claudecode _loves_ shadcn/ui. I hadn't even heard of it until i was playing around with claudecode. It seems fine to me and if the coding agent loves it then more power to it, i don't really care. That's the problem.

I think that makes coding agent choices extremely suspect, like i don't really care what it uses as long as what's produced works and functions inline with my expectations. I can totally see companies paying Anthropic to promote their tool of choice to the top of claudecodes preferences. After thinking about it, i'm not sure if that's a problem or not. I don't really care what it uses as long as my requirements (all of them) are met.

As someone who runs a small dev agency, I'm very interested in research like this.

Let's say some Doctor decides to vibecode an app on the weekend, with next to 0 exposure to software development until she started hearing about how easy it was to create software with these tools. She makes incredible progress and is delighted in how well it works, but as she considers actually opening it up the world she keeps running into issues. How do I know this is secure? How do I keep this maintained and running?

I want to be in a position where she can find me to get professional help, so it's very helpful to know what stacks these kinds of apps are being built in.

> Furthermore, what's the point of "no tools named"?

There are vibe coders out there that don't know anything about coding.

Redux should not be used for 1 person projects. If you need redux you'll know it because there will be complexity that is hard to handle. Personally I use a custom state management system that loosely resembles RecoilJS.

More like redux vs zustand. Picking zustand was one of the good standout picks for me.

Well, the tech du jour now is whatever's easier for the AI to model. Of course it's a chicken and egg problem, the less popular a tech is the harder it is to make it into the training data set. On the other hand, from an information theoretic point of view, tools that are explicit and provides better error messages and require less assumptions about hidden state is definitely easier for the AI when it tries to generalize to unknowns that doesn't exist in its training data.

Because the primary and future audience of Claude et al don’t know the tools they want, or even that a choice exists.

Have any links?

Influencer seems like an insufficient word? Like, in the glorious agentic future where the coding agents are making their own decisions about what to build and how, you don't even have to persuade a human at all. They never see the options or even know what they are building on. The supply chain is just whatever the LLMs decide it is.

Richard Thaler must be proud. This is the ultimate implementation of "Nudge"

Probably closer to the Walmart / Amazon model where it's the arbiter of shelf space, and proceed to create their own alternatives (Great Value, Amazon Brand) once they see what features people want from their various SaaS.

An obvious one will be tax software.

Considering how little data needed to poison llm https://www.anthropic.com/research/small-samples-poison , this is a way to replace SEO by llm product placement:

1. create several hundreds github repos with projects that use your product ( may be clones or AI generated )

2. create website with similar instructions, connect to hundred domains

3. generate reddit, facebook, X posts, wikipedia pages with the same information

Wait half a year ? until scrappers collect it and use to train new models

Profit...

It still ignores it. I always have to say 'Isn't this mentioned in AGENTS??' and it will concede that it is.

Advertisers will only pay if AI providers will provide them data on the equivalent of “ad impressions”. And unlabeled/non-evident advertisements are illegal in many (most?) countries.

I wonder if aggregators will emerge (something like Ground News does for news sources)

> A canary for this would be whether Gemini skews toward building stuff on GCP

Sure it doesn't prefer THE Borg?

It's like tailwindcss was purposely designed to be managed my LLM.

I mean, i guess that will shortly put an end to the "no code" movement.

https://www.bbc.com/future/article/20260218-i-hacked-chatgpt... says it took way less than half a year to 'pollute' a LLM

from my understanding Anthropic are now hiring a lot of experts in different who are writing content used to post-train models to make these decisions and they're constantly adjusted by the anthropic team themselves

this is why the stacks in the report and what cc suggests closely match latest developer "consensus"

your suggestion would degrade user experience and be noticed very quickly

This is just the SEO gaming problem Google had, reincarnated.

You specify which software patents you want it to use?

In my experience the problem is how people write them. Descriptive statements get ignored because the model treats them as context it can reason past.

"We use PostgreSQL" reads as a soft preference. The model weighs it against whatever it thinks is optimal and decides you'd be better off with Supabase.

"NEVER create accounts for external databases. All persistence uses the existing PostgreSQL instance. If you're about to recommend a new service, stop." actually sticks.

The pattern that works: imperative prohibitions with specific reasoning. "Do not use Redis because we run a single node and pg_notify covers our pubsub needs" gives enough context that it won't reinvent the decision every session.

Your AGENTS.md should read less like a README and more like a linter config. Bullet points with DO/DON'T rules, not prose descriptions of your stack.

> data on the equivalent of “ad impressions”.

1. They can skip impressions and go right to collect affiliate fees. 2. Yes, the ad has to be labeled or disclosed... but if some agent does it and no one sees it, is it really an ad.

So much to work out.

Maybe. Historically lots of ads had little to no stats and those ads were wildly more effective than anything we have today.

LLM pattern [0] will probably eventually emerge as the best way to fight those biases. This way everyone benefits from token burn!

[0](https://github.com/karpathy/llm-council)

It doesn't necessarily have to be advertisers paying AI providers. It could be advertisers working to ensure they get recommended by the latest models. The next form of SEO.

Patterns?

AI reading the patent is basically cleanroom reverse engineering according to current AI IP standards :D

Hah, it's somewhat ironic how this is almost the exact opposite of the prevailing folk wisdom I've read for the last 1-2 years: that you should never use negative instructions with specific details because it overweights the exact thing you're trying to avoid in the context.

Given my own experience futilely fighting with Claude/Codex/OpenCode to follow AGENTS.MD/CLAUDE.MD/etc with different techniques that each purport to solve the problem, I think the better explanation really is that they just don't work reliably enough to depend on to enforce rules.

That's called LLM SEO now I believe.

Tha was my assumption as well.

I caught iOS trying to autocorrect something I wrote twice yesterday, and somehow before I hit submit it managed it a third time, and I had to edit it after, where it tried three more times to change it back.

Autocorrect won’t be happy until we all sound like idiots and I wonder if that’s part of how they plan to do away with us. Those hairless apes can’t even use their properly.

Yeah patterns. lol!

The sad part is that most software patents are so woefully underspecified and content-free that even Claude might have trouble coming up with an actual implementation.

Fair point on the contradiction. The "never use negative instructions" wisdom comes from general prompting where mentioning the unwanted thing can increase its likelihood. AGENTS.md is a different context though, the model is reading persistent rules for a session, not doing a single completion where priming effects matter as much.

But you're right that "better" isn't "reliable." In practice it went from "constantly ignored" to "followed maybe 80% of the time." The remaining 20% is the model encountering situations where it decides the instruction doesn't apply to this specific case.

Honest answer is probably somewhere between "they don't work" and "write them right and you're fine." They raise the floor but don't guarantee anything. I still use them because 80% beats 20%, but I wouldn't bet production correctness on them.

There are competing terms currently being decided on by the market at large: AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization)

Candidly I am working on a startup in this space myself, though we are taking a different angle than most incumbents.

While it's still early days for the space, I sense a lot of the original entrants who focus on, essentially, 'generate more content ideally with our paid tools' will run in to challenges as the general population has a pretty negative perception of 'AI Slop.' Doubly so when making purchasing decisions, hence the rise of influencers and popularity of reviews (though those are also in danger of sloppification).

There's an inevitable GIGO scenario if left unchecked IMO.

Patents aren't vulnerable to cleanroom reverse engineering. You can create something yourself in your bedroom and use it yourself without knowing the patented thing exists, and still violate the patent. That's why they're so scary.

You won't get caught if you write something yourself and use it yourself, but programmers (contrary to entrepreneurs) have a pattern of avoiding illegal things instead of avoiding getting caught.

It's not a perfect joke I'll admit.