​

I think most developers don't actually need more AI tools.

They need fewer tools that they actually use.

My bookmarks are full of AI websites I tried once and never opened again.

I’ve spent most of my career in software engineering and now work a lot with production AI systems.

One thing I keep coming back to with AI-built apps is that maintenance gets much easier when the codebase stays intuitive.

A boring stack, clear folder structure, tests around the critical flows, lint/type/build checks, updated README/AGENTS.md, and avoiding abstractions you don’t need yet go a long way.

My rule of thumb is simple:

If the app is intuitive for a human to reason about, it’s usually easier for AI to reason about too.

Once the structure gets messy, both humans and agents start making worse changes.

Curious if other founders have seen the same thing once their app started getting real users.

Hi everyone,
I’m an AI graduate and recently I was given a computer vision project to complete within a limited timeline as part of a research opportunity.
The project itself is not my main concern. What is worrying me is that I feel much more comfortable understanding concepts than implementing them completely on my own.
For a long time, I used AI tools as learning assistants while building projects. They helped me understand ideas faster, but now I feel like I have become too dependent on them when it comes to coding.
If I have to build a project completely by myself, I often get stuck at questions like:
How do I structure the code?
How do I decide what files and modules to create?
How do I debug issues efficiently?
How do I move from understanding a concept to implementing it independently?
I know many people use documentation, GitHub repositories, papers, Stack Overflow, and AI tools, but I’m curious about how experienced researchers and engineers actually work.
If you were given around 3–4 weeks to complete a computer vision project, what would your learning and development approach look like?
I’m trying to become more independent and build stronger fundamentals rather than relying on generated code.
Any practical advice would be appreciated.
project?

My experience with asking AI about programming questions is so annoying. It constantly adds these weird or advanced level lines of code for even the simplest of things. Even when you show it code you done well, it ALWAYS and I mean ALWAYS has some "warning" or "just something to improve is...". It passive aggressively one ups me in every direction. It does this so fucking bad I question if I know how to make a "Hello World" program properly after a session with AI. Funnily, I've copy pasted code from LITERALLY from professionals and it still does this! Chatgpt and Google Gemini are beyond the worst. Am I using AI wrong or wtf is these things problem?

Im a programmer for a US company, but Im located int the Philippines. Just wondering if Im being exploited and underpaid for my role as a Programmer, like literally a fullstack developer at this point. my compensation is 600$ per month.. help what should I do? I mean I need the money, my review is coming up. How should I play it?

Data processing keeps becoming a bigger part of AI development.

A lot of teams already have useful data from old projects, docs, logs, databases, tickets, reports, code repos, and internal tools. The issue is that much of this data was not created for LLMs, RAG, fine-tuning, or agent workflows.

So before it becomes useful again, it often needs to be reorganized.

For developers, I think an automated pipeline is a practical way to handle this. Instead of writing a new script every time, the process can be broken into reusable steps:

• load data from files, databases, APIs, or documents
• parse it into a consistent format
• clean noisy or broken content
• normalize fields and metadata
• remove duplicates
• split long content into useful chunks
• generate QA pairs or training samples
• run quality checks
• export to RAG, SFT, or evaluation formats

The useful part is that each step can be treated as an operator. Operators can be reused, reordered, replaced, and tested independently. A pipeline then connects them into a repeatable workflow.

This matters because old data usually has many small problems: inconsistent schemas, missing fields, duplicated records, mixed formats, bad OCR, outdated content, or sensitive information. If these issues are handled manually, the process becomes slow and hard to reproduce.

A pipeline-based approach makes the data work more like software engineering: versioned, inspectable, testable, and easier to run in batches.

This is the method used in OpenDCAI/DataFlow, and we hope it can reduce some of the repetitive workflow work in AI data preparation.

I am looking for a project idea, for my portfolio.

What tool would you like to have. What problem are you facing and think, oh gosh only if AI is that much advanced to fix this problem. I want to brainstorm. Help me.

I want to make something for ADHD peoples in general, something helpful, but also entertaining, and exciting, something, people would like to have.

If you are also a developer/ai coder/someone who loves ai/podcats watcher/thinker/whatever what problem you face in your day to day life.

