[1] Example: https://www.reddit.com/r/webdev/comments/xaksu6/on_ios_safar...

https://grack.com/demos/adventure/

The trick for me was just using a hidden input and updating the state of an in game input box. The code is ancient by today's standards but uses a reasonably simple technique to get the selection bounds of the text.

It works with auto complete on phones and has been stable for a decade.

One of the frustrating things about web dev, I find, is the staggering gulf between apparently nearly identical tasks and unpredictability of it. So often I will find myself on gwernnet asking Said Achmiz, 'this letter is a little too far left in Safari, can we fix that?' and the answer is 'yes but fixing that would require shipping our own browser in a virtual machine.' ¯\_(ツ)_/¯

https://gwern.net/blog/2025/conference-size

Here's the game, BTW (requires multiple people in the same location): https://home.stavros.io/justone/

This has worked extremely well for me.

Early on, I realized that you have to start a new "chat" after so many messages or the LLM will become incoherent. I've found that gpt-4.1 has a much lower threshold for this than o3. Maybe that's affecting your workflow and you're not realizing it?

I am on full-rework iteration three, learning as I go on what works best, and this is definitely the way. I'm going to be making a presentation to my team about how to use AI to accelerate and extend their day-to-day for things like this and here's my general outline:

1. Tell the LLM your overall goal and have it craft a thoughtful product plan from start to finish.

2. Take that plan and tell it to break each of the parts into many different parts that are well-planned and thoroughly documented, and then tell it to give you a plan on how to best execute it with LLMs.

3. Then go piece by piece, refining as you go.

The tool sets up an environment, gets the data from the warehouse, models it, and visualizes it in great detail. It took me about 22 hours of total time and roughly 2 hours of active time.

It's beautiful, fast, and fully featured. I am honestly BLOWN AWAY by what it did and I can't wait to see what others on my team do w/this. We could have all done the setup, data ingestion, and modeling, no question; the visualization platform it built for me we absolutely could NOT have done w/the expertise we have on staff--but the time it took? The first three pieces probably were a few days of time, but the last part, I have no idea. Weeks? Months?

Amazing.

100% agree, if that was the case I would not use LLMs either. Point is, at least for my use case and using my workflow it's more like 2 hours vs 10 minutes which suddenly changes the whole equation for me.

A technique that works well for me is to get the AI to one-shot the basic functionality or gameplay, and then build on top of that with many iterations.

The one-shot should be immediately impressive, if not then ditch it and try again with an amended prompt until you get something good to build on.

Where are you reading “a lot of posts” making this specific claim? I’ve never seen any serious person make such a claim

> a strong and thorough idea of what you want, broken up into hundreds of smaller problems, with specific architectural steers on the really critical pieces.

This is how I’ve been using LLM bots since CGPT preview and it’s been phenomenally useful and 100x my productivity

The gap seems to be between people who never knew how to build, looking for a perfect Oracle that would be like a genie in a lamp, then mad when its actual work

The thing the last few years have beat into me is that most engineers are actually functionally bad engineers who only know 1:1000th of what they should know in order to know how to build a successful project end to end

My assumption was that all of the bad engineers I worked with in person were a accidental sample of some larger group of really good ones (who I’ve also been able to work with over the years) and that it’s just rare to find an actual capable engineer who understands the whole process

Turns out that’s a trivial minority (like every other field) and most people are pretty bad at what they do

The key limiting factor to any project as somebody else in this thread said was “people alignment are the number one hindrance in project speed”

So 10 years ago if I wanted to make a web application that does complex shit I’d have to go and hire a handful of experts have them coordinate, manage the coordination of it, deliver it, monitor it everything else all the way through ideation storyboarding and everything else

I can do 100% of that myself now, now it’s true I could’ve done 100% of myself previously, but again it took a year of side effort to do it

I believe with LLM’s were set to relive the same phenomenon again.

100x means that I can finish something that would have taken me 10 years in a little over a month.

It would be obvious not because people are posting “I get a 100x productivity boost”, but because show HN would be filled with “look at this database engine I wrote in a month”, and “check out this OS that took me 2 months”.

And people at work would be posting new repos where they completely rewrote entire apps from the ground up to solve annoying tech debt issues.

It’s probably higher tbh because there’s things I prototyped to test an assumption on, realized it was O(N^2) then dumped it and tried 4 more architecture simulations to get to one that was implementable with existing tool chains I know

So you’re doing exactly what i called out which is evaluating it as a magic oracle instead of what I said which is that it makes me personally something like 100x more productive as a support tool, which often means quickly ruling out bad ideas

Preventing a problem in architecture is worth way more than 100x

I have many times delivered a year of value in a few days by figuring out that we didn’t actually need to build something instead of just building exactly what someone asked for.

Knowing what not to do more of a superpower than knowing what to do - cause it’s possible to know

Tech that powers up an individual so they can go faster can be a bit of a liability for a company, bus factor 1 and all that.

Reddit.

As a tech lead who also wears product owner hats sometimes: This is how you should do it with humans also. At least 70% of my job is translating an executive’s “Time travel tower game. No bugs” into that long series of prompts with a strong architectural vision that people can work on as a team with the right levels of abstraction to avoid stepping on each other’s toes.

All future features will have enough patterns defined from the first one (schema, folder structure, modules, views, components, etc), that very few explicit vibe coding rules need to be defined.

Serious question: at what point is it easier to just write the code?

>You would be faster by the fact that you know that it's correct and you don't have to review it. Helps greatly with mental load.

I keep thinking maybe it's me who's just not getting the vibe coding hype. Or maybe my writing vs reading code efficiency is skewed towards writing more than most people's. Because the idea of validating and fixing code vs just writing it doesn't feel efficient or quality-oriented.

Then, there's the idea that it will suddenly break code that previously worked.

Overall, I keep hearing people advocating for providing the AI more details, new approaches/processes/etc. to try to get the right output. It makes me wonder if things might be coming full circle. I mean, there has to be some point where it's better to just write the code and be done with it.

My aunt would always tell me that making fresh pasta or grounding your own meat was basically just as fast as buying it. And while it may have have been true for her it definitely wasn’t for me.

this is the idea behind my recent post actually[1] where I recommend people use AI to write specs before they code. If all you have to do is a human is edit the spec, not write it from scratch, you're more likely to actually make one.

[1] https://lukebechtel.com/blog/vibe-speccing

What I've taken to lately is getting the robots to write "scientific papers" on what I want them to get up to so instead of iterating over broken code I can just ask them "does this change follow the specification?" Seems to stop them from doing overly stupid things...mostly.

Plus, since what I've been working on is just a mash-up of other people's ideas, it provides a good theoretical foundation of how all the different bits fit together. Just give them the paper you've been working on and some other paper and ask how the two can be used together, a lot of the time the two ideas aren't compatible so it saves a lot of time trying to force two thing to work when they really shouldn't. Very good way to explore different ideas without the robots going all crazy and producing a full code project (complete with test and build suites) instead of just giving a simple answer.

Yeah it isn't a panacea but it has afforded me less frustration than the alternative of jumping straight in.

> Since what I've been working on is just a mash-up of other people's ideas

Totally, I find most work I do, if I'm honest, is in this bucket. LLMs are pretty good at "filling in the gaps" between two ideas like this

A friend called me for advice on trouble he was having with an LLM and I asked “What exactly do you want the LLM to do?” He said “I want it to knock this project out of the park.” And I had to explain to him it doesn’t work that way. You can’t just ask for perfection.

I mean, you can, but you won’t get it.

You forgot “Don’t hallucinate.” Noob.