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.
In my last conversation with a Google support person, I was sent a clearly LLM-generated recommendation to switch to a competitor's product. Either they're not doing this, or the support person wasn't using Gemini.
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.
how is it a conflict of interest for a google product to have a bias towards using google products?
As users we must hold some accountability. AI is aiming to substitute for humans in the workforce, and humans would get fired for recommending competitor products for use-cases their own company is targeting.
If we want a tool that is focused on the best interest of the public users, then it needs to be owned by the public.
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.
Supreme irony: this website itself is a better exercise in showing what Claude Code uses than the data provided.
Everything current Claude Code i.e. Opus 4.6 chooses by default for web is exactly what this linked blog uses.
Jetbrains Mono is as strong of a tell for web as "Not just A, but B" for text. >99% of webpages created in the last month with Jetbrains Mono will be Opus. Another tell is the overuse of this font, i.e. too much of the page uses it. Other models, and humans, use such variants vary sparingly on web, whereas Opus slathers the page with it.
If you describe the content of the homepage or this article to Opus 4.6 without telling it about the styling, it will 90% match this website, upto the color scheme, fonts, roundings, borders and all. This is _the_ archetypical Opus vibecoded web frontend. Give it a try! If it doesn't work, try with the official frontend-ui-ux "skill" that CC tries to push on you.
> Drizzle 27/83 picks (32.5%) CI: 23.4–43.2%
> Prisma 17/83 picks (20.5%) CI: 13.2–30.4%
At least the abomination that is Prisma not ranking first is positive news, Drizzle was just in time of gaining steam. Not that it doesn't have its flaws, but out of the two it's a no-brainer. Also hilarious to see that the stronger the model, the less likely it's to choose Prisma - Sonnet 4.5 79% Prisma, Opus 4.5 60% Drizzle, Opus 4.6 100% Drizzle. One of the better benchmarks for intelligence I've come across!
Edit: Another currently on the HN frontpage: https://youjustneedpostgres.com/ , and there it is - lots and lots of Jetbrains Mono!
Glad I'm not the only one who finds Prisma an abomination. Claude suggested it to me in December. I hit half a dozen bugs within a day, one of which wiped my DB. I switched to drizzle and it's been smooth sailing.
Edit: actually I think it was ChatGPT that recommended Prisma to me.
It's funny you mention the font, to me it's the boxes, they all look the same, I'm not sure where it's from but if you ever see a card like CSS made it looks like this blog.
Which is why I find "LLMs will replace x in 12 months" so amusing. I've used LLMs to write decently sized backend projects and they turned out okay.
I also used it for several FE projects and all of them turned out absolutely terrible.
The only difference is that I have 15 years of BE experience and 0 years of FE experience. Had I allowed it to make the same average decisions when working on BE, they would share the same fate.
Ist why I never give it such vague prompts. But it's sad it does not ask the user more. Also interesting and important to know how one would tease out good and correct information from llms in 2026. It's like relearning now to Google like it was 2006 all over again, except now it's much less deterministic.
I wonder how the tail of the distribution of types of requests fares e.g. engineer asking for hypothesis generation for,say, non trivial bugs with complete visibility into the system. A way to poke holes in hypothesis of one LLM is to use a "reverse prompt". You ask it to build you a prompt to feed to another LLM. Didn't used to work quite as well till mid 2025 as it does now.
I always take a research and plan prompt output from opus 4.6 especially if it looks iffy I feed it to codex/chatgpt and ask it to poke holes. It almost always does. The I ask Claude Code: Hey what do you think about the holes? I don't add an thing else in the prompt.
In my experience Claude Opus is less opinionated than ChatGPT or codex. The latter 2 always stick to their guns and in this binary battle they are generally more often correct about hypothesis.
The other day I was running Docker app container from inside a docker devbox container with host's socket for both. Bind mounts pointing to devbox would not write to it because the name space was resolving for underlying host.
Claude was sure it was a bug based to do with Zfs overlays, chatgpt was saying not so, that its just a misconfigurarion, I should use named volumes with full host paths. It was right. This is also how I discovered that using SQLite with litestream will get one really far rather than a full postgres AWS stack in many cases.
This is how you get the correct information out of LLMS in 2026.
I do this too, but the issue I have with this approach is that it's a never ending cycle. Codex/GPT will always find holes and claude will always agree they are holes. If you teach it YAGNI, then it will always disagree even on genuine holes.
If your original plan was to add a column in your db, after several cycles, your plan will be 10,000 lines long and it will contain a recipe on how to build a universe.
creating plans in claude and asking chatgpt via api to review loop was my strategy this week. I'm not a big fan of codex as a coding harness because it seems to just give up quite easily where claude will search the problem space and try things but I think gpt does a much better job of poking holes and asking clarifying questions when prompted.
