A good case study. I have found these two to be good categories of win:
> Use these tools as a massive force multiplier of your own skills.
Claude definitely makes me more productive in frameworks I know well, where I can scan and pattern-match quickly on the boilerplate parts.
> Use these tools for rapid onboarding onto new frameworks.
I’m also more productive here, this is an enabler to explore new areas, and is also a boon at big tech companies where there are just lots of tech stacks and frameworks in use.
I feel there is an interesting split forming in ability to gauge AI capabilities - it kinda requires you to be on top of a rapidly-changing firehose of techniques and frameworks. If you haven’t spent 100 hours with Claude Code / Claude 4.0 you likely don’t have an accurate picture of its capabilities.
“Enables non-coders to vibe code their way into trouble” might be the median scenario on X, but it’s not so relevant to what expert coders will experience if they put the time in.
This is a good takeaway. I use Claude Code as my main approach for making changes to a codebase, and I’ve been doing so every day for months. I have a solid system I follow through trial and error, and overall it’s been a massive boon to my productivity and willingness to attempt larger experiments.
One thing I love doing is developing a strong underlying data structure, schema, and internal API, then essentially having CC often one-shot a great UI for internal tools.
Being able to think at a higher level beyond grunt work and framework nuances is a game-changer for my career of 16 years.
This is more of a reflection of how our profession has not meaningfully advanced. OP talks about boilerplate. You talk about grunt work. We now have AI to do these things for us. But why do such things need to exist in the first place? Why hasn't there been a minimal-boilerplate language and framework and programming environment? Why haven't we collectively emphasized the creation of new tools to reduce boilerplate and grunt work?
Yes. The author essentially asked Claude to port a driver from Linux 2.4 to Linux 6.8. Very certainly there must be sufficient amounts of training material, and web-searchable material, that describes such tasks. The author provided his own expertise where Claude could not find a good analogue in the training corpus, that is, the few actually non-trivial bits of porting.
"Use these tools as a massive force multiplier of your own skills" is a great way to formulate it. If your own skills in the area are near-zero, multiplying them by a large factor may still yield a near-zero result. (And negative productivity.)
You can still ask, generate a list of things to learn etc. basically generate a streamlined course based on all tutorials, readmes and source code available when the model was trained. You can call your tutor 24/7 as long as you got tokens.
We have members on my team that definitely feel empowered to wade into new territory, but they make so much misdirected code with LLMs, even when we make everyone use Claude 4 thinking agents.
It seems to me that if you have been pattern matching the majority of your coding career, then you have a LLM agent pattern match on top of that, it results in a lot of headaches for people who haven't been doing that on a team.
I think LLM agents are supremely faster at pattern matching than humans, but are not as good at it in general.
just points to the fact that they've no idea what they're doing and would produce different, pointless code by hand, though much slower. this is the paradigm shift - you need a much bigger sieve to filter out the many more orders of magnitude of crap that inexperienced operators of LLMs create.
One of the things I’ve noticed is that those people are the same people who before would spend 3 weeks on something before coming out with a copy of the docs that doesn’t actually solve the problem at hand, but it spits out a result that almost matches what you asked for. They never understood the problem in the first place, they always just hammered until the nail went in - now they just have a different tool.
For me is not so. It makes me way faster in languages that I don't know, but makes me slower on the ones I know because a lot of times, it creates code that will fail eventually.
Then I need to expend extra time following everything it did so I can "fix" the problem.
My daily experience suggests that this happens primarily when the developer isn't as good as they assume that they are at expressing the ideas in their head into a structure that the LLM can run with. That's not intended to be a jab, just an opportunity for reflection.
> Use these tools as a massive force multiplier of your own skills.
I've felt this learning just this week - it's taken me having to create a small project with 10 clear repetitions, messily made from AI input. But then the magic is making 'consolidation' tasks where you can just guide it into unifying markup, styles/JS, whatever you may have on your hands.
I think it was less obvious to me in my day job because in a startup with a lack of strong coding conventions, it's harder to apply these pattern-matching requests since there are fewer patterns. I can imagine in a strict, mature codebase this would be way more effective.
