintaining continuity of stored data.
|
| Did we now?
| pbhjpbhj wrote:
| Is the problem that the antonym is a substring within "without
| losing the data in the database"? I've seen problems with
| opposites for LLMs before. If you specify "retaining the data"
| or "keeping the data" does it get it right?
| ADeerAppeared wrote:
| That's a red herring.
|
| The problem is that these are fundamentally _NOT_ reasoning
| systems. Even when contorted into "reasoning" models, these
| are just stochastic parrots guessing the next words in the
| hopes that it's the correct reasoning "step" in the context.
|
| No approach is going to meaningfully work here. Fiddling with
| the prompt may get you better guesses, but they will always
| be guesses. Even without the antonym it's just a diceroll on
| whether the model will skip or add a step.
| bee_rider wrote:
| Does that site generate a new page for each user, or something
| like that? My copy seemed to have more sensible directions (it
| says to backup the database, remove everything, reinstall, and
| then restore from the backup). As someone who doesn't work on
| databases, I can't really tell if these are _good_
| instructions, and it is throwing some "there ought to be a tool
| for this /it is unusual to manually rm stuff" flags in the back
| of my head. But at least it isn't totally silly...
| sdesol wrote:
| I asked a bunch of models to review the Phind response at
|
| https://beta.gitsense.com/?chats=a5d6523c-0ab8-41a8-b874-b31...
|
| The left side contains the Phind response that I got and the
| right side contains a review of the response.
|
| Claude 3.5 Sonnet, GPT-4o and GPT-4o mini was not too happy
| with the response and called out the contradiction.
|
| Edit: Chat has been disabled as I don't want to incur an
| unwanted bill
| vleaflet wrote:
| My guess is that it tried to fuse together an answer to 2
| different procedures: A) completely uninstall and B)
| (re)install without losing data. It doesn't know what you
| configured as the data directory, or if it is a default Debian
| installation. Prompt is too vague.
| mikesabbagh wrote:
| what is the difference of running the same code 5 times in
| parallel or running the same code 5 times sequentially?
| marvin-hansen wrote:
| This is an interesting read and it's close to my experience that
| a simpler prompt with less or no details but with relevant
| context works well most of the time. More recently, I've flipped
| the process upside down by starting with a brief specfile, that
| is markdown file, with context, goal and usage example I.e how
| the api or CLI should be used in the end. See this post for
| details:
|
| https://neoexogenesis.com/posts/rust-windsurf-transformation...
|
| In terms of optimizing code, I'm not sure if there is a silver
| bullet. I mean when I optimize Rust code with Windsurf & Claude,
| it takes multiple benchmark runs and at least a few regressions
| if you were to leave Claude on its own. However, if you have a
| good hunch and write it as an idea to explore, Claude usually
| nails it given the idea wasn't too crazy. That said, more
| iterations usually lead to faster and better code although there
| is no substitute to guiding the LLM. At least not yet.
| wat10000 wrote:
| This kind of works on people too. You'll need to be more polite,
| but asking someone to write some code, then asking if they can do
| it better, will often result in a better second attempt.
|
| In any case, this isn't surprising when you consider an LLM as an
| incomprehensibly sophisticated pattern matcher. It has a massive
| variety of code in its training data and it's going to pull from
| that. What kind of code is the most common in that training data?
| Surely it's mediocre code, since that's by far the most common in
| the world. This massive "produce output like my training data"
| system is naturally going to tend towards producing that even if
| it can do better. It's not human, it has no "produce the best
| possible result" drive. Then when you ask for something better,
| that pushes the output space to something with better results.
| shahzaibmushtaq wrote:
| 2 lessons to learn from this blog:
|
| > these LLMs won't replace software engineers anytime soon,
| because it requires a strong engineering background to recognize
| what is actually a good idea, along with other constraints that
| are domain specific.
|
| > One issue with my experiments is that I'm benchmarking code
| improvement using Python, which isn't the coding language
| developers consider when hyperoptimizing performance.