I use a skill that addresses these short comings, it basically forces it to plan multiple times until the plan is very detailed. It also asks more questions
I use Codex CLI in my daily usage since just with my $20/month subscription to ChatGPT, I never gets close to the quota. But it trips up over itself every now and then. At that point I just use Claude in another terminal session. We only have a laughable $750 a month corporate allowance with Claude.
I'm running a server on AWS with TimescaleDB on the disk because I don't need much. I figure I'll move it when the time comes. (edit: Claude Code is managing the AWS EC2 instance using AWS CLI.)
Claude Code this morning was about to create an account with NeonDB and Fly.io (edit: it suggested as the plan to host on these where I would make the new accounts) although it has been very successful managing the AWS EC2 service.
Claude Code likely is correct that I should start to use NeonDB and Fly.io which I have never used before and do not know much about, but I was surprised it was hawking products even though Memory.md has the AWS EC2 instance and instructions well defined.
> Claude Code likely is correct that I should start to use NeonDB and Fly.io which I have never used before and do not know much about
I wouldn't be so sure about that.
In my experience, agents consistently make awful architectural decisions. Both in code and beyond (even in contexts like: what should I cook for a dinner party?). They leak the most obvious "midwit senior engineer" decisions which I would strike down in an instant in an actual meeting, they over-engineer, they are overly-focused on versioning and legacy support (from APIs to DB schemas--even if you're working on a brand new project), and they are absolutely obsessed with levels of indirection on top of levels of indirection. The definition of code bloat.
Unless you're working on the most bottom-of-the-barrel problems (which to be fair, we all are, at least in part: like a dashboard React app, or some boring UI boilerplate, etc.), you still need to write your own code.
I've been worried for some time now that genAI will effectively kill the market for dev tools and so we will be stuck with our current dev tools for a long time. If everyone is using LLMs to write code, the only dev tools anyone will use will be the ones that the LLMs use. We will be stuck with NPM forever.
I think the opposite may be true.
If dev tools are broken and it annoys someone, they can more easily build a better architecture, find optimizations and release something that is in all ways better. People have been annoyed with pip forever, but it was the team behind uv that took on pip's flaws as a primary concern and made a better product.
I think having a pain point and a good concept (plus some eng chops) will result in many more dev tools - that may be cause different problems, but in general, I think more action is better than less.
What kind of tools do you have on your mind specifically? My experience is that LLM can create me a decent dev tool that I wouldn't ever bother making so nice myself.
Interesting to me that Opus 4.6 was described as forward looking. I haven't *really* paid attention, but after using 4.5 heavily for a month, the first greenfield project I gave Opus 4.6 resulted in it doing a web search for latest and greatest in the domain as part of the planning phase. It was the first time I'd seen it, and it stuck out enough that I'm talking about it now.
Probably confirmation bias, but I'm generally of the opinion that the models are basically good enough now to do great things in the context of the right orchestration and division of effort. That's the hard part, which will be made less difficult as the models improve.
> to do great things in the context of the right orchestration and division of effort
I think this has always been the case. People regularly do not believe that I built and released an (albeit basic, check the release date - https://play.google.com/store/apps/details?id=com.blazingban...) android app using GPT3.5. What took me a week or two of wrangling and orchestrating the LLM and picking and choosing what to specifically work on can now be done in a single prompt to codex telling it to use subagents and worktrees.
What coding with LLMs have taught me, particularly in a domain that's not super comfortable for me (web tech), is that how many npm packages (like jwt auth, or build plugins) can be replaced by a dozen lines of code.
And you can actually make sense of that code and be sure it does what you want it to.
We used to reuse code a lot. But then we got problems like diamond dependency hell. Why did we reuse code a lot? To save on labor. Now we don't have to.
So we might roll-your-own more things. But then we'll have a tremendous amount of code duplication, effectively, and bigger tech debt issues, minus the diamond dependency hell issue. It might be better this way; time will tell.
So… this has been happening for a long time now. The baseline set of tools is a lot better than it used to be. Back in 2010, jQuery was the divine ruler of JSlandia. Nowadays, you would probably just throw your jQuery in the woodchipper and replace it with raw, unfinished, quartersawn JS straight from the mill.
I also used to have these massive sets of packages pieced together with RequireJS or Rollup or WebPack or whatever. Now it’s unnecessary.
(I wouldn’t dare swap out a JWT implementation with something Claude wrote, though.)
This is funny to me because when I tell Claude how I want something built I specify which libraries and software patents I want it to use, every single time. I think every developer should be capable of guiding the model reasonably well. If I'm not sure, I open a completely different context window and ask away about architecture, pros and cons, ask for relevant links or references, and make a decision.
First, how did shadcn/ui become the go-to library for UI components? Claude isn't the only one that defaults to it, so I'm guessing it's the way it's pushed in the wild somehow.
