7 ms·

It is incredibly easy now to get an idea to the prototype stage, but making it production-ready still needs boring old software engineering skills. I know count

by paxys 7mo ago

It is incredibly easy now to get an idea to the prototype stage, but making it production-ready still needs boring old software engineering skills. I know countless people who followed the "I'll vibe code my own business" trend, and a few of them did get pretty far, but ultimately not a single one actually launched. Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort.

dominotw 7mo ago
all they did is annoy their friends and family by sharing their vibeslop app and asking for "feedback". I really dont know how to respond to these requests. I am going to hide out and not talk to anyone till this fad passes. Reminds of the trend where everyone was dj wanting you to listen their mixtrack they made on abbleton live
- 101008 7mo ago
  "I think it's great, you should deploy it! Let me know when it's in production"
- highstep 7mo ago
  Is it really that big of a deal to help/encourage a friend/family in these simple ways?? Do you have no time in life to smell the flowers?
  - coffeebeqn 7mo ago
    AI slop is not the flowers
  - kevinsync 7mo ago
    Devil's advocate (because honestly I do agree with you, but..) -- help/encouragement often ends up turning into far more time and effort than it sounds like up front. ~18 months ago a friend of mine had a very viable, good idea for a physical product, but very fuzzy on the details of where to begin. My skillset backfilled everything he was missing to go from idea to reality to in-market. I began at arm's length with just advice and validation, then slowly got involved with CAD and prototyping to make sure it kept moving forward, then infrastructure/admin, graphic design, digital marketing and support, etc, while he worked on manufacturing, physical marketing, networking, fulfillment, sales, etc. Long story short, because I both deeply believe in the vision and know that teamwork makes the dream work, I am fully, completely, inextricably involved LOL -- and I don't have a single complaint about it either, but man, watch out, because if you don't believe in the vision but do have skills/expertise they're lacking, and opt out, friends and family will be the quickest and most aggrieved people you'll ever meet that think you're gatekeeping them from success.
    - pak9rabid 7mo ago
      I hope this at least resulted in some equity of this project for you.
      - kevinsync 7mo ago
        Yeah it turned out to be very fair, I just initially wasn't expecting to get as involved as I have hahaha
  - moduspol 7mo ago
    I sure do. I hook up Claude to my browser via MCP and have it review and give feedback for my family and friends' projects. It's a win/win.
  - pesus 7mo ago
    In this case, it's more like asking your friends to take time to smell some feces instead of flowers. Or to be a little less pessimistic, it's like asking them to stop and smell the flowers, except the flowers are fake and plastic and it makes your friends question your sanity. Either way, it's not a normal or enjoyable flower smelling experience, and doesn't add any enjoyment or simple pleasure to one's life like normal flower smelling would.
- bogwog 7mo ago
  When someone sends me an AI generated project or proposal, I just send them an AI generated reply I know they're not going to bother reading either.
  - 72deluxe 7mo ago
    This is a genius move. My wife should start doing that with emails from her boss who sends AI-generated emails and instructions to her.
calvinmorrison 7mo ago
It's helping with that part too. I was able to configure a grafana stack with the help of claude for our ansible scripts.
- skeeter2020 7mo ago
  That's no where near the end stage of launching a business.
  - stronglikedan 7mo ago
    I used it to design my business cards!
  - ghywertelling 7mo ago
    It is already having impact in triaging support tickets and faster resolution using logs.
  - calvinmorrison 7mo ago
    it's past the end stage, we are already in business. it's just something I am not an expert in, I have used in the past (by having real ops engineers build it for me) and now I have something that gives us insight into our production stack, alerts, etc, that isnt janky and covers my goals. So... yeah that is valuable and improves my business.
adriand 7mo ago
> Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort. This is true, and I bet there are thousands of people who are in this stage right now - having gotten there far faster than they would have without Claude Code - which makes me predict that the point made in the article will not age well. I think it’s just a matter of a bit more time before the deluge starts, something on the order of six more months.
- lebuin 7mo ago
  I'd argue that LLMs are not yet capable of the last step, and because most sufficiently large AI-generated codebase are an unmaintainable mess, it's also very hard for a human developer to take over and go the last mile.
  - raw_anon_1111 7mo ago
    So what is the “last step”? I have one shotted a complete AWS CDK app to create infrastructure on an empty AWS account and deploy everything - networking, VPC endpoints, Docker based lambdas, databases, logging, monitoring alerts etc. Yes I know AWS well and was detailed about the requirements z
prhn 7mo ago
Even beyond the engineering there are 100 other things to do. I launched a vibe coded product a few months ago. I spent the majority of my time - making sure the copy / presentation was effective on product website - getting signing certificates (this part SUCKS and is expensive) - managing release version binaries without a CDN (stupid) - setting up LLC, website, domain, email, google search indexing, etc, etc
- ryandrake 7mo ago
  Exactly. The "writing code" part is literally the easiest part of building a software business. And that was even before LLM assisted coding. Now it's pretty much trivial to just spew slop code until something works. The hard parts are still: making the right thing, making it good, getting feedback and idea validation, and the really hard part is turning it into a business.
- ryanbuening 7mo ago
  Agreed. However, I just recently "launched" a side project and Cloudflare made a lot of the stuff you mentioned easier. I also found that using AI helped with setting up my LLC when I had questions.
