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My LLM codegen workflow

• runoisenze 2y ago
  Great write up! Roughly how many Claude tokens are you using per month with this workflow? What’s your monthly API costs? Also what do you mean by “I really want someone to solve this problem in a way that makes coding with an LLM a multiplayer game. Not a solo hacker experience.” ?
  • harper 2y ago
    most of this is aider / codegen: Total tokens in: 26,729,994 Total tokens out: 1,553,284 Last month anthropic bill was $89.30 -- I want to program with a team, together. not a group of people individually coding with an agent, and then managing merges. I have been playing a lot with merging team context - but haven't gotten too far yet.
    • fragmede 2y ago
      Have you tried using OpenHands so you can just give it the todo.md until it gets stuck/is finished?
      • harper 2y ago
        when it first was released i played with it - but haven't since. i should try again.
• ggulati 2y ago
  Nice, I coincidentally wrote a blog post today exploring workflows as well: https://ggulati.wordpress.com/2025/02/17/cursorai-for-frontend-dev-first-impressions/ https://ggulati.wordpress.com/2025/02/17/cursorai-for-fronte... Your workflow is much more polished, will definitely try it out for my next project
  • harper 2y ago
    let me know how it works!
    • hnuser123456 2y ago
      Looks like your blog crashed, I've been wanting to read it
    • hnuser123456 2y ago
      Thank you for fixing it
  • fragmede 2y ago
    > paste in prompt into claude copy and paste code from claude.ai into IDE is more polished? What's your workflow, banging rocks together?
    • shoemakersteve 2y ago
      This made me laugh audibly. Thank you.
    • ggulati 2y ago
      More or less, I tried out Cursor for the first time a week ago. So very much in the newbie stage and looking to learn
• sprobertson 2y ago
  Strange capitalization of atm as ATM in the HN title, but great tips in there
  • Philpax 2y ago
    HN will implicitly modify the title, including uppercasing acronyms. Very possible this was one of those changes.
• jrexilius 2y ago
  The first part of this, where you told it to ask YOU questions, rather than laboriously building prompts and context yourself was the magic ticket for me. And I doubt I would have stumbled on that sorta inverse logic on my own. Really great write up!
  • danphilibin 2y ago
    This is the key to a lot of my workflows as well. I'll usually tack some form of "ask me up to 5 questions to improve your understanding of what I'm trying to do here" onto the end of my initial messages. Over time I've noticed patterns in information I tend to leave out which has helped me improve my initial prompts, plus it often gets me thinking about aspects I hadn't considered yet.
    • daxfohl 2y ago
      Frankly getting used to doing this may help our communication with other engineers as well.
      • fragmede 2y ago
        promo from L5->L7 confirmed.
        • daxfohl 2y ago
          The big question is, which level will be replaced by GPT first?
  • theturtle32 2y ago
    Can confirm, this is an excellent tactic when working with LLMs!
  • CamperBob2 2y ago
    That's one of the key wins with o1-pro's deep research feature. The first thing it tends to do after you send a new prompt is ask you several questions, and they tend to be good ones. One idea I really like here is asking the model to generate a todo list.
• rotcev 2y ago
  This is the first article I’ve come across that truly utilizes LLMs in a workflow the right way. I appreciate the time and effort the author put into breaking this down. I believe most people who struggle to be productive with language models simply haven’t put in the necessary practice to communicate effectively with AI. The issue isn’t with the intelligence of the models—it’s that humans are still learning how to use this tool properly. It’s clear that the author has spent time mastering the art of communicating with LLMs. Many of the conclusions in this post feel obvious once you’ve developed an understanding of how these models "think" and how to work within their constraints. I’m a huge fan of the workflow described here, and I’ll definitely be looking into AIder and repomix. I’ve had a lot of success using a similar approach with Cursor in Composer Agent mode, where Claude-3.5-sonnet acts as my "code implementer." I strategize with larger reasoning models (like o1-pro, o3-mini-high, etc.) and delegate execution to Claude, which excels at making inline code edits. While it’s not perfect, the time savings far outweigh the effort required to review an "AI Pull Request." Maximizing efficiency in this kind of workflow requires a few key things: - High typing speed – Minimizing time spent writing prompts means maximizing time generating useful code. - A strong intuition for "what’s right" vs. "what’s wrong" – This will probably become less relevant as models improve, but for now, good judgment is crucial. - Familiarity with each model’s strengths and weaknesses – This only comes with hands-on experience. Right now, LLMs don’t work flawlessly out of the box for everyone, and I think that’s where a lot of the complaints come from—the "AI haterade" crowd expects perfection without adaptation. For what it’s worth, I’ve built large-scale production applications using these techniques while writing minimal human code myself. Most of my experience using these workflows has been in the web dev domain, where there's an abundance of training data. That said, I’ve also worked in lower-level programming and language design, so I can understand why some people might not find models up to par in every scenario, particularly in niche domains.
