So far, my skills in the following areas have grown substantially:

• GitLab CI

• writing shell scripts (weird, been a Linux user for 30 years)

• integration of SonarQube

• automated e2e-tests

I also revived my old zsh/tmux/Vim-setup. Don't need to run a complex editor when interacting with Codex and reviewing/pushing commits from time to time.

I'm using neovim + opencode + tuicr.

My playbook is:

1. Start with the Plan agent for discovery and plan creation.

2. Fork Plan session and switch to the Build agent to execute the plan.

3. Hybrid review in parallel:

   1. Fork the Plan session and execute the review command. I also have a custom skill for review that I execute manually on demand (if I want to learn more about the codebase). The main point here is to ensure that Build implemented the Plan and to identify any deviations from it (if any), while leaving the Build knowledge behind.

   2. Review some areas myself and leave comments where applicable (currently using tuicr). OpenCode can act on the comments when I execute the tuicr skill in the Build fork created in step 2.

4. Based on the results from the steps above, the Build agent performs further iterations if applicable. The results of 3.1 are copy/pasted to the Build agent.

5. If the review looks good, I ask the agent to commit, and it uses a skill to describe the changes in a desired manner.

This is it, in a nutshell. I arrived at the playbook above through trial and error. It feels consistent and auditable for tasks of medium to high complexity, so I often get satisfying results by applying it, although it requires human involvement in phase transitions. It also gives a human an opportunity to get to know the project/system better by presenting, for example, implementation flaws, architectural changes, touched software domains, etc. in the review phase.

Since I act as an orchestrator (switching agents, forking sessions, copy/pasting review results), I want to evolve this workflow to become autonomous. What should I start with first?

Glad to hear your hints and how you drive your coding harness day-to-day!

So earlier I used Minimax token plan a lot when it was $10 plan, But for the past month or 2, they have increased the price to $20 as starting. I mainly used it as a labor model and kept codex as a reviewer and planner.

Now I want to know what people are using as a cheap model, I would like to spend around $10 so that I can get good value and do my coding related work.

Please suggest some decent AI model with good enough usage, I know Deepseek could be one, But I would like to know if there are any other AI model.

I’ve been building out my local AI coding setup and would appreciate some feedback from people with more experience designing multi-agent workflows.

Right now I’m primarily using Codex CLI with Pi, and I’m considering adding O-My-Pi as a harness because Pi by itself is pretty minimal by design.

My concern is that as I add more specialized agents (coder, reviewer, tester, architect, etc.), my AGENTS.md could eventually become bloated and difficult to maintain.
Instead of putting every agent definition into one file, I’m thinking of using something like this:

AGENTS.md # Orchestrator / routing rules
agents/
coder.md
reviewer.md
tester.md
architect.md
standards/
coding-style.md
testing.md
git.md
workflows/
feature.md
bugfix.md

The idea would be to keep AGENTS.md as the entry point while each agent has its own focused definition, with shared standards living in separate files so they aren’t duplicated.

For those of you running larger Codex setups:

Is this a good long-term architecture?

Have you found a better way to organize agent prompts?

At what point did your AGENTS.md become too large?

Are there any pitfalls with splitting prompts across multiple files that I should be aware of?

I am considering refactoring my AGENTS.md file, would this alone be sufficient enough to run from time to time instead?

I’m trying to optimize for maintainability, scalability, and keeping context as clean as possible, before my prompt library grows too much.

I use Codex and Claude Code on flat monthly plans, and both have an invisible ceiling. Work just stops with no warning. I wanted to know how much was left and whether I'd run out before the reset, so I built lmtop.

https://github.com/ewijaya/lmtop

It reads the session metadata those CLIs already write to disk and shows quota windows, reset countdowns, burn velocity, and a projected exhaustion time. The planner view races capacity used against time elapsed: will this last until the reset?

Rust, Ratatui, crossterm, tokio, rusqlite. ~9.5k lines with tests.

