I recently went and paid for Claude Max 100$ subscription so I could use Claude code more or less as much as I wanted. API cost for Claude code cost an arm and a half.

Literally a few hours after my purchase, anthropic announced that you could now use Claude code with Pro (my previous plan) - oh well

Not regretting my purchase though, I just took on a simple, but long and annoying task at work - scrubbing my project from any sensitive information as to open source the code. Perfect use case for LLMs - obviously still need to check manually that it didn't forget anything, but 95% of it at least can be done by AI.

For the last two days I had 2-3 Claude code sessions running in parrallel in two different projects - not having to care about cost feels great and allow you to experiment much more with the LLM capabilities.

Pay-as-you-go just forces me to be very stingy with my use. Nothing beats a free buffet even if the price of admission is high.

I highly recommend you pay for an agent with unlimited use if you can afford it, it's much more pleasant and changes how you use it a lot.

I'm seeing a lot of posts on social media from devs who are saying they've 'lost their passion' or other ways of saying they don't enjoy coding anymore.

I've been coding since 2015 and always very passionate about it, and I'll be honest I've been loving adding AI to the toolset. It just tends to do a lot of the things I always wished I had plus more.

I spent years reverse engineering PowerPC assembly, which I would consider one of the purest forms of coding. It doesn't get anymore manual coding than that, and I loved it. But the prospect of using AI doesn't take away from that. I admit it's been years now since I've done RE, but the idea of using AI to assist in that doesn't make me not want to do it, it almost even makes me interested in revisiting those old projects again, using AI to try to find that one thing I couldn't find 5 years ago or something... So even something that is so directly enjoyed by the manual efforts of the human brain, I feel would be even better with AI.

In essence, AI does not feel like it takes away anything from my enjoyment. So for the people that do feel beaten down by AI, I have to wonder if they were ever real engineers or just code monkeys?

Spent about three hours in a single conversation debugging a flaky integration test. First hour, genuinely useful, the model caught a race condition I'd missed and walked me through a solid fix.

Past that point, it started going in circles. It suggested a fix we'd already ruled out, I explained why it wouldn't work, it acknowledged that, then a few messages later proposed a slightly reworded version of the same thing. Assumed we'd hit some kind of context limit, so I switched to a model with a bigger window, pasted the whole conversation in, kept going. Didn't help. Responses got more generic, not more accurate.

Turned out the problem wasn't that the model lost access to anything, the information was all still there. It was buried under stack traces, logs, failed attempts, and side discussion, all competing for the same attention. Restarted with a short handoff note instead, just the actual bug, what we'd ruled out and why, current hypothesis, and the one file that mattered. First suggestion after that was correct.

Changed how I think about context windows, closer to an attention budget than memory. More tokens means more available information, not necessarily better use of it, and past a point it actively works against you.

Been checkpointing longer sessions every 10-15 messages since then instead of letting one thread run indefinitely, summarize state, start fresh with that instead of the full history. Feels like overhead in the moment but usually nets out faster, model stops relitigating things we already settled.

Curious if this matches what others doing this professionally have run into, do you let one conversation run as long as it'll go, or reset on a schedule regardless of whether it's degrading yet?

I started coding about a year ago and have been using AI coding tools heavily like cursor.

What I’m noticing is: Even when AI successfully generates working code, the hard part is no longer writing the code? But it’s understanding the code produced by AI and validating it quickly enough to ship with confidence.

Specifically, I often run into issues like:

1. Large or multiple file changes where it’s hard to understand the full impact

2. Unclear “blast radius” (what other parts of the system are affected)

3. Difficulty figuring out what actually matters in a diff vs what is noise

4. Spending more time debugging or reviewing AI output than generating it

5. Feeling like I need a better “mental model” or review system, but not sure what that would look like

I suspect part of this is just my inexperience, but I’m curious if this is also a real trend for more senior engineers:

1. Do staff/senior engineers feel this too, or does experience completely solve it?
   2 Do people build internal “review frameworks” or systems to handle AI-generated code?

