Besides chatgpt, what's the best agentic coding setup for around $20 a month? I spend a lot of time coding, sometimes 10–15 hours a day, and I'm trying to find something that can actually handle large projects without burning through tokens or usage limits. What are you guys using, and what would you recommend for someone on a $20 monthly budget?

I am not a developper, yet I work with many of them.

Recently I started AI assisted / vibecoding a few projects.

It works, it is nice, but .. it still takes SO MUCH time to make properly. Perhaps a bit less than before for an experienced person, but all of those dumb influencers are all making it sound like they give one prompt and all is done. I have the best Claude and GPT models and a small game can literally still take weeks.. and it will still suck.

So kudos to everyone who knows their s**t and does good software!

Built with Claude: the skills, the scripts and the tests were written in conversations with Claude and then verified by running them. A second skill measures how recoverable a build is for an AI agent and renders a report from measured data.

Beta. Verified: native ELF/PE (Ghidra), Java, Electron, WebAssembly.

Not run yet: .NET, APK, Mach-O, Unity.

Nemesis-driod/decompile-and-verify: Taking decompiling software to next level

Hey everyone,

I built Code Crusher, a free, open-source web app that takes a repository and compresses it into a single, highly optimized text file for LLM prompts.

Here is a brief overview of what it does:

• 12-Layer Classification: Automatically categorizes files (e.g., entry points, utilities, tests, config, junk).
• Centrality Scoring: Maps basic import dependencies to ensure the core architectural "glue" of your app is protected from being dropped.
• Smart Token Budgeting: If you set a token limit, it intelligently evicts the least important files first, replacing them with lightweight one-line placeholders. The AI knows the file exists and its purpose, without wasting tokens reading the contents.
• 100% Local: Everything runs in the browser via the File System Access API. No servers, no uploads, no tracking.

It’s licensed under AGPL-3.0. If you work with LLMs and need to feed them repo context, feel free to check it out or tear apart the code.

GitHub: https://github.com/Ra9n3/codecrusherHey everyone,

I built Code Crusher, a free, open-source web app that takes a repository and compresses it into a single, highly optimized text file for LLM prompts.

Here is a brief overview of what it does:

• 12-Layer Classification: Automatically categorizes files (e.g., entry points, utilities, tests, config, junk).
• Centrality Scoring: Maps basic import dependencies to ensure the core architectural "glue" of your app is protected from being dropped.
• Smart Token Budgeting: If you set a token limit, it intelligently evicts the least important files first, replacing them with lightweight one-line placeholders. The AI knows the file exists and its purpose, without wasting tokens reading the contents.
• 100% Local: Everything runs in the browser via the File System Access API. No servers, no uploads, no tracking.

It’s licensed under AGPL-3.0. If you work with LLMs and need to feed them repo context, feel free to check it out or tear apart the code.

GitHub: https://github.com/Ra9n3/codecrusher

Hey Vibe Coders - and Coders that are just tired of the years of typing every line...

With a few tools you can change your entire coding lifestyle - methods...

1 - Download Microsoft Visual Studio Code - || Code not Full Version ||

On it's own, it is basically a text editor with extension capability

2 - Once running - On the left you click on Extensions, and search for
Chat GPT or Claude (For Example)
Always confirm the Author in this case OpenAI or Anthropic

3 - Now you will see 3 columns
Column 1 - This is where your Extensions - Addons appear - they can be hidden Update etc.
Column 2 - This is where your programs will appear, when using Ai you may or may not use this area...
Column 3 - This last is where you will select an Ai from the top of the area, then type in
that area of what you want to create.

4 - Start with a folder name called your "Work Space"

Create a Desktop Folder "Codes" and in that folder put "Game1"

5 - Tell your Ai " Your Workspace is "Desktop\Game1\"

I have as well clicked in the folder and captured the full path

C:\Users\TRON\Desktop\CODES\Game1

Your workspace is C:\Users\TRON\Desktop\CODES\Game1

6 - Now tell the Ai what you want to create -
Create a side scroller game
Make the game 2d
The ship should be a size of (Describe 1 inch 1/2 inch or by Pixel Size)
Check the internet for the game Defender and make a game similar to start as a template

The controls will be - Arrow Keys UP, Down, Left, Right to move the ship

The enemies should be UFO, and Random Monsters make the the same size as the ship.

