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Comment by alexpotato | Hacker News Reader

I work as a DevOps/SRE and have been doing it FinTech (bank, hedge funds, startups) and Crypto (L1 chain) for almost 20 years.

My thoughts on vibe coding vs production code:

- vibe coding can 100% get you to a PoC/MVP probably 10x faster than pre LLMs

- This is partly b/c it is good at things I'm not good at (e.g. front end design)

- But then I need to go in and double check performance, correctness, information flow, security etc

- The LLM makes this easier but the improvement drops to about 2-3x b/c there is a lot of back and forth + me reading the code to confirm etc (yes, another LLM could do some of this but then that needs to get setup correctly etc)

- The back and forth part can be faster if e.g. you have scripts/programs that deterministically check outputs

- Testing workloads that take hours to run still take hours to run with either a human or LLM testing them out (aka that is still the bottleneck)

So overall, this is why I think we're getting wildly different reports on how effective vibe coding is. If you've never built a data pipeline and a LLM can spin one up in a few minutes, you think it's magic. But if you've spent years debugging complicated trading or compliance data pipelines you realize that the LLM is saving you some time but not 10x time.

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I'm building a Java HFT engine and the amount of things AI gets wrong is eye opening. If I didn't benchmark everything I'd end up with much less optimized solution.

Examples: AI really wants to use Project Panama (FFM) and while that can be significantly faster than traditional OO approaches it is almost never the best. And I'm not taking about using deprecated Unsafe calls, I'm talking about using primative arrays being better for Vector/SIMD operations on large sets of data. NIO being better than FFM + mmap for file reading.

You can use AI to build something that is sometimes better than what someone without domain specific knowledge would develop but the gap between that and the industry expected solution is much more than 100 hours.

AI is extremely good at the things that it has many examples for. If what you are doing is novel then it is much less of a help, and it is far more likely to start hallucinating because 'I don't know' is not in the vocabulary of any AI.

> because 'I don't know' is not in the vocabulary of any AI.

That is clearly false. I’m only familiar with Opus, but it quite regularly tells me that, and/or decides it needs to do research before answering.

If I instruct it to answer regardless, it generally turns out that it indeed didn’t know.

I haven't had that at all, not even a single time. What I have had is endless round trips with me saying 'no, that can't work' and the bot then turning around and explaining to me why it is obvious that it can't work... that's quite annoying.

Try something like:

> Please carefully review (whatever it is) and list out the parts that have the most risk and uncertainty. Also, for each major claim or assumption can you list a few questions that come to mind? Rank those questions and ambiguities as: minor, moderate, or critical.

> Afterwards, review the (plan / design / document / implementation) again thoroughly under this new light and present your analysis as well as your confidence about each aspect.

There's a million variations on patterns like this. It can work surprisingly well.

You can also inject 1-2 key insights to guide the process. E.g. "I don't think X is completely correct because of A and B. We need to look into that and also see how it affects the rest of (whatever you are working on)."

Ok! I will try that, thank you very much.

Of course! I get pretty lazy so my follow-up is often usually something like:

"Ok let's look at these issues 1 at a time. Can you walk me through each one and help me think through how to address it"

And then it will usually give a few options for what to do for each one as well as a recommendation. The recommendation is often fairly decent, in which case I can just say "sounds good". Or maybe provide a small bit of color like: "sounds good but make sure to consider X".

Often we will have a side discussion about that particular issue until I'm satisfied. This happen more when I'm doing design / architectural / planning sessions with the AI. It can be as short or as long as it needs. And then we move on to the next one.

My main goal with these strategies is to help the AI get the relevant knowledge and expertise from my brain with as little effort as possible on my part. :D

A few other tactics:

- You can address multiple at once: "Item 3, 4, and 7 sound good, but lets work through the others together."

- Defer a discussion or issue until later: "Let's come back to item 2 or possibly save for that for a later session".

