Yes, there are, but you're in the home of Silicon Valley investments, so I don't know what you expect to see here. "Look, we're optimizing turbines since the 90's, we need a tech lead for a replacement"?

Well, if the physical object doesn't change, the encryptor won't matter. You can build another one that reads the key and stores it, and voila, a digital duplicate. I don't understand how this will protect stuff beyond your neighboor level attack. Given that your neighboor can steal you both the key and the reader.

I won't even bother read this time.

Why do people keep ralking about AI as it actually worked? I still don't see ANY proof that it doesn't generate a total unmaintainable unsecure mess, that since you didn't develop, you don't know how to fix. Like running a F1 Ferrari on a countryside road: useless and dangerous

Because it's working for a lot of people. There are people getting value from these products right now. I'm getting value myself and I know several other folks at work who are getting value.

I'm not sure what your circumstances are but even if it's not true for you, it's true for many other people.

It's interesting that the people IRL I encounter who "get the most value" tend to be the devs who couldnt distinguish well written code from slop in the first place.

People online with identical views to them all assure me that theyre all highly skilled though.

Meanwhile I've been experimenting using AI for shopping and all of them so far are horrendous. Cant handle basic queries without tripping over themselves.

If you are a 2600 chess player, a bot that plays 1800 chess is a horrendous chess player.

But you can understand why all the 1700 and below chess players say it is good and it is making them better using it for eval?

Don't worry, AI will replace you one day, you are just smarter than most of us so you don't see it yet.

The thing is, AI is playing "tech chess" wonderful the first two moves, and then starts turning pieces around without much care, letting the queen die for no reason, but well, what a wonderful opening.

The analogy of thinking of coding AI like it's chess AI is terrible. If chess AI was at the level of coding AI, it wouldn't win a single game.

This kind of thinking is actually a big reason why execs are being misinformed into overestimating LLM abilities.

LLM coding agents alone are not good enough to replace any single developer. They only make a developer x% faster. That dev who is now x% faster may then allow you to lay off another dev. That is a subtle yet critical difference.

I like the chess analogy as it answer the question: why can't i see those gain?

To adress your point, let's try another one analogy. Imagine secreterial assistants, discussing their risk of been replaced by computers in the 80s. They would think: someone still need to type those letters, sit next to that phone and make those appointments, I am safe. Computers won't replace me.

It is not that AI will do all of your tasks and replace you. It is that your role as a specialist in software development won't be necessary most of the time (someone will do that, and that person won't call themselves a programmer).

Secretarial assistant as a profession is still very alive, and the title has been inflated to stratospheric heights (and compensation): "Chief of Staff"

If it worked, I would agree. AI just helped me finding bugs in some hashing function today, ok, nice. But only after 3 hours I got any result out of it, with 13 years of experience. My feeling is that newbies are creating todo lists with react , just like it has been copied from someone's tutorial they didnt bother to read before, and now they feel powerful. But hey, let them do our taxes then! And they get screwed in 0 seconds.

I imagine if they tried to replace typists with keyboards that produced plausible looking words that were entirely wrong half the time then we'd probably still have plenty of typists.

I tend to find that the volume of automation predictions inversely correlates to how real they are.

When capitalists actually have the automation tech they dont shout about it they just do it quietly and collect the profits.

When, say, Bezos is worried about his unionizing workforce and wants to intimidate - that's when the hot takes and splashy media articles about billions invested in automation "coming for yer jerb" you read about get published.

Idk basically everyone is my org has seen some good value out of it. We have people complaining about limitations, but would still rather have that tooling than not.

For me the main difference is now some people can explain what their code does. While some other only what it wants to achieve

It's not a first experiment it's experiment 50 or 60 and, a reaction to AI gaslighting.

Most people dont really understand coding but shopping is a far simpler task and so it's easier to see how and where it fails (i.e. with even mildly complex instructions).

Do you mind sharing examples of the prompts you are using?

Agreed.

Im very confident the experts in every field are not all that impressed by LLMs, relative of course, to those who were 'meh' in the first place. Experts meaning those who actually understand the content, not simply regurgitate or repeat it on demand.

I'd even go as far as to say there are many out there who have a feeling of disdain of the experts and want to see LLMs flourish because of this.

I see more value on the business side than the tech side. Ask the AI to transcribe images, write an email, parse some excel data, create a prototype, etc. Some of which you might have hired a tech resource to write a script for.