  • brokencode 2y ago
    > “I appreciate the time and effort the author put into breaking this down.” Let’s be honest. The author was probably playing cookie clicker while this article was being written.
• hooverd 2y ago
  I think LLM codegen still requires a mental model of the problem domain. I wonder how many upcoming devs will simply never develop one. Calculators are tools for engineers /and/ way too many people can't even do basic receipt math.
  • jack_pp 2y ago
    Calculations are for calculators. I was good at math in school but now I struggle / take so much time doing receipt math and for what? What's the purpose of the time you spend doing it, when do you need to have your brain trained for this specific task?
    • hooverd 2y ago
      For me, being able to notice when you mess up your own calculations. It doesn't help that we teach arithmetic operations ass-backwards (smallest to largest instead of largest to smallest).
• rd 2y ago
  Has anyone who evolved from a baseline of just using Cursor chat and freestyling to a proper workflow like this got any anecdata to share on noticeable improvements? Does the time invested into the planning benefit you? Have you noticed less hallucinations? Have you saved time overall? I’d be curious to hear because my current workflow is basically 1. Have idea 2. create-next-app + ShadCN + TailwindUI boilerplate 3. Cursor Composer on agent mode with Superwispr voice transcription I’m gonna try the author’s workflow regardless, but would love to hear others opinions.
  • cynicalpeace 2y ago
    I'm wondering the same thing. Most of these workflows are just context management workflows and in Cursor it's so simple to manage the context. For large files I just highlight the code and cmd+L. For short files, I just add them all by using /+downarrow I constantly feed context like this and then usually come to a good solution for both legacy and greenfield features/products. If I don't come to a good solution it's almost always because I didn't think through my prompt well enough and/or I didn't provide the correct context.
  • mike_hearn 2y ago
    Aider + AI generated maps and user guides for internal modules has worked well for me. Just today I did my own version of a script that uses Gemini 2 Flash (1M context window) to generate maps of each module in my codebase, i.e. a short one or two sentence description of what's in every file. Aider's repo maps don't work well for me, so I disable them, and I think this will work better. I also have a scratchpad file that I tell the model it can update to reflect anything new it learns, so that gives it a crude form of memory as it works on the codebase. This does help it use internal utility APIs.
    • manmal 2y ago
      LLMs forcing us to improve our documentation habits. Seriously though, many languages allow API doc generation out of comments. Maybe these docs can just be flattened into a file.
• Isamu 2y ago
  I’m curious, is adding “do not hallucinate” to prompts effective in preventing hallucinations? The author does this.
  • krainboltgreene 2y ago
    It seems absurd, but I suppose it’s the same as misspelling with similar enough trigrams as to get the best autocorrect results.
  • becquerel 2y ago
    I don't know about this specific technique, but I have found it useful to add a line like 'it's OK if you don't know or this isn't possible' at the end of queries. Otherwise LLMs have a tendency to tilt at whatever windmill you give them. Managing tone and expectations with them is a subtle but important art.
  • simonw 2y ago
    Apple were using that in their Apple Intelligence system prompts last year, I don't know if they still have that in there. https://simonwillison.net/2024/Aug/6/apple-intelligence-prompts/ https://simonwillison.net/2024/Aug/6/apple-intelligence-prom... I have no idea if it works or not!
