Ask HN: Become a 10x developer with LLM? Myth? Reality?

35 点作者 FallDead大约 2 年前

I keep hearing from VC's on the internet mainly the right now in the `all in podcast` there are people who have leveraged LLM to become a 10x dev.What stack are they using? I am thinking it's pretty much any LLM (GPT-3/GPT-4), Lang chain with GPT Index.I am guessing they build boilerplate templates and filling in functions with GPT outputs then using them to write unit tests for base cases?I am guessing the truth is really closer to the centre.

26 条评论

Someone1234大约 2 年前

It gives me faster access to documentation/Stackoverflow, and creates bespoke templates. So I am "10x" but for the most tedious parts of "copy/paste" development. For the hard parts or the time spent thinking about design/flow, it does basically nothing. So am I "10x" overall? Absolutely not. I am barely even "2x" relative to before (since this is a minority of my time).For example (ChatGPT):- Prompt:<pre><code> Create a HTML select list with the following text values: "First In, Last Out", "Last In, First Out", "Random Order", "Round-Robin" give each option and the select list appropriate names and IDs. </code></pre> - Result:<pre><code> <select name="order" id="order"> <option value="fifo">First In, Last Out</option> <option value="lifo">Last In, First Out</option> <option value="random">Random Order</option> <option value="round-robin">Round-Robin</option> </select> </code></pre> Keep in mind if I hand-wrote this I still have to write out the text-values which are 50% of the prompt's length. It did the names/IDs and the boilerplate.

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it_citizen大约 2 年前

It helps with the tedious tasks but great programmers are great because of the way they approach and solve problems, not because they write code faster.On a sidenote: You shouldn't take anything said in the "all in podcast" more seriously than a political discussion overheard a saturday night in a bar. Be careful with the halo effect.

yawnxyz大约 2 年前

I've been able to use phind + Co-pilot to learn python, build and deploy a FastAPI service on the OpenAPI spec, without really knowing any of those things. Just ask GPT-4 how FastAPI works, how OpenAPI works, and ask it to format and write your YAML for you.It's an amazing TA — better than any I've had in undergrad, and way better than office hours, where your prof don't really care and wants to go back to their research.I don't have to read through docs for hours and build a mental model — that stuff is taken care of by asking Chat.Now I'm learning how to deploy my own pytorch model for a real-life use case, by using a combination of SAM and Roboflow. I have absolutely no background in any of these, I just ask it dumb questions until I get it. It's also helping me understand how and when to use hashing and merkle trees for a related project.For me this is xInfinite because I'm doing stuff I'd been too lazy to learn and understand (e.g. vision).And I'm at the core a UX / product designer and absolutely not an engineer.

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ben_w大约 2 年前

You can easily write x10 lines of code; but what matters is deliverables.My guess (I'm using free ChatGPT rather than v4) is that it's really good at small chunks, but not big.On this basis, if you're bad at software architecture then it will rapidly get confused and produce a spaghetti monster nobody can even debug; whereas if you're good at architecture, even though you probably didn't feel limited by your typing speed in the first place, it's enough of a boost to make a difference.

TuringNYC大约 2 年前

It helps me not break my flow to go to StackOverflow/documentation, I can stay in the zone more continuously. It does worry me about the future of StackOverflow, because it is unclear how they continue to snowball and stay current if folks are not using the site directly.Also, this is tangential, but: when I'm doing PoC application websites, etc it is also helpful to generate images/placeholders/etc easily with image generators (not LLM, but in the realm) so i can stay focused on the backend functionality. I recently created a PoC internal use website in an hour because I didnt have to mess around with finding just the right stock images, etc. Good PoCs, IMHO, should have enough visuals so as not to distract from testing and the goals of the PoC.

fzzzy大约 2 年前

Reality. ChatGPT has been working fine for me but I have a ton of programming experience so I can easily fix any mistakes. I will be upgrading to plus soon.Also, Copilot sometimes just does nothing, but there have been many instances now where it literally does exactly what I wanted. It's pretty random.