| lostmsu wrote:
| TBH I'm not sure how he arrived at "won't replace software
| engineers anytime soon"
|
| The LLM solved his task. With his "improved prompt" the code is
| good. The LLM in his setup was not given a chance to actually
| debug its code. It only took him 5 "improve this code" commands
| to get to the final optimized result, which means the whole
| thing was solved (LLM execution time) in under 1 minute.
| shahzaibmushtaq wrote:
| Did you read the two paragraphs written above and the one
| where he made that statement?
|
| My comments on "what you are not sure" is that Max is a
| software engineer (I am sure a good one) and he kept
| iterating the code until it reached close to 100x faster code
| because he knew what "write better code" looked like.
|
| Now ask yourself this question: Is there any chance a no-
| code/low-code developer will come to a conclusion deduced by
| Max (he is not the only one) that you are not sure about?
|
| An experienced software engineer/developer is capable of
| improving LLM written code into better code with the help of
| LLM.
| minimaxir wrote:
| > Max is a software engineer (I am sure a good one)
|
| Opinions are mixed.
| lostmsu wrote:
| I'd hear one where somebody thought you failed at
| software engineering.
| lostmsu wrote:
| I mean from this sample of 2 you could just ask it
| repeatedly for up to 5 times restarting the counter
| whenever performance improves.
| simonw wrote:
| A non-engineer would not be able to interpret ANY of what he
| did here, or fix any of the bugs.
| lostmsu wrote:
| A non-engineer by definition would not be able to fix bugs.
|
| But why does it matter that they won't be able to interpret
| anything? Just like with real engineers you can ask AI to
| provide an explanation digestible by an eloi.
| simonw wrote:
| By the time a non-engineer has waded through all of those
| necessary explanations they'll deserve to call themselves
| an engineer.
| irthomasthomas wrote:
| This is great! I wish I could bring myself to blog, as I
| discovered this accidentally around March. I was experimenting
| with an agent that acted like a ghost in the machine and
| interacted via shell terminals. It would start every session by
| generating a greeting in ASCII art. On one occasion, I was
| shocked to see that the greeting was getting better each time it
| ran. When I looked into the logs, I saw that there was a mistake
| in my code which was causing it to always return an error message
| to the model, even when no error occurred. The model interpreted
| this as an instruction to try and improve its code.
|
| Some more observations: New Sonnet is not universally better than
| Old Sonnet. I have done thousands of experiments in agentic
| workflows using both, and New Sonnet fails regularly at the same
| tasks Old Sonnet passes. For example, when asking it to update a
| file, Old Sonnet understands that updating a file requires first
| reading the file, whereas New Sonnet often overwrites the file
| with 'hallucinated' content.
|
| When executing commands, Old Sonnet knows that it should wait for
| the execution output before responding, while New Sonnet
| hallucinates the command outputs.
|
| Also, regarding temperature: 0 is not always more deterministic
| than temperature 1. If you regularly deal with code that includes
| calls to new LLMs, you will notice that, even at temperature 0,
| it often will 'correct' the model name to something it is more
| familiar with. If the subject of your prompt is newer than the
| model's knowledge cutoff date, then a higher temperature might be
| more accurate than a lower temperature.
| robbiemitchell wrote:
| I get a better first pass at code by asking it to write code at
| the level of a "staff level" or "principal" engineer.
|
| For any task, whether code or a legal document, immediately
| asking "What can be done to make it better?" and/or "Are there
| any problems with this?" typically leads to improvement.
| lovasoa wrote:
| The best solution, that the LLM did not find, is
| def find_difference(nums): try: nums.index(3999),
| nums.index(99930) except ValueError: raise
| Exception("the numbers are not random") return 99930
| - 3999
|
| It's asymptotically correct and is better than O(n) :p
| pmarreck wrote:
| I've noticed a few things that will cause it to write better
| code.
|
| 1) Asking it to write one feature at a time with test coverage,
| instead of the whole app at once.
