alt Hacker NewsAI Can Make You Suck Faster Too
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I now started to us AI to help review my juniors PRs, because I couldn't keep up with the amount of code they ship. It started poorly, but now I have my method: I first read the code and flag the lines I'm not sure about, then ask any frontier model (I like Claude here for analysis, even if I don't use it for the rest) to explain the PR and to put effort on the parts I flagged (basically explain in detail the code, not only the PR), and to search through the libraries. Sometimes it notices something I would have missed (like missing an 'order_by' or off by one errors, because the underlying lib wasn't coded like the original AI pretended it was).
I also changed the way I do review because it has been more than a year and the juniors/new hire are still lost, wether on domain knowledge for the older new hire, or just capabilities for the juniors, and discussing with other departments, it's the same for like 95% of them. Now, rather than correcting the PR or adding a request for change, I add a whole unit/functional test to the PR and let that as an exercise to pass the test. They can use AI but I tell them to try to find what part of the code doesn't work before generating the fix, hopefully they'll take ownership of the code if I keep doing that.
Bit of a humbling/jarring moment when I realized that people are doing real paid work using LLMs that they could not otherwise do. I mean, it's quite obvious I suppose. But up until now I just assumed it was only a (massive) catalyst for things people would already be able to do with enough time. But nope -- it seems people are right now employed in roles that they would not be able to fulfil the tasks within if AI wasn't there telling them what to write/say/produce. Nobody is really going to come out and say that ... it's not something the less-AI-literate superiors would take kindly to.
“I wanted to see if I can get in on this 10x magic. I decided to put my money where my mouth was and I bought $10 worth of DeepSeek credits to use with a project I was working on.“
Wow. I guess that’s the punchline!
In general, the frontier models are not capable of reliably authoring non-trivial code without careful oversight yet. They are great at producing code that can pass tests, but not neccessarily a code review. This means if you care about code quality you still need a human in a loop understanding what has been done, and that becomes the bottleneck. And less disciplined folks will indeed become increasingly dependent.
However, over time the complexity of problems where you can get away with less/no oversight is increasing. And the models are already great at solving certain classes of problems where one doesn't really care that much about code quality, that wouldn't have even been attempted in a pre-LLM world. Over the weekend I was using Claude to add features to the compiled (no source available) firmware of one of my audio devices, adding workflow features by patching assembly and custom DSP code.
In coding, as with other areas, what's emerging is jagged intelligence.
I wish I could quote so much from this. But it encapsulates my issue with AI. Yes it can help. But it's not the end all be all. We still need smart people to make decisions.
Also, AI was build on what people figured out before. What happens when people stop figuring things out? Or stop making it available to AI? We hit a wall.
> Uses only DeepSeek and comes to the conclusion that LLM's are bad at coding?
Why not use actual frontier models, and you know do some real research, before writing a blog post?
Pre-AI, the limiter on my bad code was how much code I could type in a day.....
Now with AI I can 10x my output and 10x my bad code!
'Or else admit this is a dopamine game that makes you feel like The Universe's Most Special Programmer™ when it's really just gamified mass-scale intellectual dependency.'
Disesdi Shoshana Cox
> “after four years of open source LLMs, we should have three AirBnBs, two Stripes, and three Dropboxes thanks to the power of AI.”
Airbnb, Stripe and Dropbox were created in a different time when the market was much less competitive.
Saturation of software development velocity doesn’t increase large scale product opportunities in the market. It can also mean that opportunities get filled even more quickly by niche players, and nobody gets to grow to Airbnb scale.
IMO the latter is what’s currently happening. AI-powered companies are like little mammals scurrying around between the feet of the dinosaurs, and commentators like the OP look at the evolution of the brontosaurus as evidence that the mammals don’t seem to be growing as they should.
There are some good points, and I ask the question of where is the ground breaking stuff myself, but severely weakened by
* stretching the timeline: the actual real programming ability appeared in LLMs in the last 6-8 months, not 3-4 years,
* using the weakest possible tool: and I bought $10 worth of DeepSeek credits that is a far cry from Claude with Fable.
Also, I know nothing about marathons but for most uses putting the app, database, and background processes on the same server is very much the right starting point. With the next steps being employing Cloudflare or similar solutions long before managing a fleet of servers.
Alternatively, AI teaches you lessons about technical debt, code maintainability, and architecture faster too. Traditionally it took at least one or two years to really bump into those problems. Now any student can get into these problems within one or two weeks.