  - zeroonetwothree 7mo ago
    Getting legal advice from AI is certainly an option you can take.
    - habinero 7mo ago
      Legal advice is maybe the worst thing to get from an LLM.
Cthulhu_ 7mo ago
Exactly, there have been loads of tools over time to make software development easier - like Dreamweaver and Frontpage to build websites without coding, or low/no-code platforms to click and drag software together, or all frameworks ever, or libraries that solve issues that often take time - and I'm sure they've had a cumulative effect in developer productivity and / or software quality. But there's not one tool there that triggered a major boost in output or number of apps / libraries / products created - unless I missed something. Sure, total output has increased, especially since the early 2010's thanks to both Github becoming the social network of software development, and (arguably) Node / JS becoming one of the most popular languages/runtimes out there attracting a lot of developers to publish a lot of tools. But that's not down to productivity or output boosting developments.
zdc1 7mo ago
Even if you have the app, you get to start the fun adventure of marketing it and actually trying to grow the damn thing
- TeMPOraL 7mo ago
  Right. Which is something you neither need nor want if you just wanted to have an app.
  - deadbabe 7mo ago
    Almost all apps anyone needs have already been created.
- parpfish 7mo ago
  And you need to find a way to market it that prevents people from thinking “cool, I bet I can get Claude to whip up something similar real quick”
- achenatx 7mo ago
  one of the apps I built is an app that you give it a website, it scrapes it, then places the google ads. Over time it tries to improve the ads. Right now it is showing a 5% click through rate over a few weeks. I have no idea if that is good or not, but it saved me the hassle of having to worry about google ads for another app If the marketing platform works, then it would drive traffic to itself.
TeMPOraL 7mo ago
> It is incredibly easy now to get an idea to the prototype stage Yup. And for most purposes, that's enough. An app does not have to be productized and shipped to general audience to be useful. In fact, if your goal is to solve some specific problem for yourself, your friends/family, community or your team, then the "last step" you mention - the one that "takes majority of time and effort" - is entirely unnecessary, irrelevant, and a waste of time. The productivity boost is there, but it's not measured because people are looking for the wrong thing. Products on the market are not solutions to problems, they're tools to make money. The two are correlated, because of bunch of obvious reasons (people need money, solving a problem costs money, people are happy to pay for solutions, etc.), but they're still distinct. AI is dropping the costs of "solving the problem" part, much more than that of "making a product", so it's not useful to use the lack of the latter as evidence of lack of the former.
- threetonesun 7mo ago
  I agree, although I'd also say for the majority of problems the first part of even prototyping it is probably a waste of time and most people would be better off asking a simple AI hooked up to search if an appropriate solution already exists, or can be easily made with existing tools.
  - TeMPOraL 7mo ago
    At this point if you know about GitHub Pages[0] and know how to copy-paste, it's easier to get an LLM to make you a tool than to find one that works and is free of web entrepreneurship bullshit (ads, trackers, malware, dark patterns, etc.). -- [0] - It would've been even easier if web browsers didn't nerf file:// protocol, but that ship has sailed.
- apsurd 7mo ago
  But the last "making a product" part does apply to nearly any tool, even a solution to a personal problem. I've started tons of scratch my own itch projects. There's adoption, UX, onboarding costs even if you're the only audience. TLDR: i don't even use my own projects. I churn.
danans 7mo ago
> Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort. That's true, but even the "last step" is being accelerated. The 10% that takes 90% of the time has itself been cut in half. An example is turning debug logs and bug reports into bugfixes, and performance stats into infrastructure migrations. The time required to analyze, implement, and deploy those has been reduced by a large amount. It still needs to be coupled with software engineering skills - to decide between multiple solutions generated by an LLM, but the acceleration is significant.
- adamrezich 7mo ago
  So, how many years until we'll see results, then?
  - danans 7mo ago
    > So, how many years until we'll see results, then? -0.75 years. Software development output (features, bugs, products) - especially at smaller companies like startups - has already accelerated significantly, while software development hiring has stayed flat or declined. So there has been a dramatic increase in human-efficiency. To me, that seems like a result, although it's cold comfort as a software engineer. You probably won't see this reflected as a multiplication of new apps because the app consumer's attention is already completely tapped. There's very little attention surface area left to capture.
    - shimman 7mo ago
      You don't think capitalists are able to generate profit off of these LLMs currently? Why not? Are they just stupid or something?
      - danans 7mo ago
        > You don't think capitalists are able to generate profit off of these LLMs currently? Not sure where you are reading that. I said that they are able to be far more human-efficient because of LLMs, implying they are able to reduce costs relative to outputs/revenue, which means higher profits.
lordmathis 7mo ago
I also experienced this with my personal projects. It was really easy to just workshop a new feature. I'd talk to claude and get a nice looking implementation spec. Then I'd pass it on to a coding agent which would get 80% there but the last 20% would actually take lot more time. In the meantime I'd workshop more and more features leading to an evergrowing backlog and an anxiety that an agent should be doing something otherwise I'm wasting time. I brought this completely on myself. I'm not building a business, nothing would happen if I just didn't implement another feature.
- freedomben 7mo ago