In times of Rust and Typescript (just examples) coding standards are explicit. It‘s not optional anymore. All my vibe coding projects are using CI with tests including style and type checks. The agent makes mistakes but it sees the failing tests and fixes it. If you vibe code like we did Perl and PHP in 1999 you‘re gonna have a bad time.
>> Use these tools for rapid onboarding onto new frameworks.
Also new languages - our team uses Ruby, and Ruby is easy to read, so I can skip learning the syntax and get the LLM to write the code. I have to make all the decisions, and guide it, but I don't need to learn Ruby to write acceptable-level code [0]. I get to be immediately productive in an unfamiliar environment, which is great.
[0] acceptable-level as defined by the rest of the team - they're checking my PRs.
>>> Use these tools for rapid onboarding onto new frameworks.
> Also new languages - our team uses Ruby, and Ruby is easy to read, so I can skip learning the syntax and get the LLM to write the code.
If Ruby is "easy to read" and assuming you know a similar programming language (such as Perl or Python), how difficult is it to learn Ruby and be able to write the code yourself?
> ... but I don't need to learn Ruby to write acceptable-level code [0].
Since the team you work with uses Ruby, why do you not need to learn it?
> [0] acceptable-level as defined by the rest of the team - they're checking my PRs.
Ah. Now I get it.
Instead of learning the lingua franca and being able to verify your own work, "the rest of the team" has to make sure your PR's will not obviously fail.
Here's a thought - has it crossed your mind that team members needing to determine if your PR's are acceptable is "a bad thing", in that it may indicate a lack of trust of the changes you have been introducing?
Furthermore, does this situation qualify as "immediately productive" for the team or only yourself?
EDIT:
If you are not a software engineer by trade and instead a stakeholder wanting to formally specify desired system changes to the engineering team, an approach to consider is authoring RSpec[0] specs to define feature/integration specifications instead of PR's.
This would enable you to codify functional requirements such that their satisfaction is provable, assist the engineering team's understanding of what must be done in the context of existing behavior, identify conflicting system requirements (if any) before engineering effort is expended, provide a suite of functional regression tests, and serve as executable documentation for team members.
I'm feeling quite wary of the fact that if it's a real productivity booster, it's all in the hands of one company. Perhaps some of the others will be able to compete with it, but: still all big corporations.
those who use claude code, what you think are its best features which you cannot live without and makes your life so much easier? I am using claude code but I am not sure what stuff i should look into.
this is a fantastic summary of how LLMs can be used in general. I have found chatgpt/gemini to be useful in following scenarios
1. ELI5 with examples on any technical paper. This summarises papers and explains to me in a way i can understand.
2. At my work, we have to make a lot of proposals, so I have a project created where I put public documents that I can share for proposals and then share the Statement of Work, and it creates a technical document in my format which is about 70% right. I can add/modify the remaining 30%
3. > Use these tools as a massive force multiplier of your own skills. - This is massive when I want to start a new code base. I spend so much time in my head architecting that tools like these help create a boiler plate and structure.
4. Many times, I have stupid ideas but not enough time to waste coding those stupid ideas. The tools help me right terrible codes for my stupid ideas!! :)
One area where it really shines for me is personal projects. You know, the type of projects you might get to spend a couple hours on once the kids are in bed... Spending that couple hours guiding Claude do do what I want is way quicker than doing it all myself. Especially since I do have the skills to do it all myself, just not the time. It's been particularly effective around UI stuff since I've selected a popular UI library (MUI) but I don't use it in my day job; I had to keep looking up documentation but Claude just bangs it out very easily.
One thing where it hasn't shone is configuring my production deployment. I had set this project up with a docker-compose, but my selected CI/CD (Gitlab) and my selected hosting provider (DigitalOcean) seemed to steer me more towards Kubernetes, which I don't know anything about. Gitlab's documentation wanted me to setup Flux (?) and at some point referred to a Helm chart (?)... All words I've heard but their documentation is useless to newcomers ("manage containers in production!": yes, that's obviously what I'm trying to do... "Getting started: run this obscure command with 5 arguments": wth is this path I need to provide? what's this parameter? etc.) I honestly can't believe how complex the recommended setup is, to ultimately run 2 containers that I already have defined in ~20 lines of docker-compose...