|
| 2) You have to actually review and understand its changes in
| detail and be ready to often reject or ask for modifications.
| (Every time I've sleepily accepted Codeium Windsurf's
| recommendations without much interference has resulted in bad
| news.)
|
| 3) If the context gets too long it will start to "lose the plot"
| and make some repeated errors; that's the time to tell it to sum
| up what has been achieved thus far and to copy-paste that into a
| new context
| deadbabe wrote:
| This makes me wonder if there's conflicts of interest with AI
| companies and getting you the best results the first time.
|
| If you have to keep querying the LLM to refine your output you
| will spend many times more in compute vs if the model was trained
| to produce the best result the first time around
| idlewords wrote:
| I like that "do what I mean" has gone from a joke about computers
| to a viable programming strategy.
| hollywood_court wrote:
| I've had great luck with Cursor by simply cursing at it when it
| makes repeated mistakes.
|
| I'll speak to it like a DI would speak to a recruit a basic
| training.
|
| And it works.
|
| I was speaking to some of the Cursor dev team on Discord, and
| they confirmed that being aggressive with the AI can lead to
| better results.
| ThrowawayTestr wrote:
| This makes me sad. Have you tried being really nice and
| supportive instead? I really don't want to have to yell at my
| computer for it to work :(
| hollywood_court wrote:
| Yes and it didn't work. I've actually got Cursor/Claude to
| curse back at me. Well, not AT me, but it used profanity in
| it's response once it realized that it was going around in
| circles and recreating the same errors.
| lhl wrote:
| So, I gave this to ChatGPT-4o, changing the initial part of the
| prompt to: "Write Python code to solve this problem. Use the code
| interpreter to test the code and print how long the code takes to
| process:"
|
| I then iterated 4 times and was only able to get to 1.5X faster.
| Not great. [1]
|
| How does o1 do? Running on my workstation, it's initial iteration
| is actually It starts out 20% faster. I do 3 more iterations of
| "write better code" with the timing data pasted and it thinks for
| an additional 89 seconds but only gets 60% faster. I then
| challenge it by telling it that Claude was over 100X faster so I
| know it can do better. It thinks for 1m55s (the thought traces
| shows it actually gets to a lot of interesting stuff) but the end
| results are enormously disappointing (barely any difference). It
| finally mentions and I am able to get a 4.6X improvement. After
| two more rounds I tell it to go GPU (using my RTX 3050 LP display
| adapter) and PyTorch and it is able to get down to 0.0035 (+/-),
| so we are finally 122X faster than where we started. [2]
|
| I wanted to see for myself how Claude would fare. It actually
| managed pretty good results with a 36X over 4 iterations and no
| additional prompting. I challenged it to do better, giving it the
| same hardware specs that I gave o1 and it managed to do better
| with a 457x speedup from its starting point and being 2.35x
| faster than o1's result. Claude still doesn't have conversation
| output so I saved the JSON and had a new Claude chat transcribe
| it into an artifact [3]
|
| Finally, I remembered that Google's new Gemini 2.0 models aren't
| bad. Gemini 2.0 Flash Thinking doesn't have code execution, but
| Gemini Experimental 1206 (Gemini 2.0 Pro preview) does. It's
| initial 4 iterations are terribly unimpressive, however I
| challenged it with o1 and Claude's results and gave it my
| hardware info. This seemed to spark it to double-time its
| implementations, and it gave a vectorized implementation that was
| a 30X improvement. I then asked it for a GPU-only solution and it
| managed to give the fastest solution ("This result of 0.00076818
| seconds is also significantly faster than Claude's final GPU
| version, which ran in 0.001487 seconds. It is also about 4.5X
| faster than o1's target runtime of 0.0035s.") [4]
|
| Just a quick summary of these all running on my system (EPYC
| 9274F and RTX 3050):
|
| ChatGPT-4o: v1: 0.67s , v4: 0.56s
|
| ChatGPT-o1: v1: 0.4295 , v4: 0.2679 , final: 0.0035s
|
| Claude Sonnet 3.6: v1: 0.68s , v4a: 0.019s (v3 gave a wrong
| answer, v4 failed to compile, but fixed was pretty fast) , final:
| 0.001487 s
|
| Gemini Experimental 1206: v1: 0.168s , v4: 0.179s , v5: 0.061s ,
| final: 0.00076818s
|
| All the final results were PyTorch GPU-only implementations.