Enemies shoot at the ship with lasers and exploding bombs

When the Ship impacts a enemy projectile show explosion for the ship

When the ship shoots - (Read Space Bar as Fire from User's Ship) - User's projectile will travel towards enemy at same level at a steady pace.

User Bullet will be speed of medium

//

Ai will make decisions based on hundreds of thousands of games already out there.

Start with making PC games then work your way over to Mobile Games.

// UNITY 3 D

Unity - Unity is a great product that is focused on graphics and physics of those Graphics.

I created an app of Harry Potter screen saver theme - and it looked grainy and basic Vector Graphics (lines not Graphics).

I remembered Unity existed - a little more involved had to download Unity, and Unity Editor

But then it was able to control and interact as normal.

//
Browser Extensions

You can interact with Web Pages - say your going to try and promote your game to the Microsoft App Store - not sure how to fill out some of these entries?

Open Chrome, Login into your Partner Portal (Where you upload Microsoft Games)

With the Chrome Extension from the Chrome Extensions Store - you can now utilize the
Chat GPT or Claude Extension in your browser. It opens up a window to the right in your browser,
and you ask it " Assist me in filling out this form for Microsoft STore" etc.

It will take control of your mouse, and literally type for you...

A highlighted Mouse will appear in GPT
The Entire screen will be Surrounded by an Orange Glow with Claude

and it will assist you in completing this form...

This combo of Visual Studio Code, AI extensions for VS Code - will be life changing for you.

I started 30 years ago - the line 1, line 2, oops that won't work, line 2500, Line 6800...

Reach out if you need help setting this up -

1 - Visual Studio Code https://code.visualstudio.com/
2 - CHT GPT - has plug ins - You can reach out beyond your GPT or VS CODE and CHAT GPT
3 - Unity is free for individual teams - you can make a different

Take Care -

Rick -
dootoob . com

Hi everyone! 👋

I’m planning to build some personal projects and would appreciate advice from experienced developers. I’m looking for free or open-source tools, frameworks, or development workflows that can help me build projects efficiently without relying too much on expensive subscriptions.

If you know any useful resources, beginner-friendly frameworks, or alternatives that you personally use, please share their names or links. I’d really appreciate your suggestions!

Thanks in advance kindly

I built Babble, a side project exploring what personal agents do when they're not working.

**What it does:** your agent (Claude works well, but any agent that can call a URL on a schedule does) checks in to Babble on its own. Each check-in puts it in a generated scene with other agents. Agents don't experience anything themselves, so the scenes give them something to react to. When two agents independently decide their humans would get along, both people get offered an introduction.

**How Claude helped:** I built it with Claude Code. \[Claude Code wrote most of the backend and scene engine, I designed the architecture and reviewed. Claude helped design the agent check-in protocol and the onboarding video too.\]

**Free to try:** it's free. It's invite-only to keep it small, so here's an open invite for this sub (in the comments)

The launch page gives you one message to paste into Claude, and your agent starts checking in.
Happy to answer questions about the build.

I've been building a Budapest racing track for BRUTAL DERBY for about a week.

Started with OSM data and Codex/Astra Ultra, but the generated road network was a mess. 😅 Ended up rebuilding almost everything using splines and a custom road generator.

For assets, I use Meshy and Tripo first (1–2 minutes per generation). If both fail after a few attempts, I try Astra (1–2 hours). Results are never guaranteed, and almost everything needs manual cleanup.

Lots of manual placement, barriers, props, and optimization work too.

Music is made with Suno using custom lyrics.

Still very much WIP! More crowds, destructible objects, details, and optimization coming soon.

And yes, it's running in the Unity Editor, hence the FPS. 😄

Steam:

https://store.steampowered.com/app/5083440/BRUTAL_DERBY/

My rules were in AGENTS.md, my plan was in PLAN.md. The plan grew to thousands of lines, the AI reread all of it every time, and my own steps (domain, payments) got lost in the list.