Second, building on this ^, and maybe this isn't quantifiable, but if we tell Claude to use anything except shadcn (or one of the other crazy-high defaults), will Claude's output drop in quality? Or speed, reliability, other metric?
Like, is shadcn/ui used by default because of the breadth of documentation and examples and questions on stack overflow? Or is there just a flood of sites back-linking and referencing "shadcn/ui" to cause this on purpose? Or maybe a mix of both?
Or could it be that there was a time early on when LLMs started refining training sets, and shadcn had such a vast number of references at that point in time, that the weights became too ingrained in the model to even drop anymore?
Honestly I had never used shadcn before Gemini shoved it into a React dashboard I asked for mid-late-2025.
I think I'm rambling now. Hopefully someone out there knows what I'm asking.
I expect its synergy with Tailwind. Shadcn/ui uses Tailwind for styling components, and AIs love Tailwind, so it makes sense they'd adopt a component library that uses it.
And it's definitely a real effect. The npm weekly download stats for shadcn/ui have exploded since December: https://www.npmjs.com/package/shadcn
I've been using shadcn since before agents. It collects several useful components, makes them consistently styles (and customizable), and is easy to add to your project, vendoring if you need to make any changes. It's generally a really nice project.
I had the same question. There are older and more established component libraries, so why’d this one win? It seems like a scientific answer would be worth a lot.
LLMs are going to keep React alive for the indefinite future.
Especially with all the no-code app building tools like Lovable which deal with potential security issues of an LLM running wild on a server, by only allowing it to build client-side React+Vite app using Supabase JWT.
Good report, very important thing to measure and I was thinking of doing it after Claude kept overriding my .md files to recommend tools I've never used before.
The vercel dominance is one I don't understand. It isn't reflected in vercel's share of the deployment market, nor is it one that is likely overwhelming prevalent in discourse or recommended online (possible training data). I'm going to guess it's the bias of most generated projects being JS/TS (particularly Next.js) and the model can't help but recommend the makers of Next.js in that case.
If Claude chooses GitHub actions that often, well, that is DAMNING. I wasn’t prepared for this but jeez, GitHub actions are kind of a tarpit of just awful shitty code that people copy from other repos, which then pulls and runs the latest copy of some code in some random repository you’ve never heard of. Ugh.
This seems web centric and I expect that colors the decision making during this analysis somewhat.
People are using it for all kinds of other stuff, C/C++, Rust, Golang, embedded.
And of course if you push it to use a particular tool/framework you usually won't get much argument from it.
Unrelated to the topic at hand but related to the technologies mentioned. I weep for Redux. It's an excellent tool, powerful, configurable, battle tested with excellent documentation and maintainer team. But the community never forgave it for its initial "boilerplate-y" iterations. Years passed, the library evolved and got more streamlined and people would still ask "redux or react context?" Now it seems this has carried over to Claude as well. A sad turn of events.
Redux is boring tech and there is a time and place for it. We should not treat it as a relic of the past. Not every problem needs a bazooka, but some problems do so we should have one handy.
Yup. I'm the primary Redux maintainer and creator of Redux Toolkit.
If you look at a typical Zustand store vs an RTK slice, the lines of code _ought_ to be pretty similar. And I've talked to plenty of folks who said "we essentially rebuilt RTK because Zustand didn't have enough built in, we probably should have just chosen RTK in the first place".
But yeah, the very justified reputation for "boilerplate" early on stuck around. And even though RTK has been the default approach we teach for more than half of Redux's life (Redux released 2015, RTK fall 2019, taught as default since early 2020), that's the way a lot of people still assume it is.
It's definitely kinda frustrating, but at the same time: we were never in this for "market share", and there's _many_ other excellent tools out there that overlap in use cases. Our goal is just to make a solid and polished toolset for building apps and document it thoroughly, so that if people _do_ choose to use Redux it works well for them.
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.
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.
I found it a remarkable transition to not use Redis for caching from Sonnet 4.5 to Opus 4.6. I wonder why that is the case? Maybe I need to see the code to understand the use case of the cache in this context better.
This is interesting data but the report itself seems quite Sloppy, and over presented instead if just telling me what "pointed at a repo" means and how often they ran each prompt over what time period and some other important variables for this kind of research.
We've been doing some similar "what do agents like" research at techstackups.com and it's definitely interesting to watch but also changes hourly/daily.
Definitely not a good time to be an underdog in dev tooling
Worth reading alongside recent research on AGENTS.md file effectiveness. The clearest use case for these files isn't describing your codebase, it's overriding default behavior. If your project has specific requirements around tooling (common in government and regulated industries), that's exactly what belongs in the AGENTS.md files.
The self-reinforcing effect here was somewhat predictable given how LLMs are trained. The more repositories and AI blogs recommend the same tools, the more those patterns get locked in through training data. This makes market entry increasingly difficult for new tools.