    • harper 2y ago
      I added it because of the apple prompts! I figured it is worth a try. The results are good, but i did not test it extensively
  • watt 2y ago
    It will work - you can see it well with a Chain of Thought (CoT) model: it will keep asking itself: "am I hallucinating? let's double check" and then will self-reject thoughts if it can't find a proper grounding. In fact, this is the best part of CoT model, that you can see where it goes off rails and can add a message to fix it in the prompt. For example, there is this common challenge, "count how many r letters in strawberry", and you can see the issue is not counting, but that model does not know if "rr" should be treated as single "r" because it is not sure if you are counting r "letters" or r "sounds" and when you sound out the word, there is a single "r" sound where it is spelled with double "r". so if you tell the model, double "r" stands for 2 letters, it will get it right.
• deleted 2y ago
  [deleted]
• timmyers 2y ago
  [dead]
• bambax 2y ago
  This is all fine for a solo dev, but how does this work with a team / squad, working on the same code base? Having 7 different instances of an LLM analyzing the same code base and making suggestions would not just be economically wasteful, it would also be unpractical or even dangerous? Outside of RAG, which is a different thing, are there products that somehow "centralize" the context for a team, where all questions refer to the same codebase?
  • staindk 2y ago
    I've only recently switched to Cursor so am not clued up about everything, but they mention that the embedded indexing they do on your code is shared with others (others who have access to that repository? Unsure). It did seem to take a while to index, even though my colleagues had been using Cursor for a while, so I'm likely misunderstanding something.
  • sambo546 2y ago
    I've started substituting "human" for "LLM" when I read posts like these. Is having 7 different humans analyzing the same code base any less wasteful?
    • bambax 2y ago
      They are not analyzing the same code base, they are all contributing to the same code base, each in their own domain. It would seem relevant that any advice an LLM gives to one of them is kept consistent -- in real time -- with any other advice to any other dev, instead of having to wait for each commit or push.
  • adr1an 2y ago
    Cody/ sourcegraph provide workspaces for teams/ enterprise. Probably for this and other reasons.
• jacooper 2y ago
  I find making the LLMs think and plan the project a bit worrying, I understand this helps with procrastination but when these systems eventually get better and more integrated, the most likely thing to happen to software devs is them moving away from purely coding to more of a solution architect role (aka Planning stuff), not taking into account the negative impact of giving up critical thinking to LLMs. https://news.ycombinator.com/item?id=43057907 https://news.ycombinator.com/item?id=43057907 Other than that a great article! Very insightful.
  • harper 2y ago
    I actually think it is going to be way worse than you are suggesting. I think that the LLM codegen is going to replace most if not all of software eng workflow and teams that we see today. Software is going to be prompt wrangling with some acceptance testing. Then just prompt wrangling. I don't have a lot of hope for the software profession to survive.
• maelito 2y ago
  Given a 3 648 318 tokens repository (number from Repomix), I'm not sure what would be the cost of using a leading LLM to analyse it and ask improvements. Isn't the input token number way more limited than that ? This is part is unclear to me in the "non-Greenfield" part of the article. Iterating with aider on very limited scopes is easy, I've used it often. But what about understanding a whole repository and act on it ? Following imports to understand a Typescript codebase as a whole ?
  • kridsdale3 2y ago
    Well, do you as a human have the whole codebase loaded in to your memory with the ability to mentally reason with it? No, you work on a small scope at a time.
    • layer8 2y ago
      You may work in a limited scope at a time, but you are aware how it fits into the larger scope, and more often than not you actually have to connect things across different scopes.
      • jack_pp 2y ago
        Well you can use an LLM similarly. Have it write docs for all your files including a summary for each function / class, ideally in order of dependency. Then use only the summaries in context. This should significantly lower your token count. Haven't tried it personally but it should work
        • layer8 2y ago
          In my experience, you often remember and/or discover relationships to other parts of the system during the current development task, by delving into the implementation. These relationships also aren't necessarily explicit in the code you're looking at. For example, they can relate to domain-level invariants or to shared resources, or simply shared patterns and conventions. In general you can't prepare everything that would be relevant up front.
• rollinDyno 2y ago
  Something I quickly learned while retooling this past week is that it’s preferable not to add opinionated frameworks to the project as they increase the size of the context the model should be aware of. This context will also not likely be available in the training data. For example, rather than using Plasmo for its browser extension boilerplate and packaging utilities, I’ve chosen to ask the LLM to setup all of that for me as it won’t have any blindspots when tasked with debugging.
