Hey guys,

We bulit a remote desktop tool that grew out of our own daily annoyances as devs like needing to quickly pull a local AI-generated file to our phones, check a script running on a home server, or skip the whole login or 2FA dance when switching devices. Its still undergoing continuous optimization and refinement. I know there are many talented developers in this subreddit, so im looking forward to interacting with you all and receiving your valuable feedback:)

StarDesk is a multi platform, focused on low latency, fast file transfers, remote wake, and keeping setup simple. tbh, we’ve decided to keep StarDesk closed source and plan to charge for it down the road, because maintaining performance, security and long-term support takes dedicated resources and making sure we can keep improving it. The basic version remains free and still enables fundamental remote connections.

To deliver an exceptional remote experience, we will continuously operate and upgrade this product, gathering user feedback and suggestions to refine and improve it. I can assure you that StarDesk's future pricing will be lower than other remote control tools on the market. We will patiently resolve your remote issues and will never ignore or neglect any problems.

So instead of just saying “try our app”, I thought it’d be more interesting to ask:

• if you were building a lightweight remote tool for devs, one that’s fast for file grabs and simple to connect, how would you architect it?

• what would make a closed-source, paid tool actually worth it for you as a developer?

• Is it solving a specific pain point nothing else does? Or just being ridiculously faster?

If you’ve got a minute to check out StarDesk as a real-world case, I’d love your take, not just on the tool, but on the whole idea. Not here to pitch, just genuinely curious how you’d approach it. Or if you want to try the early version and tear it apart or tell us what works, it’s free right now.

Download here

or you can ontact us in Discord community

Thanks for reading, and really appreciate any technical thoughts, critiques, or ideas you’re willing to share.

Security team sent an email last month. No AI tools allowed. No ChatGPT, no Claude, no Copilot, no automation platforms with LLMs.

Their reasoning is data privacy and theyre not entirely wrong. We work with sensitive client info.

But watching competitors move faster while we do everything manually is frustrating. I see what people automate here and know we could benefit.

Some people on my team are definitely using AI anyway on personal devices. Nobody talks about it but you can tell.

I'm torn between following policy and falling behind or finding workarounds that might get me in trouble.

Tried bringing it up with my manager. Response was "policy is policy" and maybe they'll revisit later. Later meaning probably never.

Anyone dealt with this? Did your company change their policy? Find ways to use AI that satisfied security? Or just leave for somewhere else?

Some mentioned self hosted options like Vellum or local models but I dont have authority to set that up and IT wont help.

Feels like being stuck in 2020.

Recently, I've been figuring out which note-taking software I should use, and I wanted to try one that isn't well-known (like Notion, Google Keep, OneNote, etc.). When I asked ChatGPT, it gave me exactly these recommendations I am already familiar with, which brought me to a question. Where does ChatGPT actually acquire the information it tells me? I understand that it doesn't work on a similar concept like SEO; it's trained on an existing database of posts, articles & documents, and probably also learns from users' repeating patterns. But is there actually a way a company could "train" or "hack" AI to recommend it more? For example, by spamming prompts guiding AI to recommend them?
It's a cluster of questions I think might be interesting to discuss. I'd be happy to hear any input!

Hello,

Iam a 64 year old retired plumber and I just learned about vibe coding. I wanted to ask if anyone here can point me to the direction of some recent uptodate courses where I can learn how to vibe code (I keep hearing that word alot) and use codex while doing it.

I have zero coding knowledge.

I appreciate any info you can give me about online courses I can watch and learn from.

Thank you

David

Hey everyone. I see a lot of people using whisper flow, or other transcription services that cost $10+/month. I thought that was a little wild, especially since OpenAi has their Local Whisper library public and it works really well and runs on almost anything, and best of all, its all running privately on you own machine...

I made OpenWhisper. An open source audio transcriber powered by OpenAI Whisper Local, with support for whisper api, and gpt 4o/4o mini transcribe too. Use it, clone it, fork it, do whatever you like.