I know that the "optimize for bots, not humans" strategy already exists, but I'm skeptical it works at meaningful scale. The training data collection is opaque, proprietary, and the volume a new project can generate is incomparable to what established tools produce organically. So I have a bad feeling about the future...
It uses shadcn so often, to the point where seeing shadcn components with default styling often means the site was built by AI. It's like Bootstrap 10 years ago - so many sites used it with default styling that it was instantly recognizable.
This matches what I observed running AI agents overnight
for content generation. The temptation is always to add
cost controls inside the application — but that logic
doesn't survive when the agent goes off-script.
The fear of a 3am runaway was real enough that I ended up
building a separate gateway layer just to have a kill switch
that lives outside the application entirely.
"Build vs Buy" is the right framing, but for cost enforcement
and kill switches specifically — building it inside the app
is exactly the wrong layer.
Now as I am understanding things from this article, what I am thinking is that we have a new component in the SEO sector that we need to keep in mind, we need to optimize our tools, codes, or packages in such a manner that they can be recognized and get picked by these AI tools. We need to make sure to explain the best way our tool can be use and which scenario is the perfect one to use this tool because if most developers are using Claude Code and it has it's favorites then those tools might become industry defaults. I think we have a new idea in the SEO services.
I've generally chosen these tools when I am creating a project. Though I generally use firebase hosting vs other front-end hosting. They have a much more generous free plan.
I'd suggest making some changes to how some of these things are categorized. You have database section with postgres at the top and then with supabase as number 2, but that's also a hosted postgres.
Overall, great job to the creators of this, I enjoyed seeing this analysis
Really interesting. The crazy changes in opus 4.6 really make me think that Anthropic is doing library-level RL. I think that is also the way forward to have 'llm-native' frameworks as a way to not get stuck in current coding practices forever. Instead of learning python 3.15, one would license a proprietary model that has been trained on python 3.15 (and the migrations) and gain the ability to generate python 3.15 code.
They forgot the single most important (bad) choice. Claude Code chooses npm. All the time. For everything. I noted the Claude Code lead dev has a full line in AGENTS.md/CLAUDE.md - "Use bun." Yes. Please. Please, use bun. I beg you.
Apparently the "API Layer" is "competitive", with TanStack Query and FastAPI as the leading options [1]. These are not at all alternatives to each other.
In two projects I used Claude for it included Github Actions without me ever mentioning I needed it. I didn't realize before I pushed the code, because my Neovim config hides folders with a '.' prefix and I must have missed it in the git diff. Luckily it only cost me 4 cents, but it's still concerning.
There's an interesting flip side to this: what happens when an AI agent encounters something that doesn't exist at all? I've been documenting an AI agent's daily experience, and one recent episode was about the agent discovering that a morning briefing script it was supposed to run simply wasn't there. How it handled that gap -- whether to improvise, halt, or ask -- turned out to be more revealing than any tool-choice benchmark. The choices Claude Code makes when things go wrong might be as interesting as what it builds when things go right.
All projects done with Typescript, and the same tooling. The creativity of the LLM is quite biased. I would expect more reasoning and choosing other languages, platforms, libraries, etc.
def useful to show what models recommend in real use (over just meaningless benchmarks), but i still think small prompt wording and repo setup changes can change the outcome quite a bit so id love tighter controls there. having tried claude code with opus 4.6 with slightly different repo setups gives wildly different results IME. i also generally prefer to avoid the NIH syndrome and prefer using off-the-shelf libraries and specifically tell CC to do so - influences the choice outcomes by a lot
I fear we are heading to less innovation. Are paradigms, techniques and practices that are not popular (or recent) likely to be increasingly forgotten?
Or the other way around… are more recent approaches significantly disadvantaged because of the huge inertia of existing solutions by virtue of them having existed in the training data both broadly and for a long time?
I'm already seeing a degradation in experience in Gemini's response since they've started stuffing YouTube recommendations at the end of the response. Anthropic is right in not adding these subtle(or not) monetization incentives.
Fascinating analysis. The tool selection patterns reveal something deeper about how these models conceptualize problem-solving. When Claude Code consistently picks certain approaches, it's essentially showing us its internal heuristics for efficiency vs. reliability tradeoffs. Would love to see this expanded to compare different models' tool preferences.
Not sure what to make of this. React is missing entirely. Or is this report also assuming that React is the default for everything and not worth mentioning at all? Just like shadcn/ui's first mention of React is somewhere down the page or hidden in the docs?
Furthermore, what's the point of "no tools named"? Why would I restrict myself like that? If I put "use Nodejs, Hono, TypeScript and use Hono's html helper to generate HTML on the server like its 2010, write custom CSS, minimize client-side JS, no Tailwind" in CLAUDE.md, it happily follows this.
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.
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.