  Ha! I do this too and have also recently noticed. When scope creep is relatively cheap, it also gets unending and I'm never satisfied. I've had a couple of projects that I would otherwise open source that I've had to be realistic about and just accept it's only going to be useful for myself. Once I open it I feel a responsiblity for maintenance and stability that just adds a lot of extra work. I need to save those for the projects that might actually, realistically, be used.
raw_anon_1111 7mo ago
Software engineering is not “coding” though. Before AI for the last 8 or so years now first at a startup then working in consulting mostly with companies new to AWS or they wanted a new implementation, it’s been: 1. Gather requirements 2. Do the design 3. Present the design and get approval and make sure I didn’t miss anything 4. Do the infrastructure as code to create the architecture and the deployment pipeline 5. Design the schema and write the code 6. Take it through UAT and often go back to #4 or #5 7. Move it into production 8. Monitoring and maintenance. #4 and #5 can be done easily with AI for most run of the mill enterprise SaaS implementations especially if you have the luxury of starting from the ground up “post AI”. This is something you could farm off to mid level ticket takers before AI.
- SgtBastard 7mo ago
  What makes you think 1-2, 6-8 can’t be done by agents?
  - raw_anon_1111 7mo ago
    1. An agent is not going to talk to the “business” and solve XYProblems, conflicting agendas, and deal with strategy. I’ve had to push back on people in my own company that want to give customers “questionnaires” to fill out pre engagement and I refuse to do it on any project I lead. An agent can tell facial expressions, uncertainty etc. 2. AI is horrible at system design. One anecdote. I was vibe coding an internal website that will at most be used by 7 people in total. Part of it was uploading a file to S3 and then loading the file into an Postgres table. It got the “create pre-signed S3 url and upload it directly to that instead of sending it to the API” correct (documented best practice). But then it did the naive “upload the file from S3 and do a bulk sql insert into the database”. This would have taken 20 minutes. The optimized method that I already knew was just to use the Postgres AWS extension to load it directly from S3 - 30 seconds. I’ve heard from a lot of data engineers run into similar problems (I am not one. I play one sometime). 6. Involves talking to the customer and UX. 7. Moving to production doesn’t take AI. Automation, stage deployments, automated testing and monitoring, blue /green deployments etc is a solved problem. 8. Monitoring is also a solve problem pre AI. It’s what happens after a problem is what you need people for. So yes 1,2 and 7 are high value, high touch. If you look at the leveling guidelines for any BigTech company, you have to be good at 1 and 2 at least to get pass mid level. Then there is always “0” pre-sales. I can do inbound pre-sales (not chase customers). It’s not that much different than what I do now as the first technical person who does a deep dive strategy conversation
DanHulton 7mo ago
It's really thrown off some old adages. It's now "the first 90% takes 90% of the time, the last 10% takes the other 90,000,000% of the time." Just doesn't have the same ring to it.
- marginalia_nu 7mo ago
  It's more Zeno's paradox. You take one step, get 90% of the way to the finishing line. Now you look ahead and still a bunch of distance ahead of you. You take another step and get 90% of the way there. Now you look ahead and see there's still more distance ahead of you,...
bwfan123 7mo ago
> the "last step" is what takes the majority of time and effort Having worked extensively with vibe-coded software, the main problem for me is that I have tuned-off from the ai-code, and I dont see any skin-in-the-game for me. This is dangerous because it becomes increasingly harder to root-cause and debug problems because that muscle is atrophying. use-it or lose-it applies to cognitive skills (coding/debugging). Now, I lean negatively to ai-code because, while it seduces us with fast progress in the first 80%, the end outcome is questionable in terms of quality. Finally, ai-coding encourages a prompt-and-test or trial-and-error approach to software engineering which is frustrating and those with experience would prefer to get it right by design.
- janalsncm 7mo ago
  I also wonder about this for myself. My feeling is that my debug skills are also atrophied a bit. But I would split debugging into two buckets: 1. Debugging my own code or obvious behavior with other libraries. 2. Debugging pain-in-the-ass behavior with other libraries. My patience with the latter is significantly less now, and so is perhaps my skill in debugging them. Libraries that change their apis for no apparent reason, libraries which use nonstandard parameter names, libraries which aren’t working as advertised.
- deleted 7mo ago
  [deleted]
- genatron_ai 7mo ago
  It's also possible to heavily influence the design and what is sent to the AI model in the prompt to help ensure the output is the way you would like it. In existing codebases with your style and patterns or even in the prompt, it's possible to heavily influence the output so that you can hopefully get the best of both worlds.
npilk 7mo ago
How much longer will this be true, though? With improving computer use, it may be possible in the next ~year or so that agents will be able to wire up infrastructure and launch to production.
- ericmcer 7mo ago
  no I don't think with LLMs as the foundation we will ever have something that can build and launch something end to end. They just predict the next most likely token... no amount of clever orchestration can cover that up and make it into real intelligence.
  - bonoboTP 7mo ago
    Nice bait
hermitcrab 7mo ago
90% done, just the other 90% to do...
davmar 7mo ago
succinct and accurate.
autotune 7mo ago
I launched a draw.io competitor to the point that it is in production, but there is little activity on the site as far as signups are concerned. Doesn't deliver enough business value.
- chrisandchris 7mo ago
  Out of curiosity: What is your USP? Why should I prefer your product over draw.io? IMHO (this may not apply to you!) a lot of people launch a "competitor" of a product which seems to be a clone of the product without improving something that the other product misses/is very bad at.
  - autotune 7mo ago