- Save the review notes / analysis / design sketch to a markdown doc to use in a future session. Or just as a reference to remember why something was done a certain way when I'm coming back to it. Can be useful to give to the AI for future related work as well.

- Send the content to a sub-agent for a detailed review and then discuss with the main agent.

Eh… I am not sure if that translate to “I don’t know”.

IDK would require the LLM to be aware of the frequency of cases seen in its own training.

I can see this working as a risk ranking, which is certainly worth trying in its own right.

Does it actually say “I don’t know?”

I don’t know can be added to the vocabulary depending on the technique being used.

There are so many overlapping and also unique approaches to software development beyond vibe coding and ai driven software development.

I think the main issue is treating LLM as a unrestrained black box, there's a reason nobody outside tech trust so blindly on LLMs.

The only way to make LLMs useful for now is to restrain their hallucinations as much as possible with evals, and these evals need to be very clear about what are the goal you're optimizing for.

See karpathy's work on the autoresearch agent and how it carry experiments, it might be useful for what you're doing.

> there's a reason nobody outside tech trust so blindly on LLMs.

Man, I wish this was true. I know a bunch of non tech people who just trusts random shit that chatgpt made up.

I had an architect tell me "ask chatgpt" when I asked her the difference between two industrial standard measures :)

We had politicians share LLM crap, researchers doing papers with hallucinated citations..

It's not just tech people.

We were working on translations for Arabic and in the spec it said to use "Arabic numerals" for numbers. Our PM said that "according to ChatGPT that means we need to use Arabic script numbers, not Arabic numerals".

It took a lot of back-and-forths with her to convince her that the numbers she uses every day are "Arabic numerals". Even the author of the spec could barely convince her -- it took a meeting with the Arabic translators (several different ones) to finally do it. Think about that for a minute. People won't believe subject matter experts over an LLM.

We're cooked.

Kind of a tangent but that did make me curious about how numbers are written in Arabic: https://en.wikipedia.org/wiki/Eastern_Arabic_numerals

I guess "Western Arabic" would have been more precise.

The architect should have required Hindu numbers. Same result, but even more confusion.

Man this is maddening.

Honestly I think we're just becoming more aware of this way of thinking. It's certainly exacerbated it now that everyone has "an expert" in their pocket.

It's no different than conspiracy theorists. We saw a lot more with the rise in access to the internet. Not because they didn't put in work to find answers to their questions, but because they don't know how to properly evaluate things and because they think that if they're wrong then it's a (very) bad thing.

But the same thing happens with tons of topics, and it's way more socially acceptable. Look how everyone has strong opinions on topics like climate, rockets, nuclear, immigration, and all that. The problem isn't having opinions or thoughts, but the strength of them compared to the level of expertise. How many people think they're experts after a few YouTube videos or just reading the intro to the wiki page?

Your PM is no different. The only difference is the things they believed in, not the way they formed beliefs. But they still had strong feelings about something they didn't know much about. It became "their expert" vs "your expert" rather than "oh, thanks for letting me know". And that's the underlying problem. It's terrifying to see how common it is. But I think it also leads to a (partial) solution. At least a first step. But then again, domain experts typically have strong self doubt. It's a feature, not a bug, but I'm not sure how many people are willing to be comfortable with being uncomfortable

There’s a possibility the same people might believe anything they read on social media or via Google and it’s something worthy of attention.

And the worst part is, these people don't even use the flagship thinking models, they use the default fast ones.

In my experience, people outside of tech have nearly limitless faith in AI, to the point that when it clashes with traditional sources of truth, people start to question them rather than the LLM.

I am curious about what causes some to choose Java for HFT. From what I remember the amount of virgin sacrifices and dances with the wolves one must do to approach native speed in this particular area is just way too much of development time overhead.

Probably the same thing that makes most developers choice a language for a project, it's the language they know best.