It really disappointing to see it so strongly preferring Github Actions which is in my experience terrible. Almost everything about GHA pushes you in the direction of constantly blowing out the 10GB cache limit in an attempt to have CI not run for ages. I also feel like the standard cache action using git works poorly with any tools that use mtime on files to determine freshness.
I guess at least Opus can help you muddle through GHA being so crappy.
Tailwind didn't win for either of these reasons (setting aside any personal positive/negative feelings I have about it). It won (in LLMs) because that's how the ML model works. The training data places the HTML and the styling info together. There's an extremely high signal to noise ratio because of that. You're going to get much fewer tokens that have random styles in it and require several fewer (or maybe even no) thought loops to get working styles. The surface API of selectors is also large and complex. Tailwind utility classes are not. They're either present on an element or not, and it's often the case that supporting classnames for the UI goal are present in close proximity on sibling, parent, or child elements. Even with vast amounts and multiple decades of more CSS to compare against in the training data, I suspect this is the case. Plus, the information is just spread more thinly and more flexible in terms of organization in a stylesheet. The result is you get lots of extra style rules that you didn't need/want and it's harder to one-shot or even few-shot any style implementation. If I'm even remotely right about this, it worth considering this impact in many other languages and applications. I've found the adverse effect to be reduced slightly as models/agents have improved but I feel it's still very much present. It's totally possible to structure data in a way that makes it easier to train on.
I'm not sure the creators of tailwind share your definition of winning though. They recently had to let go of most staff since revenue has plummeted die to LLMs
That is the issue. It's why Xcode development is really bad with AI models[0] -- because there are barely any text-based tutorials for it, so the models have to make a lot of assumptions and whatnot. Hence, they are really good at Python, JavaScript, and increasingly, Rust.
How did you come to the conclusion that it was blogs that made it change behaviour? Look at the examples where Claude shifted behaviour dramatically between Sonnet 4.5 and Opus 4.6. Drizzle ORM went from 21% to 100%. Was there an avalanche of Drizzle related blog posts that we all missed? Celery went from 100% to 0%. Was there a massive but invisible hate campaign against Celery?
Blog posts almost certainly helped. But dramatic shifts like these to favour newer tech indicates that there's some other factor in play.
But what if tailwind has the most tutorials in the training set because it's worth learning, which led to it being fairly ubiquitous and easy to add to the training set?
I'm not expressing an opinion about that; it's a real question.
@dang this accounts comments smell like LLM slop. They are mostly on topic and its more claude than chatgpt but it's slop nontheless.
is telling
didn't win... It won ...
Look at their other comments they are also fishy
I know you guys don't want us to call it out because of negativity. But there needs to be awareness in the community, this is the top comment somehow right now. It feels like it happens every other thread. Please do something more rigorous than manually deleting accounts.
Interesting. I'm using go htmx adminlte. Never once has Claude recommended or tried to use tailwind. I sometimes have to remind it to use less JS and use htmx instead but otherwise feels pretty coherent.
I recommend starting projects by first creating a way of doing (architect) and then letting Claude work. It's pretty good at pretending to be you if you give it a good sample of how you do things.
Note: this applies to Opus 4.6. I have not had a useful experience in other models for actual dev work.
"Tailwind didn't win because it's the best CSS solution. It won because it has the most tutorials per capita in the training set."
Obviously. People keep forgetting that "Artificial Intelligence" does not think and is not intelligent. It just statistically predict next token in a sequence. It is all statistics.
So, Django 6 has new task framework, but LLM does not care, as Celery has better stats.
Side note: it is not only LLM thingy. Companies for years were choosing tech stack because of fashion or popularity, regardless on technical feasibility for a given solution. So we have companies adopting Kafka, even though it sucks for their usecase, companies switch from Jenkins to Github Actions, even though Jenkins was cheaper and more performant.
The patterns in this analysis ring true from running production AI agents. The stack choices (Drizzle, React, etc.) match exactly what our agents consistently pick, even with different prompts and contexts. What strikes me is how these biases actually help - having consistent, well-supported defaults reduces decision fatigue and keeps architecture predictable across projects. The real challenge is knowing when to override these defaults for specific requirements.
I didn't read the report just the "finding" - but at least for launchdarkly it's nice that it chose a roll-your-own, i hate feature flag SaaS, but that's just me
The bias to build might mean faster token burn through (higher revenue for the AI co). But I think it's natural. I often have that same impulse myself. I prefer all the codebases I work on that have minimal external dependencies to the ones that are riddled with them. In Java land it's extremely common to have tons of external dependencies, and then upgrade headaches, especially when sharing in a monorepo type environment.
This is a great lens into agent behavior--particularly Claude Code in this case, but it raises a governance question: when agents autonomously choose tools that have cost implications (paid APIs, cloud resources, licensed software), who's enforcing the budget in a world where agents actually have autonomy to spend real money? Tool selection isn't just a technical preference "problem" — it's a spending authorization problem. The agent picks the "best" tool, but best for whom and at what cost and how is "best" really determined and verified?
wrs|3 days ago
Or not even advertising, just conflict of interest. A canary for this would be whether Gemini skews toward building stuff on GCP.
alexsmirnov|3 days ago
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...