    Specifically because it generates Terraform code along with the diagrams, but I guess it's not the selling point that I thought it would be.
    - chrisandchris 7mo ago
      Terraform code for what? I mean, a Draw.IO diagram is basically just XML (so can be versioned anf stuff and so).
deleted 7mo ago
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balls187 7mo ago
> and a few of them did get pretty far, but ultimately not a single one actually launched. Having done this professionally for a very, very long time, software engineers aren't particularly good at launching products. Technology has drastically lowered the barriers to bring software products to customers, and AI is a continuation of that trend.
roadside_picnic 7mo ago
> not a single one actually launched. I think this represents a fundamental misunderstanding of how these AI tools are used most effectively: not to write software but to directly solve the problem you were going to solve with software. I used to not understand this and agreed with the "where is all the shovelware" comments, but now I've realized the real shift is not from automating software creation, but replacing the need for it in the first place. It's clear that we're still awhile away from this being really understood and exploited. People are still confusingly building webapps that aren't necessary. Here's two, somewhat related, examples I've come across (I spend a lot of time on image/video generation in my free time): A web service that automatically creates "headshots" for you, and another that will automatically create TikTok videos for you. I have bespoke AI versions of both of these I built myself in an afternoon, running locally, creating content for prices that simply can't be matched by anyone trying to build a SaaS company out of these ideas. What people are thinking: "I know, I can use AI to build a SaaS startup the sells content!" But building a SaaS company still requires real software since it has to scale to multiple users and use cases. What more and more people are realizing is "I can created the content for basically free on my desktop, now I need to figure out how to leverage that content". I still haven't cracked the code for creating a rockstar TikTok channel, but it's not because I'm blocked on the content end. Similarly I'm starting to see that we're still not thinking about how to reorganize software teams to maximally exploit AI. Right now I see lots of engineers doing software the old way with an AI powered exo-skeleton. We know what this results in: massive PRs that clog up the whole process, and small bugs that creep up later. So long as we divide labor into hyper focused roles, this will persist. What I'm increasingly seeing is that to leverage AI properly we need to re-think how these roles actually work, since now one person can be much responsible for a much larger surface area rather than just doing one thing (arguably) faster.
williamcotton 7mo ago
I agree 100%. Boring old software skills are part of what it took to "write" this DSL, complete with a fully featured LSP: https://github.com/williamcotton/webpipe https://github.com/williamcotton/webpipe https://github.com/williamcotton/webpipe-lsp https://github.com/williamcotton/webpipe-lsp (lots of animated GIFs to show off the LSP and debugger!) While I barely typed any of this myself I sure as heck read most of the generated code. But not all of it! Of course you have to consider my blog to be "in production": https://github.com/williamcotton/williamcotton.com/blob/main/app.wp https://github.com/williamcotton/williamcotton.com/blob/main... The reason I'm mentioning this project is because the article questions where all the AI apps are. Take a look at the git history of these projects and question if this would have been possible to accomplish in such a relatively short timeframe! Or maybe it's totally doable? I'm not sure. I knew nothing about quite a bit of the subsystems, eg, the Debug Adapter Protocol, before their implementation.
- dalenw 7mo ago
  I recently "vibe coded" a long term background job runner service... thing. It's rather specific to my job and a pre-existing solution didn't exist. I already knew what I wanted the code to be, so it was just a matter of explaining explicitly what I wanted to the AI. Software engineering concepts, patterns, al that stuff. And at the end of the day(s) it took about the same amount of time to code it with AI than it would've taken by hand. It was a lot of reviewing and proofreading and just verifying everything by hand. The only thing that saved me time was writing the test suite for it. Would I do it again? Maybe. It was kinda fun programming by explaining an idea in plain english than just writing the code itself. But I heavily relied on software engineering skills, especially those theory classes from university to best explain how it should be structured and written. And of course being able to understand what it outputs. I do not think that someone with no prior software engineering knowledge could do the same thing that I did.
deleted 7mo ago
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hintymad 7mo ago
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sixtyj 7mo ago
Well… because it is not almost possible do it solo. Code is just one part of puzzle. Add: Pricing, marketing and ads, invoicing, VAT, make really good onboarding, measure churn rate, do customer service… A lot of vibe coders are solopreneurs. You have to be very consistent and disciplined to make final product that sells.
arkensaw 7mo ago
Not to get too political, but there's a lot of talk this week about the US military using AI to select targets and be more effective in Iran, which is not un-similar. In both cases, AI is making people think they can achieve things that were previously judged to unachievable, whether those things are building an app without any effort and getting rich, or effecting regime change without any actual strategic planning.
tabs_or_spaces 7mo ago
I would add that getting customers, especially paid customers for your app is not easily solved with ai too. Only a few get lucky with funding, only a few have a profitable business.
bicepjai 7mo ago
OpenClaw would disagree :) they are live, in business and by all accounts not built on rigorous engineering, my experience supports it. Sometimes scrappy ships and survives, in the llm era.
ruthven-elara 7mo ago
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by paxys 7mo ago