It wasn't a matter of choosing Java for HFT, it was a matter of selecting a project that was a good fit for Java and my personal knowledge. I was a Java instructor for Sun for over a decade, I authored a chunk of their Java curriculum. I wrote many of the concurrency questions in the certification exams. It's in my wheelhouse :)

My C and assembly is rusty at this point so I believe I can hit my performance goals with Java sooner than if I developed in more bare metal languages.

"HFT" means different things to different people.

I've worked at places where ~5us was considered the fast path and tails were acceptable.

In my current role it's less than a microsecond packet in, packet out (excluding time to cross the bus to the NIC).

But arguably it's not true HFT today unless you're using FPGA or ASIC somewhere in your stack.

The one person who understands HFT yeah. "True" HFT is FPGA now and also those trades are basically dead because nobody has such stupid order execution anymore, either via getting better themselves or by using former HFTs (Virtu) new order execution services.

So yeah there's really no HFT anymore, it's just order execution, and some algo trades want more or less latency which merits varying levels of technical squeezing latency out of systems.

Software HFT? I see people call Python code HFT sometimes so I understand what you mean. It's more in-line with low latency trading than today's true HFT.

I don't work for a firm so don't get to play with FPGAs. I'm also not co-located in an exchange and using microwave towers for networking. I might never even have access to kernel networking bypass hardware (still hopeful about this one). Hardware optimization in my case will likely top out at CPU isolation for the hot path thread and a hosting provider in close proximity to the exchanges.

The real goal is a combination of eliminating as much slippage as possible, making some lower timeframe strategies possible and also having best class back testing performance for parameter grid searching and strategy discovery. I expect to sit between industry leading firms and typical retail systematic traders.

> AI really wants to use Project Panama

It would help if you briefly specified the AI you are using here. There are wildly different results between using, say, an 8B open-weights LLM and Claude Opus 4.6.

I've been using several. LM Studio and any of the open weight models that can fit my GPU's RAM (24GB) are not great in this area. The Claude models are slightly better but not worth they extra cost most of the time since I typically have to spend almost the same amount of time reworking and re-prompting, plus it's very easy to exhaust credits/tokens. I mostly bounce back and forth between the codex and Gemini models right now and this includes using pro models with high reasoning.

Maybe a silly question, but why Java? As a C# guy, my experience with AI is it hasn't been great with it, and I'd suspect similar for Java. I'd probably go with Rust, which my own efforts with AI has done really well with, even if I'm far from a Rust expert.

Then you list all of the things you want it not to do and construct a prompt to audit the codebase for the presence of those things. LLMs are much better at reviewing code than writing it so getting what you want requires focusing more on feedback than creation instructions.

Wouldn't Java always lose in terms of latency against a similarly optimized native code in, let's say, C(++)?

Not necessarily. Java can be insanely performant, far more than I ever gave it credit for in the first decade of its existence. There has been a ton of optimization and you can now saturate your links even if you do fairly heavy processing. I'm still not a fan of the language but performance issues seem to be 'mostly solved'.

"Saturating your links" is rarely the goal in HFT.

You want low deterministic latency with sharp tails.

If all you care about is throughput then deep pipelines + lots of threads will get you there at the cost of latency.

You can achieve optimized C/C++ speeds, you just can't program the same way you always have. Step 1, switch your data layout from Array of Structures to Structure of Arrays. Step 2, after initial startup switch to (near) zero object creation. It's a very different way to program Java.

You have to optimize your memory usage patterns to fit in CPU cache as much as possible which is something typical Java develops don't consider. I have a background in assembly and C.

I'd say it's slightly harder since there is a little bit of abstraction but most of the time the JIT will produce code as good as C compilers. It's also an niche that often considers any application running on a general purpose CPU to be slow. If you want industry leading speed you start building custom FPGAs.

As long as you tune the JVM right it can be faster. But its a big if with the tune, and you need to write performant code

Java has significant overhead, that most/every object is allocated on heap, synchronized and has extra overhead of memory and performance to be GC controlled. Its very hard/not possible to tune this part.

