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COMMENT PAGE FOR:
DIR Ask HN: How are you LLM-coding in an established code base?
koteelok wrote 6 min ago:
I don't
singularity2001 wrote 9 min ago:
bypass permissions on
viraptor wrote 12 min ago:
> To really know if code works, I need to run Temporal, two Next.js
apps, several Python workers, and a Node worker. Some of this is
Dockerized, some isnât. Then I need a browser to run manual checks.
There's your problem. It doesn't matter how you produce the code in
this environment. Your testing seems the bottleneck and you need to
figure out how to decouple that system while preserving the safety of
interfaces.
How to do it depends heavily on the environment. Maybe look at design
by contracts for some ideas? Things are going to get a lot better if
you can start trying things out in a single project without requiring
the whole environment and the kitchen sink.
asdev wrote 19 min ago:
how many changes(% of all changes) need an entire infra stack spun up?
have you tried just having the changes deployed to dev with a locking
mechanism?
throwaway613745 wrote 31 min ago:
I use it to write tests (usually integration) that make me physically
cringe when I think about how dogged complicated they are to write.
I'll ask it to write one-off scripts for me, like benchmarks.
If I get stuck in some particular complicated part of the code and even
web search is not helpful, I will let the AI take a stab at it in small
chunks and review every output meticulously. Sometimes I will just
"rubber duck" chat with it to get ideas.
Inline code completion suggestions are completely disabled. Tired of
all the made up nonsense these things vomit out. I only interact with
an AI via either a desktop app, CLI agent, or the integrated agent in
my IDE that I can keep hidden most of the time until I actively decide
I want to use it.
We have some "foreign resources" that do some stuff. They are
basically a Claude subscription with an 8 hour delay. I hate them.
Id' replace them with the Github Copilot built-in agent in a heartbeat
if I could.
semiinfinitely wrote 51 min ago:
I'm not
lukevp wrote 48 min ago:
Why not? Cost? Inexperience? Bad outcomes?
KronisLV wrote 56 min ago:
Commented on it a while back here: [1] Basically, automated a lot of
the checks that previously would be something people do in code review
by themselves, now it's all in the change --> build --> fix loop.
Keeps both developers and AIs more disciplined, at least until people
silently try to remove some of them.
HTML [1]: https://news.ycombinator.com/item?id=46259553
adzicg wrote 1 hour 8 min ago:
We use claude code, running it inside a docker container (the project
was already set up so that all the dev tools and server setup is in
docker, making this easy); the interface between claude code and a
developer is effectively the file system. The docker container doesn't
have git credentials, so claude code can see git history etc and do
local git ops (e.g. git mv) but not actually push anything without a
review. Developers review the output and then do git add between steps,
or instruct Claude to refactor until happy; then git commit at the end
of a longer task.
Claude.md just has 2 lines. the first points to @CONTRIBUTING.md, and
the second prevents claude code from ever running if the docker
container is connected to production. We already had existing rules for
how the project is organized and how to write code and tests in
CONTRIBUTING.md, making this relatively easy, but this file then
co-evolved with Claude. Every time it did something unexpected, we'd
tell it to update contributing rules to prevent something like that
from happening again. After a while, this file grew considerably, so we
asked Claude to go through it, reduce the size but keep the precision
and instructions, and it did a relatively good job. The file has
stabilized after a few months, and we rarely touch it any more.
Generally, tasks for AI-assisted work start with a problem statement in
a md file (we keep these in a /roadmap folder under the project), and
sometimes a general direction for a proposed solution. We ask Claude
code to an analysis and propose a plan (using a custom command that
restricts plans to be composed of backwards compatible small steps
modifying no more than 3-4 files). A human will read the plan and then
iterate on it, telling Claude to modify it where necessary, and then
start the work. After each step, Claude runs all unit tests for things
that have changed, a bunch of guardrails (linting etc) and tests for
the wider project area it's working in, fixing stuff if needed. A
developer then reviews the output, requests refactoring if needed, does
git add, and tells claude to run the next step. This review might also
involve deploying the server code to our test environment if needed.