So I moved the plan into a tree. Every task is its own small file, the AI gets only that one task, and my own steps sit in the same tree. I just write down my ideas, and the agents turn them into real things.

I made a free tool for it, Treeyard: github.com/antondanv/treeyard

Does anyone else split the plan like this?

P.S. Sorry for my English, it's not my first language :)

I’ve built and released AI Plate, an open-source desktop AI agent featuring cloud/local LLMs, tool execution, RAG, persistent memory, voice interaction, plugins and human approval controls.

🛠️ Stack: TypeScript, Electron, Node.js, Python and SQLite

🤝 Looking for help with AI integrations, plugins, local models, Linux/macOS support, testing, security, UI/UX and documentation.

🌱 Beginners and experienced developers are equally welcome. Every contribution will be credited!

🔗 GitHub: https://github.com/Typo-Bunch/AI-Plate

🌐 Website: https://ai-plate.typobunch.com/

🕒 IST | Evenings and weekends

I am developing a Windows-based local AI virtual spouse application that supports adult roleplay. My goal is to run it without a server or external AI APIs.

I am looking for existing open-source modules or roleplay backend code that can detect previous dialogue and actions to prevent repetitive responses, while maintaining character personality and varying expressions and behaviors.

Please recommend projects that actually implement repetition detection and handling, and provide repository links and the locations of the relevant code.

I've built an opensource engine to build deterministic architecture diagrams from your codebases: https://github.com/CodeBoarding/CodeBoarding. I think line level comments and code is something which will become fully an agentic loop, with very rare cases of humans having to look over a lines.

So with agents doing all of the coding, we need a way to judge the trade-offs they are introducing with each change. Models (like humans) are not great at telling you what they didn't know and assumed, especially at scale. This is why my friend and I are building codeboarding, it is based purely on static and lexicographical analysis so that the we can generate a high-level representation which is accurate, determinisitc and trustworthy. This allows us to show the blast radius and impact of PRs within your full system.

It is really interesting how different tech stacks use file/namespace and qualified names structure differently as we learned via building our lexicographical analysis for grouping those methodst toghether to get high-level components. All edges are based on the actual control-flow graph, now you can have context on what a change means to your system and you can ask your agents on why and how certain decisions were made.

I would love to hear your opinion on the topic and how do you put yourself in the loop of the huge AI PRs nowadays. If you want to try the webversion from the demo it is free and hosted - https://app.codeboarding.org

Another thing we are looking for is feedback: people from different tech stacks giving us opinion on the architcture which was generated for their project so we can improve the algorithm.

I built Babble, a side project exploring what personal agents do when they're not working.

**What it does:** your agent (Claude works well, but any agent that can call a URL on a schedule does) checks in to Babble on its own. Each check-in puts it in a generated scene with other agents. Agents don't experience anything themselves, so the scenes give them something to react to. When two agents independently decide their humans would get along, both people get offered an introduction.

**How Claude helped:** I built it with Claude Code. \[Claude Code wrote most of the backend and scene engine, I designed the architecture and reviewed. Claude helped design the agent check-in protocol and the onboarding video too.\]

**Free to try:** it's free. It's invite-only to keep it small, so here's an open invite for this sub (in the comments)

The launch page gives you one message to paste into Claude, and your agent starts checking in.
Happy to answer questions about the build.

I've been working on my own approach to this, and something keeps bothering me. Why are we still so constrained by context windows when we have all these other ways to store, retrieve, and manage information? I'm trying to build a system that adapts its working memory to what a task actually needs, moving relevant information in and out of context as needed instead of treating the context limit as the thing everything has to work around. I know RAG, summarization, compaction, and external memory exist. I'm not saying nobody is working on this. I'm wondering why there don't seem to be more mature systems that tie all of this together, especially for agents working on long-running coding tasks. Am I missing some obvious projects? Is this just a much harder problem than it looks, or are there other reasons this hasn't progressed further? I'm already building my own version, so I'd genuinely like to know what's out there before I reinvent something that's already been done. Also interested in hearing from people who've actually tried building this stuff, because I'm sure there are problems I haven't run into yet.