On the tech side I see it saving some time with stuff like mock data creation, writing boiler plate, etc. You still have to review it like it's a junior. You still have to think about the requirements and design to provide a detailed understanding to them (AI or junior).

I don't think either of these will provide 90% productivity gains. Maybe 25-50% depending on the job.

For me, the main thing is to never have it write anything based on the goal (what the end result should look like and how it should behave). And only on the implementation details (and coding practices that I like).

Sure it is not as fast to understand as code I wrote. But at least I mostly need to confirm it followed how it implemented what I asked. Not figuring out WHAT it even decided to implement in the first place.

And in my org, people move around projects quite a bit. Hasn’t been uncommon for me to jump in projects with 50k+ lines of code a few times a year to help implement a tricky feature, or help optimize things when it runs too slow. Lots of code to understand then. Depending on who wrote it, sometimes it is simple: one or two files to understand, clean code. Sometimes it is an interconnected mess and imho often way less organized that Ai generated code.

And same thing for the review process, lots of having to understand new code. At least with AI you are fed the changes a a slower pace.

> Why do people keep ralking about AI as it actually worked?

Because it does.

> I still don't see ANY proof that it doesn't generate a total unmaintainable unsecure mess, that since you didn't develop, you don't know how to fix.

I wouldn't know since it's been years since I've tried but I'd imagine that Claude Code would indeed generate a half-baked Next.js monstrosity if one-shot and left to its own devices. Being the learned software engineer I am, however, I provide it plenty of context about architecture and conventions in a bootstrapped codebase and it (mostly) obeys them. It still makes mistakes frequently but it's not an exaggeration to say that I can give it a list of fields with validation rules and query patterns and it'll build me CRUD pages in a fraction of the time it'd take me to do so.

I can also give it a list of sundry small improvements to make and it'll do the same, e.g. I can iterate on domain stuff while it fixes a bunch of tiny UX bugs. It's great.

> can give it a list of fields with validation rules and query patterns and it'll build me CRUD pages in a fraction of the time it'd take me to do so.

Ah, is this what you do for a job? I actually have to accord accounting rules between countries, evaluate different conflicting laws, understand what the government wants in a report, check that everything sums up... But if my job was CRUD apps and thats it, yes I would fear for my job.

> Ah, is this what you do for a job? I actually have to [...]

> if my job was CRUD apps and thats it,

I'm not sure whether you intended this but your post reads as presumptuous and disdainful. The whole point of these tools is to free people from having to do these parts of their job so that they can focus on the ones of greater substance. At no point did I say that was my entire job or that I was afraid of it disappearing - if that was your takeaway then I don't know what to say.

You can launch a new product in one month instead of 12 months. I think this works best for startups where the risk tolerance is high but works less than ideal for companies such Amazon where system failure has high costs

Most startups fail because of no market or money issues, not because of product issues. You can build a wonderful cathedral and pray an empty god in a wonderful desert. Then someone cuts oil supply and the cathedral is not aor conditioned anymore. So go ahead, build 50 products, hope that 1 succeeds. My bet is that 50 will fail and 1 person in 1000 will succeed. But everyone will spend so much money on Nvidia and Anthropic that they will eradicate whatever else is in the world.

so where are these one man products lauched in a month?

not talking about toys or vibecoded crap no one uses.

Aside from the things people have launched that don't count because I'm right, where are the things that have been launched?

apps with actual users is too high of a bar now?

probs look in the game space to see a bunch of muggles making money off of "vibe coded" software

They're here, I made one. Not a toy or vibecoded crap, people got immediate value. Not planning to doxx myself by linking it. This was more than a year ago when models weren't even as good yet. A year later it has thousands of consistent monthly users, and it only keeps growing. It's nothing compared to VC startups but for a solo dev, made in a month? Again, it's not a toy, it offers new functionality that simply didn't exist yet and it improves people's lives. The reality is that there's no chance I would've done it without LLMs.

Blah blah blah

Real ones don't exist. Conveniently, nobody that has claimed to have created a functional AI product is willing to "doxx themselves" by linking to their app.

Weirdly, people who have actually created functional one-man products don't seem to have the same problem, as they welcome the business.

Exactly. This is f"cking hilarious.

"oooh Im afraid of doxxing myself", wtf? lmao!

Person that can’t use a hammer “hasn’t seen any proof” that hammers work.

I definitely can use a hammer, trust me xD you're just bar chatter trying to belittle me, maybe, but do you actually know me, brah

I agree, but absence of evidence is not evidence of absence, and we currently have a lot of developers who feel very productive right now.