You program differently for this niche in any language. The hot path (number crunching) thread doesn't share objects with gateway (IO) threads. Passing data between them is off heap, you avoid object creation after warm up. There is no synchronization, even volatile is something you avoid.

> Passing data between them is off heap

how exactly you are passing data? You can pass some primitives without allocating them on heap. You can use some tiny subset of Java+standard library to write high performance code, but why would you do this instead of using Rust or C++?

In some places I'm using https://github.com/aeron-io/agrona

Strangely this is one of the areas where I want to use project panama so I might re-implement some of the ring buffers constructs.

You allocate off heap memory and dump data into it. With modern Java classes like Arena, MemoryLayout, and VarHandle it's honestly a lot like C structs.

I answered "why" in another post in this thread.

> You allocate off heap memory and dump data into it. With modern Java classes like Arena, MemoryLayout, and VarHandle it's honestly a lot like C structs.

my opinion is that no, it is not, declaring and using C struct is 20x times more transparent, cost efficient and predictable. And that's we talking about C raw stucts, which has lots of additional ergonomics/safety/expression improvements in both c++ and rust on top of it.

Depends. Many reasons, but one is that Java has a much richer set of 3rd party libraries to do things versus rolling your own. And often (not always) third party libraries that have been extensively optimized, real world proven, etc.

Then things like the jit, by default, doing run time profiling and adaptation.

Java has huge ecosystem in enterprise dev, but very unlikely it has ecosystem edge in high performance/real time compute.

There are actually cases when Java (the HotSpot JVM) runs faster than the same logic written in C/C++ because the JVM is doing dynamic analysis and selective JIT compilation to machine code.

I personally know of an HFT firm that used Java approximately a decade ago. My guess would be they're still using it today given Java performance has only improved since then.

it doesn't mean Java is optimal or close to optimal choice. Amount of extra effort they do to achieve goals could be significant.

Optimal in what sense? In the java shops I've worked at it's usually viewed as a pretty optimal situation to have everything in one language. This makes code reuse, packaging, deployment, etc much simpler.

In terms of speed, memory usage, runtime characteristics... sure there are better options. But if java is good enough, or can be made good enough by writing the code correctly, why add another toolchain?

> But if java is good enough, or can be made good enough by writing the code correctly,

"writing code correctly" here means stripping 95% of lang capabilities, and writing in some other language which looks like C without structs (because they will be heap allocated with cross thread synchronization and GC overhead) and standard lib.

Its good enough for some tiny algo, but not good enough for anything serious.

It's good enough for the folks who choose to do it that way. Many of them do things that are quite "serious"... Databases, kafka, the lmax disruptor, and reams of performance critical proprietary code have been and continue to be written in java. It's not low effort, you have to be careful, get intimate with the garbage collector, and spend a lot of time profiling. It's a totally reasonable choice to make if your team has that expertise, you're already a java shop, etc. I no longer make the choice to use java for new code. I prefer rust. But neither choice is correct or incorrect.

> Databases, kafka, the lmax disruptor, and reams of performance critical proprietary code have been and continue to be written in java.

those have low bar of performance, also they mostly became popular because of investments from Java hype, and rust didn't exist or had weak ecosystem at that time.

I would say that if AI has to make decisions about picking between framework or constructs irrelevant to the domain at hand, it feels to me like you are not using the AI correctly.

I've seen SQL injection and leaked API tokens to all visitors of a website :)

There’s a big gap between reality and the influencer posts about LLMs. I agree with you that LLMs do provide some significant acceleration, but the influencers have tried to exaggerate this into unbelievable numbers.

Even non-influencers are trying to exaggerate their LLM skills as a way to get hired or raise their status on LinkedIn. I rarely read the LinkedIn social feed but when I check mine it’s now filled with claims from people about going from idea to shipped product in N days (with a note at the bottom that they’re looking for a new job or available to consult with your company). Many of these posts come from people who were all in on crypto companies a few years ago.