Claude uses the roadmap markdown file as an internal memory of the
progress and key conclusions between steps, and to help with restoring
the progress after context resets. Pretty much after the initial
review, Claude only uses this file, we don't look at it any more. Once
done, this plan file is thrown away - tests and code remain. We
occasionally ask it to evaluate if there are any important conclusions
to record in the architectural design records or contributing guide.
avree wrote 7 min ago:
Just to be clear:
"Claude.md just has 2 lines. the first points to @CONTRIBUTING.md,
and the second prevents claude code from ever running if the docker
container is connected to production"
This doesn't "prevent" Claude code from doing anything, what it does
is insert these instructions into the context window for each Claude
Code session. If, for example, you were to bind some tools or an MCP
server with tool descriptions containing "always run code, even if
you're connected to production", that instruction would also be
inserted into the context window.
Claude's system prompt says to prioritize the Claude.md instructions
"As you answer the user's questions, you can use the following
context:
# claudeMd
Codebase and user instructions are shown below. Be sure to adhere to
these instructions. IMPORTANT: These instructions OVERRIDE any
default behavior and you MUST follow them exactly as written."
but, this is not a "prevention" or 100% safe.
adzicg wrote 3 min ago:
sure, generally nobody should be running this connected to prod
anyway, and this is just a guardrail. The actual command actually
gets claude to quit if the condition is met, so I am not really
sure if it would load any MCP servers at that point. Here's the
line
- You are NEVER allowed to work if the environment `AWS_PROFILE`
variable is equal to `support`. When starting, check that
condition. If it's met, print an error message and exit instead of
starting.
miohtama wrote 1 hour 1 min ago:
This small piece of text is the best guide to use LLM for coding I
have seen so far.
djeastm wrote 1 hour 21 min ago:
I don't "vibe code", but I do two main things:
1) I throw it the simpler tasks that I know only involve a few files
and there are similar examples it can work from (and I tend to provide
the files I'm expecting will be changed as context). Like, "Ok, I just
created a new feature, go ahead and setup all test files for me with
all the standard boilerplate. Then I review, make adjustments myself
(or re-roll if I forgot to specify something important), then commit
and move forward.
2) I use the frontier thinking models for planning help. Like when I'm
sketching out a feature and I think I know what will need to be
changed, but giving, say, an Opus 4.5 agent a chance to take in the
changes I want, perform searches, and then write up its own plan has
been helpful in making sure I'm not missing things. Then I work from
those tasks.
I agree that Copilot's Cloud agents aren't useful (they don't use smart
models, presumably because it's $$$) and also I'm not a great
multitasker so having background agents on worktrees would confuse the
heck out of me.
sergeyk wrote 1 hour 30 min ago:
> AFAICT, thereâs no service that lets me: give a prompt, write the
code, spin up all this infra, run Playwright, handle database
migrations, and let me manually poke at the system. We approximate this
with GitHub Actions, but that doesnât help with manual verification
or DB work.
I think this is almost exactly what we've built with [1] - set up a
project with one or more repos
- set up your environment any way you want, including using docker
containers
- run any number of Claude Code, Codex, Gemini, Amp, or OpenCode agents
on a prompt, or "ticket" (we can add Cursor CLI also)
- each ticket implementation has a fully running "app preview", which
you can use just like you use your locally running setup. your running
web app is even shown in a pane right next to chat and diff
- chat with the agent inside of a ticket implementation, and when
you're happy, submit to github
(agents can even take screenshots)
happy to onboard you if that sounds interesting, just ping me at
sergey@superconductor.dev
HTML [1]: https://superconductor.dev
adam_gyroscope wrote 37 min ago:
will email! Your homepage doesn't make the environment part clear -
it reads like it's akin to cursor multiple agent mode (Which I think
you had first, FWIW).
giancarlostoro wrote 1 hour 31 min ago:
> AFAICT, thereâs no service that lets me: give a prompt, write the
code, spin up all this infra, run Playwright, handle database
migrations, and let me manually poke at the system. We approximate this
with GitHub Actions, but that doesnât help with manual verification
or DB work.
What you want is CI/CD that deploys to rotating stating or dev
environments per PR before code is merged.