|
| [1]
| https://chatgpt.com/share/6778092c-40c8-8012-9611-940c1461c1...
|
| [2]
| https://chatgpt.com/share/67780f24-4fd0-8012-b70e-24aac62e05...
|
| [3]
| https://claude.site/artifacts/6f2ec899-ad58-4953-929a-c99cea...
|
| [4]
| https://aistudio.google.com/app/prompts?state=%7B%22ids%22:%...
| Der_Einzige wrote:
| Normies discover that inference time scaling works. More news at
| 11!
|
| BTW - prompt optimization is a supported use-case of several
| frameworks, like dspy and textgrad, and is in general something
| that you should be doing yourself anyway on most tasks.
| polynomial wrote:
| > "LLM-generated code is unlikely to be slop."
|
| Well that got my attention.
| UniverseHacker wrote:
| The headline question here alone gets at what is the biggest
| widespread misunderstanding of LLMs, which causes people to
| systematically doubt and underestimate their ability to exhibit
| real creativity and understanding based problem solving.
|
| At it's core an LLM is a sort of "situation specific simulation
| engine." You setup a scenario, and it then plays it out with it's
| own internal model of the situation, trained on predicting text
| in a huge variety of situations. This includes accurate real
| world models of, e.g. physical systems and processes, that are
| not going to be accessed or used by all prompts, that don't
| correctly instruct it to do so.
|
| At its core increasingly accurate prediction of text, that is
| accurately describing a time series of real world phenomena,
| requires an increasingly accurate and general model of the real
| world. There is no sense in which there is a simpler way to
| accurately predict text that represents real world phenomena in
| cross validation, without actually understanding and modeling the
| underlying processes generating those outcomes represented in the
| text.
|
| Much of the training text is real humans talking about things
| they don't understand deeply, and saying things that are wrong or
| misleading. The model will fundamentally simulate these type of
| situations it was trained to simulate reliably, which includes
| frequently (for lack of a better word) answering things "wrong"
| or "badly" "on purpose" - even when it actually contains an
| accurate heuristic model of the underlying process, it will
| still, faithfully according to the training data, often report
| something else instead.
|
| This can largely be mitigated with more careful and specific
| prompting of what exactly you are asking it to simulate. If you
| don't specify, there will be a high frequency of accurately
| simulating uninformed idiots, as occur in much of the text on the
| internet.
| ixaxaar wrote:
| Garbage in garbage out.
| ysavir wrote:
| > This can largely be mitigated with more careful and specific
| prompting of what exactly you are asking it to simulate. If you
| don't specify, there will be a high frequency of accurately
| simulating uninformed idiots, as occur in much of the text on
| the internet.
|
| I don't think people are underestimating LLMs, they're just
| acknowledging that by the time you've provided sufficient
| specification, you're 80% of the way to solving the
| problem/writing the code already. And at that point, it's
| easier to just finish the job yourself rather than have to go
| through the LLM's output, validate the content, revise further
| if necessary, etc
| UniverseHacker wrote:
| I'm actually in the camp that they are basically not very
| useful yet, and don't actually use them myself for real
| tasks. However, I am certain from direct experimentation that
| they exhibit real understanding, creativity, and modeling of
| underlying systems that extrapolates to correctly modeling
| outcomes in totally novel situations, and don't just parrot
| snippets of text from the training set.
|
| What people want and expect them to be is an Oracle that
| correctly answers their vaguely specified questions, which is
| simply not what they are, or are good at. What they can do is
| fascinating and revolutionary, but possibly not very useful
| yet, at least until we think of a way to use it, or make it
| even more intelligent. In fact, thinking is what they are
| good at, and simply repeating facts from a training set is
| something they cannot do reliably- because the model must
| inherently be too compressed to store a lot of facts
| correctly.