  • sampton 2y ago
    The end of artisan frameworks - probably for the better.
    • balls187 2y ago
      It's likely the end of a lot of abstractions that made programming easier. At some point, specialized code-gen transformer models should get really good at just spitting out the lowest level code required to perform the job.
      • hy4000days 2y ago
        This. Future programming language designers are then answering questions like: "How low-level can this language be while considering generally available models and hardware available can only generate so many tokens per second?", "Do we have the language models generate binary code directly, or is it still more efficient time-wise to generate higher level code and use a compiler?" "Do we ship this language with both a compiler and language model?" "Do we forsake code readability to improve model efficiency?"
        • superb_dev 2y ago
          I’m excited for my new woodworking career if this ever becomes a reality. LLMs are truly sucking the art out of everything.
          • 2y ago
• zackify 2y ago
  Cline over everything for me
• dkkergoog 2y ago
  [dead]
• dfltr 2y ago
  > Legacy modern code As opposed to Vintage Pioneer code?
  • harper 2y ago
    in my experience, there is quite a spectrum of legacy code. Legacy modern code would be anything from the last 5-10 years. Vintage Pioneer code (which i have both initialized, and maintained) is more than 20 years old. I am trying not to be a vintage pioneer these days.
• thedeep_mind 2y ago
  This is effing great...thanks for sharing your experience. I was just wondering how to give my edits back to in-browser tools like Claude or ChatGPT, but the idea of repo mix is great, will try! Although I have been flying bit with copilot in vscode, so right now I have essentially two AI, one for larger changes (in the browser), and then minor code fixes (in vscode).
• snowwrestler 2y ago
  Spelling nit: “Over my skis” ~ in over my head. “Over my skies” ~ very far overhead. In orbit maybe?
  • pyreal 2y ago
    Thanks for that clarification! I was wondering what skies had to do with skiing.
  • matsemann 2y ago
    > “Over my skis” ~ in over my head. Is that correct? Never heard the expression before, but as a skier if you're over your skis you're in control of them, while if you're backseated the skis will control you.
    • jdlshore 2y ago
      It seems to be currently popular corporate-speak for "overextended." I've heard it a bunch lately. Never really thought about whether it was accurate, though!
  • harper 2y ago
    fixed! thanks
• bionhoward 2y ago
  I don’t mind LLMs, but what irks me is the customer noncompete, you have these systems that can do almost anything and the legal terms explicitly say you’re not allowed to use the thing for anything that competes with the thing. But if the things can do almost anything then you really can’t use it for anything. Making a game with Grok? No, that competes with the xAI game studio. Making an agents framework with ChatGPT? No, that competes with Swarm. Making legal AI with Claude? No, that competes with Claude. Seems like the only American companies making AI we can actually use for work are HuggingFace and Meta.
  • thornewolf 2y ago
    Ignore the noncompetes. Never get sued. If you do, everyone else is on your side.
    • bionhoward 2y ago
      Meh, why pay to teach someone else’s bot? I’m sticking with open source
  • biddit 2y ago
    Form a Nonprofit X and a Corp Y: Noprofit X publishes outputs from competing AI, which is not copyrightable. Corp Y injests content published by Nonprofit X.
• avandekleut 2y ago
  This is pretty much my flow that I landed on as well. Dump existing relevant files into context, explain what we are trying to achieve, and ask it to analyze various approaches, considerations, and ask clarifying questions. Once we both align on direction, I ask for a plan of all files to be created/modified in dependency order with descriptions of the required changes. Once we align on the plan I say lets proceed one file at a time, that way I can ensure each file builds on the previous one and I can adjust as needed.
• fullstackwife 2y ago
  Looks similar to my experience, except this part: > if it doesn’t work, Q&A with aider to fix I fix errors myself, because LLMs are capable of producing large chunks of really stupid/wrong code, which needs to be reverted, and thats why it makes sense to see the code at least once. Also I used to find myself in a situation when I tried to use LLM for the sake of using LLM to write code (waste of time)
• codeisawesome 2y ago
  Would be great if there were more details on the costs of doing this work - especially when loading lots of tokens of context via repo mix and then generating code with context (context-loaded inference API calls are more expensive, correct?). A dedicated post discussing this and related considerations would be even better. Are there cost estimations in the tools like aider (vs just refreshing the LLM platform’s billing dashboard?)