Claude got me through it. Took it about 5-6 hours of trying stuff, build failing, trying again. And even then, it still doesn't deploy when I push. It builds, pushes the new container images, and spins up a new pod... which it then immediately kills because my older one is still running and I only want one pod running... Oh well, I'll just keep killing the old pod until I have some more energy to throw at it to try and fix it.
TL;DR: it's much better at some things than others.
Totally. Being able to start shipping from the first commit using something like Picocss and just add features helps gets things out of the design stage, but shipping features individually.
Some folks seem to like Docker Swarm before kubernetes as well and I've found it's not bad for personal projects for sure.
AI will always return the average of it's corpus given the chance (or not clear direction in the prompt). I usually let my opinions rip and say to avoid building myself a stack temple to my greatness. It often comes back with a nice lean stack.
I usually avoid or minimize Javascript libraries for their brittleness, and the complexity can eat up more of the AI's context and awareness to map the abstractions vs something it knows incredibly well.
Python is great, but web stuff is still emerging, FastAPI is handy though, and putting something like Pico/HTMX/alpine.js on the front seems reasonable.
Laravel is also really hard to overlook sometimes when working with LLMs on quick things, there's so much working code out there that it can really get a ton done for an entire production environment with all of the built in tools.
Happy to learn about what other folks are using and liking.
I don’t have a lot of experience with your first point. I do I have a lot of experience with your second point. And I would say that you hit the nail on the head
I had an Amiga disk image (*.adf) that I wanted to extract the files from. There are probably tools to do this but I was just starting with Claude Code, so I asked it to write a tool to extract the files by implementing the filesystem.
It took a few prompts but I know enough about FFS (the Amiga filesystem) to guide it, and it created exactly the tool I wanted.
"force multiplier of your own skills" is a great description.
I think this is illustrative of the kind of productive things you can do with an LLM if you know what you are doing. Is it perfect, no. Can they do useful things if you prompt correctly, absolutely. It helps knowing what you are doing and having enough skill to make good judgment calls yourself.
There are currently multiple posts per day on HN that escalate into debates on LLMs being useful or not. I think this is a clear example that it can be. And results count. Porting and modernizing some ancient driver is not that easy. There's all sorts of stuff that gets dropped from the kernel because it's just too old to bother maintaining it and when nobody does, deleting code becomes the only option. This is a good example. I imagine, there are enough crusty corners in the kernel that could benefit from a similar treatment.
I've had similar mixed results with agentic coding sometimes impressing me and other times disappointing me. But if you can adapt to some of these limitations it's alright. And this seems to be a bit of a moving goalpost thing as well. Things that were hard a few months ago are now more doable.
The more you use the tools, the more you're able to recognize the situations in which they're useful.
These studies keep popping up where they randomly decide whether someone will use AI to assist in a feature or not and it's hard for me to explain just how stupid that is. And how it's a fundamental misunderstanding of when and how you'd want to use these tools.
It's like being a person who hangs up drywall with screws and your boss going "Hey, I'm gonna flip a coin and if it's heads you'll have to use the hammer instead of a screwdriver" and that being the method in which the hammer is judged.
I don't wake and go "I'm going to use AI today". I don't use it to create entire features. I use it like a dumb assistant.
> I've had similar mixed results with agentic coding sometimes impressing me and other times disappointing me. But if you can adapt to some of these limitations it's alright. And this seems to be a bit of a moving goalpost thing as well. Things that were hard a few months ago are now more doable.
I actually do this now. That's one of those things that went from impossible to doable under some circumstances. Still a bit of a coin flip but it can work well in some code bases. I still have a mental block even asking for these things under the assumption it would not work anyway. But I've been pleasantly surprised a few times where this actually works.
> There are currently multiple posts per day on HN that escalate into debates on LLMs being useful or not.