dominotw 7mo ago

all they did is annoy their friends and family by sharing their vibeslop app and asking for "feedback". I really dont know how to respond to these requests. I am going to hide out and not talk to anyone till this fad passes. Reminds of the trend where everyone was dj wanting you to listen their mixtrack they made on abbleton live

101008 7mo ago
"I think it's great, you should deploy it! Let me know when it's in production"
highstep 7mo ago
Is it really that big of a deal to help/encourage a friend/family in these simple ways?? Do you have no time in life to smell the flowers?
- coffeebeqn 7mo ago
  AI slop is not the flowers
- kevinsync 7mo ago
  Devil's advocate (because honestly I do agree with you, but..) -- help/encouragement often ends up turning into far more time and effort than it sounds like up front. ~18 months ago a friend of mine had a very viable, good idea for a physical product, but very fuzzy on the details of where to begin. My skillset backfilled everything he was missing to go from idea to reality to in-market. I began at arm's length with just advice and validation, then slowly got involved with CAD and prototyping to make sure it kept moving forward, then infrastructure/admin, graphic design, digital marketing and support, etc, while he worked on manufacturing, physical marketing, networking, fulfillment, sales, etc. Long story short, because I both deeply believe in the vision and know that teamwork makes the dream work, I am fully, completely, inextricably involved LOL -- and I don't have a single complaint about it either, but man, watch out, because if you don't believe in the vision but do have skills/expertise they're lacking, and opt out, friends and family will be the quickest and most aggrieved people you'll ever meet that think you're gatekeeping them from success.
  - pak9rabid 7mo ago
    I hope this at least resulted in some equity of this project for you.
    - kevinsync 7mo ago
      Yeah it turned out to be very fair, I just initially wasn't expecting to get as involved as I have hahaha
- moduspol 7mo ago
  I sure do. I hook up Claude to my browser via MCP and have it review and give feedback for my family and friends' projects. It's a win/win.
- pesus 7mo ago
  In this case, it's more like asking your friends to take time to smell some feces instead of flowers. Or to be a little less pessimistic, it's like asking them to stop and smell the flowers, except the flowers are fake and plastic and it makes your friends question your sanity. Either way, it's not a normal or enjoyable flower smelling experience, and doesn't add any enjoyment or simple pleasure to one's life like normal flower smelling would.
bogwog 7mo ago
When someone sends me an AI generated project or proposal, I just send them an AI generated reply I know they're not going to bother reading either.
- 72deluxe 7mo ago
  This is a genius move. My wife should start doing that with emails from her boss who sends AI-generated emails and instructions to her.

calvinmorrison 7mo ago

It's helping with that part too. I was able to configure a grafana stack with the help of claude for our ansible scripts.

skeeter2020 7mo ago
That's no where near the end stage of launching a business.
- stronglikedan 7mo ago
  I used it to design my business cards!
- ghywertelling 7mo ago
  It is already having impact in triaging support tickets and faster resolution using logs.
- calvinmorrison 7mo ago
  it's past the end stage, we are already in business. it's just something I am not an expert in, I have used in the past (by having real ops engineers build it for me) and now I have something that gives us insight into our production stack, alerts, etc, that isnt janky and covers my goals. So... yeah that is valuable and improves my business.

adriand 7mo ago

> Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort. This is true, and I bet there are thousands of people who are in this stage right now - having gotten there far faster than they would have without Claude Code - which makes me predict that the point made in the article will not age well. I think it’s just a matter of a bit more time before the deluge starts, something on the order of six more months.

lebuin 7mo ago
I'd argue that LLMs are not yet capable of the last step, and because most sufficiently large AI-generated codebase are an unmaintainable mess, it's also very hard for a human developer to take over and go the last mile.
- raw_anon_1111 7mo ago
  So what is the “last step”? I have one shotted a complete AWS CDK app to create infrastructure on an empty AWS account and deploy everything - networking, VPC endpoints, Docker based lambdas, databases, logging, monitoring alerts etc. Yes I know AWS well and was detailed about the requirements z

prhn 7mo ago

Even beyond the engineering there are 100 other things to do. I launched a vibe coded product a few months ago. I spent the majority of my time - making sure the copy / presentation was effective on product website - getting signing certificates (this part SUCKS and is expensive) - managing release version binaries without a CDN (stupid) - setting up LLC, website, domain, email, google search indexing, etc, etc

ryandrake 7mo ago
Exactly. The "writing code" part is literally the easiest part of building a software business. And that was even before LLM assisted coding. Now it's pretty much trivial to just spew slop code until something works. The hard parts are still: making the right thing, making it good, getting feedback and idea validation, and the really hard part is turning it into a business.
ryanbuening 7mo ago
Agreed. However, I just recently "launched" a side project and Cloudflare made a lot of the stuff you mentioned easier. I also found that using AI helped with setting up my LLC when I had questions.
- zeroonetwothree 7mo ago
  Getting legal advice from AI is certainly an option you can take.
  - habinero 7mo ago
    Legal advice is maybe the worst thing to get from an LLM.