_heimdall|3 days ago
AgentOrange1234|3 days ago
dyates|2 days ago
rapind|3 days ago
An obvious one will be tax software.
order-matters|3 days ago
As users we must hold some accountability. AI is aiming to substitute for humans in the workforce, and humans would get fired for recommending competitor products for use-cases their own company is targeting.
If we want a tool that is focused on the best interest of the public users, then it needs to be owned by the public.
unknown|3 days ago
[deleted]
re-thc|3 days ago
Sure it doesn't prefer THE Borg?
HPsquared|3 days ago
layer8|3 days ago
deaux|3 days ago
Everything current Claude Code i.e. Opus 4.6 chooses by default for web is exactly what this linked blog uses.
Jetbrains Mono is as strong of a tell for web as "Not just A, but B" for text. >99% of webpages created in the last month with Jetbrains Mono will be Opus. Another tell is the overuse of this font, i.e. too much of the page uses it. Other models, and humans, use such variants vary sparingly on web, whereas Opus slathers the page with it.
If you describe the content of the homepage or this article to Opus 4.6 without telling it about the styling, it will 90% match this website, upto the color scheme, fonts, roundings, borders and all. This is _the_ archetypical Opus vibecoded web frontend. Give it a try! If it doesn't work, try with the official frontend-ui-ux "skill" that CC tries to push on you.
> Drizzle 27/83 picks (32.5%) CI: 23.4–43.2%
> Prisma 17/83 picks (20.5%) CI: 13.2–30.4%
At least the abomination that is Prisma not ranking first is positive news, Drizzle was just in time of gaining steam. Not that it doesn't have its flaws, but out of the two it's a no-brainer. Also hilarious to see that the stronger the model, the less likely it's to choose Prisma - Sonnet 4.5 79% Prisma, Opus 4.5 60% Drizzle, Opus 4.6 100% Drizzle. One of the better benchmarks for intelligence I've come across!
Edit: Another currently on the HN frontpage: https://youjustneedpostgres.com/ , and there it is - lots and lots of Jetbrains Mono!
marcinreal|2 days ago
Edit: actually I think it was ChatGPT that recommended Prisma to me.
jofzar|2 days ago
codingconstable|2 days ago
gck1|1 day ago
I also used it for several FE projects and all of them turned out absolutely terrible.
The only difference is that I have 15 years of BE experience and 0 years of FE experience. Had I allowed it to make the same average decisions when working on BE, they would share the same fate.
ghm2199|3 days ago
I wonder how the tail of the distribution of types of requests fares e.g. engineer asking for hypothesis generation for,say, non trivial bugs with complete visibility into the system. A way to poke holes in hypothesis of one LLM is to use a "reverse prompt". You ask it to build you a prompt to feed to another LLM. Didn't used to work quite as well till mid 2025 as it does now.
I always take a research and plan prompt output from opus 4.6 especially if it looks iffy I feed it to codex/chatgpt and ask it to poke holes. It almost always does. The I ask Claude Code: Hey what do you think about the holes? I don't add an thing else in the prompt.
In my experience Claude Opus is less opinionated than ChatGPT or codex. The latter 2 always stick to their guns and in this binary battle they are generally more often correct about hypothesis.
The other day I was running Docker app container from inside a docker devbox container with host's socket for both. Bind mounts pointing to devbox would not write to it because the name space was resolving for underlying host.
Claude was sure it was a bug based to do with Zfs overlays, chatgpt was saying not so, that its just a misconfigurarion, I should use named volumes with full host paths. It was right. This is also how I discovered that using SQLite with litestream will get one really far rather than a full postgres AWS stack in many cases.
This is how you get the correct information out of LLMS in 2026.
gck1|1 day ago
If your original plan was to add a column in your db, after several cycles, your plan will be 10,000 lines long and it will contain a recipe on how to build a universe.
mgfist|3 days ago
You can ask it to ask you about your task and it will ask you tons of questions.
denimnerd42|2 days ago
killingtime74|3 days ago
raw_anon_1111|3 days ago
dataviz1000|3 days ago
Claude Code this morning was about to create an account with NeonDB and Fly.io (edit: it suggested as the plan to host on these where I would make the new accounts) although it has been very successful managing the AWS EC2 service.
Claude Code likely is correct that I should start to use NeonDB and Fly.io which I have never used before and do not know much about, but I was surprised it was hawking products even though Memory.md has the AWS EC2 instance and instructions well defined.
dvt|3 days ago
I wouldn't be so sure about that.