The world really is changing but there’s a wave of influencers and trend followers trying to stake out their claims as leaders on this new frontier. They should be ignored if you want any realistic information.

I also think these exaggerated posts are causing a lot of people to miss out on the real progress that is happening. They see these obviously false exaggerations and think the opposite must be true, that LLMs don’t provide any benefit at all. This is creating a counter-wave of LLM deniers who think it’s just a fad that will be going away shortly. They’re diminishing in numbers but every LLM thread on HN attracts a few people who want to believe it’s all just temporary and we’re going back to the old ways in a couple years.

> I rarely read the LinkedIn social feed but when I check mine it’s now filled with claims from people about going from idea to shipped product in N days (with a note at the bottom that they’re looking for a new job or available to consult with your company).

This always seems to be the pattern. "I vibe coded my product and shipped it in 96 hours!" OK, what's the product? Why haven't I heard of it? Why can't it replace the current software I'm using? So, you're looking for work? Why is nobody buying it?

Where is the Quicken replacement that was vibecoded and shipping today? Where are the vibecoded AAA games that are going to kill Fortnite? Where is the vibecoded Photoshop alternative? Heck, where is the vibecoded replacement for exim3 that I can deploy on my self hosted E-mail server? Where are all of the actual shipping vibecoded products that millions of users are using?

I found one example of this going very wrong on reddit the other day -

https://www.reddit.com/r/selfhosted/comments/1rckopd/huntarr...

One redditor security reviews a vibe coded project

Wow, great example, and great example of what these fakers do when called out. Summary:

The maintainer, instead of listening to the security researcher and accepting feedback about his development process, instead:

1. Denied the problem

2. Censored discussion of the problem

3. Banned the people calling out the problem

...and then when the security issues were posted more publicly and got traction...

4. Made the subreddit private

5. Wiped and deleted his account

6. Wiped and deleted the GitHub repo

7. Took the project's web site off the web

Absolutely wild and unhinged behavior.

The self hosted reddit has been inundated with slop and is trying to ban it. It's not a good idea to run anyone else's vibe code!

holy fuck this is awesome.. I haven't laughed this hard in a while

I agree with your general point but ... "Where are the vibecoded AAA games". A game dev team is typically less than 15% programmers. Most of the team are artists, followed by game designers. Maybe someday those will be replaced too but at the moment, while you can get some interesting pictures from stable-diffusion techniques it's unlikely to make a cohesive game and even prompting to create all of it would still take many person years.

That said, I have had some good experiences getting a few features from zero to working via LLMs and it's helped me find lots of bugs far easier than my own looking.

I can imagine a vibe coded todo app. I can also kind of imagine a vibe coded gIMP/Photoshop though it would still take several person years, prompting through each and every feature.

> Where are all of the actual shipping vibecoded products that millions of users are using?

Claude Code and OpenClaw - they are vibecoded. And I believe more coming.

Claude Code is not vibecoded, it is made using Claude Code but it is not vibecoded using Claude Code.

kind of - https://x.com/bcherny/status/2004897269674639461

But it's like crypto then, good for buying other crypto, or illegal stuff.

Also people are using CC for the cheap access to the model, otherwise they'd be using opencode.

Yeah, I really wonder if someone would trust to do their taxes in a vibe-coded version of Turbotax...

Do you really need Turbotax? Just feed it the tax code, your financial data, and the relevant forms and it should be good to go. Now we have freed up the labor of accountants so they can go be productive in another segment of society. /s

Looking from outside the US tax system, I feel taxes are intentionally complex to keep some people employed and to hide from questions.

well, you still need to drive some data: say you purchased some food, was that a personal purchase or was it business-related (e.g. a meal with a client). Software can help with that decision making.

But even if human interaction wasn't needed, the question still stands, would you trust an LLM to calculate your tax liability just by feeding it data?