Cthulhu_ 7mo ago

Exactly, there have been loads of tools over time to make software development easier - like Dreamweaver and Frontpage to build websites without coding, or low/no-code platforms to click and drag software together, or all frameworks ever, or libraries that solve issues that often take time - and I'm sure they've had a cumulative effect in developer productivity and / or software quality. But there's not one tool there that triggered a major boost in output or number of apps / libraries / products created - unless I missed something. Sure, total output has increased, especially since the early 2010's thanks to both Github becoming the social network of software development, and (arguably) Node / JS becoming one of the most popular languages/runtimes out there attracting a lot of developers to publish a lot of tools. But that's not down to productivity or output boosting developments.

zdc1 7mo ago

Even if you have the app, you get to start the fun adventure of marketing it and actually trying to grow the damn thing

TeMPOraL 7mo ago
Right. Which is something you neither need nor want if you just wanted to have an app.
- deadbabe 7mo ago
  Almost all apps anyone needs have already been created.
parpfish 7mo ago
And you need to find a way to market it that prevents people from thinking “cool, I bet I can get Claude to whip up something similar real quick”
achenatx 7mo ago
one of the apps I built is an app that you give it a website, it scrapes it, then places the google ads. Over time it tries to improve the ads. Right now it is showing a 5% click through rate over a few weeks. I have no idea if that is good or not, but it saved me the hassle of having to worry about google ads for another app If the marketing platform works, then it would drive traffic to itself.

TeMPOraL 7mo ago

> It is incredibly easy now to get an idea to the prototype stage Yup. And for most purposes, that's enough. An app does not have to be productized and shipped to general audience to be useful. In fact, if your goal is to solve some specific problem for yourself, your friends/family, community or your team, then the "last step" you mention - the one that "takes majority of time and effort" - is entirely unnecessary, irrelevant, and a waste of time. The productivity boost is there, but it's not measured because people are looking for the wrong thing. Products on the market are not solutions to problems, they're tools to make money. The two are correlated, because of bunch of obvious reasons (people need money, solving a problem costs money, people are happy to pay for solutions, etc.), but they're still distinct. AI is dropping the costs of "solving the problem" part, much more than that of "making a product", so it's not useful to use the lack of the latter as evidence of lack of the former.

threetonesun 7mo ago
I agree, although I'd also say for the majority of problems the first part of even prototyping it is probably a waste of time and most people would be better off asking a simple AI hooked up to search if an appropriate solution already exists, or can be easily made with existing tools.
- TeMPOraL 7mo ago
  At this point if you know about GitHub Pages[0] and know how to copy-paste, it's easier to get an LLM to make you a tool than to find one that works and is free of web entrepreneurship bullshit (ads, trackers, malware, dark patterns, etc.). -- [0] - It would've been even easier if web browsers didn't nerf file:// protocol, but that ship has sailed.
apsurd 7mo ago
But the last "making a product" part does apply to nearly any tool, even a solution to a personal problem. I've started tons of scratch my own itch projects. There's adoption, UX, onboarding costs even if you're the only audience. TLDR: i don't even use my own projects. I churn.

danans 7mo ago

> Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort. That's true, but even the "last step" is being accelerated. The 10% that takes 90% of the time has itself been cut in half. An example is turning debug logs and bug reports into bugfixes, and performance stats into infrastructure migrations. The time required to analyze, implement, and deploy those has been reduced by a large amount. It still needs to be coupled with software engineering skills - to decide between multiple solutions generated by an LLM, but the acceleration is significant.

adamrezich 7mo ago
So, how many years until we'll see results, then?
- danans 7mo ago
  > So, how many years until we'll see results, then? -0.75 years. Software development output (features, bugs, products) - especially at smaller companies like startups - has already accelerated significantly, while software development hiring has stayed flat or declined. So there has been a dramatic increase in human-efficiency. To me, that seems like a result, although it's cold comfort as a software engineer. You probably won't see this reflected as a multiplication of new apps because the app consumer's attention is already completely tapped. There's very little attention surface area left to capture.
  - shimman 7mo ago
    You don't think capitalists are able to generate profit off of these LLMs currently? Why not? Are they just stupid or something?
    - danans 7mo ago
      > You don't think capitalists are able to generate profit off of these LLMs currently? Not sure where you are reading that. I said that they are able to be far more human-efficient because of LLMs, implying they are able to reduce costs relative to outputs/revenue, which means higher profits.

lordmathis 7mo ago

I also experienced this with my personal projects. It was really easy to just workshop a new feature. I'd talk to claude and get a nice looking implementation spec. Then I'd pass it on to a coding agent which would get 80% there but the last 20% would actually take lot more time. In the meantime I'd workshop more and more features leading to an evergrowing backlog and an anxiety that an agent should be doing something otherwise I'm wasting time. I brought this completely on myself. I'm not building a business, nothing would happen if I just didn't implement another feature.