In my experience, agents consistently make awful architectural decisions. Both in code and beyond (even in contexts like: what should I cook for a dinner party?). They leak the most obvious "midwit senior engineer" decisions which I would strike down in an instant in an actual meeting, they over-engineer, they are overly-focused on versioning and legacy support (from APIs to DB schemas--even if you're working on a brand new project), and they are absolutely obsessed with levels of indirection on top of levels of indirection. The definition of code bloat.
Unless you're working on the most bottom-of-the-barrel problems (which to be fair, we all are, at least in part: like a dashboard React app, or some boring UI boilerplate, etc.), you still need to write your own code.
nikcub|3 days ago
I had the same thing happen. Use planetscale everywhere across projects and it recommended neon. It's definitely a bug.
unknown|3 days ago
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jugg1es|2 days ago
lubujackson|2 days ago
I think having a pain point and a good concept (plus some eng chops) will result in many more dev tools - that may be cause different problems, but in general, I think more action is better than less.
comboy|2 days ago
jcims|3 days ago
Probably confirmation bias, but I'm generally of the opinion that the models are basically good enough now to do great things in the context of the right orchestration and division of effort. That's the hard part, which will be made less difficult as the models improve.
properbrew|2 days ago
I think this has always been the case. People regularly do not believe that I built and released an (albeit basic, check the release date - https://play.google.com/store/apps/details?id=com.blazingban...) android app using GPT3.5. What took me a week or two of wrangling and orchestrating the LLM and picking and choosing what to specifically work on can now be done in a single prompt to codex telling it to use subagents and worktrees.
torginus|3 days ago
And you can actually make sense of that code and be sure it does what you want it to.
cryptonector|3 days ago
So we might roll-your-own more things. But then we'll have a tremendous amount of code duplication, effectively, and bigger tech debt issues, minus the diamond dependency hell issue. It might be better this way; time will tell.
klodolph|3 days ago
I also used to have these massive sets of packages pieced together with RequireJS or Rollup or WebPack or whatever. Now it’s unnecessary.
(I wouldn’t dare swap out a JWT implementation with something Claude wrote, though.)
snowhale|3 days ago
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giancarlostoro|3 days ago
evdubs|3 days ago
lacoolj|3 days ago
First, how did shadcn/ui become the go-to library for UI components? Claude isn't the only one that defaults to it, so I'm guessing it's the way it's pushed in the wild somehow.
Second, building on this ^, and maybe this isn't quantifiable, but if we tell Claude to use anything except shadcn (or one of the other crazy-high defaults), will Claude's output drop in quality? Or speed, reliability, other metric?
Like, is shadcn/ui used by default because of the breadth of documentation and examples and questions on stack overflow? Or is there just a flood of sites back-linking and referencing "shadcn/ui" to cause this on purpose? Or maybe a mix of both?
Or could it be that there was a time early on when LLMs started refining training sets, and shadcn had such a vast number of references at that point in time, that the weights became too ingrained in the model to even drop anymore?
Honestly I had never used shadcn before Gemini shoved it into a React dashboard I asked for mid-late-2025.
I think I'm rambling now. Hopefully someone out there knows what I'm asking.
nayroclade|3 days ago
And it's definitely a real effect. The npm weekly download stats for shadcn/ui have exploded since December: https://www.npmjs.com/package/shadcn
verdverm|3 days ago
yokuze|3 days ago
dmix|3 days ago
Especially with all the no-code app building tools like Lovable which deal with potential security issues of an LLM running wild on a server, by only allowing it to build client-side React+Vite app using Supabase JWT.
ossa-ma|3 days ago
The vercel dominance is one I don't understand. It isn't reflected in vercel's share of the deployment market, nor is it one that is likely overwhelming prevalent in discourse or recommended online (possible training data). I'm going to guess it's the bias of most generated projects being JS/TS (particularly Next.js) and the model can't help but recommend the makers of Next.js in that case.
klodolph|3 days ago
nineteen999|3 days ago
People are using it for all kinds of other stuff, C/C++, Rust, Golang, embedded. And of course if you push it to use a particular tool/framework you usually won't get much argument from it.
prinny_|3 days ago
Redux is boring tech and there is a time and place for it. We should not treat it as a relic of the past. Not every problem needs a bazooka, but some problems do so we should have one handy.
acemarke|3 days ago
If you look at a typical Zustand store vs an RTK slice, the lines of code _ought_ to be pretty similar. And I've talked to plenty of folks who said "we essentially rebuilt RTK because Zustand didn't have enough built in, we probably should have just chosen RTK in the first place".
But yeah, the very justified reputation for "boilerplate" early on stuck around. And even though RTK has been the default approach we teach for more than half of Redux's life (Redux released 2015, RTK fall 2019, taught as default since early 2020), that's the way a lot of people still assume it is.