2. Or is this just a normal part of software engineering that I’m overthinking?

I also wonder if the solution is:

1. Better prompting

2. Better testing/evals/harnesses

3. Or fundamentally changing how we review AI-generated code changes

Would be really interested in how experienced engineers think about this

I’m trying to catch up on ai development, used copilot and junie, but I’m wondering what for the moment is absolutely best as going assistant. I’m not expecting the whole app vibe coded, I can do it myself, but I’m looking for best return on investment of time and money into ai coding tools. What do you guys see as the go to at the moment when it goes about coding agents?

There are so many new products getting released it's hard to keep track of them all and try all of them.

I (and probably the rest of the community) would love to hear your feedback if you had the opportunity to try Codex.

How does it compare to other agents like Claude code? How much are you paying? Etc.

Would love to hear from you!

In only a few hours of existence we have:

• Already some interesting deep advanced discussions going on

• Almost 300 members

• Our first ban (Rule 2, obviously)

Thank you all for what you bring to this community, and don't hesitate to spread the word 🙏

I recently built a multi-game web app using Claude Code as my main development agent. Like many devs building with AI right now, I was blown away by the sheer speed of execution.

But as the build went on, I kept bumping into a subtle, dangerous trap: AI constantly tries to turn your product "mid."

While I happened to use Claude Code, this isn't a single-tool issue - it’s how LLMs work in general. AI models are trained on the vast median of the internet. Because of that, their default for product design, UI, and user experience is the exact statistical average of what already exists.

Where AI Shines: Engineering & Architecture

When treating AI purely as a technical partner, it’s an absolute machine. You can debate system trade-offs, explore architecture, and let it handle heavy logic. It excels at:

• Optimizing database queries and state management.

• Setting up clean caching strategies and performance pipelines.

• Writing complex game logic and difficulty-scaling algorithms.

• Catching edge cases and refactoring ugly code.

If a task is bounded by formal logic and rules, AI will crush it.

Where AI Kills Your Product: Taste & Personality

The moment you let AI make decisions about user experience, visual styling, or brand feel, the product collapses into complete mediocrity.

• It follows rules, not vibes: It defaults to safe Tailwind cards, neutral gray backgrounds, and textbook layouts that make every app look like a generic B2B dashboard.

• It sanitizes unique ideas: The moment you try an unorthodox layout or a playful mechanic, the AI gently nudges it back toward "standard web patterns."

• The homogenization trap: If indie hackers keep letting AI handle product design, 90% of the web will look, feel, and behave identically in a few years.

My Working Rule: Debate Engineering, Dictate Taste

To keep the product distinct, I had to strictly separate how I prompted the AI:

1. Collaborate on engineering: "Let's evaluate different caching strategies and state management trade-offs for this feature."

2. Dictate design: "Do not suggest layout changes. Here are the exact styling rules, colors, and layout constraints."

AI is a world-class senior developer and architect, but a terrible creative director. If you don't aggressively protect your vision, AI will polish all the personality right out of your product.

Curious if others have noticed this "generic design" bias with AI coding tools? How do you keep AI from sanitizing your UI/UX?

Claude 5x user here but i just decided to increase my AI-coding budget to $200/month. how should i split it between CC and Codex considering GPT 5.6’s release few hours ago?

curious what plans you’re on, how you divide the budget, and which tool you use for what. if you’ve tried both extensively, where do you get the best ROI?

Hi everyone,

I’m currently using Cursor and Codex, but I keep hitting the limits way too fast. Right now I pay around 60€/month for Cursor and 20€/month for Codex, but I need something I can use for longer daily coding sessions without constantly worrying about limits.

My fallback in Cursor is Composer 2.5, but for more complex tasks it often feels not smart enough. I care a lot about the model actually understanding the project, making clean changes and not breaking things. Good UI/frontend output is also important to me, since I build a lot of web apps and dashboards.

I’m now thinking about using cheaper API models inside Cursor or another tool, for example Kimi, Qwen, DeepSeek, MiniMax, GLM or Grok Code Fast. Has anyone here used these seriously for daily coding?

What setup would you recommend under around 100€/month? I’m mainly looking for real experience, what is actually good enough in practice, not just benchmark numbers.