freedomben 7mo ago
Ha! I do this too and have also recently noticed. When scope creep is relatively cheap, it also gets unending and I'm never satisfied. I've had a couple of projects that I would otherwise open source that I've had to be realistic about and just accept it's only going to be useful for myself. Once I open it I feel a responsiblity for maintenance and stability that just adds a lot of extra work. I need to save those for the projects that might actually, realistically, be used.

raw_anon_1111 7mo ago

Software engineering is not “coding” though. Before AI for the last 8 or so years now first at a startup then working in consulting mostly with companies new to AWS or they wanted a new implementation, it’s been: 1. Gather requirements 2. Do the design 3. Present the design and get approval and make sure I didn’t miss anything 4. Do the infrastructure as code to create the architecture and the deployment pipeline 5. Design the schema and write the code 6. Take it through UAT and often go back to #4 or #5 7. Move it into production 8. Monitoring and maintenance. #4 and #5 can be done easily with AI for most run of the mill enterprise SaaS implementations especially if you have the luxury of starting from the ground up “post AI”. This is something you could farm off to mid level ticket takers before AI.

SgtBastard 7mo ago
What makes you think 1-2, 6-8 can’t be done by agents?
- raw_anon_1111 7mo ago
  1. An agent is not going to talk to the “business” and solve XYProblems, conflicting agendas, and deal with strategy. I’ve had to push back on people in my own company that want to give customers “questionnaires” to fill out pre engagement and I refuse to do it on any project I lead. An agent can tell facial expressions, uncertainty etc. 2. AI is horrible at system design. One anecdote. I was vibe coding an internal website that will at most be used by 7 people in total. Part of it was uploading a file to S3 and then loading the file into an Postgres table. It got the “create pre-signed S3 url and upload it directly to that instead of sending it to the API” correct (documented best practice). But then it did the naive “upload the file from S3 and do a bulk sql insert into the database”. This would have taken 20 minutes. The optimized method that I already knew was just to use the Postgres AWS extension to load it directly from S3 - 30 seconds. I’ve heard from a lot of data engineers run into similar problems (I am not one. I play one sometime). 6. Involves talking to the customer and UX. 7. Moving to production doesn’t take AI. Automation, stage deployments, automated testing and monitoring, blue /green deployments etc is a solved problem. 8. Monitoring is also a solve problem pre AI. It’s what happens after a problem is what you need people for. So yes 1,2 and 7 are high value, high touch. If you look at the leveling guidelines for any BigTech company, you have to be good at 1 and 2 at least to get pass mid level. Then there is always “0” pre-sales. I can do inbound pre-sales (not chase customers). It’s not that much different than what I do now as the first technical person who does a deep dive strategy conversation

DanHulton 7mo ago

It's really thrown off some old adages. It's now "the first 90% takes 90% of the time, the last 10% takes the other 90,000,000% of the time." Just doesn't have the same ring to it.

marginalia_nu 7mo ago
It's more Zeno's paradox. You take one step, get 90% of the way to the finishing line. Now you look ahead and still a bunch of distance ahead of you. You take another step and get 90% of the way there. Now you look ahead and see there's still more distance ahead of you,...

bwfan123 7mo ago

> the "last step" is what takes the majority of time and effort Having worked extensively with vibe-coded software, the main problem for me is that I have tuned-off from the ai-code, and I dont see any skin-in-the-game for me. This is dangerous because it becomes increasingly harder to root-cause and debug problems because that muscle is atrophying. use-it or lose-it applies to cognitive skills (coding/debugging). Now, I lean negatively to ai-code because, while it seduces us with fast progress in the first 80%, the end outcome is questionable in terms of quality. Finally, ai-coding encourages a prompt-and-test or trial-and-error approach to software engineering which is frustrating and those with experience would prefer to get it right by design.

janalsncm 7mo ago
I also wonder about this for myself. My feeling is that my debug skills are also atrophied a bit. But I would split debugging into two buckets: 1. Debugging my own code or obvious behavior with other libraries. 2. Debugging pain-in-the-ass behavior with other libraries. My patience with the latter is significantly less now, and so is perhaps my skill in debugging them. Libraries that change their apis for no apparent reason, libraries which use nonstandard parameter names, libraries which aren’t working as advertised.
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genatron_ai 7mo ago
It's also possible to heavily influence the design and what is sent to the AI model in the prompt to help ensure the output is the way you would like it. In existing codebases with your style and patterns or even in the prompt, it's possible to heavily influence the output so that you can hopefully get the best of both worlds.

npilk 7mo ago

How much longer will this be true, though? With improving computer use, it may be possible in the next ~year or so that agents will be able to wire up infrastructure and launch to production.

ericmcer 7mo ago
no I don't think with LLMs as the foundation we will ever have something that can build and launch something end to end. They just predict the next most likely token... no amount of clever orchestration can cover that up and make it into real intelligence.
- bonoboTP 7mo ago
  Nice bait

hermitcrab 7mo ago

90% done, just the other 90% to do...

davmar 7mo ago

succinct and accurate.

autotune 7mo ago

I launched a draw.io competitor to the point that it is in production, but there is little activity on the site as far as signups are concerned. Doesn't deliver enough business value.

