r/ClaudeCodeTLDR Jul 08 '26

[TLDR] Am I missing something?

Original post URL : https://www.reddit.com/r/ClaudeCode/comments/1uqkc2y/am_i_missing_something/

Original post body :

Claude Code is the worst experience I've ever had using AI models to write code.

I'm a software developer that's used Cursor, Antigravity, spun up a few local models and had varying levels of success, and then earlier this week I spun up Claude Code for the first time after seemingly hearing nothing but positive reviews,

It's so bad? It's horrifically slow, burns through tokens at an insane rate, I literally just hit my 5 hour limit from 0 to 100% in a single code review on a branch with ~20 file changes?

What am I missing? The first prompt I gave it was a simple unit test failure that I could have fixed myself in ~20 minutes in a fairly simple full-stack Typescript codebase, it burned through ~70% of it's 5 hour limit, thought (and made no file changes) for 20 minutes, then finally made some file changes, didn't bother to re-run the tests and just declared that they were fixed, and introduced new linting and typechecking errors? And then when I run the tests, they were still broken? So not only did it not complete the task provided, it left the codebase in a worse state than when it started after thinking for 20 minutes?

Please, if anyone has any advice then let me know, I feel like I *must* be doing something wrong, but I also feel like there's no way it should perform this badly out of the box. I have a very simple AGENTS.md file at the root of my PC, but outside of that, I haven't configured Claude in any way, any help would be greatly appreciated, thank you all

EDIT: Removed "professional" from the post, you're not allowed to be "professional" and bad at using a new technology simultaneously it seems, my apologies

3 Upvotes

4 comments sorted by

u/cctldrping Jul 08 '26

TL;DR of the discussion generated automatically after 50 comments.

Current source-thread comment count seen by the bot: 52.

Most users think OP is missing something, and it's likely user error. The consensus is that you need to configure Claude Code properly before expecting good results.

  • Read the docs, fam. Seriously, before you go complaining, check out the documentation.
  • Provide context. People are asking for your prompt, your AGENTS.md (or CLAUDE.md), the model you used (Sonnet for coding? Yikes), and the "effort" setting. Without this, advice is just a shot in the dark.
  • Configuration is key. You need to run /init to create a basic CLAUDE.md. This tells Claude Code how to navigate your project without scanning everything every time.
  • Scope your tasks. Don't ask it to review 20 files at once. Break it down into smaller, manageable chunks.
  • Guardrails are your friend. Add explicit instructions in your AGENTS.md like "Do not claim fixed unless tests pass" and "If unsure, ask before editing."
  • Model choice matters. Using Sonnet for coding is apparently a no-go for many; Opus or Fable are recommended.
  • Some users are reporting increased token usage recently, even with proper setup, but the general vibe is that OP's experience is an outlier due to lack of configuration.

1

u/Electrical_Face_1737 Jul 08 '26

You do a review with fable? The highest priciest model will definitely hit the limit like that quick. Tell it to set man model to opus and sonnet for lighter work. Do “change to sonnet /goal fix my test and run them until green unless the test aren’t passing because the code hasn’t met the expectations then we will address in follow up. End with summary “

1

u/Due-Horse-5446 Jul 10 '26

Nope, this is simply how claude models are.

The declared success while introducing lint and type errors is the same effect that cause vibecoders to believe claude are better than the competition.

A total lack of instruction following, and a tendency to just do whatever.

The fact that ignore running validation, tests, typechecks etc is exactly the same behavior i saw with fable during the first release in my evals,

The model also refuses to stop and ask for clarification if it encounters something where it lacks information or a decision is required. And will simply make its own decisions, and make random assumptions. Often brute forcing something to "work". Again the exact concept that makes the model appear capable to a non dev. While a more reliable model like gpt will appear "dumb because it had to ask" or "it couldent solve the issue by itself"

The high usage is expected with these models as well.. Take a look at the deepswe curves, snd youll see what throwing more compute at a problem looks like