My main worry is whether they will be useful when priced above actual cost. I worry about becoming depending on these tools only for them to get prohibitively expensive.
I've used Claude Code in the past month to do development on CoMaps [1] using the 20 USD/month plan.
I've been able to do things that I would not have the competence for otherwise, as I do not have a formal software engineering background and my main expertise is writing python data processing scripts.
E.g., yesterday I fixed a bug [2] by having Claude compare the CarPlay and iOS search implementations. It did at first suggest another code change than the one that fixed it, but that felt just like a normal part of debugging (you may need to try different things)
Most of my contributions [3] have been enabled by Claude, and it's also been critical to identify where the code for certain things are located - it's a very powerful search in the code base
And it is just amazing if you need to write a simple python script to do something, e.g., in [4]
Now this would obviously not be possible if everyone used AI tools and no one knew the existing code base, so the future for real engineers and architects is bright!
I mainly use Claude Code for things I know, where I just don't want to focus on the coding part. However, I recently found a very niche use. I have a small issue with an open source project. Instead of just accepting it, it occurred to me I can just clone the repo, and ask CC to look into my issue. For example, I was annoyed with Helix/Zed that replacing parameter in Zig code only works for function declarations, not function calls. I suspected it will be in the tree-sitter grammar, but I let it go through the Zed source code, then it asked for the grammar, so I cloned it and gave it access to that, and it happily fixed the grammar for me and tested the results. It needed a few nudges to make the fix properly, but I spent maybe 5 minutes on this, while CC was probably working for half an hour. I even had it fork the repo, and open the PR for me. In the end I have an useful change that people will benefit from, that I'd never attempt myself.
LLMs are also good for writing quick experiments and benchmarks to satisfy someone's curiosity. For example, once I was wondering, how much time does it take to migrate a cache line between cores when several processes access the same variable - and after I wrote a detailed benchmark algorithm, LLM generated the code instantly. Note that I described the algorithm completely and what it did is just translated it into the code. Obviously I could write the code myself, but I might need to lookup a function (how does one measure elapsed time?), I might make mistakes in C, etc. Another time a made a benchmark to compare linear vs tree search for finding a value in a small array.
It's very useful when you get the answer in several minutes rather than half a hour.
Also I wanted to add that LLMs (at least free ones) are pretty dumb sometimes and do not notice obvious thing. For example, when writing tests they generate lot of duplicated code and do not move it into a helper function, or do not combine tests using parametrization. I have to do it manually every time.
Maybe it is because they generate the code in one pass and cannot return back and fix the issues. LLM makers, you should allow LLMs to review and edit the generated code.
I see that often enough too, but if I then ask it to review what it's done and look for opportunities to factor out duplicated code, it does a decent job.
https://github.com/terryyin/lizard has been useful to track when functions get too convoluted or long, or when there's too much duplication -- in code generated by agents. Still have to see how well it works long term but it's caught things here and there, I have it in the build steps in my scripts so the agent sees its output.
Also I wanted to add that LLMs (at least free ones) are pretty dumb sometimes and do not notice obvious thing. For example, when writing tests they generate lot of duplicated code and do not move it into a helper function, or do not combine tests using parametrization. I have to do it manually every time.
> using these tools as a massive force multiplier…
Even before tools like CC it was the case that LLMs enabled venturing into projects/areas that would be intimidating otherwise. But Claude-Code (and codex-cli as of late) has made this massively more true.
For example I recently used CC to do a significant upgrade of the Langroid LLM-Agent framework from Pydantic V1 to V2, something I would not have dared to attempt before CC:
I also created nice collapsible html logs [2] for agent interactions and tool-calls, inspired by @badlogic/Zechner’s Claude-trace [3] (which incidentally is a fantastic tool!).