chrisandchris 7mo ago
Out of curiosity: What is your USP? Why should I prefer your product over draw.io? IMHO (this may not apply to you!) a lot of people launch a "competitor" of a product which seems to be a clone of the product without improving something that the other product misses/is very bad at.
- autotune 7mo ago
  Specifically because it generates Terraform code along with the diagrams, but I guess it's not the selling point that I thought it would be.
  - chrisandchris 7mo ago
    Terraform code for what? I mean, a Draw.IO diagram is basically just XML (so can be versioned anf stuff and so).

deleted 7mo ago

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balls187 7mo ago

> and a few of them did get pretty far, but ultimately not a single one actually launched. Having done this professionally for a very, very long time, software engineers aren't particularly good at launching products. Technology has drastically lowered the barriers to bring software products to customers, and AI is a continuation of that trend.

roadside_picnic 7mo ago

> not a single one actually launched. I think this represents a fundamental misunderstanding of how these AI tools are used most effectively: not to write software but to directly solve the problem you were going to solve with software. I used to not understand this and agreed with the "where is all the shovelware" comments, but now I've realized the real shift is not from automating software creation, but replacing the need for it in the first place. It's clear that we're still awhile away from this being really understood and exploited. People are still confusingly building webapps that aren't necessary. Here's two, somewhat related, examples I've come across (I spend a lot of time on image/video generation in my free time): A web service that automatically creates "headshots" for you, and another that will automatically create TikTok videos for you. I have bespoke AI versions of both of these I built myself in an afternoon, running locally, creating content for prices that simply can't be matched by anyone trying to build a SaaS company out of these ideas. What people are thinking: "I know, I can use AI to build a SaaS startup the sells content!" But building a SaaS company still requires real software since it has to scale to multiple users and use cases. What more and more people are realizing is "I can created the content for basically free on my desktop, now I need to figure out how to leverage that content". I still haven't cracked the code for creating a rockstar TikTok channel, but it's not because I'm blocked on the content end. Similarly I'm starting to see that we're still not thinking about how to reorganize software teams to maximally exploit AI. Right now I see lots of engineers doing software the old way with an AI powered exo-skeleton. We know what this results in: massive PRs that clog up the whole process, and small bugs that creep up later. So long as we divide labor into hyper focused roles, this will persist. What I'm increasingly seeing is that to leverage AI properly we need to re-think how these roles actually work, since now one person can be much responsible for a much larger surface area rather than just doing one thing (arguably) faster.

williamcotton 7mo ago

I agree 100%. Boring old software skills are part of what it took to "write" this DSL, complete with a fully featured LSP: https://github.com/williamcotton/webpipe https://github.com/williamcotton/webpipe https://github.com/williamcotton/webpipe-lsp https://github.com/williamcotton/webpipe-lsp (lots of animated GIFs to show off the LSP and debugger!) While I barely typed any of this myself I sure as heck read most of the generated code. But not all of it! Of course you have to consider my blog to be "in production": https://github.com/williamcotton/williamcotton.com/blob/main/app.wp https://github.com/williamcotton/williamcotton.com/blob/main... The reason I'm mentioning this project is because the article questions where all the AI apps are. Take a look at the git history of these projects and question if this would have been possible to accomplish in such a relatively short timeframe! Or maybe it's totally doable? I'm not sure. I knew nothing about quite a bit of the subsystems, eg, the Debug Adapter Protocol, before their implementation.

dalenw 7mo ago
I recently "vibe coded" a long term background job runner service... thing. It's rather specific to my job and a pre-existing solution didn't exist. I already knew what I wanted the code to be, so it was just a matter of explaining explicitly what I wanted to the AI. Software engineering concepts, patterns, al that stuff. And at the end of the day(s) it took about the same amount of time to code it with AI than it would've taken by hand. It was a lot of reviewing and proofreading and just verifying everything by hand. The only thing that saved me time was writing the test suite for it. Would I do it again? Maybe. It was kinda fun programming by explaining an idea in plain english than just writing the code itself. But I heavily relied on software engineering skills, especially those theory classes from university to best explain how it should be structured and written. And of course being able to understand what it outputs. I do not think that someone with no prior software engineering knowledge could do the same thing that I did.

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hintymad 7mo ago

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sixtyj 7mo ago

Well… because it is not almost possible do it solo. Code is just one part of puzzle. Add: Pricing, marketing and ads, invoicing, VAT, make really good onboarding, measure churn rate, do customer service… A lot of vibe coders are solopreneurs. You have to be very consistent and disciplined to make final product that sells.

arkensaw 7mo ago

Not to get too political, but there's a lot of talk this week about the US military using AI to select targets and be more effective in Iran, which is not un-similar. In both cases, AI is making people think they can achieve things that were previously judged to unachievable, whether those things are building an app without any effort and getting rich, or effecting regime change without any actual strategic planning.

tabs_or_spaces 7mo ago

I would add that getting customers, especially paid customers for your app is not easily solved with ai too. Only a few get lucky with funding, only a few have a profitable business.

bicepjai 7mo ago

OpenClaw would disagree :) they are live, in business and by all accounts not built on rigorous engineering, my experience supports it. Sometimes scrappy ships and survives, in the llm era.

ruthven-elara 7mo ago

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