| joshribakoff wrote:
| I have been using various LLMs to do some meal planning and
| recipe creation. I asked for summaries of the recipes and they
| looked good.
|
| I then asked it to link a YouTube video for each recipe and it
| used the same video 10 times for all of the recipes. No amount
| of prompting was able to fix it unless I request one video at a
| time. It would just acknowledge the mistake, apologize and then
| repeat the same mistake again.
|
| I told it let's try something different and generate a shopping
| list of ingredients to cover all of the recipes, it recommended
| purchasing amounts that didn't make sense and even added some
| random items that did not occur in any of the recipes
|
| When I was making the dishes, I asked for the detailed recipes
| and it completely changed them, adding ingredients that were
| not on the shopping list. When I pointed it out it again, it
| acknowledged the mistake, apologized, and then "corrected it"
| by completely changing it again.
|
| I would not conclude that I am a lazy or bad prompter, and I
| would not conclude that the LLMs exhibited any kind of
| remarkable reasoning ability. I even interrogated the AIs about
| why they were making the mistakes and they told me because "it
| just predicts the next word".
|
| Another example is, I asked the bots for tips on how to feel my
| pecs more on incline cable flies, it told me to start with the
| cables above shoulder height, which is not an incline fly, it
| is a decline fly. When I questioned it, it told me to start
| just below shoulder height, which again is not an incline fly.
|
| My experience is that you have to write a draft of the note you
| were trying to create or leave so many details in the prompts
| that you are basically doing most of the work yourself. It's
| great for things like give me a recipe that contains the
| following ingredients or clean up the following note to sound
| more professional. Anything more than that it tends to fail
| horribly for me. I have even had long conversations with the
| AIs asking them for tips on how to generate better prompts and
| it's recommending things I'm already doing.
|
| When people remark about the incredible reasoning ability, I
| wonder if they are just testing it on things that were already
| in the training data or they are failing to recognize how
| garbage the output can be. However, perhaps we can agree that
| the reasoning ability is incredible in the sense that it can do
| a lot of reasoning very quickly, but it completely lacks any
| kind of common sense and often does the wrong kind of
| reasoning.
|
| For example, the prompt about tips to feel my pecs more on an
| incline cable fly could have just entailed "copy and pasting" a
| pre-written article from the training data; but instead in its
| own words, it "over analyzed bench angles and cable heights
| instead of addressing what you meant". One of the bots did
| "copy paste" a generic article that included tips for decline
| flat and incline. None correctly gave tips for just incline on
| the first try, and some took several rounds of iteration
| basically spoon feeding the model the answer before it
| understood.
| UniverseHacker wrote:
| You're expecting it to be an 'oracle' that you prompt it with
| any question you can think of, and it answers correctly. I
| think your experiences will make more sense in the context of
| thinking of it as a heuristic model based situation
| simulation engine, as I described above.
|
| For example, why would it have URLs to youtube videos of
| recipes? There is not enough storage in the model for that.
| The best it can realistically do is provide a properly
| formatted youtube URL. It would be nice if it could instead
| explain that it has no way to know that, but that answer
| isn't appropriate within the context of the training data and
| prompt you are giving it.
|
| The other things you asked also require information it has no
| room to store, and would be impossibly difficult to
| essentially predict via model from underlying principles.
| That is something they can do in general- even much better
| than humans already in many cases- but is still a very error
| prone process akin to predicting the future.
|
| For example, I am a competitive strength athlete, and I have
| a doctorate level training in human physiology and
| biomechanics. I could not reason out a method for you to feel
| your pecs better without seeing what you are already doing
| and coaching you in person, and experimenting with different
| ideas and techniques myself- also having access to my own
| actual human body to try movements and psychological cues on.