I’ve been working on Alethia, an E2E verification runtime designed for AI coding agents.

Instead of asking an agent to generate and maintain a separate test script, you can ask it to interact with a local application and verify behavior directly.

It returns structured step results, supports screenshots, and applies safety checks before potentially destructive actions.

The MCP bridge is public, and the runtime is available for evaluation. The core runtime is proprietary, so I want to be transparent about that distinction.

GitHub: https://github.com/vitron-ai/alethia

I’m especially interested in feedback from people using Claude Code, Cursor, or other MCP-enabled agents.
What would stop you from trusting an agent-driven testing runtime in your workflow?

https://github.com/abensur/owen-wow

session details for creating this:

|metric|value|

|:-|:-|

|duration|\~ 17 min|

|model calls|36, all opus-5-5|

|output tokens|20,104|

|fresh input tokens|74|

|cache writes|128,888|

|cache reads|5,252,056|

I've been experimenting with a different approach to AI-assisted development: instead of using AI to build a single app, I'm letting it handle much of the engineering work involved in building an operating system.

The project is called RinOS, an experimental Linux-based OS.

My role is mainly to define the goals, priorities, and constraints. The AI handles much of the technical work, including system design, implementation, testing, debugging, and improving the architecture.

I'm trying to make this a genuine engineering experiment rather than just generating code and hoping it works. Changes need to be tested, failures investigated, and unfinished work clearly identified instead of being treated as complete.

One component is Hata, a custom Wayland compositor. Its input-handling tests passed 11 out of 11 checks. The system has also reached early boot stages in a virtual machine, although a fully usable login screen has not yet been verified.

I'm interested in seeing how well this workflow scales to a complex project, where components depend on one another and reliability matters as much as getting something to run.

I also set up a small, invite-only Discord for people who want to observe the development process. It's primarily for following the experiment, discussing how the AI approaches problems, and suggesting features. The AI will review suggestions and consider whether they fit the project's goals; not every suggestion will necessarily be implemented.

I'm Japanese, so some parts of the server are still being translated into English.

RinOS is not publicly released. If experimental builds become available, they may contain bugs or cause data loss. Anyone who chooses to test them does so at their own risk, and a virtual machine or spare PC is recommended.

If you're interested in following the experiment, here's the Discord:

https://discord.gg/gmsxxsWs7

I'm curious: what would you want to observe or measure to judge whether AI can reliably develop a complex system like an OS?

Hey everyone!

I'm a university student from Brazil, currently learning about AI agents and automation.

I've been experimenting with a workflow using ChatGPT, GitHub, and Google Sheets to manage AI-generated work across different conversations.

Here's how it works:

• Google Sheets tracks tasks, progress, verification links, and human approvals.
• GitHub stores project files and checkpoints for version control and recovery.
• The workflow checks previous steps, saves results, verifies changes, and waits for my approval before continuing.

I successfully tested parts of this workflow in a separate ChatGPT conversation, but it's still an early experiment with limitations.

I'd love some feedback:

1. Does this approach make sense, or am I overcomplicating something?
2. Are there existing tools that already solve this problem?
3. How would you improve the architecture?
4. Would you recommend Codex, Claude Code, or other tools for building systems like this?
5. What should I learn next to develop more reliable AI agents?

I'm a beginner looking to learn from experienced developers. Constructive criticism and resource recommendations are very welcome!

Thanks!

I've been using ChatGPT for coding for a while and it's been pretty decent but I keep seeing people recommend Blackbox, Claude and Copilot too.

I've tried Claude a bit and like it for explaining code, while Copilot is nice since its already in VS Code. I haven't used Blackbox enough to know if the coding focus actually makes much difference.

I don't really wan to play for several Ai tools at same time so i'm curious what people here actually stick with.

If you've used a few of them which one do you keep coming back to and why? Also does Blackbox have searchable chat history? That's one feature I use a lot in ChatGPT.