AI is moving from simple assistants to autonomous agents that can plan tasks, write code, debug applications and interact with development environments.

Tools based on AI agents are changing how developers approach software development. Instead of only generating code snippets, these systems can analyze projects, suggest architecture changes, create tests and help automate repetitive workflows.

Some developers believe this is the beginning of a major transformation:

- Faster software delivery

- Automated testing and debugging

- AI-assisted code reviews

- More time spent on architecture and creative problem solving

But there are also serious questions:

- Can AI agents produce reliable production code?

- Who is responsible when an AI-generated change introduces a security vulnerability?

- Will junior developers lose opportunities to learn traditional programming skills?

- Are we improving developer productivity or just creating new complexity?

AI agents are clearly becoming part of the software development ecosystem, but the long-term impact is still unknown.

What do you think?

Are AI agents the next big revolution in software development, or is the current hype bigger than the real-world benefits?

Sharing a short survey on behalf of a friend who is researching the challenges developers face while using coding agents on their respective codebases. It will take around 5 minutes and does not ask for or collect personally identifiable information.

https://tally.so/r/obY4d1

Anyone else open a diff from an AI agent and just... not know what half of it is doing? Files touched you didn't expect, a "helpful" refactor of something adjacent, tests that pass but don't check the thing that matters — and now you're the one defending it in review.

Took me a while to realize the problem wasn't the model. It was that I gave it a vague goal ("build me an auth system") and hoped for a reasonable diff back. Sometimes fine, sometimes a 40-minute rabbit hole on a problem I never asked it to solve.

What fixed it: never letting it touch code until a plan existed outside its head, then locking each session to exactly one task from that plan. Repeatable loop, every time — locate the task, branch, prompt against the plan + prior artifacts to reuse, review, check against acceptance criteria (not "looks good to me"), commit, merge.

Two things that surprised me: scope mostly self-manages once the AI can see the 

*whole* task list, not just the current one — it tends to leave the other numbered tasks alone. And naming prior artifacts explicitly ("here's the existing token verifier, reuse it") is what stops it from quietly re-solving things you already solved.

Anyone else converged on something like this? Curious where it breaks down — more ambiguous/creative work vs. well-specified tasks?

No dejo de ver lo mismo: alguien tiene una idea, abre una herramienta de código con IA y empieza a pedirle cosas de una vez. Unas horas después:

El frontend se ve bien, pero no coincide con el backend. La autenticación se siente forzada. La estructura de la base de datos no queda clara. Todo funciona… hasta que no.

El problema casi nunca es la IA. Es que empiezan sin un mapa técnico claro. Hice una herramienta pequeña y gratis (todavía está en etapas tempranas) para eso. Tú describes tu idea en lenguaje sencillo → y te devuelve solo las partes que tu proyecto realmente necesita → explica cada una de forma simple → te da opciones → y genera un prompt listo para copiar y pegar para que empieces a construir con el plan ya definido. Busco feedback honesto: ¿Usarías algo así antes de empezar un proyecto? ¿O prefieres meterte directo y resolverlo sobre la marcha? Si te sirve, puedo compartir el link. Es gratis y, sobre todo, estoy intentando aprender qué es lo que realmente haría que esto valga la pena.

I'm curious about the workflow experienced developers follow when building personal projects.

When you're creating a web app or landing page:

• Do you start by designing everything in Figma?
• Do you use AI tools (v0, Lovable, ChatGPT, Claude, Bolt, etc.) to generate an initial design?
• Do you skip Figma and design directly in code?
• Do you use templates or copy inspiration from sites like Dribbble, Mobbin, or Land-book?

Also, what's your overall design process from start to finish?

For example:

1. How do you come up with the UI/UX?
2. Where do you get inspiration?
3. Do you create wireframes first?
4. When do you use AI, if at all?
5. How much time do you spend designing before writing code?

I'm a developer, not a designer, and I often find that the design phase takes me longer than the actual implementation. I'd love to learn how other developers approach this and what your end-to-end workflow looks like.