• randomcatuser 2y ago
  > I really want someone to solve this problem in a way that makes coding with an LLM a multiplayer game. Not a solo hacker experience. There is so much opportunity to fix this and make it amazing. This i think is the grand vision -- what could it look like? in my mind programming should look like a map -- you can go anywhere, and there'll be things happening. and multiple people. If anyone wants to work on this (or have comments, hit me up!)
• mark_mcnally_je 2y ago
  I'm a bit confused here, what promt do you use to start Aider and how do you just let Aider run wild so you can play cookie clicker?
  • fragmede 2y ago
    Am I overthinking yes | aider
    • mark_mcnally_je 2y ago
      Hahahaa well that might work but I wish you could just say `aider --go-hog-wild`
      • fragmede 2y ago
        fwiw it doesn't/that was a joke. In some cases the LLM will suggest running a command that doesn't terminate (eg npm run dev to run a webserver), so it'd get stuck running that command just waiting for user input.
        • mark_mcnally_je 2y ago
          :( big sad
        • e12e 2y ago
          Surely you can just ask the ai to make sure the code will terminate before it runs it? /s
          • fragmede 2y ago
            You jest, but given something that's in it's training data, ChatGPT's able to say the code won't terminate (eg a code to give all the Fibonacci numbers), so there are subsets of the halting problem it can solve for.
  • matsemann 2y ago
• fallinditch 2y ago
  Great post and discussion. Also, don't forget that your favorite AI tools can be of great help with the factors that cause us to make software: research, subject expertise, marketing, business planning, etc.
• keyle 2y ago
  I have been using LLM for a long time but these prompts ideas are fantastic; they really opened up a world for me. Because a lot of the benefits of LLM is bringing ideas or questions I am not thinking of right now, and this really does that. Typically this would happen as I dig through a topic, not before hand. So that's a net benefit. I also tried it and it worked a charm, the LLM did respect context and the step by step approach, poking holes in my ideas. Amazing work. I still like writing codes and solving puzzles in my mind so I won't be doing the "execution" part. From there on, I mostly use LLM as auto complete and I'm stuck here or obscure bug solving. Otherwise, I don't get any satisfaction from programming, having learnt nothing.
• tarkin2 2y ago
  Most new programmers forget the specification and execution plan part of programming. I ended up finishing my side projects when I kept these in mind, rather than focusing on elegant code for elegant code's sake. It seems the key to using LLMs successfully is to make them create a specification and execution plan, through making them ask /you/ questions. If this skill--specification and execution planning--is passed onto LLMs, along with coding, then are we essentially souped-up tester-analysts?
• briga 2y ago
  Absolutely LLMs are great for greenfield projects. They can get you to a prototype for a new idea faster than any tool yet invented. Where they start to break down, I find, is when you ask it to make changes/refactors to existing code and mature projects. They usually lack context, so they doesn't hesitate to introduce lots of extra complexity, add frameworks you don't need, and in general just make the situation worse. Or if they get you to some solution it will have taken so long that you might as well have just done the heavy lifting yourself. LLMs are still no substitute for actually understanding your code.
