Shepherd's Dog: A Game by Fable

I feel like this is a recurring theme. Someone posts about one shotting something and then it’s revealed that you could also have one-shot it buy git cloning the repo it’s based off of

Do you happen to have any videos of a Tervuren doing actual herding work or competing in herding trials?

I was apparently under the wrong impression that Tervurens are no longer bred for herding. My girlfriend just told me that there are still dedicated herding lines, but when I started looking into it, I could hardly find any footage. Compared to the thousands of videos of Border Collies working stock, there seems to be very little material available.

I'd also be interested to hear more about what your dogs are like. Are they from a specialized herding line?

Do you see them as being closer to the typical working Malinois - very intense, high-drive dogs with a strong prey focus - or more like a blend of the classic German Shepherd and Border Collie traits?

The latter type, at least in my experience, can make excellent everyday companions as long as their owners know what they're doing and provide enough mental and physical stimulation.

The reason I'm asking is that I've been considering getting another Malinois. I used to own a fourth-hand Belgian Shepherd who eventually turned into a reasonably good everyday companion, but it was a long and sometimes difficult road. There were also certain situations where he could never really be trusted.

He also had a habit of finishing arguments that my Pointer started in a very Malinois-like fashion, while my Pointer was generally too stupid(and proud) to learn anything from that experience.

Feel free to write a wall of text if you feel like it.

This is really no different from working with humans. A visionary founder has to spend tremendous effort to get their engineers to will a vision into existence. This will be the key skill with AI.

This is what spec .md files are for, skill issue

Like you said, working and maintainable are very different things. One-shot hits a wall the moment you need to do anything non-trivial after the initial generation. Bug fixing is extremely hard, even with AI assistance. Same with feature additions. It's pretty much black box at this point on. AI that wrote it now goes in loops wasting tokens without being able to can't reliably fix it either, because it has no memory of the architectural decisions it made (or didn't make, for that matter) the first time round.

What I realized is that the failure here is the absence of a shared mental model between you and the code.

I'm a product designer with average front-end know-how, and a solid understanding on HTML/CSS and how the web works, coming from the era of hand-coding html/css files. After vibe-coding a few products early this year, purely to learn how AI works, how to design AI interaction patterns etc., I built something called Intent Model. (largely inspired by SDD / BDD.

Intent model is a structured, typed artifact (basically a JSON contract) that captures actors, entities, journeys, rules, and constraints before I write (or make the AI write) any code. It sits upstream of everything. Think of it like a condensed, strict distillation of your PRD / BRD / requiremnt doc.

When you hand the AI a well-defined intent file instead of a vague brief, this one-shot becomes structured and bound by rules. Now you're giving it an architecture and to conform to. You define (or make the AI define) the precise variable names, their types, lifecycle, user roles, responsibilities, business rules and constraints in the file. Every generated artifact can trace back to a decision you made deliberately, reviewd and signed-off.

In the design world, we already do this by using design tokens. We can tell the AI that it needs to strictly use design tokens and not use stray properties like a hex color value or raw values not defined in the token contract. This is easily auditable by AI as well.

The result is you can still move absurdly fast and still maintain the understanding, which the one-shot approach throws away. This way, you know why every piece exists because you defined the intent before the AI implementated it.

AI is the accelerant, and you're the architect. The intent is the blueprint you generate to guide/harness the AI.

The best part is, once you have an intent contract at the heart of your project, it becomes impossible to break things too, logically or experience-wise.

If the end is a combination of education and product discovery, then yeah maybe, although those are also dimensions of personal productivity that can be amplified by leveraging AI tools.

If the end goals of programming is leveraging computer automation, then nobody actually cares how the automation infrastructure gets established, and the less distractions with low value implementation complexity, the better.

Or is there some other AI usage described in this article that is not supported by cursor?

I have pointed out on here before that instances of truly unique human ideas not grounded in nature or previous ideas from others is almost nil, there are not many examples that someone can give me. All human ideas and work is derivative.

Elves? Humans with pointy ears. Werewolves? Humans mixed with wolves. Car tyre? Cart wheel...stone wheel/roller. Etc.

Ah well, it’s still fun and it does appear to measure how good AI is in creating these kind of games.

Like I remember in college I had something akin to the idea of “50 people 1 question.” I was starting to become interested in shooting my own documentaries and was particularly interested in man on the street style interviews. I pitched it to a friend who then told me about 50p1q, which baffled him because it was like the hot thing already a year or two prior haha.

Anyway that’s just something I think happens a lot. And now with genAI people don’t throw the idea around even, they quickly do a crappy version of the thing, present it, then find out it exists. Which isn’t terrible I guess but it’s one less filter for my better or for worse.

But also, how original can a game idea ever (now) really be – there's always going to be things you can describe it as 'like' or a mix of, even if not identical. And for such simple things, very little room for being non-identical to whatever they're like.

In case it all just comes from training data, "one shotting" a game would be more comparable to "git pull" and changing some assets than "generating code".

I'm not saying this is how it works, I'm trivializing LLMs with this statement, but when I see someone on linkedin excited about generating checkers and chess my first thought is "you could have done that with git pull for the past 20 years".

Most software is not needed, YCombinator itself works on the philosophy of “maybe 2/300 ideas are good” and even among those their biggest hitters were social media platforms and undercutting existing services using VC money.

It was a big game that didn’t make a lot of sense in retrospect, and now with these AI super coders it just doesn’t make sense faster.

Software ate the world, and AI is the garbage disposal meant to chew up the leftovers.

Even with the perfect AI to write, one would need to iterate through many different ideas, play testing constantly, getting people to play test and analyze what they found fun and where they got stuck. And to get the best ideas you'll need to be playing lots of different kinds of games.

IMO the ability to describe a game and let the AI implement a PoC is pretty wild. It's a signal as to whether such an idea is worth pursuing further to me rather than a finished product. And I am enjoying all the experimentation with existing genres as well as the occasional truly original experience due to the dramatically lower cost of entry. What these efforts lack currently is the playtesting and polish that is hard without a human in the loop. So much like agentic engineering, the productive work is in being a centaur. It surprises me how much pushback this is getting from the demographic that embraced the relatively inscrutable git over simpler alternatives for small teams along with the tower of Babel of equally inscrutable frameworks and APIs.

It's not unlike Martin Scorsese admitting upfront he's using GenAI as a creative tool to visualize scenes for his scripts. The predictable backlash that he dare use AI in any way for any aspect of his craft despite his irrefutable oeuvre is a sign of the times more than a legitimate objection to me. Ask the users of deviantArt to stop working with Photoshop and see how that goes.

Having worked in the game industry in the past and adjacent to Hollywood over my career, they were already top heavy exploitative cultures before AI. And any auteur that thinks they can replace humans with agents is as tuned in to GenAI as the tech CEOs and VCs that happily announced layoffs and instituted tokenmaxxing benchmarks to measure the "incredible" boost in productivity AI enables.

So my question, ahead of the mandatory downvotes for not chanting along with the torch-bearing mob against AI in every way is: beyond the CapEx and the buildout issues (both legit IMO), how is AI impacting you negatively and personally?

https://en.wikipedia.org/wiki/IKEA_effect

> make me a simple html5 canvas game, top down, where you play as a dog who herds sheep into a pen

I like Gemma 31B's version (free model on OpenRouter): https://jsbin.com/mufirivixo/edit?html,output

> // Sky-like background

comment for the ground! :) No doubt most of the training data is for sky...