Hm. That was interesting, so I ran it as prompt to Qwen3-Coder (which I run locally). It spat out a bunch of C functions together with a C struct and commented:
This implementation provides:
Generic Tuple Structure: A flexible tuple that can hold different types of elements (int, double, string, float)
Constructor Function: tuple_create() initializes a new tuple with specified size
Type-Safe Setters:
tuple_set_int(), tuple_set_double(), tuple_set_string(), tuple_set_float()
Type-Safe Getters:
tuple_get_int(), tuple_get_double(), tuple_get_string(), tuple_get_float()
Memory Management: Automatic memory allocation and deallocation
Utility Functions:
tuple_print() for displaying tuple contents
tuple_destroy() for cleanup
Seems to be a common enough request to be present in the model's training data :D Even the code looked fine, apart from being a very naive and thus slow implementation.
I asked ChatGPT to make a system of macros that would generate a struct with given fields (for example: int a, bool b, char *c) and a function that would print the struct contents. Instead of refusing it started inventing recursive macros that were never going to work. It seems LLMs are especially bad with C macros.
If you have access to state-of-art coding assistants, try asking this. You can additionally add a requirement to generate a destructor function (that would free dynamically-allocated fields).
> I need a C class that has a constructor for a tuple
| I'll create a simple C++ class with a constructor that takes a tuple.
Here's an example...
> nah C
| C doesn't have classes or constructors - those are C++ features. In C, you
would use a struct and create an initialization function. Here's how
you'd handle a tuple-like structure:
When I read an article like this it makes me think about how the demand for work to be done was nowhere close to being fully supplied by the pre-LLM status quo.
LLM assisted coding can get you from an idea to MVP in an evening (within maybe 1 or 2 Claude 5 hour quota windows).
I've done _so_ many of these where I go "hmm, this might be useful", planned the project with gemini/chatgpt free versions to a markdown project file and then sic Claude on it while I catch up on my shows.
Within a few prompts I've got something workable and I can determine if it was a good idea or not.
Without an LLM I never would've even tried it, I have better and more urgent things to do than code a price-watcher for very niche Blu-ray seller =)
This, for me, is the actual gain and I don't see a lot of people talking about it: it's not that I finish a project faster the LLMs. From what I've read and personally experienced, it probably takes about as long to complete a project with or without the LLMs. But the difference is, without it I spend all that time deeply engaged, unable to do anything else. With the LLMs I no longer require continuous focus. It may be the same wall-clock time but my own mental capacity is not being used at or near capacity.
It's never about lack of work but lack of people who have the prerequisite expertise to do it. If you don't have experience w/ kernel development then no amount of prompting will get you the type of results that the author was able to achieve. More specifically, in theory it should be possible to take all the old drivers & "modernize" them to carry them forward into each new version of the kernel but the problem is that none of the LLMs are capable of doing this work w/o human supervision & the number of people who can actually supervise the LLMs is very small compared to the amount of unmaintained drivers that could be ported into newer kernels.
There is a good discussion/interview¹ between Alan Kay & Joe Armstrong about how most code is developed backwards b/c none of the code has a formal specification that can be "compiled" into different targets. If there was a specification other than the old driver code then the process of porting over the driver would be a matter of recompiling the specification for a new kernel target. In absence of such specification you have to substitute human expertise to make sure the invariants in the old code are maintained in the new one b/c the LLMs has no understanding of any of it other than pattern matching to other drivers w/ similar code.
There is usually a specification for how hardware works. But:
1. The original hardware spec is usually proprietary, and
2. The spec is often what the hardware was supposed to do. But hardware prototype revisions are expensive. So at some point, the company accepts a bunch of hardware bugs, patches around them in software, ships the hardware, and reassigns the teams to a newer product. The hardware documentation won't always be updated.
This is obviously an awful process, but I've seen and heard of versions of it for over 20 years. The underlying factors driving this can be fixed, if you really want to, but it will make your product totally uncompetitive.
AI doesn’t need to replace a specialist in their entirety for it to tank demand for a skill. If the people that currently do the work are significantly more productive, fewer people will be necessary to the same amount of work. Then, people trying to escape obsolescence in different, more popular specialties move into the niche ones. You could easily pass the threshold of having less work than people without having replaced a single specialist.
IDK, the bottleneck really still seems to be "marketable ideas" rather than their implementation. There's only so much stuff people are willing to actually pay for.