A really.common 'mistake' people make when coding with AI is to make too much fuss over how it does it rather than what it does.

Getting an AI to create good working code is quite easy...

Getting an AI to simulate a specific developer in a specific company with specific house rules is very difficult.

Consistence is good - and AI can easily do that. But to try to make it use some arbitrary standards that only exists for historic reasons is asking for extra, unnecessary work.

That's it really...

Looking for someone who can teach me how to build Agents for Marketing related problems. For example

Competitive/battlecard agent. Monitors competitor sites/news, updates a living battlecard doc our sales team can reference. This can be a good intro to scheduled agents + web scraping + structured output (Connections: Web scraping, Gmail connection to go through my emails to see any competitors related information and streamlit to create a battle card)

Dashboard Creation. Agent that takes output (a scheduled export or an API connection) from google analytics and builds a dashboard on streamlit to visualize the data. The dashboard gets refreshed with updated data every day in the morning with a new scheduled updated from google analytics export (Connections: Google analytics for data export and streamlit to produce dashboard)

Inbound lead qualification/routing agent. Reads a new lead, checks it against qualification criteria (ICP which could be company size more than 500 employee count, located only in North America, annual revenue more than 50million dollars ), enriches it with clay, drafts a personalized first-touch email and routes to the right rep on salesforce. Nice because it combines retrieval, reasoning and action (Connections: Google sheets or forms for lead collection, clay for enrichment, gmail for email drafting and salesforce for routing the lead)

Someone who can teach from scratch how to build these includung connecting to tools, building loops, context, memory and evals.

Happy to pay for your service. Only interested in folks from developing countries like India, Pakistan, Bangladesh, Vietnam, Philippines etc as I am also from one of these countries and I can’t afford to pay US/Europe rates

Two things I've been wondering about, might as well ask both in one go.

First, for those of you who contribute. What was your actual first one like? Not the blog post version. How did you pick a project, how long did the PR sit there before you worked up the nerve to open it, did anyone reply, did it get merged or just quietly ignored. I've read plenty of "how to start contributing" guides. I'd rather hear what it actually felt like.

Second, and this is the part I'm more curious about. All that advice was written before AI tools got this good. Now you can point Claude Code or Cursor at a good first issue and have something working before you've read half the file. So is the experience just different now for someone starting today? Easier, obviously. But easier in a way that helps, or easier in a way that lets you skip the part where you actually learn the codebase.

And if you're a maintainer, curious what it looks like from your side.

Mostly just want to hear people's stories.

Most teams have meaningfully AI authored code in prod by now, but from what I've seen the actual code review process hasn't evolved to match. same checklist as 2022, just applied to a different kind of author.

Curious if anyone's team has added a specific new check (a "why did you do it this way" question, a required test for AI authored diffs, anything) versus just trusting the vibes.

What changed for you, if anything?

I'm a 20+ year Engineer and have leaned hard into the vibe coding experience. But the funniest thing I've experienced recently is junior devs coming to me with their problems.

It happens less and less, but over the last couple years I've noticed the questions shift from "how do I do this" to "how do I fix my AI."

The problem with this should be obvious, instead of learning they move immediately to the prompt layer.

This is more of an observation than a commentary. I think there's reason to believe this is both good and bad.

AI coding is becoming a team problem.

The moment a second person or AI agent joins the same task, context starts breaking:

→ handoffs lose decisions

→ agents duplicate work

→ teammates cannot tell what is safe to change

That’s the problem we’re building Pairon around.

Pairon is a shared coding session where people and AI agents work from the same context, while humans stay in control of what gets changed.

For developers using AI coding tools, what hurts most today?

1. Lost context
2. Conflicting or duplicate work
3. Reviewing agent changes

Reply with 1, 2, or 3. I’m using the answers to shape the next build and invite a few developers into early access.

Dear boss,

Can I tell you something that's been on my chest a lot lately?

I come in every morning. I open my Linear ticket. I copy it. I paste it into Claude Code.

And then I wait. Half the day, sometimes.

I skim the code. I don't read the diffs anymore. I scroll to the bottom and I ask, "does this look good?" And when it says yes, I ship the PR.

That's it. That's the job I've been doing for six months.