  • wilkystyle 2y ago
    100% agree. My experience to date across the major LLMs is that they are quick to leap to complex solutions, and I find that the code often is much harder to maintain than if I were to do it myself. But complex code is only part of the problem. Another huge problem I see is the rapid accumulation of technical debt. LLMs will confidently generate massive amounts of code with abstractions and design patterns that may be a good fit in isolation, but are absolutely the wrong pattern for problem you're trying to solve or the system you're trying to build. You run into the "existing code pattern" problem that Sandi Metz talked about in her fantastic 2014 RailsConf talk, "All the little things" [0]: > "We have a bargain to follow the pattern, and if the pattern is a good one then the code gets better. If the pattern is a bad one, then we exacerbate the problem." Rapidly generating massive amounts of code with the wrong abstractions and design patterns is insidious because it feels like incredible productivity. You see it all the time in posts on e.g. Twitter or LinkedIn. People gushing about how quickly they are shipping products with minimal to zero other humans involved. But there is no shortcut to understanding or maintainability if you care about building sustainable software for the medium to long-term. EDIT: Forgot to add link [0] https://www.youtube.com/watch?v=8bZh5LMaSmE&t=8m11s https://www.youtube.com/watch?v=8bZh5LMaSmE&t=8m11s
    • williamcotton 2y ago
      But why follow the wrong abstraction and why try to build something that you don't fundamentally understand? I've built some rather complex systems: Guish, a bi-directional CLI/GUI for constructing and executing Unix pipelines: https://github.com/williamcotton/guish https://github.com/williamcotton/guish WebDSL, fast C-based pipeline-driven DSL for building web apps with SQL, Lua and jq: https://github.com/williamcotton/webdsl https://github.com/williamcotton/webdsl Search Input Query, a search input query parser and React component: https://github.com/williamcotton/search-input-query https://github.com/williamcotton/search-input-query
• pyreal 2y ago
  I'm curious to see his mise tasks. He lists a few of them near the end but I'm not sure what his LLM CLI is. Is that an actual tool or is he using it as a placeholder for "insert your LLM CLI tool here"?
  • nickrj 2y ago
    https://github.com/simonw/llm https://github.com/simonw/llm It is linked to in the article - a brilliant utility from Simon.
• bcoates 2y ago
  That lonely/downtime section at the end is a giant red flag for me. It looks like the sort of nonproductive yak-shaving you do when you're stuck or avoiding an unpleasant task--coasting, fooling around incrementally with your LLM because your project's fucked and you psychologically need some sense of progress. The opposite of this is burnout--one of the things they don't tell you about successful projects with good tools is they induce much more burnout than doomed projects. There's a sort of Amdahl's Law in effect, where all the tooling just gives you more time to focus on the actual fundamentals of the product/project/problem you’re trying to address, which is stressful and mentally taxing even when it works. Fucking around with LLM coding tools, otoh, is very fun, and like constantly clean-rebuilding your whole (doomed) project, gives you both some downtime and a sense of forward momentum--look how much the computer is chugging! The reality testing to see if the tool is really helping is to sit down with a concrete goal and a (near) hard deadline. Every time I've tried to use an LLM under these conditions it just fails catastrophically--I don't just get stuck, I realize how basically every implicit decision embedded in the LLM output has an unacceptably high likelihood of being wrong, and I have an amount of debug cycles ahead of me exceeding the time to throw it all away and do it without the LLM by, like, an order of magnitude. I'm not an LLM-coding hater and I've been doing AI stuff that's worked for decades, but current offerings I've tried aren't even close to productive compared to searching for code that already exists on the web.
  • getnormality 2y ago
    It sounds like LLMs are the new futzing with emacs configuration.
    • wilkystyle 2y ago
      Old and busted: Futzing around with my Emacs configuration. New hotness: Having an LLM do it for me.
  • krupan 2y ago
    Seriously!! Coding with LLM's is marketed as a huge time saver, but every time I've tried, it hasn't been. I'm told I just need to put in the time (ironic, no?) to learn to use the LLM properly. Why don't I just use that time to learn to write code better myself?
• jdenning 2y ago
  Question to folks with good workflows: Are you using tools like DSPy to generate prompts? Any other tools/tips about managing prompts?
  • harper 2y ago
    i really wanted to use DSPy to generate prompts, but it wasn't quite as compatible with my workflow as i wanted. I love the idea tho - code instead of strings. i will dig in again. It is an exciting idea.
• bill_lau19 2y ago
  I learn this things from this blog: 1. Use multi turn with LLM tools to finish a job. 2. Work step by step.
• psadri 2y ago
  One more tool he could make is one to wrap that entire process so there is less copy/pasting needed.
  • sejje 2y ago
    Aider, a tool he uses, can do that automatically. He could just use that feature.