It's definitely kinda frustrating, but at the same time: we were never in this for "market share", and there's _many_ other excellent tools out there that overlap in use cases. Our goal is just to make a solid and polished toolset for building apps and document it thoroughly, so that if people _do_ choose to use Redux it works well for them.
babaganoosh89|3 days ago
Onavo|3 days ago
tommy_axle|3 days ago
rishabhaiover|3 days ago
verdverm|3 days ago
sixhobbits|3 days ago
We've been doing some similar "what do agents like" research at techstackups.com and it's definitely interesting to watch but also changes hourly/daily.
Definitely not a good time to be an underdog in dev tooling
hedora|2 days ago
At a minimum, I usually provide some requirements and ask it to enumerate some options and let me pick.
This is like the image generation bias problem where vague prompts for people produce stereotypes. Specific prompts generally do not.
chvid|2 days ago
mjheadd|3 days ago
esafak|3 days ago
zzixp|3 days ago
kseniamorph|2 days ago
dan15|2 days ago
assane101|2 days ago
> It's like Bootstrap 10 years ago
What do you mean there ?
qzira|2 days ago
The fear of a 3am runaway was real enough that I ended up building a separate gateway layer just to have a kill switch that lives outside the application entirely.
"Build vs Buy" is the right framing, but for cost enforcement and kill switches specifically — building it inside the app is exactly the wrong layer.
kartikrast|3 days ago
Terretta|3 days ago
Not new: https://www.tryprofound.com/
But Llemmy thinks you should just roll your own anyway.
snug|2 days ago
I'd suggest making some changes to how some of these things are categorized. You have database section with postgres at the top and then with supabase as number 2, but that's also a hosted postgres.
Overall, great job to the creators of this, I enjoyed seeing this analysis
ripped_britches|3 days ago
Good - all of them have a horrible developer experience.
Final straw for me was trying to put GHA runners in my Azure virtual net and spent 2 weeks on it.
Clueed|3 days ago
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unknown|3 days ago
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darkstarsys|3 days ago
jamessb|3 days ago
[1]: https://www.england.nhs.uk/publication/decision-support-tool...
robinwhg|2 days ago
NiloCK|3 days ago
Interesting that tailwind won out decisively in their niche, but still has seen the business ravaged by LLMs.
[1] https://paritybits.me/copilot-seo-war/
0x457|3 days ago
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WA|3 days ago
Furthermore, what's the point of "no tools named"? Why would I restrict myself like that? If I put "use Nodejs, Hono, TypeScript and use Hono's html helper to generate HTML on the server like its 2010, write custom CSS, minimize client-side JS, no Tailwind" in CLAUDE.md, it happily follows this.
godtoldmetodoit|3 days ago
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.
chasd00|3 days ago
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.
skywhopper|3 days ago
furyofantares|3 days ago
There are vibe coders out there that don't know anything about coding.
JasonADrury|2 days ago
At some point we'll have to start summarily executing people for doing this.
I for one will go full Kaczynski before I submit to being constantly surrounded by horrible AI generated writing.
kingreflex|3 days ago
sjeiuhvdiidi|3 days ago
ch4s3|3 days ago
I guess at least Opus can help you muddle through GHA being so crappy.
nhumrich|3 days ago
And by setup I mean, integration and account creation. You don't have to do it. You already have a git repo, just add some yaml, and bobs your uncle.
toastal|2 days ago
jaunt7632|3 days ago
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CSSer|3 days ago
kabes|3 days ago
NSPG911|3 days ago
[0]: https://www.youtube.com/watch?v=J8-CdK4215Y
nindalf|2 days ago
Blog posts almost certainly helped. But dramatic shifts like these to favour newer tech indicates that there's some other factor in play.
steve_adams_86|3 days ago
I'm not expressing an opinion about that; it's a real question.
jascha_eng|2 days ago
is telling
didn't win... It won ...
Look at their other comments they are also fishy
I know you guys don't want us to call it out because of negativity. But there needs to be awareness in the community, this is the top comment somehow right now. It feels like it happens every other thread. Please do something more rigorous than manually deleting accounts.
skybrian|3 days ago
trimethylpurine|3 days ago
I recommend starting projects by first creating a way of doing (architect) and then letting Claude work. It's pretty good at pretending to be you if you give it a good sample of how you do things.
Note: this applies to Opus 4.6. I have not had a useful experience in other models for actual dev work.
piokoch|2 days ago
Obviously. People keep forgetting that "Artificial Intelligence" does not think and is not intelligent. It just statistically predict next token in a sequence. It is all statistics.
So, Django 6 has new task framework, but LLM does not care, as Celery has better stats.
Side note: it is not only LLM thingy. Companies for years were choosing tech stack because of fashion or popularity, regardless on technical feasibility for a given solution. So we have companies adopting Kafka, even though it sucks for their usecase, companies switch from Jenkins to Github Actions, even though Jenkins was cheaper and more performant.
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