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> Use these tools as a massive force multiplier of your own skills.
Claude definitely makes me more productive in frameworks I know well, where I can scan and pattern-match quickly on the boilerplate parts.
> Use these tools for rapid onboarding onto new frameworks.
I’m also more productive here, this is an enabler to explore new areas, and is also a boon at big tech companies where there are just lots of tech stacks and frameworks in use.
I feel there is an interesting split forming in ability to gauge AI capabilities - it kinda requires you to be on top of a rapidly-changing firehose of techniques and frameworks. If you haven’t spent 100 hours with Claude Code / Claude 4.0 you likely don’t have an accurate picture of its capabilities.
“Enables non-coders to vibe code their way into trouble” might be the median scenario on X, but it’s not so relevant to what expert coders will experience if they put the time in.
One thing I love doing is developing a strong underlying data structure, schema, and internal API, then essentially having CC often one-shot a great UI for internal tools.
Being able to think at a higher level beyond grunt work and framework nuances is a game-changer for my career of 16 years.
Dead Comment
"Use these tools as a massive force multiplier of your own skills" is a great way to formulate it. If your own skills in the area are near-zero, multiplying them by a large factor may still yield a near-zero result. (And negative productivity.)
It seems to me that if you have been pattern matching the majority of your coding career, then you have a LLM agent pattern match on top of that, it results in a lot of headaches for people who haven't been doing that on a team.
I think LLM agents are supremely faster at pattern matching than humans, but are not as good at it in general.
just points to the fact that they've no idea what they're doing and would produce different, pointless code by hand, though much slower. this is the paradigm shift - you need a much bigger sieve to filter out the many more orders of magnitude of crap that inexperienced operators of LLMs create.
Then I need to expend extra time following everything it did so I can "fix" the problem.
I've felt this learning just this week - it's taken me having to create a small project with 10 clear repetitions, messily made from AI input. But then the magic is making 'consolidation' tasks where you can just guide it into unifying markup, styles/JS, whatever you may have on your hands.
I think it was less obvious to me in my day job because in a startup with a lack of strong coding conventions, it's harder to apply these pattern-matching requests since there are fewer patterns. I can imagine in a strict, mature codebase this would be way more effective.
Also new languages - our team uses Ruby, and Ruby is easy to read, so I can skip learning the syntax and get the LLM to write the code. I have to make all the decisions, and guide it, but I don't need to learn Ruby to write acceptable-level code [0]. I get to be immediately productive in an unfamiliar environment, which is great.
[0] acceptable-level as defined by the rest of the team - they're checking my PRs.
> Also new languages - our team uses Ruby, and Ruby is easy to read, so I can skip learning the syntax and get the LLM to write the code.
If Ruby is "easy to read" and assuming you know a similar programming language (such as Perl or Python), how difficult is it to learn Ruby and be able to write the code yourself?
> ... but I don't need to learn Ruby to write acceptable-level code [0].
Since the team you work with uses Ruby, why do you not need to learn it?
> [0] acceptable-level as defined by the rest of the team - they're checking my PRs.
Ah. Now I get it.
Instead of learning the lingua franca and being able to verify your own work, "the rest of the team" has to make sure your PR's will not obviously fail.
Here's a thought - has it crossed your mind that team members needing to determine if your PR's are acceptable is "a bad thing", in that it may indicate a lack of trust of the changes you have been introducing?
Furthermore, does this situation qualify as "immediately productive" for the team or only yourself?
EDIT:
If you are not a software engineer by trade and instead a stakeholder wanting to formally specify desired system changes to the engineering team, an approach to consider is authoring RSpec[0] specs to define feature/integration specifications instead of PR's.
This would enable you to codify functional requirements such that their satisfaction is provable, assist the engineering team's understanding of what must be done in the context of existing behavior, identify conflicting system requirements (if any) before engineering effort is expended, provide a suite of functional regression tests, and serve as executable documentation for team members.
0 - https://rspec.info/features/6-1/rspec-rails/feature-specs/fe...