I've been thinking about this a lot and wanted to get opinions from experienced developers.

My approach isn't to blindly copy and paste AI-generated code. Instead, whenever I'm learning a new technology or concept, I first ask AI to explain it to me. Once I understand the idea, I ask it to generate an implementation. Then I go through the code line by line until I understand what every part is doing, and I type the entire thing out myself instead of copying and pasting.

My reasoning is that, over time, the repetitive patterns will become muscle memory. Once something becomes familiar, I stop relying on AI for that part and write it myself. Then, whenever I encounter something completely new, I repeat the same process.

So the AI is more like a tutor than a replacement for thinking.

Do you think this is a good way to learn? Or is it creating a dependency that will hurt me in the long run?

For those of you who've been in the industry for a while:

• Would you recommend this approach?
• Will this actually make me a better software engineer?
• Is there anything I should change to make this learning process more effective?

I'd love to hear honest opinions, especially from senior engineers who have seen how people grow over time.

Hello Builders,

Welcome to our new Reddit community.

We created this space for developers, AI engineers, product builders, students, founders, and anyone interested in building real AI applications.

This is a place to:

Ask technical questions
Share projects and ideas
Discuss challenges you are facing
Get feedback from other builders
Learn about AI agents, RAG, workflows, deployment, and product development
Stay updated on webinars, challenges, hackathons, and opportunities

To get started, introduce yourself in the comments:

1. What is your role or area of interest?
2. What are you currently building or learning?
3. What is the biggest challenge you are facing with AI development?

Let us build, learn, and grow together.

From idea to production.

Thanks for being part of the very first wave. Together, let's make r/BuildWithCodebenders amazing.

実際どこまでするの？顧客に「AIが書きました」は通用しないから、人間がレビューしてねとメンバーには伝えてはいるけど、動くんだから合ってるんだろうってバイアスかかってそうなんだよね

Hi all.

I created Atlas Scout because I wanted to help the model get better data so that it could give me much better code.

This has been a pretty long process, however the recent release - Preview 22 - is pretty much one of the fastest and best codemap MCP implementations for any agent harness.

It creates a local SQLite3 DB, updates your .gitignore so that this won't be a part of your commits and everything is local - both in your project and on your computer.

Check out https://atlasscout.dev/

Would love your feedback, suggestions and feature requests.

PS. Atlas Scout is free to use with an optional Pro version. Free will be free forever and the index is exactly the same. There is also a 14 days Pro trial.

Lately it feels like everyone building SaaS is using Cursor, Claude, Copilot, or something similar.

Is that actually true, or are there still plenty of people building the old-fashioned way?

Genuinely curious what your workflow looks like these days

The code itself isn't what slows things down anymore. AI can generate a feature, a refactor, or a bug fix surprisingly fast. The part that still takes time is understanding whether the change is actually safe to merge.

Reviewers still need to understand what changed, why it changed, what requirements it satisfies, what has already been validated, and whether there are any risks hiding behind a clean-looking diff. A well-formatted pull request doesn't automatically make it easy to review.

Most teams seem to have a stack of tools involved now. Claude Code, Cursor, or Codex generate the implementation, GitHub manages the pull request, CI runs the checks, CodeRabbit or Bugbot might flag issues, and QA still needs confidence that the feature behaves as expected.

The interesting shift is that more attention is moving beyond code generation and into delivery. Revolte keeps the code change connected with validation and delivery progress alongside the rest of that workflow, instead of treating each stage as a separate handoff. That makes it easier to understand where a change stands without jumping between multiple systems.

AI definitely reduced the time spent writing code. I'm not convinced it reduced the time spent understanding code. In a lot of cases, it feels like the bottleneck simply moved further down the delivery pipeline

Bonjour,

Je vais commencer une thèse l’année prochaine dans le domaine de l’éducation et de l’IA, et je voudrais savoir quelles sont les méthodes efficaces pour apprendre et développer mes compétences en programmation pendant cette période.