I regret only having one upvote for this.

I note that games are mostly art assets and things like level design, and players are already happy to instantly consign such products to the slop bin.

The whole thing is "market for lemons": app stores filling with dozens of indistinguishable clones of each product category will simply scare users off all of them.

"I come from a state that raises corn and cotton and cockleburs and Democrats, and frothy eloquence neither convinces nor satisfies me. I am from Missouri. You have got to show me."

The “store on the chain” thing turned out to be a fad in terms of technology, even though it made a lot of money (in the billions and more) to some people via the crypto thing. That was less than 10 years ago, so many of us do remember the similarities of the discourse being made then to what’s happening now.

With all that said, today’s LLMs do seem so provide a little bit more value compared to the bit chain thing, for example OCR/.pdf parsing is I’d say a solved thing right now thanks to LLMs, which is nice.

>Many of these posts come from people who were all in on crypto companies a few years ago.

This is ditto my observation. There seems to be a certain "type" of people like this. And it's not just people looking for work.

My guess is either they have super low critical thinking, a very cynical view of the world where lies and exaggeration are the only way to make it, or something more pathological (narcissism etc).

The "type" is simply the get-rich-quick schemers.

I have a relative who was late to crypto, late to drop shipping, late to carbon credits, but is now absolutely all-in on AI as his ticket out. It honestly depresses the hell out of me trying to talk to him because everything is about money and getting rich.

People like this don't care about underlying technologies or learning past the most basic surface level of understanding.

Day 7 of using Claude Code here are my takes...

“Day 7" would be amazing - all that I see YouTube recommending is "I tried it for 24 hours"

I was listening to an "expert" on a podcast earlier today up until the point where the interviewer asked how long his amazing new vibe-coded tooling has been in production, and the self-proclaimed expert replied "actually we have an all-hands meeting later today so I can brief the team and we will then start using the output..."

> The LLM makes this easier but the improvement drops to about 2-3x b/c there is a lot of back and forth + me reading the code to confirm etc

This makes sense when you stop viewing the LLM as a "vending machine" for apps and start seeing it as a repository of software deltas.

LLMs aren't just trained on final code; they are trained on the entire history of pull requests, review comments, and issue discussions that move a project from one version to the next.

When I use an LLM now, my workflow has shifted entirely. I’ve stopped trying to be the "coder" and have instead stepped into the role of PR Reviewer and Power User. My job is to point out edge cases, define the spec, and catch regressions—effectively managing a "virtual team" that handles the boilerplate and feature implementation.

Expecting a one-shot 1.0 release is unrealistic because it bypasses the thousand micro-decisions that happen in a real dev cycle. By embracing the "review and refine" loop, I’m becoming a better maintainer, even if that 100-hour gap to a polished product still exists.

This is exactly my experience at Lovable. For some parts of the organization, LLMs are incredibly powerful and a productivity multiplier. For the team I am in, Infra, it's many times distraction and a negative multiplier.

I can't say how many times the LLM-proposed solution to a jittery behavior is adding retries. At this point we have to be even more careful with controlling the implementation of things in the hot path.

I have to say though, giving Amp/Claude Code the Grafana MCP + read-only kubectl has saved me days worth of debugging. So there's definitely trade-offs!

My colleague recently shipped a "bug fix" that addresses a race condition by adding a 200ms delay somewhere, almost completely coded by LLM. LLM even suggests that "if this is not good enough, increase it to 300ms".

That says something about how much some people care about this.

Even doubly so because that's how most people have solved a similar problem, so that the LLM suggests that

More generally: LLM effectiveness is inversely proportional to domain specificity. They are very good at producing the average, but completely stumble at the tails. Highly particular brownfield optimization falls into the tails.

The magic is testing. Having locally available testing and high throughput testing with high amount of test cases now unlocks more speed.

The test cases themselves becomes the foci - the LLM usually can't get them right.

