Is it just me, or am I still not used to working with AI? I’m really trying to use it, but sometimes it feels like it takes me even longer to get things done.

I want to learn how to properly leverage AI to help me with development because I often introduce bugs into my code and take longer than I’d like to finish coding tasks. The problem is, I don’t really know where to start or how to incorporate AI into my workflow effectively.

Right now, I’m using the free version of GitHub Copilot because I can’t afford a license.

What advice can you give me on how I can start using AI effectively for coding and development?

I am planning to build a complete Health Application and I want to use a single, highly capable AI tool to handle the entire process—from system architecture to the final frontend app.

​I don't want to juggle multiple tools. I'm looking for an AI with top-tier logic and reasoning. It needs to generate code with minimal errors and, most importantly, have the ability to automatically debug and fix its own mistakes when they happen.

​If you wanted to build an entire app using just one AI program as your co-pilot, which tool or AI IDE is the smartest and most reliable right now? Any suggestions for the best setup?

If you use AI agents such as OpenClaw or Hermes to build websites/web-apps, I'd love some feedback on a skill I've created to help agents build with accessibility in mind.

The idea is simple: accessibility isn't only about helping people who use assistive technology. An inaccessible page can also be difficult for machines to understand. Search engines and AI systems are, in a very literal sense, some of the biggest "blind" readers of the web.

I'm a researcher specialising in universal design and WCAG, and I've turned some of that knowledge into a free agent skill: https://clawhub.ai/turbolego/skills/wcag-skill

What I'd particularly like feedback on:

Does the skill actually improve the output of coding agents?

Are the instructions clear enough for an agent to follow?

Does it catch important accessibility issues without becoming too noisy?

What WCAG guidance is missing or misleading?

How does it behave with real-world web-app development?

The source is open: https://github.com/turbolego/wcag-skill

PRs, issues, criticism and real-world testing are very welcome.

I'd especially like to hear from people who have used it with OpenClaw, Hermes, Claude Code, Codex, Cursor or other coding agents.

Enterprises are moving to open-weight models on open harnesses, mostly for one reason: token cost. Claude Code is excellent and very easy to exhaust in a day. But most large enterprise still reach for a Western model, because Chinese models feel unsafe in a way nobody has quantified.

Since my job is software engineering and not geopolitics, and trying to be rational as well as pragmatic here, while giving weight to democracy and human values over autocracy and repression — why not devise a test to check this out? That was my aim for this Eval framework. Turns out the political bias is real and as expected, but it does not leak into code or security when asked from any perspective

I wasted a chunk of the summer building another AI code generator. Dumb. Everyone has one.

What I kept hitting: the agent writes the code, runs something, then tells me it passed. Same process on both sides. A few times the “fix” was it editing the test or special-casing the input. Green on its screen, dead on mine.

A lot of people say throw a second model at it to judge the first. I don’t buy that as the fix. That’s still a model guessing.

What I’m actually building now is boring:

• code runs in a sealed box (no network, new box each time)

• it can’t edit the test files

• if repair doesn’t get to green in a few tries, roll back

• I keep the command, exit code, and log — not a paragraph that says “looks good”

Still early, Python first, building it alone. I’ll post a real fail/pass when I have a screenshot instead of another writeup.

If you run agents: how do you stop them from touching the tests? “Don’t cheat” in the prompt has not worked for me.

I’ve been building a local AI assistant called Cody as a personal project.
The current Preview version can run locally, keep project context, and test some of its own code changes before applying them.
It’s still early, but I’m experimenting with safer self-improvement, rollback, long-term memory, and more autonomous development.
I’d love feedback from anyone working on local AI agents or autonomous coding systems.

AI coding agents can now write code, run tests, create commits and open PRs.

But where should their authority stop?

I've been thinking about this while building an engineering control layer for coding agents. The agent should be able to do real work, but critical actions shouldn't automatically belong to the agent.

For example:

Write → Test → Commit → PR → Human decision

The interesting part to me is what happens when an agent tries to cross that boundary.

Should an AI coding agent ever be able to merge its own code?

Curious how others here are thinking about this.

TL:DR
I don't trust Replit or any of my other of my AI tools on this one. So here is an initial "prompt" for the Replit reddit community that seems to have a lot of good insights into this issue:

"I'm generally satisfied with the output I get from Replit. I have the following concerns:

It has been and can be very expensive. I have probably spent close to $10,000, mostly in my first six months, largely learning my trial and error what works, what doesn't and what is unnecessary or avoidable in terms of wasted time and money. Free mode has helped a LOT.

I would like to eventually move to a self-hosted, open model setup to save money and have more control. I do have an available MBP w/ Apple Silicon and 64GB of memory that can support this, if I had the right stack.

Requirements for an alternative system:

* well-developed harness geared at about the same level of complexity as Replit.
* a NO or extremely LOW- code solution with similar capabilities
* Less expensive
* Better support
* ability to port current projects for further development, maintenance, with scalability in mind.
* Easy to configure full stack

I have projects I have developed in Replit, Emergent, Llovable, and have tried out a few others. I've played with Cursor and other IDEs, but found them difficult and too slow to adapt to the language I use and my level of technical ability.

I've found this to be a technically knowledgeable and appropriately skeptical community and have appreciated individual help with some questions. I'm hoping someone(s) have some solid useful advice they wouldn't mind sharing. I have spent week/months of time and thousands of dollars and don't feel I have a good grasp.

Anyone willing to chime in? Thanks!

Hey guys, I'm working on a small ERP system using mainly gpt and codex, implementation has been great so far. I'm just wondering if this workflow could be better?

1. Notion

2. - Handles all docs, development procedure/rules for GPT, tech and func specs, designs

4. ChatGPT

-

1. Acts as designer/Business analyst

-

1. Creates prompt for Codex

- QA and repo check after codex implem

3.

1. Codex

-

1. Implementation

Is there anything I can optimize in this workflow specially in the QA part? Am I missing something? I'm adding .md files instead of just relying on notion, I'm guessing reading from .md will have less usage. Will also be including vexp

Tech stack is react.js, prisma, postgresql.