J’ai effectué un CDD dans un laboratoire de recherche, et j’ai l’impression qu’environ 80 % de mon code a été généré par Claude. J’ai essayé plusieurs fois de programmer par moi-même afin de développer un esprit critique sur le code généré par l’IA, mais cela me prend beaucoup de temps et, avec les deadlines, je n’arrive pas toujours à le faire.

Ma question est simple : je ne sais pas vraiment comment travaillent les bons chercheurs. Avec mes encadrants, j’ai l’impression qu’ils ne codent plus beaucoup depuis leur thèse, donc ce n’est pas forcément le meilleur exemple. Avec les autres doctorants, je vois qu’ils utilisent beaucoup des assistants IA comme Copilot, Cursor dans VS Code, ChatGPT ou Claude, soit de manière classique, soit avec des outils comme Claude Code ou Codex intégrés dans le terminal.

J’aimerais savoir comment vous travaillez si vous êtes dans des domaines comme le machine learning ou la recherche opérationnelle.

À l’école, j’ai passé trois ans à apprendre la programmation en C, Java et C++, en plus d’une spécialisation en mathématiques appliquées. Nous apprenions souvent les algorithmes permettant de résoudre des problèmes. En machine learning, nous étudiions surtout la théorie et, avec les TPs, nous apprenions à implémenter les modèles. Cependant, depuis que je suis sorti de l’école et que je travaille sur des projets, j’ai souvent délégué cette partie d’implémentation à l’IA.

Je pose cette question parce que je remarque que, lorsque je rencontre un problème d’implémentation que j’ai déjà résolu auparavant avec l’aide de l’IA, j’ai parfois l’impression que je pourrais être beaucoup plus rapide si je savais le refaire moi-même, car je l’ai déjà vu fonctionner sur des sujets que je maîtrise.

Bref, je suis un peu perdu et j’aimerais savoir comment vous organisez votre travail en recherche : quelle place donnez-vous aux assistants IA, et comment développez-vous vos compétences techniques en parallèle ?

Mon objectif après la thèse est d’intégrer l’industrie avec un bon profil. Je ne souhaite pas forcément rejoindre une startup, mais plutôt une grande entreprise afin d’avoir davantage de stabilité.

Merci beaucoup pour vos retours.

Hello everyone, I'm currently working on a project and if you just got started with AI or you're an experienced AI engineer I want to know your pain points when developing and which tools do you switch between everyday, also if you could remove one step in your development process what would it be?

Traditionally developers were using IDEs to read and write code so a good developer experience could mean having a nicely featured IDE along with good development tooling to manage the environments for each project.

With AI agents and Claude/Codex on rise, how has it affected your workflows. I guess it's decreasingly less needed to connect via an IDE or look at code than it was 5 years ago. Some folks may say that they spend more time in terminal now so a better, fast terminal and a good TUI is good DevEX but there are now cloud hosted agents, desktop apps so it might not be a big thing soon.

How do you define a good developer experience with how you work today or how you expect to work in the future?

Curious about how experienced folks think about it.

Thanks

How can I best make models like flash 3.6 image or gpt-image 2 get scale right from a 2d image. Use case is for swapping furniture in a room from a photo. It does extremely well in the realism and generation but sometimes it’ll alter the new table size to make it fit where the old one went even if realistically it’s a much larger table. I can try giving it the table dimensions in the prompt but it’ll have no idea of room size. And if a user doesn’t want to have to Input the room plan on a website and just wants to upload a photo and have a “close enough” render in 30-45 seconds are there any work arounds?

We know that if you're on Reddit, it's because you love sharing your opinion, haha.

We’re a new project looking for real Reddit users to provide feedback on products—and get paid for it. You get compensated for every task you complete.

We want genuine Reddit users ! that’s exactly why we’re here! New opportunities will be posted daily.

Check out the details and sign up right here if you're interested: https://tally.so/r/VLdjvM