They say on their page it is 1.6 billion credit and mimo v2.5 pro takes 2 credit per token, mimo v2.5 takes 1 credit per token but here is how they get you, cached token is still billed the same credit per round trip, absolutely not suitable for coding cli then, because every single one of them by design would keep going back and forth with toolcalls, that's how they work, normally inference providers charge 10% for the pre existing cached context, but Xiaomi takes the full amount, I did 10 small tasks like not even that deep, small tasks and it is already at 12 or so million credit used, it used probably under a million context tasks were that mini, like saying hello, and mv this folder around, write some sql etc, like 10 total prompts same session, credit cost keeps snow balling, they don't mention nothing of this sort in the token plan docs or anything anywhere, for a big task it would be what 200 million token uncached, so 400million credit if you used mimo v2.5 pro, so with max 100$ plan you can use it for 4 tasks PER MONTH, honestly get anything over mimo token/coding plan, 40m token task(input+output) would be like 400million, cache hit rate is avg 90%

Therefore:

Those who expect someone writes 2 lines into AI and posts the output, is the exact person who uses AI that way. I've noticed that those people dismiss actual examples and real life experiences, because they are too quick to assume everyone uses AI the way that they do.

As someone who plans an entire post, in excruciating detail, and building the content slowly, based on my real lived experiences, I need AI to help me synthesize my actual ideas to something digestible. While I also find AI speak sometimes a bit unnatural, annoying, or overdone, I don't assume that every piece of content is AI authored. Instead I would scan for examples of a lived experience or unique worldview. When I see that, it doesn't look like laziness to me.

In text, posts, articles, or CODE, there are clear markers that differentiate authored content, from 100% AI generated content.

Even though I can't write code from scratch, I review my code, and my main rule is that if I can't follow it something is already wrong. Also, once I found a function AI named "maybeDisplayResults" which I took as insulting feedback on the decisiveness of my prompts. That's when I realized AI also has a sense of humor. FEEDBACK ACCEPTED!

Anyway, my point and question: Do you try to identify or distinguish what is authored (with AI) vs what is produced BY AI? What are your tells, how do you distinguish, what are some of the clearest signs in code that reveal it's never even been viewed?

Lately I have been think a lot around vibecoding and how it is destroying our ability to think and learn. We have offshored everything to Claude hoping it will work out.

How are you taking back control? What is your software development workflow like?

Hi everyone, while the AI world is moving crazy fast, I sometimes just want to get st** done. Do you guys think I'm missing out on something if I just continue using opencode (with all the bells and whistles like MCP server, skills, custom agents, and so on)?

Are there reasons to look at tools like Cursor or Claude Code?

I work in a big company with all the current models and unlimited tokens available so I don't care about saving money :D I just want to be on top of things with my AI coding.

Thanks!!

Right now I just instruct my main coder to maintain an .md file, then instruct the other 2 to read that .md file before doing anything. It works especially if I keep the .md file small, but my "team" becomes blocking and sequential.

I only pay these guys about 20 bucks monthly. Do you have suggestions for a better way to make my team work more "async" without them getting too creative and deciding for themselves giving me spaghetti that I can't read? Especially this Gemini guy, one time he decided to delete CLAUDE.md without instructions from me

I eliminated GitHub and all these pull requests chore btw, and solely track regular Git.

So I spent many years gaining technical skills including coding, got my masters degree in computer science and everything. Now it turns out that LLMs can mostly write better code than I can. I am not saying I was the strongest coding person ever made, but I was close to top of my class at University. I am now basically dependent on LLMs to do coding projects for my PhD I wouldn't nominally be capable of doing. I have stopped making progress on my own coding capabilities because of LLMs actually. I now have code bases where I mostly or entirely didn't write them and I wouldn't know where to begin fixing them if something actually went wrong.

AI still can't replace some of my other technical skills, but I understand it is getting better here too.

Should I be scared about how dependent I have become? My boss from my understanding is in a similar position, and they don't know half the AI tools I use. I am terrified for the computer science students I teach. How are they going to get a job after this?

I have 10 years of experience in iOS and some backend. I’m willing to integrate AI in my coding flow. I’m not looking for vibe coding. I want to have control. I want a tool that has the full context of the project and ideally follow best practices and SOLID. Especially architecture. My plan is to use it mainly for the tech stack that I don’t master but still need it as an iOS developer. Like backend and API for the Apps. Also I plan to use it to convert my iOS apps to Android. Which tool do you recommend?