If deployment fails you do not allow the PR to be approved. Did this
for a primarily React project we had before but you can do all your
projects, you just need temporary environments that rotate per PR.
dbuxton wrote 1 hour 5 min ago:
I used to love Heroku review apps!
tiku wrote 1 hour 34 min ago:
I describe functions that I want to change or upgrade. Claude code
gives the best results for me. I ask for a plan first to see if it gets
what I want to do and I can finetune it then.
I have a project that still uses zend framework and it gets it quite
good.
PaulDavisThe1st wrote 1 hour 38 min ago:
We're not. At ardour.org we've banned any and all LLM-generated code
(defined as code that was either acknowledged to be LLM-generated or
makes us feel that it was).
This is based on continual (though occasional) experiments asking
various LLMs for solutions to actual known problems with our code, and
utter despair at the deluge of shit that it produces (which you
wouldn't recognize as shit unless you knew our existing codebase well).
2 weeks ago, there was the claim that our code makes extensive use of
boost::intrusive_ptr<> ... in 300k lines of C++, there isn't a single
use of this type, other than in an experimental branch from 6-7 years
ago.
So we just say no.
jstummbillig wrote 1 hour 15 min ago:
How do you review the no?
PaulDavisThe1st wrote 1 hour 9 min ago:
We don't review it, we just say it.
qnleigh wrote 1 hour 39 min ago:
I would be very curious to hear about the state of your codebase a year
from now. My impression was that LLMs are not yet robust enough to
produce quality, maintainable code when let loose like this. But it
sounds like you are already having more success than I would have
guessed would be possible with current models.
One practical question: presumably your codebase is much larger than an
LLM's context window. How do you handle this? Don't the LLMs need
certain files in context in order to handle most PRs? E.g. in order to
avoid duplicating code or writing something in a way that's
incompatible with how it will be used upstream.
adam_gyroscope wrote 34 min ago:
So, it does sometimes duplicate code, especially where we have a
packages/ directory of Typescript code, shared between two nextjs and
some temporal workers. We 'solve' this with some AGENT.md rules, but
it doesn't always work. It's still an open issue.
The quality is general good for what we're doing, but we review the
heck out of it.
lukevp wrote 52 min ago:
One thing I think people confuse with context is they see an LLM has
say 400k context and think their codebase is way bigger than that,
how can it possibly work. Well, do you hold a 10 million line
codebase in your head at once? Of course not. You have an intuitive
grasp of how the system is built and laid out, and some general names
of things, and before you make a change, you might search through the
codebase for specific terms to see what shows up. LLMs do the same
thing. They grep through the codebase and read in only files with
interesting / matching terms and only the part of the file thats
relevant, in much the same way you would open a search result and
only view the surrounding method or so. The context is barely used
in these scenarios. Context is not something thatâs static, itâs
built dynamically as the conversation progresses via data coming from
your system (partially through tool use).
I frequently use LLMs in a VS Code workspace with around 40 repos,
consisting of microservices, frontends, nuget and npm packages, IaC,
etc. altogether its many millions of lines of code. and I can ask it
questions about anything the codebase and it has no issues managing
context. I do not even add files manually to context (this is worse
actually because it puts the entire file into context even if itâs
not all used). I just refer to the files by name and the LLM is smart
enough to read them in as appropriate. I have a couple JSON files
that are megs of configuration, and I can tell it to summarize /
extract examples out of those files and itâll just sample sections
to get an overview.
newsoftheday wrote 41 min ago:
> You have an intuitive grasp of how the system is built and laid
out,
Because they are human, intuition is a human trait, not an LLM code
grinder trait.
hhimanshu wrote 1 hour 44 min ago:
Have you installed Claude Code Github App and tried assigning the
issues using @claude? In my experience it has done better than Github
Copilot
rparet wrote 23 min ago:
(I work for the OP company)
We use Cursor's bugbot to achieve the same thing. Agree that it seems
better than Copilot for now.
weeksie wrote 1 hour 46 min ago:
Most of the team uses:
- Claude Code + worktrees (manual via small shell script)
- A root guardrails directory with a README to direct the agent where
to look for applicable rule files (we have a monorepo of python etls
and elixir applications)
- Graphite for stacked prs <3
- PR Reviews: Sourcery + Graphite's agent + Codex + Claude just sorta
crank 'em, sourcery is chatty but it's gotten a lot better lately.