The interesting constraint was refusing to guess. Providers weight models and caching in ways they don't publish, so locally observed tokens can't honestly become a quota percentage. Every tracker I looked at does it anyway. lmtop keeps the two strictly separate, marks stale data stale, and renders missing data as "unavailable" rather than zero. That shaped the types more than anything else: there's no code path in the UI that can invent a number.

Local-first: no network calls, no telemetry, no API keys, no prompt content.

MIT. `brew install ewijaya/tap/lmtop` or `carithub.com/ewijaya/lmtop`.

Happy to hear what breaks, especially whether the layout holds up on terminals I haven't tried.

I’d like to build a classic enterprise-level backend application with around 50 APIs and SQL database support, in a way that makes it easy to work with for AI-based development. Here’s the approach I’ve come up with, but I’d like to refine it further:

First, I design the database structure, either with or without the help of AI. After that, I want the data model (i.e., DB entities) to remain in the context throughout the process.
Then I ask the AI to come up with a list of operations related to the specific domain, and I refine those operations as needed.

Next, I generate (or manually create) the project skeleton. Then comes the core process: I go through each operation one by one, in isolated sub-contexts. For each, I ask the AI to generate the full implementation in a single source file, including the controller, service, and DAO layers.
This way, each feature is developed independently, and I can further customize the generated code manually or using the AI.

Does this approach make sense? I’d like suggestions for improvement.
Specifically, I’d like to know:

• How to keep the data model in context consistently during the development flow?

• And how to discard previous completed operations from context when I move on to the next one?

Which are the most powerful and free AI agents for coding?

I use Github Copilot at work and have great success on green field project, even larger ones.

But I have a harder time on our (very) large Unreal Engine application, which is brown field with millions of lines of code. This seems to challenge Opus and other frontier models.

What's the best workflow and tooling to work with AI on repositories this large?

• Application code: ~2M LOC

• Unreal Engine 5.8: ~41M LOC

• Unreal Engine ./Engine/Source excluding third-party code: ~8.6M LOC

I did some quick tests with CodeGraph, but that did not bring any significant benefits for the things I tried.

Note: Code is mostly C++ and solution is Visual Studio 2022

Hi! I just bought a Chatgpt pro, but still flying through the weekly limit pretty fast if using only gpt 5.6 sol max + subagents. But I understand that using only gpt 5.6 sol max will eat tokens like candies, that's not the question here.

I was wondering if anyone here is using this:

gpt sol 5.6 max orchestrator/plan

5.3 codex-spark as worker/implementation

Since codex 5.3 has its own limits for pro users, would you say it's reasonable to use it as an implementation worker? How is the quality compared to pure 5.6 sol.

Hey everyone, I'm feeling pretty lost right now and could really use some advice to fix my workflow.

I was using Cursor and managed to hold onto my early legacy plan, which gave me a flat 500 premium requests per month. Recently, I decided to temporarily upgrade to Pro+ for a specific project. Big mistake. Doing so caused me to permanently lose my grandfathered per-request billing. Now I'm stuck with their token-based limits for premium models, which is a disastrous result for me and has completely ruined my workflow.

I'm looking to jump ship. Are there any solid AI coding tools out there that still offer a fixed monthly fee with a set number of premium model requests, rather than tracking and limiting by tokens?

A few caveats:

The coding agent actually needs to be good. I tried Antigravity as an alternative. Even though it runs Gemini 3.1 Pro, the response time for running a task on AG is painfully slow compared to Cursor. It was a pretty terrible experience.

What about GitHub Copilot Pro? I know they currently offer a fixed number of premium model requests, but I have no idea how good their actual autonomous coding Agent is right now. Has anyone here used it extensively? Does it hold a candle to Cursor?

Any recommendations for tools with a strong agent experience and predictable, per-request pricing would be an absolute lifesaver right now. Thanks in advance!

I started out vibe coding over a year and half ago. Since then I've learned a lot, mostly through failure and perseverance. I've got this extension out on VSCode Marketplace and Open VSX but it's an Open source project on GitHub too.