We are very much in need of an actual way to measure real economic impact of AI-assisted coding, over both shorter and longer time horizons.

There's been an absolute rash of vibecoded startups. Are we seeing better success rates or sales across the industry?

> "absence of evidence is not evidence of absence"

That's the same false argument that the religious have offered for their beliefs and was debunked by Bertrand Russell's teapot argument: https://en.wikipedia.org/wiki/Russell%27s_teapot

I'm not speaking of burdens of proof about unfalsifiable statements.

I'm saying that I think this is an important enough question that I think we should seek real evidence in either direction, especially since apparently everyone already has a strong opinion (warranted or not.)

I see a lot of people talk about 'insecure code' and while I don't doubt that's true, there's a lot of software development where security isn't actually a concern because there's no need for the software to be 'secure'. Maintainability is important I'll grant you.

Oh, finally someone who speaks the truth :D yeah, security su*ks everywhere. True. But when you grow a product with time, you fill the holes one by one when you start losing water. With AI, you will have so many holes in your 15 days made battleship, that... good luck putting it at sea, you can tank in the first minute. As Moltbook has shown. I don't have to find proofs (which I dont actually find). I just need a counter example and... first big vibe product I've seen, first gigantic security failure. Plain to see.

For sure, but I haven't written a single piece of software where security would ever be considered a factor. Not all software runs on the web, not all software deals with accounts etc.

It works. You’re just not doing it right if it doesn’t work for you. It’s hard to convince me otherwise at this point.

Have fun then. I don't need to convince you, I asked reasons to be convinced. And this thread is offering me none at all! :D

Consider it this way as a reasoning step: We've invented a cross compiler that can handle the natural languages too. That's definitely useful; but it's still GIGO so you still need your brain.

Yeah its great but as the OP said you have to watch every P.R. like a hawk

OP said you stop doing that at some point

I’ve been using it to develop firmware in c++. Typically around 10-20 KLOC. Current projects use Sensors, wire protocols, RF systems , swarm networks, that kind of stuff integrated into the firmware.

If you use it correctly, you can get better quality, more maintainable code than 75% of devs will turn in on a PR. The “one weird trick” seems to be to specify, specify, specify. First you use the LLM to help you write a spec (document, if it’s pre existing). Make sure the spec is correct and matches the user story and edge cases. The LLM is good at helping here too. Then break down separations of concerns, APIs, and interfaces. Have it build a dependency graph. After each step, have it reevaluate the entire stack to make sure it is clear, clean, and self consistent.

Every step of this is basically the AI doing the whole thing, just with guidance and feedback.

Once you’ve got the documentation needed to build an actual plan for implementation, have it do that. Each step, you go back as far as relevant to reevaluate. Compare the spec to the implementation plan, close the circle. Then have it write the bones, all the files and interfaces, without actual implementations. Then have it reevaluate the dependency graph and the plan and the file structure together. Then start implementing the plan, building testing jigs along the way.

You just build software the way you used to, but you use the LLM to do most of the work along the way. Every so often, you’ll run into something that doesn’t pass the smell test and you’ll give it a nudge in the right direction.

Think of it as a junior dev that graduated top of every class ever, and types 1000wpm.

Even after all of that, I’m turning out better code, better documentation, and better products, and doing what used to take 2 devs a month, in 3 or 4 days on my own.

On the app development side of our business, the productivity gain also strong. I can’t really speak to code quality there, but I can say we get updates in hours instead of days, and there are less bugs in the implementations. They say the code is better documented and easier to follow , because they’re not under pressure to ship hacky prototype code as if it were production.

On the current project, our team size is 1/2 the size it would have been last year, and we are moving about 4x as fast. What doesn’t seem to scale for us is size. If we doubled our team size I think the gains would be very small compared to the costs. Velocity seems to be throttled more by external factors.

I really don’t understand where people are coming from saying it doesn’t work. I’m not sure if it’s because they haven’t tried a real workflow, or maybe tried it at all, or they are definitely “holding it wrong.” It works. But you still need seasoned engineers to manage it and catch the occasional bad judgment or deviation from the intention.

If you just let it, it will definitely go off the rails and you’ll end up with a twisted mess that no one can debug. But use a system of writing the code incrementally through a specification - evaluation loop as you descend the abstraction from idea to implementation you’ll end up winning.

