Hello community,
Long-time reader, first-time poster. Please be kind 👀

I need help. Reading online about which AI Coding tool is better, cheaper, the most beneficial for your money, or best for your profession is impossible. Almost every "unbiased" article on available tools is somehow owned by, or financially backed by, the company that the article says is the best tool to choose. I need real-people responses to real-world utilization.

Backstory:

I use VS Code today, and I have a Copilot Pro subscription (pending downgrade). I loved this product. I used it for everything (personal and work). It was, IMHO, it was the best product on the market. It has a lot of features that I'm not fond of; the most recent inline suggestions are split into two: Ghost Text (free) and Next Edit ($$$). I'm also not fond of reducing services/features while still finding a way to charge you more.

I started using Cursor (free trial), and I really like it. It could do everything I needed, just like in VS Code. My trial is up, and I'm about to hit the UPGRADE button, but I'm still not sold.

Recently, my work has been powered by Claude. The platform we primarily use for daily use is "Claude-driven" for AI (chat/coding/analysis), so interacting with Claude has been more beneficial than using Gemini or GPT. For example, in VSCode, if I ask a question about our platform and I select "Auto", VSCode mostly uses Codex or GPT and gets the answer wrong (usually). If I turn off auto and ask the same question to a Claude model, I get the right answer, but at a higher cost. Most of the GPT/Grok/other models are just too far behind in adapting to platform changes, while our platform is trained on all recent changes using Claude.

TL;DR: From the community, for Python/React/Spark (PySpark/Spark SQL) and heavy coding, which is the recommended platform you would suggest for AI-assisted coding among VSCode, Cursor Pro+, and Claude Code Pro that gives you the most for your investment?

Thank you

I'm a 4th-year CSE student from India, and I'm looking to buy one AI coding subscription with a budget of around $20/month.

My primary use case is agentic coding, not chat. I want to build websites and full-stack projects mostly by giving prompts and letting the AI work through the codebase.

My workflow would look something like this:

\- "Redesign this entire landing page to feel like Linear/Vercel."

\- "Implement authentication, dashboard, and database."

\- "Refactor the whole codebase."

\- "Fix all TypeScript errors."

\- "Build this feature end-to-end."

So I'm looking for something that can handle large repositories, maintain context well, and let me run long autonomous coding sessions without constantly worrying about hitting limits.

I'm not looking for the best chatbot. I'm specifically looking for the best coding agent.

The options I'm considering are:

\- Cursor Pro

\- Windsurf Pro

\- Google AI Pro (Gemini CLI/VS Code workflow)

\- OpenCode (if it's worth considering)

A few things about me:

\- Student in India, so value for money matters.

\- I mainly build websites using Next.js, React, TypeScript, Tailwind, Node.js, etc.

\- I expect to code several hours almost every day.

\- I don't mind using VS Code if it means significantly higher usage limits.

\- I care more about real-world agent capability and practical limits than benchmark scores.

My biggest concern is usage limits. Marketing pages are often vague, so I'd love to hear from people who actually use these tools daily.

For those of you who code professionally or use AI for hours every day:

1. Which plan gives you the most agentic coding for around $20/month?

2. Which one hits limits the least?

3. If you could only subscribe to one today, which would you pick and why?

Looking for real-world experiences rather than marketing claims. Thanks!

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!!

Hey everyone,

I'm a student working on a project and keep running into the same issue — I hit the usage limit on whatever AI I'm using, so I'm forced to switch to another one, and I lose all my context/progress in the process. It's getting frustrating.

Also heard GitHub Copilot's free Student plan recently got cut down a lot (premium models removed, new sign-ups paused), so that's not really reliable anymore either.

Currently using a mix of Claude's free tier and a couple others, but none of them alone feel like enough for a full project without hitting caps.

For students out there — what's actually working for you right now? Looking for:

• Free or student-discounted AI tools with decent usage limits

• Anything that helps keep context when switching between tools

• General tips for managing a real project without constantly running out of quota

Appreciate any suggestions, thanks!

I have been experimenting with different ai coding setups over the past few months and need any tips or better combination which u have tried...

last month I mainly used the $200 codex plan with $20 cursor and $20 claude code.

this month i'm trying something different: $20 codex, $20 claude code, $20 kimi 2.7 and putting the rest of the budget into cursor(60 dollar).

i'm trying to figure out which combination gives the best value for day-to-day software development.

if you've tried different combinations, what ended up working best for you?

Hi all, curious if folks have used Claude Code in the Claude desktop app and wondering how it compares to using Cursor or VS Code IDEs (without subscriptions). My initial take is that it doesn't easily allow new windows for multiple concurrent projects or it doesn't seem as clear or effective. Has anyone moved over to CC in Claude desktop app and likes it more and could share their experience and benefits? Thanks.