How does that test suite get built and validated? A comprehensive and high quality test suite is usually much larger than the codebase it tests. For example, the sqlite test suite is 590x [1] the size of the library itself

1. https://sqlite.org/testing.html

By sweat and tears, and unfortunately, AI can only help so much in those cases. You'll have to have a really concrete idea about what your product is and how it should work.

sqlite is an extreme outlier not a typical example, with regard to test suite size and coverage.

> The magic is testing.

No it is not.

There os no amount of testing that can fix a flawed design

That is a given, similar to how no amount of implementation can fix a wrong product. Both statements are not very meaningful.

The word "Testing" is a very loaded term. Few non-professionals, or even many professionals, fully understand what is meant by it.

Consider the the following: Unit, Integration, System, UAT, Smoke, Sanity, Regression, API Testing, Performance, Load, Stress, Soak, Scalability, Reliability, Recovery, Volume Testing, White Box Testing, Mutation Testing, SAST, Code Coverage, Control Flow, Penetration Testing, Vulnerability Scanning, DAST, Compliance (GDPR/HIPAA), Usability, Accessibility (a11y), Localization (L10n), Internationalization (i18n), A/B Testing, Chaos Engineering, Fault Injection, Disaster Recovery, Negative Testing, Fuzzing, Monkey Testing, Ad-hoc, Guerilla Testing, Error Guessing, Snapshot Testing, Pixel-Perfect Testing, Compatibility Testing, Canary Testing, Installation Testing, Alpha/Beta Testing...

...and I'm certain I've missed dozens of other test approaches.

You forgot a hope-driven development and release process and other optimism based ("i'm sure it's fine" method), or faith based approaches to testing (ship and pray, ...). Customer driven invluntary beta testing also comes to mind and "let's see what happens" 0-day testing before deployment. We also do user-driven error discovery, frequently.

There is no science to testing, no provable best way, despite many people's vehement opinions

Why did you assume I'm talking about a "provable best way"? I meant that it doesn't make sense to talk simply about "testing" without clarifying what one means by it. If you assume that the absence of a "provable best way" implies a lack of utility, let me remind you that there is no "provable best way" for training LLMs either. Does that matter in practice?

> - This is partly b/c it is good at things I'm not good at (e.g. front end design)

Everyone thinks LLMs are good at the things they are bad at. In many cases they are still just giving “plausible” code that you don’t have the experience to accurately judge.

I have a lot of frontend app dev experience. Even modern tools (Claude w/Opus 4.6 and a decent Claude.md) will slip in unmaintainable slop in frontend changes. I catch cases multiple times a day in code review.

Not contradicting your broader point. Indeed, I think if you’ve spent years working on any topic, you quickly realize Claude needs human guidance for production quality code in that domain.

Yes I’ve seen this at work where people are promoting the usage of LLMs for.. stuff other people do.

There’s also a big disconnect in terms of SDLC/workflow in some places. If we take at face value that writing code is now 10x faster, what about the other parts of the SDLC? Is your testing/PR process ready for 10x the velocity or is it going to fall apart?

What % of your SDLC was actually writing code? Maybe time to market is now ~18% faster because coding was previously 20% of the duration.

It’s the Gell-Mann amnesia effect applied to LLM instead of media

> Testing workloads that take hours to run still take hours to run with either a human or LLM testing them out (aka that is still the bottleneck)

Actually I had some terrible experiences when asking the agent to do something simple in our codebase (like, rename these files and fix build scripts and dependencies) but it spent much longer time than a human, because it kept running the full CI pipelines to check the problems after every attempted change.

A human would, for example, rely on the linter to detect basic issues, run a partial build on affected targets, etc. to save the time. But the agent probably doesn't have a sense of time elapsed.

Went through something similar recently with database calls.

Co-pilot said something about having too many rows returned and had some complex answer on how to reduce row count.

I just added a "LIMIT 100" which was more than adequate.

Can't this be solved with something like "Don't run any CI commands" in the AGENTS.md?