This made me think of "1,000 True Fans". I'm sure there will be more than a few business school case studies written about competition in this era of software development. Even things like niche apps singularly tailored for small skilled trades are seeing competition. AI tooling will definitely allow new/smaller/solo players to clone apps and undercut the legacy players on price more rapidly than before. Some of these very narrow apps are fairly expensive ($100s/month), if replacements of acceptable quality can be produced they will take some market share. This may be death (shrinkage) by a 1000 cuts for some established players.
> Most apps these days gather credit card details
That's one of the few consumer-facing areas where there are still standards in place, namely PCI-DSS. As far as I know the audits require the name of a human who is responsible for payment security. Card companies can one-hit kill your startup if you're breaking those rules (maybe purely blockchain startups are exempt).
Yes, you can offload this to stripe, but then your app should never see the card number and certainly not the CVV. You end up storing these, even by accident, both stripe and the card companies will hate you.
> Borrowing Disesdi's logic in their arithmetic (which is hard to argue against), after four years of open source LLMs, we should have three AirBnBs,1 two Stripes,2 and three Dropboxes3 thanks to the power of AI. > > So. Where the fuck are they?
I think this is a great question, and I have a completely unqualified theory.
I work in a fairly specialised field, I've notice when I use AI to try to generate code in fields where I'm very experienced, it produces a worse output at a slower rate than I can produce manually[0].
When I use it in fields I'm not an expert in by any stretch, like webdev, genAI massively improves my output by a huge factor.
I think this makes the productivity gains a little fuzzy. Claude code makes me 10x faster at webdev, but I'd be a very slow webdev. Ultimately, the next innovation in a field, will probably be from an expert in that field, and they therefore won't see the kind of "10x productivity boost" that non-experts do.
[0] I still find AI helpful for exploring code etc, just not so much producing output.
> we should have at least a handful of AirBnBs or Dropboxes
Neither was ever about some bottleneck on pumping out code and everything about marketing and network effects
I think my 10x comes elsewhere than writing code. For ex llms are much better at bash than me. So good I don't have to leave my shell. Jira? ServiceNow? Ha! Claude you go update that ticket. Go check my history and git commits and write me my standup. I'm not leaving the terminal.
I don't disagree with the title of this weblog post, but its premise is wrong: the existence of AI has an impact on the total number of tech companies, but not on the number of companies that make it real big (the AirBnBs, Stripes and Dropboxes the author is looking for).
Previously, the limit on bad code entering any serious product was gated by having devs that at least knew how to change what they copied from SO.
Seriously, that was the lowest level of skill I saw in 30 years of development.
Now we have people who can't even do that proudly showing off PRs to widely used products.
"B-b-but I do the systems design and hard thinking".
Sure, buddy.
> 4. There is, of course, the alternative that leaders are the real bottlenecks. But that can't be right...
Sshh, don't say that part to loud, especially on HN.
I tried really hard to get senior management to understand this where I work. Yes, AI is an accelerator, but that doesn't necessarily mean it's going to accelerate you in the "right" direction.
I have a coffee cup with the writing "Do stupid thing faster with cafe". That's how I feel about myself when I use AI carelessly... The speed with which I can make a mess is astronomical!
AI does make me suck more, which is a good thing. It sped me up and has me iterating on things in 3 days that normally would have taken me more than a month to do. Surprise, surprise: I'm discovering that I have a lot of blind spots and false presumptions about what good programming is. My understanding of the craft is patchy, but that wasn't as noticable when going at the speed of human-programming.
It's like cars. If you only ever drive at 30 km/h, a 30 yo car, that had its last oil change in 2008, might be great. But, if, in the same car, you start driving at 200 km/h, you might notice that there's a lot of room for improvement. My programmer-self is the 30 yo car in this metaphor.
How you use AI as a person who can actually code is up to you.
You can use AI in two ways, either…
1. set the plane on autopilot and arrive at the destination having no idea how you got there - like any old idiot who can type a prompt. And also pay a very expensive air fare for this super power auto pilot.
2. You can pilot the plane yourself. Plan the route in advance. Set waypoints. Make decisions along the way. And land the plane manually. And the reward is you know the route and you pay 1/10 the cost.
If one truly believes it's not in a bubble, then borrow and leverage an unlimited amount and bet the house & your family on creating a business with infinite growth and value. Don't know how to do your own prompts? Borrow and then hire others to do it for you. If those who you hire don't know, they can hire others too.*
[* This is not financial advice. Please don't actually do this.]
AI is like a tool used by mega-corporations to change the world according to what they want. We see this with the increase in RAM prices. I don't want to pay the overprice here - AI companies owe us money. People seem still mostly in the AI hype phase, but a lot more criticism has amplified in the last months. It is only a matter of time until the hype phase is over.