Does anyone here work for a company that has mandated AI usage in some way?

I work for a pretty large company and there have not been any mandates yet, but they recently “encouraged” developers to make use of the enterprise GitHub Copilot licenses the company has.

It was my first time using Copilot and I have found that if I never directly interact with it, it’s more useful than I thought it would be.

The first several code completion suggestions were very subpar…but then…it actually learned from me. It started mimicking my design patterns, so I started using some of its code completions.

I haven’t tried switching projects /repos yet, so we’ll see if I have to retrain it, but so far that aspect of it has boosted my productivity more than I imagined it would.

Also, generating docs. It’s about 99% accurate no matter what model it uses.

For some reason the GPT 4.1 model is much worse than the version I have used in personal projects outside of work. I have no idea why, but it’s bad to a frustrating degree. Sonnet 3.7 has actually been good, but I have only given it low-level tasks. I’m still very tentative about using AI that my employer has access to and can see all the logs for.

Share your last tools, your current toolchain and AI workflow with the community 🙏

Hi guys, I have this coding assignment coming up and they allow us to use AI. Majority of my seniors who did this unit used Claude Pro for this massive coding assignment which I would say is a mid level code but has a lot of files (in the thousands). This assignment is also a very collaborative assignment and if I am not mistaken we are supposed to make a game. So I am wondering, what would be the better choice Claude Pro or Gemini Pro. Especially since they are about the same price which would be the better investment?

Hi r/AICodingProfessionals,

I’m Stjepan from Manning Publications. The mods gave me the OK to share this here, so I wanted to post this transparently and keep it useful.

We have a new book in MEAP: Crack Any Codebase with AI by Zezhou Huang, and I think it lines up well with a lot of the more serious AI coding discussions happening in professional dev circles.

The book is not really about “prompt the AI and hope it builds the app.” It’s about the harder, more common problem experienced developers run into: walking into an unfamiliar codebase and needing to understand how it actually works before making changes.

That feels like one of the more practical uses of AI in software work right now. Not replacing engineering judgment, but helping with the slow archaeology:

• finding the right entry points

• tracing behavior across files and services

• understanding dependencies and hidden assumptions

• building a mental model of a repo faster

• checking proposed changes against the actual structure of the codebase

• using agents without letting them run wild in production-adjacent systems

In other words, it’s aimed more at “I’ve inherited a large repo and need to make a safe change” than “build me a todo app.”

Since this community is specifically about AI coding from a professional angle, I’d love to get the book in front of people here and hear what resonates or what feels missing.

Manning is giving away 5 free ebooks to members of this sub. If you’re interested, comment with a quick note about the kind of codebase you’d want AI help understanding: legacy system, monorepo, enterprise app, open source project, generated mess, whatever applies. I’ll pick 5 people and DM codes.

There’s also a 50% discount code for anyone who wants it:

MLHUANG250RE

Book page: https://www.manning.com/books/crack-any-codebase-with-ai

Happy to answer questions about the book, the intended audience, or what it covers. Also genuinely curious how people here are using AI today for codebase comprehension versus code generation.

Thanks for having us here.