    • harper 2y ago
      Yea - i have had OK luck with the architect mode. but was using that as part of this (using deepseek for reasoning) and it good. but oh so slow. Ultimately, i would love to just use one tool
      • sejje 2y ago
        I don't mean the architect mode, I mean the copy-paste mode. https://aider.chat/docs/usage/copypaste.html https://aider.chat/docs/usage/copypaste.html
• cipehr 2y ago
  Am I the only one that doesn’t see the hype with Claude? I recently tried it, hit the usage limit, read around found tons of blogs and posts from devs saying Claude is the best code assistant LLM… so I purchased Claude pro… and I hate it. I have been asking it surface level questions about Apache spark (configuring the number of tasks retries, errors, error handling, etc.) and it hallucinated so much, so frequently. It reminds me of like ChatGPT 3… What am I doing wrong or what am I missing? My experience has been so underwhelming I just don’t understand the hype for why people use Claude over something else. Sorry I know there are many models out there, and Claude is probably better than 99% of them. Can someone help me understand the value of it over o1/o3? I honestly feel like I like 4o better. /frustration-rant
  • btucker 2y ago
    It seems like you might be trying to use it like a search engine, which is a common mistake people make when first trying LLMs. LLMs are not like Google. The key is to give it context so it can help you. For example, if you want it to help you with Spark configuration, give it the Spark docs. If you want it to help you write code, give it your codebase. Tools like cursor and the like make this process very easy. You can also set up a local MCP server so the LLM can get the context and tools it needs on its own.
    • cipehr 2y ago
      Thank you very much for the ideas here, i will try the approach of giving it context. I havent got into cursor, since i use helix and intellij… i need to look into the MCP server thing Thanks again!
      • foretop_yardarm 2y ago
        Giving examples of inputs and outputs can also help
        • cipehr 2y ago
          thank you, I will try this too. I feel like I didn't have to do this much work with other models like o1/o3/4o... but if it provide the return value I'm hearing from the hype around Claude I am willing to try.
• oars 2y ago
  Good for greenfield projects.
• insin 2y ago
  I liked the bit where he asked it not to hallucinate
• blah2244 2y ago
  This is a great article -- I really appreciate the author giving specific examples. I have never heard of mise (https://mise.jdx.dev/ https://mise.jdx.dev/) before either, and the integration with the saved prompts is a nifty idea -- excited to try it out!
  • abrookewood 2y ago
    Mise is great - it's an alternative to ASDF and remains call compatible from memory, but is much faster.
• junto 2y ago
  Something I’ve started to do recently is mob programming with LLM’s. I act as the director, creativity and ideas person. I have one LLM that implements, and a second LLM that critiques and suggests improvements and alternatives.
  • krupan 2y ago
    I hope this is a joke, but I'm guessing it isn't, lol!
    • triyambakam 2y ago
      It's a good idea. Get diverse model output.
• tyiz 2y ago
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• krupan 2y ago
  If I have to go to this much effort, what is AI buying us here? Why don't we just put the effort in to learn to write code ourselves? Instead of troubleshooting AI problems and coming up with clever workarounds for those problems, troubleshoot your code, solve those problems directly!
  • triyambakam 2y ago
    Speed. The abstraction layer has moved up. You probably aren't writing machine code anymore.
  • harper 2y ago
    the speed is way faster. I am a good programmer with more than 25 years of professional experience. The AI is a better programmer in every way. Why do it myself when i can outsource it and play cookie clicker? The real thing that sold me is the entire workflow takes 10 minutes to plan, and then 10-15 minutes to execute (let's say a python script of medium complexity). after a solid ~20-30 min I am largely done. no debugging necessary. it would have taken me an hour or two to do the same script. this means i can spend a lot more time with the fam, hacking on more things, and messing about.
• deleted 2y ago
  [deleted]
• ChrisRob 2y ago
  In our company we are only allowed to use GitHub Copilot with GPT or Claude, but not Claude directly. I'm quite struggling with getting good results from it, so I'll try to adapt your workflow into that setup. To the community: Do you have some additional guidance for that setup?
  • debian3 2y ago
    Use vs code insider if you can. They double the context size and it really makes a difference. You get 128k input token.
• mrklol 2y ago
  About the first step, you probably also need some kind of context that the LLM has most information to iterate with you about the new feature idea. so either you put the whole codebase into the context (will mostly lead to problems as tokens are limited) or you have some kind of summary with your current features etc. Or you do some kind of "black box" iterations, which I feel won’t be that useful for new features, as the model should know about current features etc? What’s the way here?