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gregjor大约 2 年前

If you're just copying someone else's code, whether from StackOverflow or an LLM, you haven't made yourself a 10x developer. You may have found a shortcut through the boilerplate and repetitive code that occurs across many applications, but have you learned anything? Do you understand the code? Could you write it yourself? Using a forklift doesn't make me a 10x weight lifter. Finishing a paint-by-numbers canvas doesn't make me an artist.Learning programming languages and writing the trivial bits doesn't describe the hard part of programming: domain knowledge and skillfully translating complex business requirements into working code.

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rsd79大约 2 年前

I don’t think GPT4 beats an expert yet. That’s why expectations to ask it to build a complex commercial project from scratch are not realistic.Where it shines is up-levelling the developer in areas he is not an expert. Need to jump out of Java development and process some data in Python with a special library? It will likely be more than 10x speed up for you to ask GPT4 for help. Learning a functional language? It will send tons of best practices your way. It’s also great in translating ideas between domains, one example are your requirements to code, but also code to tests, data to SQL etc.I’m happy with how helpful it is in exploring implementation concepts - you can discuss problems you imagine and it will improve the suggestions. A bit like a brainstom guide for you. And sometimes it will surprise you with a much simpler idea which you’ve been overthinking.It’s really great when you don’t expect it to be perfect. It fails very often do to unclear commands (limited prompt „memory” is counter intuitively for humans limiting what can be understood - how can it not remember what you can see a bit to the top?). It will always try to do what you ask for, but if you assume it will know what you know you will be disappointed.

ryanSrich大约 2 年前

Idk if it can turn a 1x developer into a 10x developer, but it can certainly turn a product manager with enough coding knowledge into a passable developer. I just wrote a tweet thread about this today - <a href="https://twitter.com/rrichrs/status/1646885705790914564?s=20" rel="nofollow">https://twitter.com/rrichrs/status/1646885705790914564?s=20</a>

bestcoder69大约 2 年前

See <a href="https://simonwillison.net/2023/Mar/27/ai-enhanced-development/" rel="nofollow">https://simonwillison.net/2023/Mar/27/ai-enhanced-developmen...</a>> What stack are they using? I am thinking it's pretty much any LLM (GPT-3/GPT-4), Lang chain with GPT Index.I'm skeptical of any 'stack' that isn't just ChatGPT + GPT-4. It's probably whatever their flash in the pan startup is building right now. To me this seems like a horrible time to build up such a stack (personal or otherwise) rather than just getting proficient at ChatGPT and the plain old OpenAI API while waiting for new stuff like plugins to come out.Personally I just have a go-to tech stack for GPT-based projects, composed of python, poetry, datasette, and click CLI's written by GPT-4 that can call out to OpenAI in order to manipulate databases (to then be viewed in datasette). But this is more for building little demos to try out GPT rather than incorporating into other work. Otherwise I just use ChatGPT alone. But TBH I'm far from 10x.

MattPalmer1086大约 2 年前

I don't know about 10x, but it has enabled me to explore new approaches much faster and write a lot of the code.My old workflow would be to search documentation, blog posts, stack overflow and anything else that might give me a clue. Usually there isn't something that does exactly what I want and the documentation usually leaves a lot to be desired.New workflow: please write some code using X library in Y language that does Z. Code usually mostly works. If there is anything I don't understand, I ask it to explain. It's like having an expert by my side teaching and writing for me.Not sure it makes me 10x, but it does let me explore more things and get to an understanding a lot faster. I use some of that time saved to try out more ideas, so overall I'm not sure I'm hugely faster but what I can produce is a lot higher quality with better features.

fbrncci大约 2 年前

I would say that since LLMs made it on the scene my code output efficiency went 2-3x. As a backend dev I ended up implementing several frontend features at work, stuff I would have not touched previously. I did not blindly implement them. But had ChatGPT teach me how to do so. But I would say that I could still not fully understand it without ChatGPT. And as for debugging and googling things goes. Efficiency has definitely gone up 5x in many cases. Maybe for some cases as much as 10-20x.However my overal efficiency and productivity maybe only went up maybe 2-3x? I don’t know. It’s hard to assess. Writing code is really only part of the job, so it can only do so much. But I am absolutely sure that even now there will be outliers who went from being 1x developers to the mythical 10x, based on the improvements I have seen with myself.

throwawaysleep大约 2 年前

I have used ChatGPT to cut down one of my jobs to maybe 3 hours of coding work a week. The job is extracting data from JSON and putting it into differently formatted JSON. It does a great job of designing extractors and putting it into a different format.

