>"AI is really good until you know what you’re talking about."

Maybe this is a case well represented by the bell curve meme? "AI is great; can do everything" (but you've no domain knowledge so cannot guide it and everything means autonomous creation, so when eventually reaches a roadblock will have no idea what to do), "AI is really good until you know what you’re talking about" (then seemingly doesn't work and is even counterproductive), "AI is great; can do everything" (you've domain knowledge and can guide it and everything means application and assistance).

Essentially rather hope for the LLM to create all by itself as seems to be the current case for many, you should be able utilize your knowledge and have it assist you to both generate an initial code and converge it to where you want.

I agree you need to know what you’re doing. But Claude Code is definitely better than I am at some things- probably the most important of which is starting some mundane task that I would otherwise procrastinate indefinitely.

It’s very good at Typescript, search, and research, but still does stupid stuff and requires review and steering.

I don’t get into the same flow while using it, either, but I think that might be a matter of time. I find it allows me to spend more of my time thinking at a higher level. I could see myself learning to really enjoy that. Code review is exhausting, though, and has always been my least favorite aspect of the job. It seems my future is going to be code-review-heavy, and that is probably the biggest drawback.

yeah- this is a fair concern and I should have been clearer. I wouldnt do this on anything with real data or production traffic. that hetzner instance was a side project with nothing sensitive on it. the point was more about claudes ability to reason through infrastructure problems not that everyone should hand over ssh keys. you're right to be cautious

> But then I mentioned I had credits on Fly.io, was eligible for Vercel's free tier, had a Cloudflare account, and a Neon database.

I miss the days where deploying an app was just uploading some files. Maybe we need AI to understand this artificial complexity we introduced ourselves?

I've also noticed Claude "running away" and doing a bunch of work I never asked it to do.

And I'm sure just "restarting docker instances" fixed the root problem here...

The maintenance costs here are going to be eye watering.

Context is garbage in, garbage out.

And then hallucinating APIs that don't exist, breaking all the unit tests and giving up saying they're an "implementation detail", and over engineering a horrific class that makes Enterprise Fizzbuzz look reasonable

I've been running claude code on a 13 year old potato and it's never used 136GB of RAM - possibly because I only have 8GB.

Except a god-level engineer wouldn't write unit tests that pass but don't actually test anything because it mocked the responses instead of testing the _actual_ responses, so your app is still broken despite tests passing and "victory!" claims by the "engineer".

Just one example of many personal experiences.

It is helpful, and very very fast at looking things up, sifting through logs and documentation to figure out a bug, writing ad-hoc scripts, researching solutions; but definitely junior-level when it comes to reasoning, you really have to keep your thinking cap on and guide it.

I suspect that this is already a big reason why we get so many conflicting signals on "the best coding model". People tune into the style of the model they use the most, and hit snags and friction when they take another model for a spin.

Most iOS users report that Android is a disaster of an operating system, with layers and layers of user frustration. In reality, they actually are just totally in tune with how iOS does stuff. I can only imagine we have something similar going on here.

> I still haven't figured out MCP or how/why to use them or why to bother. You run servers. I guess. It's too complex for my smol brain to understand

I know this is self-deprecating humor, but you do NOT have a smol brain: MCP servers are not as needed anymore now that Claude Code supports "Skills". They are also very token hungry as their spec is not lazy-loaded like the skills.

It was / and still is very useful if you collaborate with other engineers or want to perform operations in a non-stochastic fashion.

MCP servers are a way to expose a set of APIs (openAPI spec) to an LLM to perform the listed operations in a deterministic fashion (including adding some auditing, logging, etc). LLMs are fine-tuned for tool calling, so they do it really well and consistently.

Examples:

- Documentation / Glossary: MCP server that runs somewhere that reads a specific MD file or database that is periodically updated: think "what are my team members working on" / "let me look up what this means in our internal wiki".

- Gating operations behind authentication: a MCP server that is connected to your back office and allows you to upgrade a customer's plan, list existing customers, surface current. Super useful if you're a solo-founder for example.

It's more like you want Claude to interact with X, and you go to see if there's an MCP server for it.

Claude could use the API directly but most MCP now comes with OAuth so you can let it act as you, in case API keys are hard to come by or chargeable. Sometimes with a good skill or a preconfigured CLI tool skills can be just as good if not far more powerful than an MCP server.

But the trigger you'd look for to decide to use an MCP is 'i wish Claude could access X'. My top examples:

- pulling designs from figma to implement them - fetching ticket context for a job from JIRA - getting a stack trace to investigate from Sentry

If you haven't grokked MCP yet don't bother now; it's on the way out. Instead do learn to write an AGENT.md file (create a CLAUDE.md file to point to it) then list all the tools you have at its disposal. It will probably know how to use them; it just needs to be told what's available.