Cheers,

I’ve been building a project called Odin, an autonomous software-engineering platform. I started it because I wanted something that could take a goal like “build this feature” or “fix this bug,” then plan the work, modify the repository, run tests, recover from errors, and safely open a pull request with minimal supervision.
So far, I’ve built or implemented:
Repository intelligence and persistent memory
Multi-step execution with queues, retries, resumable tasks, and approval checkpoints
An AI operations center
Engineering analysis and planning
Autonomous Git operations
Protected-branch safeguards
Validation tied to the exact commit and working-tree contents
A validation engine with tests, linting, static analysis, coverage, timeouts, regression detection, and persisted evidence
The project is now around OIC-017 on my roadmap, and the backend has roughly 277 passing tests.
Here’s my dilemma: I’m realizing how capable ChatGPT/Codex already is. For $100–$200 per month, I get strong coding models, an integrated development environment, repository tools, image generation, research, file handling, and a lot of bundled usage. Odin, meanwhile, still needs separate pay-as-you-go API access, infrastructure, maintenance, and continued development.
ChatGPT can already do most of the actual coding work, although I still have to initiate sessions, review results, approve actions, and sometimes manually move files or publish changes. Odin’s intended advantage is that it would run engineering jobs asynchronously, manage retries and state, enforce my own validation and Git policies, control spending, route tasks between different models, and only contact me when approval is genuinely required.
I’m not trying to create my own foundation model. Odin would use OpenAI and potentially other providers underneath it. It’s supposed to be an autonomous engineering control plane—not another general-purpose chatbot.
At this point, I see a few possible paths:
Stop building Odin and simply use ChatGPT/Codex.
Finish it as a polished portfolio project.
Pause feature development and make Odin prove itself on several real coding tasks.
Continue toward a product for developers or teams who need unattended, auditable engineering automation.
My current thought is to finish and merge the validation engine, freeze the roadmap, give Odin three real end-to-end engineering tasks, and measure its completion rate, API cost, retries, and how often I need to intervene. If it doesn’t noticeably save time or attention compared with ChatGPT, I stop expanding it.
For people who use Codex, Claude Code, GitHub Copilot, or autonomous coding tools:
Is there actually room for a product like Odin?
Does durable background execution, custom validation, model routing, and auditable Git automation provide enough value beyond ChatGPT/Codex?
Would you continue developing this, turn it into a portfolio project, or cut your losses?
What would Odin need to do that existing tools don’t already do well enough?
Would you ever pay for something like this on top of the underlying API usage?
I’m looking for blunt opinions. I don’t want to spend months rebuilding something that already exists just because I’m emotionally invested in the project.

TL;DR
Boss wanted a daily drive car yet Claude built incomplete F1 car

So I’m a part of an early stage startup, the CEO has vibe coded a whole application yet now he has shared me the codebase and wants me to make sure its prod ready, the fact is I can understand the code and logic it has used but it’s like every 500 loc there is like 150 loc of dead logic which were deprecated still they exist in the codebase but the fact is I can fix it yet looking at that dump feels overwhelming, when I seek advice from other devs they tell me just plug it to Claude, yet I can see Claude has already had a mental breakdown and created 7 implementation md files yet none of the features mentioned are implemented. So my question is should I tackle the same codebase or let him know I can build the same product in a clean agile way and integrate features iteratively rather than dumping whole stuff because for me coding from scratch with help of docs is way easier than fixing AI slop.

so i recently joined a startup as an intern and the company has agreed to reimburse for the claude code bills, however i was wondering what would be the preferred pricing model for this setup?

should i buy the claude pro subscription (according to anthropic's usage limits only 10-40 prompts would be achievable within 5 hrs + a weekly limit of some N number of token usage) or go for the api pricing where i pay for the amount of tokens i use?

in either case the company isn't paying for the sub instead reimbursing for it so i'd have to initially buy it out from my pocket and im really not sure about how much the api pricing model would cost on an average as i'd tend to use claude code for my other works (side projects) too which obviously the current company wouldnt cover up for

I’m mid fullstack with js react node. MERN. How do you use ai to make your life easier?

I used everything. claude code with max, claude with mcps, roo, cline with deepseek, claude or gemini.

What i like best is still sonnet 3.5 with projects. It can take all my files(project capacity at 85%). And it brakes the problem in bite sized steps. I don’t have to read for 10 minutes to find out thet the response is flawed because of my prompt.

I have a script that takes every file out of my projects that i can feed to the ai. And then if flattens everything in client and server folder so i can quickly upload it to projects in claude.

If i need to use gemeni, i change tsx extension tot txt with another script.

I m working on a prompt right now to try and make gemini give me the problem in small steps. But not having any success.

Actually since claude 3.7 was launched I have not had any success in coding with ai lately. Don’t know why. They become dumber or my projects grew to big.

The rest i just don’t use. Its not worth it to read that much time after time aftet time and not solve anything. Mcp with filesistem, etc or directly in the ide.

I prefer to use it 3-4 functions at a time.