kcartlidge大约 2 年前

I can sort-of answer with a personal example. A week or two back I asked Bing to give me the code for a Go API to list books. Here's what I did, and each bullet point was one single step in the conversation.---- It gave me the complete code for a Go API using JSON, with handler and routing, using a map to hold the data- I asked it to switch to a website and it gave me the same code, with the route adjusted and the rendering returning HTML to view the books- I said I needed a SQLite back-end and it updated the code to fetch from a database, including stuff to open the connection- I asked it to seed the database with some random data and it added a method to do that- I asked it for a page to add a book and it gave me the full code back with a route, handler, database code, and form handling logic to do add a book- I said I needed the option to delete any book and it gave me updated HTML with a small form tag and delete button for every book in the item list, with code to delete the right book when a button is clicked---I'm not exaggerating when I say that each of the above was literally a single request. I pasted the code returned into a file and ran it and in each case it built and worked.At only one stage did it get something wrong. I never kept the prompts/code as I was experimenting, but basically it had an off-by-one error in a loop where it did less than or equals to rather than less than. I described the problem (one prompt) and it "worked it out" and gave me the code back with the line fixed.So that is 6 prompts plus an extra one for the bug, and the result was a working (trivial) site written in Go with a database back-end.The other main use I've had is pasting in a screen or two's worth of existing Python code. No questions, just the code. It explained what the code did (including a nuance I'd missed) and proactively pointed out a bug I hadn't seen.It was incredible to see, then it was worrying (am I needed any more?), then reassuring when I realised this was great for getting started with green-field stuff or iterating over small stuff, but less so with big-picture stuff on a large codebase.

dpbriggs大约 2 年前

In my admitted limited experience (small amounts at work and in personal projects) it's some integer multiplier. For routine code it's really nice. But for something more complicated it was distracting. Copilot had an opinion of what the software should be which wasn't quite right and had to be ripped out later. It feeds on itself and your program is further shaped in that direction.Of course I could have made a similarly poor decision but it's interesting to see that automated to the point of convenience.

amelius大约 2 年前

Let's first try to become 10x system administrators with LLM.

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bt1a大约 2 年前

What I would like to know is what is the best way to integrate an LLM and its output into my IDE without paying for something like Github Copilot. Any ideas here?

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kabes大约 2 年前

Maybe, but everybody has access to it. So with the playing field remaining equal, everybody becomes 10x what they were before and you're back at square 1

juanre大约 2 年前

I pay for ChatGPT-4. It's helping me program noticeably better and much faster, even with mid-sized projects involving many files. This is the process that I follow:- I write a system prompt with a succinct description of what I want to implement: "X is an online app that does Y, with these features: ..." I try to be exhaustive, and I write as I would if I were describing what I want to do to a very proficient programmer who needs to understand the problem and the solution. I save this to a prompts/0-system.txt file that will be part of the project.- I design an architecture and define the general boundary conditions. I may ask ChatGPT for opinions, but at this stage, it's mostly based on experience. I add it to the system prompt.- I write a description of the first chunk of functionality that I want to implement, usually a set of functions or a class, at the individual file level. For example, prompts/1-dal.txt.- I (actually ChatGPT) wrote a very simple recursive cat script (<a href="https://github.com/juanre/catprompt">https://github.com/juanre/catprompt</a>) that combines several files into a single prompt (a file can include other files, etc). I add a line to prompt/1-dal.txt pointing to the system prompt, and use catprompt to create a combined prompt that will include the overall description and the specifics of the functionality I am after.- I give this prompt to ChatGPT, and it produces the code. It's typically perfectly fine. I add it to the project, for example, as dal.py, review, and fix whatever may need fixing.- I ask ChatGPT to create tests in the same session. This also tends to work fine.- Then I move to the next piece of functionality, in a new session. I write another description, in another file, including the system prompt and usually the code already created.The prompts remain: they become a description of the code (and of my intentions) for other programmers, and they can help ChatGPT write documentation as well.I enjoy programming like this. The effort I make in writing concise descriptions helps me think more clearly. I used to love literate programming back in the 90s, and this feels very much like it. Knuth described it thus:"Let us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do."Replace "human beings" with LLMs, and I think that's where we are now.