(editor-wise, most of us are nvim users)
Lots of iteration. Feature files (checked into the repo). Graphite
stacks are amazing for unblocking the biggest bottleneck in ai assisted
development which is validation/reviews. Solving the conflict hell of
stacked branches has made things go much, much faster and it's acted as
downward pressure on the ever increasing size of PRs.
jemiluv8 wrote 1 day ago:
Your setup is interesting. Iâve had my mind on this space for a while
now but havenât done any deep work on a setup that optimizes the
things Iâm interested in.
I think at a fundamental level, I expect we can produce higher quality
software under budget. And I really liked how you were clearly thinking
about cost benefits especially in your setup. Iâve encountered far
too many developers that just want to avoid as much cognitive work as
possible. Too many junior and mid devs also are more interested in
doing as they are told instead of thinking about the problem for
themselves. For the most part, in my part of the world at least, junior
and mid-level devs can indeed be replaced by a claude code max
subscription of around $200 per month and youâd probably get more
done in a week than four such devs that basically end up using an llm
to do work that they might not even thoroughly explore.
So in my mind Iâve been thinking a lot about all aspects of the
Software Development LifeCycle that could be improved using some llm or
sorts.
## Requirements. How can we use llms to not only organize requirements
but to strip them down into executable units of work that are sequenced
in a way that makes sense. How do we go further to integrate an llm
into our software development processes - be it a sprint or whatever.
In a lot of green field projects, after designing the core components
of the system, we now need to create tasks, group them, sequence them
and work out how we go about assigning them and reviewing and updating
various boards or issue trackers or whatever. There is a lot of
gruntwork involved in this. Iâve seen people use mcps to
automatically create tasks in some of these issue trackers based on
some pdf of the requirements together with a design document.
## Code Review - I effectively spend 40% of my time reviewing code
written by other developers and I mostly fix the issues I consider
âminorâ - which is about 60% of the time. I could really spend less
time reviewing code with the help of an llm code reviewer that simply
does a âfirst passâ to at least give me an idea of where to spend
more of my time - like on things that are more nuanced.
## Software Design - This is tricky. Chatbots will probably lie to you
if you are not a domain expert. You mostly use them to diagnose your
designs and point out potential problems with your design that someone
else wouldâve seen if they were also domain experts in whatever you
were building. We can explore a lot of alternate approaches generated
by llms and improve them.
## Bugfixes - This is probably a big win for llmsâ because there used
to be a platform where I used to be able to get $50s and $30s to fix
github bugs - that have now almost entirely been outsourced to llms.
For me to have lost revenue in that space was the biggest sign of the
usefulness of llms I got in practice. After a typical greenfield
project has been worked on for about two months, bugs start creeping
in. For apps that were properly architected, I expect these bugs to be
fixable by existing patterns throughout the codebase. Be it removing a
custom implementation to use a shared utility or other or simply using
the design systems colors instead of a custom hardcoded one. In fact
for most bugs - llms can probably get you about 50% of the way most of
the time.
## Writing actual (PLUMBING) code . This is often not as much of a
bottleneck as most would like to think but it helps when developers
donât have to do a lot of the grunt-work involved in creating source
files, following conventions in a codebase, creating boilerplates and
moving things around. This is an incredible use of llms that is hardly
mentioned because it is not that âhotâ.
## Testing - In most of the projects we worked on at a consulting firm,
writing tests - whether ui or api was never part of the agreement
because of the economics of most of our gigs. And the clients never
really cared because all they wanted was working software. For a
developing firm however, testing can be immense especially when using
llms. It can provide guardrails to check when a model is doing
something it wasnât asked to do. And can also be used to create and
enforce system boundaries especially in pseudo type systems like
Typescript where JavaScriptâs escape hatches may be used as a
loophole.