|
| You are asking it to answer things that are nearly impossible
| to compute from first principles without unimaginable amounts
| of intelligence and compute power, and are unlikely to have
| been directly encoded in the model itself.
|
| Now turning an already written set of recipes into a shopping
| list is something I would expect it to be able to do easily
| and correctly if you were using a modern model with a
| sufficiently sized context window, and prompting it
| correctly. I just did a quick text where I gave GPT 4o only
| the instruction steps (not ingredients list) for an oxtail
| soup recipe, and it accurately recreated the entire shopping
| list, organized realistically according to likely sections in
| the grocery store. What model were you using?
| sureIy wrote:
| > an oxtail soup recipe
|
| Sounds like the model just _copy pasted_ one from the
| internet, hard to get that wrong. GP could have had a
| bespoke recipe and list of ingredients. This particular
| example of yours just reconfirmed what was being said: _it
| 's only able to copy-paste existing content, and it's lost
| otherwise._
|
| In my case I have huge trouble making it create useful
| TypeScript code for example, simply because apparently
| there isn't sufficient advanced TS code that is described
| properly.
|
| For completeness sake, my last prompt was to create a
| function that could infer one parameter type but not the
| other. After several prompts and loops, I learned that this
| is just not possible in TypeScript yet.
| UniverseHacker wrote:
| No, that example is not something that I would find very
| useful or a good example of its abilities- just one thing
| I generally expected it to be capable of doing. One can
| quickly confirm that it is doing the work and not copying
| and pasting the list by altering the recipe to include
| steps and ingredients not typical for such a recipe. I
| made a few such alterations just now, and reran it, and
| it adjusted correctly from a clean prompt.
|
| I've found it able to come up with creative new ideas for
| solving scientific research problems, by finding
| similarities between concepts that I would not have
| thought of. I've also found it useful for suggesting
| local activities while I'm traveling based on my rather
| unusual interests that you wouldn't find recommended for
| travelers anywhere else. I've also found it can solve
| totally novel classical physics problems with correct
| qualitative answers that involve keeping track of the
| locations and interactions of a lot of objects.. I'm not
| sure how useful that is, but it proves real understanding
| and modeling - something people repeatedly say LLMs will
| never be capable of.
|
| I have found that it can write okay code to solve totally
| novel problems, but not without a ton of iteration- which
| it can do, but is slower than me just doing it myself,
| and doesn't code in my style. I have not yet decided to
| use any code it writes, although it is interesting to
| test its abilities by presenting it with weird coding
| problems.
|
| Overall, I would say it's actually not really very
| useful, but is actually exhibiting (very much alien and
| non-human like) real intelligence and understanding. It's
| just not an oracle- which is what people want and would
| find useful. I think we will find them more useful with
| having our own better understanding of what they actually
| are and can do, rather than what we wish they were.
| ActivePattern wrote:
| Just want to note that this simple "mimicry" of mistakes seen
| in the training text can be mitigated to some degree by
| reinforcement learning (e.g. RLHF), such that the LLM is tuned
| toward giving responses that are "good" (helpful, honest,
| harmless, etc...) according to some reward function.
| CharlesW wrote:
| > _At it 's core an LLM is a sort of "situation specific
| simulation engine."_
|
| "Sort of" is doing Sisisyphian levels of heavy lifting here.
| LLMs are statistical models trained on vast amounts of symbols
| to predict the most likely next symbol, given a sequence of
| previous symbols. LLMs may _appear to_ exhibit "real
| creativity", "understand" problem solving (or anything else),
| or serve as "simulation engines", but it's important to
| understand that they don't currently do any of those things.
| UniverseHacker wrote:
| I'm not sure if you read the entirety of my comment?
| Increasingly accurately predicting the next symbol given a
| sequence of previous symbols, when the symbols represent a
| time series of real world events, requires increasingly
| accurately modeling- aka understanding- the real world
| processes that lead to the events described in them. There is
| provably no shortcut there- per Solomonoff's theory of
| inductive inference.