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soeptical大约 2 年前

I have made 2 iphone apps in the past week. I have never before been able to finish an app as i would run into some insurmountable issue and lose interest. The apps are simple but functional. It has really made me excited about doing lots of things that I could never get over the competence hump of doing previously.

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adoxyz大约 2 年前

It has def helped me become more efficient in the sense that I can get ideas of how things could be implemented. Or for use cases like creating a regex to do something.Has it made me 10x, probably not, has it improved my efficiency, 100%.

aero-glide2大约 2 年前

I can only speak for myself but my productivity has definitely improved 10x. It's not that I do everything 10x faster, but when I'm stuck on a long standing problem i resolve it much quicker.

RecycledEle大约 2 年前

GPT 4 gives me example code so I can learn the missing system call or library call that I need to make something work. Previously, searching for those @#$@ calls took most of my time.

beebmam大约 2 年前

In my 20+ year career I've still yet to see the myth of a "10x developer" as a reality. It just doesn't exist.

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kledru大约 2 年前

it will be difficult because the people with potential to become 10x developers are too busy trying to stay up to date with advances in LLM technology.

MasterScrat大约 2 年前

I've been using GPT4 very intensively for the past week. I've mostly been writing frontend code and writing Python CLI utilities.- First, there's a MAJOR difference between GPT-3.5 and GPT-4. From a branding point of view it's almost surprising that both are grouped under the name "ChatGPT". Anytime I hear someone saying "yeah I tried out ChatGPT and wasn't impressed" you can be sure they're using the free version (ie GPT-3.5)- GPT-3.5 is great for simple scripts and data transformation, eg "rename files from this pattern to that pattern", "write a script to reformat from this CSV format to this JSON format". GPT-4 can do that as well of course but is generally overkill, it generates more slowly, and your GPT-4 requests are currently throttled so you want to keep them for higher value questions.- GPT-4 is GREAT for frontend code (React/TypeScript/TailwindCSS). For example I had a few ideas yesterday to improve one of our pages: I wanted to add "infinite loading" (loading data progressively instead of all at once), make the search input "sticky" so they stay at the end of the page, and add a "Clear" button in the main input field that only appear when it's focused. I gave ALL THREE changes at once to GPT-4, along with the 160 lines of relevant code (15K+ characters). It came up with comprehensive steps to make all these changes, including installing some npm packages, changing codes here and there, adding custom CSS styles. And IT ALL JUST WORKED ON FIRST TRY.- GPT-4 is also suprisingly good at design. I build pages which are tools for engineers so I don't need award winning UI, but having nice matching colors always makes things better. You can ask GPT-4 to make some display "classy", or "old school", or "make it really stand out" and it'll follow your instructions, matching the style of your webapp. Before that I was using TailwindUI, I now haven't checked it in a week.- GPT-4 can do non-trivial code refactor. For example I was able to give it all the code for request handling, asked to make it "more concise and maintainable" and it suggested major architecture changes, then gave me back mostly working code. The last small problems I was able to either fix myself or if I was lazy i just had GPT4 fix them. The question then really becomes a tradeo-off of how much I want to think vs how much I'm willing to do mindless copy-paste between the ChatGPT interface and my IDE.- What I find most enjoyable is that it makes it very "cheap" in terms of time and energy to try out new things. Frontend is not my speciality so for example I wasn't sure how long it'd take to add an "infinite loading" feature. Without GPT4, it wasn't a priority so i just wouldn't have added it, because it would have meant checking recommended libraries, checking their docs, finding Medium blog posts with code screenshots and behind paywall etc. Here I got a solution i could try out in a few minutes. In some cases I try things out and i realise they inccur too many changes - no worries i just bin the changes and move on with my life.- For simple Python CLI utilities it's also been insanely useful. For example I was able to say: "any time it's making a network request, i want a cool animation to be displayed" and bam it integrated the "halo" package, worked right away. I realise that this is an "easy case" because the whole code fits in context (300 short lines).So yeah overall, I'd definitely say it has been increasing my efficiency by a factor of 2-3x for these specific tasks. Again these tasks are relatively "easy", writing lots of code to do simple things that could reasonably easily be explained in plain words.