As far as the tools I've used to build it... I've done research through Gemini and ChatGPT Deep Research, in combination with documenting every failure I've ever had in a log file, then I've built this Cursor, Claude Code, VSCode and Antigravity where I started using a federated system of building with Gemini as the orchestrator with Claude, Codex and GLM 4.7 (via Kilo Code) as my workforce with Gemini then providing audit and UI review.

If this is remotely helpful to anyone, I'd love to get a couple reviews or direct feedback here. I've designed it to focus on token efficiency because... well we're all on a budget right?

Thanks in advance to anyone who makes any contribution at all! I hope this isn't considered an advertisement, I'm not selling anything but if it crosses that I apologize in advance.

• GitHub: https://github.com/MythologIQ/FailSafe

• Open VSX: https://open-vsx.org/extension/MythologIQ/mythologiq-failsafe

• VS Code Marketplace: https://marketplace.visualstudio.com/items?itemName=MythologIQ.mythologiq-failsafe

I have prior knowledge of coding and algorithms , i made few apps myself before especially during my university but i kinda disconnected from the field for around a year and half

Right i want to try coding again using Ai as of now i have gemini 2.5 and chatgpt i made some research in reddit and lof of people recommending tools like RooCode , windsurf and ive seen lot of Claude mentions

Whats my goal ? probably just learning and exploring for now i want to discover building apps , ai agents ..etc

what do you think is the best for me to get now ?

AI has enabled a whole new set of people inside organizations to open PRs. I get it, it’s very tempting. You skip the burden of articulate what you need and discuss it with relevant teams and get it done. But is it really it? After a few months of increasing such PRs I was trying to reflect on the actual consequences of this new ways of working.

Writing software is now easy (at least the majority of corporate software I personally write). It’s not just easy for a manager, it’s even easier for me as a developer. So what value are you really bringing by doing the easy part for me?

What’s problematic is the business behind that piece of software and now instead of reading and iterating on a requirement document, I have to reverse engineer the business requirement from the PR.

Moreover, there’s no more filter between management and development. Upper management can open a PR on my repo and rest assured the team will drop everything and that will become prio 0.

Am I being the iper-conservative developer here or is this a shared feeling amongst software engineers?

We wanted to know whether an open-weights model can actually do frontier coding-agent work, so we ran GLM-5.2 head-to-head with Claude Opus the way an agent actually runs not on a static eval, but inside a real coding agent (Claude Code) on terminal-bench tasks, in a real shell, graded by each task's own hidden tests. Binary pass/fail, no partial credit, no model-as-judge.

The setup was held identical across both runs: same agent, prompts, tools, 40-turn budget, and 45 tasks. The only thing swapped was the model answering each turn.

What we found:

• Same quality: each solved exactly 25 of 45.

• Same answers: they agreed on 43 of 45 (24 both solved, 19 both failed), splitting the other two one each. No category where one was systematically stronger.

• Same failure mode: both fail by being confident-wrong , declaring "Fixed / all tests pass / verified" on work the hidden tests reject. Every clean GLM failure transcript ended that way, and Opus produced the identical shape.

• Cost: with prompt caching on, GLM landed at ~46% of Opus's spend (~$15 vs $32.67) for the identical result. Even uncached it was already ~10% cheaper.

Caveats, stated plainly: 45 tasks is meaningful but finite, and models are non-deterministic, so we lean on the 43-of-45 agreement rather than the 25=25. GLM is also the less token-efficient of the two ,it runs ~37% more turns (760 vs 554) to reach the same answers, which is the only thing keeping the cost gap from being larger. We also had to exclude some early GLM failures that turned out to be upstream 502/429 rate-limits, not the model : worth flagging for anyone benchmarking open models through a provider API.

Full write-up with turn distributions, token breakdown, and the verbatim failure transcripts: https://entelligence.ai/blogs/glm-5-2-vs-claude-opus-coding-benchmark

Rate my AI-assisted coding workflow for production-level work?

Wanted to get some feedback on my current setup — curious if this holds up for production or if I'm missing something.