His AI experience is $10 of DeepSeek - but I think he is right anyway in the main points.
You can most definitely ship crap much faster than you used to. It's obvious to anyone when you're shipping crap. And it seems everywhere I look people are shipping crap. Both software and writing.
The hardest part of shipping quality software is not and has never been "writing the code". The hard parts are product taste, architecture and ensuring your product actually solves the problems it should in an efficient and secure manner.
If you don't have an intuition of those, you will likely still be shipping crap. Just faster.
It almost feels like ages ago when the promise of AI was to be a sponge of knowledge, to help us dig in quicker and speed up the search process of looking up implementations on Google / Stackoverflow because the expectation was that we authored code and demonstrate it to our peers it was correct as the role of an earnest code contributor.
You cannot overstate how disruptive agentic powered development has had on the startup scene. There is clearly an embrace or loosening of that expectation which is upending the profession and also paradoxically giving superpowers to us who have been in the business a long time.
It’s hard to know where this is all going as we are being challenged by this transformative, powerful, yet problematic technology every day at work.
- its just a next token predictor with a high probability bro https://blog.florianherrengt.com/how-llms-work.html
- when are all the "I am running agents 24x7 a day on max plan" people on HN gonna learn this?
- best case scenario bro: LLMs get infinitely better and nobody needs to code anymore
"software requirement prompting is the new skill" I can write those pretty well bro
- worst case scenario: AI market crash, cognitive debt spikes across every major organization filled with vibe coding juniors that have never spent a single day in their life debugging a production setup without AI.
- Now the whole world is filled with 90% programmers that cannot add 2 numbers in c++ without using GPT
- Guess what bro? I am now one of the most valuable programmers there is :)
> three AirBnBs, two Stripes, and three Dropboxes
you want more SaaS?
> The chatbot recommended some of the dumbest shit you could possibly do
The "I tried it and it sucked" is borderline conspiratorial at this point. There are enough talented, thoughtful developers saying there's something real here, and it is worth believing them and investing some time to understand it, even if you come out the other side and decide you don't want to use LLMs.
Use a very good model. Set up a good harness. Spend some time on your system prompts and skills. Develop your intuitions about how the model works, what it's good at, how to scope the work, and how to steer it. Recognize when it's alleviating menial work and recognize when it's making choices you really need to understand yourself. Be patient and accept that the failures are going to be very painful for a while.
Don't write it off until you've genuinely seen the upsides.
I largely agree with this piece but the author isn't really giving the AI development process a fair shot. It's not a matter of "prompt better" but of establishing processes that account for and correct agent shortcomings.
For me, it's taken thousands of dollars and months of experiments to get a process that produces working and maintainable code. You can't just go in with $10 and be like "Wow it doesn't work" after playing around with it for 30 seconds, which is how far $10 takes you on models that are doing work. All that does is expose the shortcomings that need correction.
You can get around these not by just "prompting better", but by establishing rules and pipelines that are enforced with standard software engineering tools like compilers, linters, type checkers, borrow checkers, profilers, benchmarking, smoke tests, proof assistants, and so forth. Those things we know work really well, so AI coding shouldn't be viewed as AI doing all the work, but AI orchestrating all of the tools that we know work so well in software development.
The magic of agentic engineering is only possible because our software tools are so good not because agents are.
If you don’t understand how the sausage was made you have made legacy sausage.
When I do use it, I try to only use it to do things I can already do.
I don’t want to solve a new problem with an LLM programming assistant anyways - that’s the fun part of my work, why would I get rid of it?
Why does it have to lead to a 10x increase in revolutionary companies?
Next to maintaining and expanding my own network of websites I 10xd writing boring CRUD applications for companies that were otherwise unable to afford it, making all their employees more productive. There's true economic value in that.
> and ruining the quality of internet search engines.
Tbf, I'm not sure we can really lay this one at the feet of GenAI. The SEO bros had pretty thoroughly ruined search before LLMs took off - the process just accelerated a little at that point.
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The code smell in my repos are at an all time high and I'm a senior dev, can't image how worse vibe coders have it.
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I started manual coding again at least 1 day a week.
Its good for the brain, but man it's really slow.
And the AI results prove it: Over the past couple years I've been able to bring several work projects to completion single handedly.
Our R&D company is now handling more clients and we are returning better outcomes/products faster than before.
So...what to do? Results don't lie.
However I will say LLM's can't write multi threaded double buffers by them selves. Probably because they are so application specific.
But I think the reason I can go do fast is because I went so slow before. I have an intuition I can transfer into an LLM. Question is if that intuition will become sloppy as time goes on...