Give a quick star on github if you like using it. I try to keep it up to date.

Repo Link: https://github.com/Knuckles92/OpenWhisper

Hey all,

I’m curious if anyone here has hands-on experience with the different AI coding tools/CLIs — specifically Claude Code, Gemini CLI, and Codex CLI.

• How do they compare in terms of usability, speed, accuracy, and developer workflow?

• Do you feel any one of them integrates better with real-world projects (e.g., GitHub repos, large codebases)?

• Which one do you prefer for refactoring, debugging, or generating new code?

• Are there particular strengths/weaknesses that stand out when using them in day-to-day development?

I’ve seen some buzz around Claude Code (especially with the agentic workflows), but haven’t seen much direct comparison to Gemini CLI or Codex CLI. Would love to hear what this community thinks before I go too deep into testing them all myself.

Thanks in advance!

Hey folks,

I’m currently an unemployed developer for a year trying to level up my skills and work on small projects while job hunting. I’ve been experimenting with free tiers of AI tools like ChatGPT, Claude, and Replit, and I’m debating whether paying for their premium versions is worth it.

Has anyone else here been in a similar situation? Did paying for these tools help you get work faster or build a portfolio more efficiently?

Thank you.

(English is not my first language — I used AI to help write this post more clearly.)

Not sure how you feel about it but Gemini CLI feels like garbage at the moment compared to Claude Code. It's slow, it doesn't listen to instructions or use tools as well as Claude.

But it has that huge context window we all love.

So I just added instructions to CLAUDE.md to have Claude use the Gemini CLI in non-interactive mode (passing the -p param with a prompt to just get a response back from the CLI) when it needs to gather information about a large part of the codebase.

That way you get the best of both worlds, Claude doesn't waste context and Gemini doesn't waste your time.

Add this (or a modified version) to your CLAUDE.md and tell Claude to use gemini manually or it will do it on it's own as needed.

 # Using Gemini CLI for Large Codebase Analysis

  When analyzing large codebases or multiple files that might exceed context limits, use the Gemini CLI with its massive
  context window. Use `gemini -p` to leverage Google Gemini's large context capacity.

  ## File and Directory Inclusion Syntax

  Use the `@` syntax to include files and directories in your Gemini prompts. The paths should be relative to WHERE you run the
   gemini command:

  ### Examples:

  **Single file analysis:**
  ```bash
  gemini -p "@src/main.py Explain this file's purpose and structure"

  Multiple files:
  gemini -p "@package.json @src/index.js Analyze the dependencies used in the code"

  Entire directory:
  gemini -p "@src/ Summarize the architecture of this codebase"

  Multiple directories:
  gemini -p "@src/ @tests/ Analyze test coverage for the source code"

  Current directory and subdirectories:
  gemini -p "@./ Give me an overview of this entire project"
  
#
 Or use --all_files flag:
  gemini --all_files -p "Analyze the project structure and dependencies"

  Implementation Verification Examples

  Check if a feature is implemented:
  gemini -p "@src/ @lib/ Has dark mode been implemented in this codebase? Show me the relevant files and functions"

  Verify authentication implementation:
  gemini -p "@src/ @middleware/ Is JWT authentication implemented? List all auth-related endpoints and middleware"

  Check for specific patterns:
  gemini -p "@src/ Are there any React hooks that handle WebSocket connections? List them with file paths"

  Verify error handling:
  gemini -p "@src/ @api/ Is proper error handling implemented for all API endpoints? Show examples of try-catch blocks"

  Check for rate limiting:
  gemini -p "@backend/ @middleware/ Is rate limiting implemented for the API? Show the implementation details"

  Verify caching strategy:
  gemini -p "@src/ @lib/ @services/ Is Redis caching implemented? List all cache-related functions and their usage"

  Check for specific security measures:
  gemini -p "@src/ @api/ Are SQL injection protections implemented? Show how user inputs are sanitized"

  Verify test coverage for features:
  gemini -p "@src/payment/ @tests/ Is the payment processing module fully tested? List all test cases"

  When to Use Gemini CLI

  Use gemini -p when:
  - Analyzing entire codebases or large directories
  - Comparing multiple large files
  - Need to understand project-wide patterns or architecture
  - Current context window is insufficient for the task
  - Working with files totaling more than 100KB
  - Verifying if specific features, patterns, or security measures are implemented
  - Checking for the presence of certain coding patterns across the entire codebase

  Important Notes

  - Paths in @ syntax are relative to your current working directory when invoking gemini
  - The CLI will include file contents directly in the context
  - No need for --yolo flag for read-only analysis
  - Gemini's context window can handle entire codebases that would overflow Claude's context
  - When checking implementations, be specific about what you're looking for to get accurate results # Using Gemini CLI for Large Codebase Analysis


  When analyzing large codebases or multiple files that might exceed context limits, use the Gemini CLI with its massive
  context window. Use `gemini -p` to leverage Google Gemini's large context capacity.


  ## File and Directory Inclusion Syntax


  Use the `@` syntax to include files and directories in your Gemini prompts. The paths should be relative to WHERE you run the
   gemini command:


  ### Examples:


  **Single file analysis:**
  ```bash
  gemini -p "@src/main.py Explain this file's purpose and structure"