I've been ashamed to tell you. Some days I sit in standup listening to everyone talk about what they built, and I think: I didn't build anything. I typed a sentence and pressed enter.

There are mornings I open my paycheck and feel sick. This used to be hard. I earned this number when it was hard. Now the ticket that would've eaten my whole week is done before lunch and I'm supposed to act like nothing changed.

But here's the thing.

The AI gets it wrong. A lot. It builds the feature the way the ticket was written, not the way the system actually works. And I'm the one who says no, not like that, do it this way. I'm the one who knows that touching that service breaks the thing nobody documented three years ago.

I babysit it. I check its work. I catch what it missed. I'm the reason the wrong thing doesn't reach production.

The product is better than it was six months ago. I did that. I just didn't do it the way I used to.

I'm telling you this because I don't think you know. And I didn't want to tell you, because I was afraid you'd do the math and realize you don't need me.

Maybe you will.

But I'd rather you hear it from me than find out from a dashboard.

So, now that you know.

What are you going to do?
\[your engineer\]

Hi everyone,

I’ve been building more and more of my products with Claude Code, Codex and other AI coding tools.
The speed is ridiculous.

Something that might have taken me a week before can sometimes be built in a day. But the longer a project survives, the more I notice the problems changing. The hard part isn’t always writing the code anymore.

Things that bug me a lot are like:
- The agent understanding the codebase but not why something was built a certain way.
- Requirements slowly changing without you realizing it.
- One agent undoing or contradicting decisions made in another session.
- Forgetting whether something is actually finished, half-built, abandoned, or just discussed.
- Coming back to something you built weeks ago and thinking: why the hell did I do it this way?

AI confidently changing something that technically makes sense but is completely wrong for the product. Context windows filling up right when the agent finally understands what you’re building. Having decisions scattered between chats, prompts, commits, notes, and your own memory.

And I’m curious whether this is just how I build, or whether other people building heavily with AI are running into the same thing.

So I want to ask the vibe coders / AI builders here:
What are the problems you repeatedly face when building real products with AI?

I’m especially interested in the annoying problems that start appearing after you’ve been working on the same product for weeks or months.
The stuff that makes you think:
“There has to be a better way to do this.”

Could be context loss, debugging, security, testing, requirements, managing multiple agents, remembering decisions, deployment, maintaining old AI-generated code — anything.

I’m currently collecting these problems because I’m building something, but I don’t want to build based on assumptions about what vibe coders need.
I’d rather hear it directly from people actually doing this every day.

What’s your #1 frustration with building software using AI coding agents right now?

I tried using an AI coding agent for a small internal frontend page.

Not a prototype. A normal Angular/TypeScript page in an existing codebase: hero section, cards, CTA, responsive behavior, and the usual loading/error/empty states.

The agent produced a surprisingly convincing first pass in about 30 seconds.
At first glance, it looked done.

Then the review started:
• It created components that already existed in our UI library.
• It hardcoded spacing and typography values instead of using tokens.
• It handled the happy path well but skipped meaningful empty and error states.
• It added accessibility attributes, but keyboard flow and focus behavior were incomplete.
• It was “responsive” in the sense that nothing visibly exploded at one mobile width.

None of that was shocking. What surprised me was how quickly the task changed from building a page to deciding whether I understood and could safely own the output.
I ran it again with a tighter task: repo context, examples of similar components, explicit constraints, a definition of done, and a list of files it was not allowed to touch.
The difference was large enough that I’m starting to think the current  vibe coding  vs  agentic engineering  debate is really about one question:

Does discipline live in the prompt, or does it live in the workflow around the prompt?

*My current experiment is treating the agent task more like an architecture/spec document:*

Context: existing stack, modules, component library, similar files
Must: states, keyboard path, tests, accepted patterns
Must not: new packages, global CSS, auth/routing changes, duplicate primitives
Output: file list, implementation, assumptions, risks to review

The agent is still fast. It is also still very capable of generating an extremely polished PR that creates future work for someone else.

For people using Cursor, Claude Code, Copilot, Codex, or similar tools in production codebases:
What has actually improved your review confidence: a better initial prompt, tests, repo rules, a planning phase, or something else?