One thing where it hasn't shone is configuring my production deployment. I had set this project up with a docker-compose, but my selected CI/CD (Gitlab) and my selected hosting provider (DigitalOcean) seemed to steer me more towards Kubernetes, which I don't know anything about. Gitlab's documentation wanted me to setup Flux (?) and at some point referred to a Helm chart (?)... All words I've heard but their documentation is useless to newcomers ("manage containers in production!": yes, that's obviously what I'm trying to do... "Getting started: run this obscure command with 5 arguments": wth is this path I need to provide? what's this parameter? etc.) I honestly can't believe how complex the recommended setup is, to ultimately run 2 containers that I already have defined in ~20 lines of docker-compose...
Claude got me through it. Took it about 5-6 hours of trying stuff, build failing, trying again. And even then, it still doesn't deploy when I push. It builds, pushes the new container images, and spins up a new pod... which it then immediately kills because my older one is still running and I only want one pod running... Oh well, I'll just keep killing the old pod until I have some more energy to throw at it to try and fix it.
TL;DR: it's much better at some things than others.
Some folks seem to like Docker Swarm before kubernetes as well and I've found it's not bad for personal projects for sure.
AI will always return the average of it's corpus given the chance (or not clear direction in the prompt). I usually let my opinions rip and say to avoid building myself a stack temple to my greatness. It often comes back with a nice lean stack.
I usually avoid or minimize Javascript libraries for their brittleness, and the complexity can eat up more of the AI's context and awareness to map the abstractions vs something it knows incredibly well.
Python is great, but web stuff is still emerging, FastAPI is handy though, and putting something like Pico/HTMX/alpine.js on the front seems reasonable.
Laravel is also really hard to overlook sometimes when working with LLMs on quick things, there's so much working code out there that it can really get a ton done for an entire production environment with all of the built in tools.
Happy to learn about what other folks are using and liking.
It took a few prompts but I know enough about FFS (the Amiga filesystem) to guide it, and it created exactly the tool I wanted.
"force multiplier of your own skills" is a great description.
Dead Comment
Dead Comment
There are currently multiple posts per day on HN that escalate into debates on LLMs being useful or not. I think this is a clear example that it can be. And results count. Porting and modernizing some ancient driver is not that easy. There's all sorts of stuff that gets dropped from the kernel because it's just too old to bother maintaining it and when nobody does, deleting code becomes the only option. This is a good example. I imagine, there are enough crusty corners in the kernel that could benefit from a similar treatment.
I've had similar mixed results with agentic coding sometimes impressing me and other times disappointing me. But if you can adapt to some of these limitations it's alright. And this seems to be a bit of a moving goalpost thing as well. Things that were hard a few months ago are now more doable.
These studies keep popping up where they randomly decide whether someone will use AI to assist in a feature or not and it's hard for me to explain just how stupid that is. And how it's a fundamental misunderstanding of when and how you'd want to use these tools.
It's like being a person who hangs up drywall with screws and your boss going "Hey, I'm gonna flip a coin and if it's heads you'll have to use the hammer instead of a screwdriver" and that being the method in which the hammer is judged.
I don't wake and go "I'm going to use AI today". I don't use it to create entire features. I use it like a dumb assistant.
> I've had similar mixed results with agentic coding sometimes impressing me and other times disappointing me. But if you can adapt to some of these limitations it's alright. And this seems to be a bit of a moving goalpost thing as well. Things that were hard a few months ago are now more doable.
Exactly my experience too.
I actually do this now. That's one of those things that went from impossible to doable under some circumstances. Still a bit of a coin flip but it can work well in some code bases. I still have a mental block even asking for these things under the assumption it would not work anyway. But I've been pleasantly surprised a few times where this actually works.
My main worry is whether they will be useful when priced above actual cost. I worry about becoming depending on these tools only for them to get prohibitively expensive.
I've been able to do things that I would not have the competence for otherwise, as I do not have a formal software engineering background and my main expertise is writing python data processing scripts.