Except for the times you do want it to run the CI.

LLM issues can often be solved by being more and more specific, but at some point being specific enough is just as time consuming as jumping in and doing it yourself.

>Testing workloads that take hours to run still take hours to run with either a human or LLM testing them out (aka that is still the bottleneck)

Absolutely. Tight feedback loops are essential to coding agents and you can’t run pipelines locally.

This is where I think we need better tooling around tiered validation - there's probably quite a bit you can run locally if we had the right separation; splitting the cheap validation from the expensive has compounding benefits for LLMs.

What I do now is I make an MVP with the AI, get it working. And then tear it all down and start over again, but go a little slower. Maybe tear down again and then go even more slowly. Until I get to the point where I'm looking at everything the AI does and every line of code goes through me.

I concur on the DevSecOps aspect for a more specific reason: If you're failing a pipeline because ThirdPartyTOol69 doesn't like your code style or W/E, you can have the LLM fix it. Or get you to 100% test coverage etc. Or have it update your Cypress/Jest/SonarQube configs until the pipeline passes without losing brain cells doing it by hand. Or finds you a set of dependency versions that passes.

> The LLM makes this easier but the improvement drops to about 2-3x b/c there is a lot of back and forth + me reading the code to confirm etc (yes, another LLM could do some of this but then that needs to get setup correctly etc)

> The back and forth part can be faster if e.g. you have scripts/programs that deterministically check outputs

This is where configuration language like CUE can be useful in complementing LLM [1].

It's the deterministic NLP cousin of the stochastic LLM based on mathematically sound latticed-value logic [2].

[1] Guardrailing Intuition: Towards Reliable AI:

https://cue.dev/blog/guardrailing-intuition-towards-reliable...

[2] The Logic of CUE:

https://cuelang.org/docs/concept/the-logic-of-cue/

Also, now you're reading someone else's code and not everybody likes that. In fact, most self-proclaimed 10x coders I know hate it.

So instead of the 10x coder doing it, the 1x coder does it, but then that factor of 3x becomes 0.3x.

Absolutely. In my experience there are more “good coders” than people who are good at code review/PR/iterative feedback with another dev.

A lot of people are OCD pedants about stuff that can be solved with a linter (but can’t be bothered to implement one) or just “LGTM” everything. Neither provide value or feedback to help develop other devs.

> A lot of people are OCD pedants about stuff that can be solved with a linter (but can’t be bothered to implement one) or just “LGTM” everything. Neither provide value or feedback to help develop other devs.

This many be one of the best quotes on HN in a while.

Thank you, I felt old & cranky today

Isn’t that the reason why people advocate for spec-driven development instead of vibe coding?

Do you ever think that maybe your biased? I ask because I get the feeling from a lot of professional programmers that they feel like they are better and smarter than everyone and everything else. No matter how good an LLM or AI in general gets at programming task, people who make a living programming will always have a problem with it. There is going to come a time when you're going to be obsolete. I hate to say it, but it's coming and the hostility twords the tech isn't going to save your job.

At this point, every programmer who claims that vibecoding doesn't make you at least 10 times more productive is simply lying or worst, doesn't know how to vibe code. -So, you want to tell me that you don't review the code you write? Or that others don't review it? - You bring up ONE example with a bottleneck that has nothing to do with programming. Again, if you claim it doesn't make you 10x more productive, you don't know how to use AI, it is that simple. - I pin up 10 agents, while 5 are working on apps, 5 do reviews and testing, I am at the end of that workflow and review the code WHILE the 10 agents keep working.

For me it is far more than 10x, but I consider noobs by saying 10x instead of 20x or more.

Just goes to show that most programmers have no idea what most programmers are mostly programming. Great that it works for you, but don't assume that this applies to everyone else.

Can you link to one launched product with users for us?

I can't tell if this is real or a joke.

What exactly are you producing? LinkedIn posts?

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