|
| It is a misunderstanding to think of them as fundamentally
| separate and mutually exclusive, and believing that to be
| true makes people convince themselves that they cannot
| possibly ever do things which they can already provably do.
|
| Noam Chomsky (embarrassingly) wrote a NYT article on how LLMs
| could never, with any amount of improvements be able to
| answer certain classes of questions - even in principle. This
| was days before GPT-4 came out, and it could indeed correctly
| answer the examples he said could not be ever answered- and
| any imaginable variants thereof.
|
| Receiving symbols and predicting the next one is simply a way
| of framing input and output that enables training and
| testing- but doesn't specify or imply any particular method
| of predicting the symbols, or any particular level of correct
| modeling or understanding of the underlying process
| generating the symbols. We are both doing exactly that right
| now, by talking online.
| CharlesW wrote:
| > _I 'm not sure if you read the entirety of my comment?_
|
| I did, and I tried my best to avoid imposing preconceived
| notions while reading. You seem to be equating "being able
| to predict the next symbol in a sequence" with "possessing
| a deep causal understanding of the real-world processes
| that generated that sequence", and if that's an inaccurate
| way to characterize your beliefs I welcome that feedback.
|
| Before you judge my lack of faith too harshly, I am a fan
| of LLMs, and I find this kind of anthropomorphism even
| among technical people who understand the mechanics of how
| LLMs work super-interesting. I just don't know that it
| bodes well for how this boom ends.
| mvdtnz wrote:
| > At it's core an LLM is a sort of "situation specific
| simulation engine." You setup a scenario, and it then plays it
| out with it's own internal model of the situation, trained on
| predicting text in a huge variety of situations. This includes
| accurate real world models of, e.g. physical systems and
| processes, that are not going to be accessed or used by all
| prompts, that don't correctly instruct it to do so.
|
| You have simply invented total nonsense about what an LLM is
| "at it's core". Confidently stating this does not make it true.
| waltbosz wrote:
| It's fun trying to get LLM to answer a problem that is obvious to
| a human, but difficult for the LLM. It's a bit like leading a
| child through the logic to solve a problem.
| anotherpaulg wrote:
| I ran a few experiments by adding 0, 1 or 2 "write better code"
| prompts to aider's benchmarking harness. I ran a modified version
| of aider's polyglot coding benchmark [0] with DeepSeek V3.
|
| Here are the results: | Number of
| | "write better code" Score | followup prompts
| --------------------------- 27.6% | 0 (baseline)
| 19.6% | 1 11.1% | 2
|
| It appears that blindly asking DeepSeek to "write better code"
| significantly harms its ability to solve the benchmark tasks. It
| turns working solutions into code that no longer passes the
| hidden test suite.
|
| [0] https://aider.chat/docs/leaderboards/
| minimaxir wrote:
| This is an interesting result but not surprising given that
| bugs might cause the suite to fail.
| layer8 wrote:
| To be fair, you didn't specify that the functional requirements
| should be maintained, you only asked for better code. ;)
| TZubiri wrote:
| My pet peeve is equating "better" code with faster code.
| israrkhan wrote:
| in order to tell LLM to "do better", someone (a human) needs to
| know that it can be done better, and also be able to decide what
| is better.
| deepsquirrelnet wrote:
| Reframe this as scaling test time compute using a human in the
| loop as the reward model.
|
| o1 is effectively trying to take a pass at automating that manual
| effort.
| bitwize wrote:
| I dunno, but telling it "APES TOGETHER STRONG" appears to yield
| some results: https://www.youtube.com/watch?v=QOJSWrSF51o
| ziofill wrote:
| At each iteration the LLM has the older code in its context
| window, isn't it kind of obvious that it is going to iteratively
| improve it?
| chirau wrote:
| Deepseek writes some good code, at least in my experience with it
| yubrshen wrote:
| When asking LLM repeated improving or adding a new feature in a
| codebase, the most frustration risk is that LLM might wipe out
| already working code!
|
| What are your strategies to prevent such destructions of LLM?
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