As a side note, and this is a little strange and I might be wrong because it’s hard to quantify and all vibes, but:

I have the AI keep a journal about its observations and general impressions, sort of the “meta” without the technical details. I frame this to it as a continuation of “awareness “ for new sessions.

I have a short set of “onboarding“ documents that describe the vision, ethos, and goals of the project. I have it read the journal and the onboarding docs at the beginning of each session.

I frame my work with the AI as working with it as a “collaborator” rather than a tool. At the end of the day, I remind it to update its journal of reflections about the days work. It’s total anthropomorphism, obviously, but it seems to inspire “trust” in the relationship, and it really seems to up-level the effort that the AI puts in. It kinda makes sense, LLMs being modelled on human activity.

FWIW, I’m not asserting anything here about the nature of machine intelligence, I’m targeting what seems to create the best result. Eventually we will have to grapple with this I imagine, but that’s not today.

When I have forgotten to warm-start the session, I find that I am rejecting much more of the work. I think this would be worth someone doing an actual study to see if it is real or some kind of irresistible cognitive bias.

I find that the work produced is much less prone to going off the rails or taking shortcuts when I have this in the context, and by reading the journal I get ideas on where and how to do a better job of steering and nudging to get better results. It’s like a review system for my prompting. The onboarding docs seem to help keep the model working towards the big picture? Idk.

This “system” with the journal and onboarding only seems to work with some models. GPT5 for example doesn’t seem to benefit from the journal and sometimes gets into a very creepy vibe. I think it might be optimized for creating some kind of “relationship” with the user.

Yeah, I guess that for small projects, you can prototype and generate well and good. 20k loc is not much. A seasoned engineer can put code generation to good use. Yet, the cost of maintaining 4x the code grows exponentially, especially when you have to communicate your specs to humans, or have stakeholders discuss how they want the product to be. Firmware is also a nice ground. Sensors also behave mechanically, which is good for AIs who can pattern match more effectively than with actual humans. I won't say AI is useless. I see the incremental gains. I don't see the exponential gains for everyone that is going to kill the world - yet. And the fact that it takes 50+ comments to actually get someone who can explain real gains tells me much about how overhyped the whole domain is.

This is one of the best descriptions of using AI effectively I’ve read. It becomes clear that using AI effectively is about planning, architecture, and directing another intelligent agent. It’s essential to get things right at each high level step before drilling in deeper as you clearly outlined.

I suspect you either already were or would’ve been great at leading real human developers not just AI agents. Directing an AI towards good results is shockingly similar to directing people. I think that’s a big thing separating those getting great results with AI from those claiming it simply does not work. Not everyone is good at doing high level panning, architecture, and directing others. But those that already had those skills basically just hit the ground running with AI.

There are many people working as software engineers who are just really great at writing code, but may be lacking in the other skills needed to effectively use AI. They’re the angry ones lamenting the loss of craft, and rightfully so, but their experience with AI doesn’t change the shift that’s happening.

I'm a nerd, and I have plenty of reasons to disagree

If you name a scale "corporate bullshit" you kinda want some kinda outcome

Embrace it. Make your life worse and get less productive now. Wow. Is that how you're going to put down my throat another useless AI tool that makes too much code and doesn't understand what product, user experience and security mean? I really hoped the article dealt with Doom and porn somehow

Aaaand 100% yes. Correct. I don't know why people can be so drunk to put billions after this feature that serves nothing but top notch translation, and text interaction to a mindless machine. Like, speaking love to a parrot, and since the parrot replies back, marrying him.

Still more market share (for Mozilla's browser) than all the Linux distros.

We use sheets at Odoo, but also our internal version of the Spreadsheer app. It boils down to preference. Zealots will preach their side.

Where I work, it's not a matter of preference, it's a matter of security policies. If we had the enterprise version of Google Docs, with the policies applied there, and managed corporate accounts, that would be a different story. In that case, the policy would push us to Sheets instead of Excel. Same story, but a different destination.

The OP didn't really frame how this question came up. But using what all your co-workers use and staying in the same ecosystem has a lot of value, even without sensitive data involved. If the whole company uses Excel, no one wants to deal with the one person using Sheets, and vice versa.

> The OP didn't really frame how this question came up.

Hey, OP here. Valid. It's a pre-seed, pre-revenue start-up going through the motions to get the company on the right footing ahead of seeking funding. We have a company Workspace domain, but one of our advisors is a grizzled old CFO and did not enjoy seeing one his formulas borked by a difference in how Excel and Sheets handles xlookup().