E.g., yesterday I fixed a bug [2] by having Claude compare the CarPlay and iOS search implementations. It did at first suggest another code change than the one that fixed it, but that felt just like a normal part of debugging (you may need to try different things)
Most of my contributions [3] have been enabled by Claude, and it's also been critical to identify where the code for certain things are located - it's a very powerful search in the code base
And it is just amazing if you need to write a simple python script to do something, e.g., in [4]
Now this would obviously not be possible if everyone used AI tools and no one knew the existing code base, so the future for real engineers and architects is bright!
[1] https://codeberg.org/comaps/comaps [2] https://codeberg.org/comaps/comaps/pulls/1792 [3] https://codeberg.org/comaps/comaps/pulls?state=all&type=all&... [4] https://codeberg.org/comaps/comaps/pulls/1782
Hope to make the bridge soon with i18n of cartes.app.
I also use LLMs to work on it. Mistral, mostly.
It's very useful when you get the answer in several minutes rather than half a hour.
Maybe it is because they generate the code in one pass and cannot return back and fix the issues. LLM makers, you should allow LLMs to review and edit the generated code.
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You can tell it to move it and they'll move it and use this shared code from now on.
Even before tools like CC it was the case that LLMs enabled venturing into projects/areas that would be intimidating otherwise. But Claude-Code (and codex-cli as of late) has made this massively more true.
For example I recently used CC to do a significant upgrade of the Langroid LLM-Agent framework from Pydantic V1 to V2, something I would not have dared to attempt before CC:
https://github.com/langroid/langroid/releases/tag/0.59.0
I also created nice collapsible html logs [2] for agent interactions and tool-calls, inspired by @badlogic/Zechner’s Claude-trace [3] (which incidentally is a fantastic tool!).
[2] https://github.com/langroid/langroid/releases/tag/0.57.0
[3] https://github.com/badlogic/lemmy/tree/main/apps/claude-trac...
And added a DSL to specify agentic task termination conditions based on event-sequence patterns:
https://langroid.github.io/langroid/notes/task-termination/
Needless to say, the docs are also made with significant CC assistance.
If you don't have the technical understanding of a language or framework, there is going to be a lot of ambiguity in your prompts.
This specificity gap leads the LLM to fill in those gaps for you, which may not be what you intended. And that's usually where bugs hide.
I think this is the flip side to being a "force multiplier"
I would hope Claude would be like “Hold on man…”
If you have access to state-of-art coding assistants, try asking this. You can additionally add a requirement to generate a destructor function (that would free dynamically-allocated fields).
| I'll create a simple C++ class with a constructor that takes a tuple. Here's an example...
> nah C
| C doesn't have classes or constructors - those are C++ features. In C, you would use a struct and create an initialization function. Here's how you'd handle a tuple-like structure:
I've done _so_ many of these where I go "hmm, this might be useful", planned the project with gemini/chatgpt free versions to a markdown project file and then sic Claude on it while I catch up on my shows.
Within a few prompts I've got something workable and I can determine if it was a good idea or not.
Without an LLM I never would've even tried it, I have better and more urgent things to do than code a price-watcher for very niche Blu-ray seller =)
There is a good discussion/interview¹ between Alan Kay & Joe Armstrong about how most code is developed backwards b/c none of the code has a formal specification that can be "compiled" into different targets. If there was a specification other than the old driver code then the process of porting over the driver would be a matter of recompiling the specification for a new kernel target. In absence of such specification you have to substitute human expertise to make sure the invariants in the old code are maintained in the new one b/c the LLMs has no understanding of any of it other than pattern matching to other drivers w/ similar code.
¹https://www.youtube.com/watch?v=axBVG_VkrHI
1. The original hardware spec is usually proprietary, and
2. The spec is often what the hardware was supposed to do. But hardware prototype revisions are expensive. So at some point, the company accepts a bunch of hardware bugs, patches around them in software, ships the hardware, and reassigns the teams to a newer product. The hardware documentation won't always be updated.
This is obviously an awful process, but I've seen and heard of versions of it for over 20 years. The underlying factors driving this can be fixed, if you really want to, but it will make your product totally uncompetitive.