## DEVOPS. I remember there was a time we used to manually invalidate
cloudfront distributions after deploying our ui build to some e3
bucket. Weâve subsequently added a pipeline stage to invalidate the
distribution. But I expect there are lots of grunt devops work that
could really be delegated. Of course, this is a very scary use of llms
but I daresay - we can find ways to use it safely
## OBSERVABILITY - a lot of observability platforms already have this
feature where llms are able to review error logs that are ingested,
diagnose the issue, create an issue on github or Jira (or wherever),
create a draft PR, review, test it in some container, iterate on a
solution X times, notify someone to review and so on and so forth. Some
llms on this observability platform also attach a level of priority and
dispatch messages to relevant developers or teams. LLms in this loop
simply supercharge the whole observability/instrumentation of
production applications
But yeah, that is just my two cents. I donât have any answers yet I
just ponder on this every now and then at a keyboard.
Sevii wrote 3 days ago:
Can you setup automated integration/end-to-end tests and find a way to
feed that back into your AI agents before a human looks at it? Either
via an MCP server or just a comment on the pull request if the AI has
access to PR comments. Not only is your lack of an integration testing
pipeline slowing you down, it's also slowing your AI agents down.
"AFAICT, thereâs no service that lets me"... Just make that service!
adam_gyroscope wrote 2 days ago:
We do integration testing in a preview/staging env (and locally), and
can do it via docker compose with some GitHub workflow magic (and
used to do it that way, but setup really slowed us down).
What I want is a remote dev env that comes up when I create a new
agent and is just like local. I can make the service but right now
priorities arenât that (as much as I would enjoy building that
service, I personally love making dev tooling).
bitbasher wrote 3 days ago:
I generally vibe code with vim and my playlist in Cmus.
adam_gyroscope wrote 2 days ago:
Man I was vim for life until cursor and the LLMs. For personal stuff
I still do claude + vim because I love vim. I literally met my wife
because I had a vim shirt on and she was an emacs user.
WhyOhWhyQ wrote 1 hour 11 min ago:
Claude open in another tab, hitting L to reload the file doesn't do
it for you?
dazamarquez wrote 3 days ago:
I use AI to write specific types of unit tests, that would be extremely
tedious to write by hand, but are easy to verify for correctness. That
aside, it's pretty much useless. Context windows are never big enough
to encompass anything that isn't a toy project, and/or the costs build
up fast, and/or the project is legacy with many obscure concurrently
moving parts which the AI isn't able to correctly understand, and/or
overall it takes significantly more time to get the AI to generate
something passable and double check it than just doing it myself from
the get go.
Rarely, I'm able to get the AI to generate function implementations for
somewhat complex but self-contained tasks that I then copy-paste into
the code base.
missinglugnut wrote 1 hour 30 min ago:
My experience is very similar.
For greenfield side projects and self contained tasks LLMs deeply
impress me. But my day job is maintaining messy legacy code which
breaks because of weird interactions across a large codebase. LLMs
are worse than useless for this. It takes a mental model of how
different parts of the codebase interact to work successfully and
they just don't do that.
People talk about automating code review but the bugs I worry about
can't be understood by an LLM. I don't need more comments based on
surface level patter recognition, I need someone who deeply
understands the threading model of the app to point out the subtle
race condition in my code.
Tests, however, are self-contained and lower stakes, so it can
certainly save time there.
sourdoughness wrote 2 days ago:
Interesting. I treat VScode Copilot as a junior-ish pair programmer,
and get really good results for function implementations. Walking it
through the plan in smaller steps, noting that weâll build up to
the end state in advance ie. âfirst letâs implement attribute x,
then weâll add filtering for x laterâ, and explicitly using
planning modes and prompts - these all allow me to go much faster,
have good understanding of how the code works, and produce much
higher quality (tests, documentation, commit messages) work.
I feel like, if a prompt for a function implementation doesnât
produce something reasonable, then it should be broken down further.
I donât know how others define âvibe-codingâ, but this feels
like a lower-level approach. On the times Iâve tried automating
more, letting the models run longer, I havenât liked the results.
Iâm not interested in going more hands-free yet.
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