I definitely relate with your sentiment and I like your term "configuration bankruptcy"

on MCP, the mental model that clicked for me is "giving claude access to tools it can call" so that instead of copy pasting from your database or API, claude can just... query it

playwright MCP for me is godsend

>Is there a first step in programming? If there is, that would be thinking

I'd argue that a first step (regardless of field) is either a necessity or curiosity.

Thinking comes later. (And to be honest I can't really think of it as a "step", simply because this is a process).

I use Claude and not crapping on it or the boosters, but most of the people who I've encountered in real life who really gush about it, are either semi-technical, and who struggle in some way when coding. Non technical CEO of small startup I was at, manager who didn't go to college, etc.

It's a cool tool! Just am tired of being treated like anti AI because I don't outsource my brain to it, or gush over their DIY UI demos.

How much AI did you use to write up this article? It tripped up my "fake AI-written article" detector a few times despite being interesting enough to read to the end

> I wrote this because everyone is talking about Claude Code right now and it's all over my timeline.

Feels more like peer pressure induced post, than evaluating a tool critically for pros and cons.

> Claude Code has this effect where you KNOW it's good but can't quite say WHY.

Definitely gives the "vibe" of social media's infinite scroll induced dopamine rush.

Overall, this post just seems to be enforcing the idea that "fuzzy understanding of business domain will be enough to get a mature product using some AI, and the AI will somehow magically figure out most non-functional requirements and missing details of business domain". Thing is that figuring out "most non-functional requirements and missing details of business domain" is where most of the blood and sweat goes.

Do you have any sources you've found that document Claude Code's UI in detail? I'm really curious about what they've built, as a UI designer.

because devs will have no clue how their systems work, the only ones who do will be LLMs, gatekept behind an ever-increasing cost-per-usage.

It is a very significant consideration for every one of those tools. The introduction of the "ribbon" in Excel was moderately controversial in 2007.

The default tools made available in Photoshop is why it remains on top to this day.

good question. the difference with AI tools is the interface isn't stable in the same way photoshop or excel is. with traditional software you learn it once and muscle memory carries you. with LLM tools the model itself changes, the optimal prompting style shifts, features interact with model behavior in unpredictable ways. so the cognitive load compounds differently. not saying features are bad, just that the tradeoffs are different

Developer Experience (as in UX - User Experience)

Developer Experience

They both make stuff up and make very obvious mis-interpretations of evidence. If you take the output of an LLM, and ask another LLM to check it, this dramatically reduces this. Even if you do it with the same LLM but without the existing context. I was able to write a detailed analysis of a rule system by doing this with 3 steps, claude -> chatgpt -> gemini3. It caught all the mistakes, including overstatements and vague statements. It wasn't perfect, but even after one review the # of mistakes or stupid statements was almost 0.

If a coding agent was released that never made anything up, how much would that change things for you?

Yet when I try it, it feels like a developer fresh out of a coding bootcamp with no real experience. There’s no real reasoning, problem solving is still brute forced. It still rewrites rather than modifies. The context is way too limiting and it gets lost in its own “thinking”

I had it One-shot the full architecture for a fairly advanced distributed system for a client. It then one shot the actual code design (following absolutely all our our internal requirements on auth, stack to use, security, code styling, documentation, etc). It then one shot (and we code reviewed everything thoroughly) each of the 5 micro services needed.

It one shot the infrastructure to use and created the terraform file to put it up anywhere. It deployed it.

It caught some of the errors it had made by itself after load-testing, and corrected them. It created the load test itself (following patterns from previews projects we had).

It did all of this in a week. With human supervision on each step, but in a fucking week. We gave it all the context it needed and one-shotted everything.

It is more than god-level. If you are not getting these increases in productivity, you are using it wrong.

Thesis: LLMs are a powerful tool

Antithesis: LLMs are emasculating

Synthesis: Programmers should be feminized

I'm pretty enthusiastic about LLMs and use them on my 8 year old codebase with ~500kloc. I work at a hedge fund and can trace most of my work to dollars.

I dunno if I’d fall myself “enthusiastic” but I successfully use AI on a large production monorepo. The onus is on the user to break down the problem into llm-sized bites. How to do this effectively is a skill that takes time to develop. You’re not crazy: if you go in and ask it to do things in broad strokes it won’t work.

I'm quite sure "loading the project" isn't putting every single line of code into the context. Probably just a huge CLAUDE.md or something.

Either that or this author is completely out of touch with reality.

should have been clearer here. by "loading the project" I meant the initial context claude builds like CLAUDE.md, directory structure, etc... not literally putting every line of code into context. 7M tokens would obviously not fit in a 200k window

I think the agent mode stuff only works well on trivial projects. But the top tier models can be very productive with carefully constructed prompts and manually curated contexts for large mono repos.

I’m not clear what “just loading the project” even means here - if that’s how many tokens are consumed by system prompt plus Claude.md and MCP tools well that has nothing to do with the size of the project

You obviously don't have any idea how any of this actually works.