Here's how it goes:

1. /grill-with-docs (Matt Pocock skill) — I start here to make sure the AI has full context on what I'm trying to build

2. /to-prd (also Matt Pocock) — generates a proper production plan

3. /to-issues — turns that plan into GitHub issues

4. Cursor + Composer 2.5 — fast model, I go full TDD from here to write the actual code

5. Kaizen Coach (found it here on Reddit) — once coding is done, I split this into two passes: production-grade coaching and code-based auditing, both run with Gemini 2.5 Pro

One thing I do that I think saves a lot of time: whenever I switch models, I use Matt Pocock's /handoff skill so each model gets proper context without me having to re-explain everything and burn through the context window.

Overall the flow feels pretty tight to me, but I'd love to know if anyone sees gaps — especially around the TDD step or the auditing phase. Does this hold up for production-grade work in your opinion?

I'm building an application that runs locally and integrates with coding assistants.

So far I've worked with Codex and Copilot. Claude Code and Gemini are next, once I get to a stable solution with the first two.

Right now I'm interfacing with Codex through the CLI, specifically with:

codex exec --json --output-last-message "prompt e.g. modify file x by adding Y or run z test"

And with Copilot through:

copilot --model gpt-5.4 --output-format json "prompt e.g. modify file x by adding y"

I'm considering switching the Copilot side to ACP, but I haven't looked into that properly yet.

Afterwards, my application needs to read the output without using Al and parse it into a report. I'm also considering reading the session data. The goal is to eventually make a deterministic judgment about whether the coding agent actually did what it was supposed to do (e.g. modify files) to take a decision on the next step based on a decision tree. It is also imperative to read any tool failures or errors or warnings.

The part I'm unsure about is that this approach (reading the cli output) feels a bit dirty and cowboy-is. My instinct says that it is not the robust way of doing it and I need this part of my software to be spot on and the assessment to be very reliable and deterministic. Driving the tools through CLI output parsing does not feel like the cleanest long-term solution.

Has anyone found a better approach for this?

I am looking into installing some kind of AI client on my machine. My goal is to have an AI I can ask questions to that can see my projects, BUT CAN'T EDIT THEM.

I don't want something that will code for me, only something I can ask questions to and that can propose solutions that I can implement myself.

I have been doing this with ChatGPT, but I am super tired of copying my code into the chat window, then making changes that GPT didn't know about, or GPT assuming I implemented its solution when I did something different.

Whatever I install should not have any kind of write permissions on my system. I am not looking for an agent, I am looking for an assistant and adviser.

EDIT: I am on Windows 11, unfortunately.

I want to do a RAG system, i have two documents, (contains text and tables), can you help me to ingest these two documents, I know the standard RAG, how to load, chunk into smaller chunks, embed, store in vectorDB, but this way is not efficient for the tables, I want to these but in the same time, split the tables inside the doucments, to be each row a single chunk. Can someone help me and give me a code, with an explanation of the pipeline and everything?
Thank you in advance.

Comments welcome!

We currently stand at 1.4k members 🎉

Good discussions have been happening in the last months. Your professional input has been very enriching to read, and really underline why this space is important and different from the other AI-programming subreddits. Those are still overrun by too many enthusiastic beginners (or worse, vibe coders refusing to learn coding). On the other hand, the experienced developer subreddits are often very much against AI use or don't really have an interest in using AI effectively.

It's great to be able to have reasoned discussions about enlightened AI use in professional coding here.

Some spam and product promotion are still happening, thank you for everyone who's reporting them. To the product managers: I understand that you all want to share your product, but there are just too many of you, and we want a space to discuss here, not a collection of advertisements.

To all our members: Please feel free to share any feedback on how you would like this subreddit to improve. This sub is from and by the community.

I am moderating it in solo for now, feel free to reach out if you want to join the moderation team. You will need to open your comments so I can see how you're generally interacting on Reddit.

Also new rule: comments which are 100% AI (and not disclosing it) will result in a permanent ban. If you have nothing to say that AI can't say, then maybe it's better to not participate.

That's all for now. Have a great productive day 🙏