My lead asked me to recommend the best AI coding tool—Cursor, Antigravity, or any other alternatives. For context, I’m currently using Claude Code. I’d love to hear your opinions, especially on pricing, usage limits, context window, and any other trade-offs or limitations worth considering.

Android developers,

I'm curious ,what AI coding tool do you actually use in your daily workflow?

- Cursor

- Claude

- GitHub Copilot

- ChatGPT

- Windsurf

- Something else?

I'm building an Android startup using Kotlin, Jetpack Compose, MVVM, Firebase, and Agora, so I'd love to know what real Android developers are using today ,not what you'd recommend, but what you personally use.

Feel free to mention why you chose it.

Colegas desenvolvedores e arquitetos de sistemas, preciso compartilhar uma enorme frustração e um alerta para que vocês não desperdicem seu tempo e dinheiro como eu desperdicei.

Caí na armadilha do Minimax 2.7 e agora caí na armadilha do M3. Deixe-me ser absolutamente claro: o Minimax M3 é terrível. Se você estiver construindo algo além de um script simples, como mecanismos ERP proprietários, soluções de dados para varejo ou qualquer coisa que exija raciocínio lógico sério, este modelo irá desmoronar em suas mãos.

Ele é completamente incapaz de manter o contexto em um ambiente de desenvolvimento do mundo real. Quando você o alimenta com um ADR (Registro de Decisão de Arquitetura) bem documentado baseado em grafos, ele fica completamente confuso. Ele alucina conexões, perde o controle das restrições e quebra a lógica arquitetural.

Pior ainda, ele falha completamente em respeitar o arquivo AGENTS.md. Definimos documentação, regras e limites claros nesse arquivo Markdown diretamente no repositório, e o Minimax simplesmente ignora tudo. Ele age como se a documentação não existisse, o que torna impossível confiar nele para uma integração séria com a base de código.

Comparar o Minimax com o Claude Opus é uma piada completa. Ele nem chega perto das capacidades do Claude Sonnet, nem alcança o Kimi 2.7. É um produto de brinquedo disfarçado por um marketing pesado para enganar os consumidores.

No meu fluxo de trabalho diário, sempre que o Minimax causa uma bagunça estrutural, aqueles que realmente se esforçam para limpar e terminar o trabalho com absoluto profissionalismo são o **Qwen 3.7 Plus** e o **Kimi 2.7**. Esses modelos realmente leem a documentação, entendem arquiteturas complexas, têm resiliência contextual genuína e levam as instruções sistêmicas a sério.

Considere isso um anúncio de utilidade pública: eu mordi a isca para que você nunca precise. Se você estiver gerenciando projetos grandes e complexos, fique bem longe do Minimax. Não se deixe enganar pelos benchmarks sintéticos, pois, na prática, ele é um desastre completo.

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 posted here recently asking whether understanding and reviewing code is mostly what software engineers do now, and got a lot of helpful responses pointing out things like:

1. Improving fundamentals by writing more code manually
2. Treating code review as a skill that develops with experience
3. Relying on things like tests, git history, and better system design

That made sense, so i'm trying to go one level deeper and understand what this actually looks like in practice for experienced engineers.

Most recently i ran into this on my own side project, an AI powered ads diagnostics tool. I had claude plan out a research/reasoning pipeline, the logic looked sound when i read it, but when i ran the actual tests the output quality was way off. Turns out the retry logic was hammering the same endpoint on failure, and the AI output fields weren't matching the schema a downstream dependency expected. I only caught it by running the tests and reading through the reasoning output manually, the plan looked completely fine on paper.

So my question is specifically, when you're reviewing a big PR in a real production codebase, what is your actual step by step process?

For example:

1. How do you decide what to look at first?
2. How do you quickly build enough context about the change?
3. How do you figure out blast radius / what might break?
4. How do you decide what matters vs what can be skimmed?
5. How do you catch the gap between "the logic looks right" and "this will actually behave correctly at runtime"?

I need help with this from people with the same or similar machine

Like a lot of you, I was spending too much on Claude and GPT API costs while building side projects. Official pricing is great but

adds up fast if you're running Claude Code or Cursor all day.

I built aiapiflow.com to solve this for myself and opened it up.

The idea is simple:

- You get one API key

- It works exactly like the official Anthropic/OpenAI API — same endpoints, same format

- Works with Claude Code, Cursor, Cline, any SDK you already use

- Up to 85% cheaper than going direct

- Credits never expire, no monthly subscription

https://preview.redd.it/t9hzt99t629h1.png?width=1536&format=png&auto=webp&s=13e3530d6ca9eee446c1be49d1bf66d9f13fe700

Been debating internally to go with Codex or Claude Code?

Tried the Chinese 1’s but found them not good enough. SuperGrok a bit of a let down. Would like advice from serious coders on which they prefer, ideally if they’ve used both actively. Looking for agentic AI plus App coding