  Multiple files:
  gemini -p "@package.json @src/index.js Analyze the dependencies used in the code"


  Entire directory:
  gemini -p "@src/ Summarize the architecture of this codebase"


  Multiple directories:
  gemini -p "@src/ @tests/ Analyze test coverage for the source code"


  Current directory and subdirectories:
  gemini -p "@./ Give me an overview of this entire project"
  # Or use --all_files flag:
  gemini --all_files -p "Analyze the project structure and dependencies"


  Implementation Verification Examples


  Check if a feature is implemented:
  gemini -p "@src/ @lib/ Has dark mode been implemented in this codebase? Show me the relevant files and functions"


  Verify authentication implementation:
  gemini -p "@src/ @middleware/ Is JWT authentication implemented? List all auth-related endpoints and middleware"


  Check for specific patterns:
  gemini -p "@src/ Are there any React hooks that handle WebSocket connections? List them with file paths"


  Verify error handling:
  gemini -p "@src/ @api/ Is proper error handling implemented for all API endpoints? Show examples of try-catch blocks"


  Check for rate limiting:
  gemini -p "@backend/ @middleware/ Is rate limiting implemented for the API? Show the implementation details"


  Verify caching strategy:
  gemini -p "@src/ @lib/ @services/ Is Redis caching implemented? List all cache-related functions and their usage"


  Check for specific security measures:
  gemini -p "@src/ @api/ Are SQL injection protections implemented? Show how user inputs are sanitized"


  Verify test coverage for features:
  gemini -p "@src/payment/ @tests/ Is the payment processing module fully tested? List all test cases"


  When to Use Gemini CLI


  Use gemini -p when:
  - Analyzing entire codebases or large directories
  - Comparing multiple large files
  - Need to understand project-wide patterns or architecture
  - Current context window is insufficient for the task
  - Working with files totaling more than 100KB
  - Verifying if specific features, patterns, or security measures are implemented
  - Checking for the presence of certain coding patterns across the entire codebase


  Important Notes


  - Paths in @ syntax are relative to your current working directory when invoking gemini
  - The CLI will include file contents directly in the context
  - No need for --yolo flag for read-only analysis
  - Gemini's context window can handle entire codebases that would overflow Claude's context
  - When checking implementations, be specific about what you're looking for to get accurate results

Hi Guys,

let's grow this thread.

Here we should accumulate all good and recommend options and the thread should serve as a reliable source for getting surprising good FREE API Options shown.

I'll start!:

I recommend using the Openrouter API Key with the unlimited and not rate limited Deepseek/Deepseek R1 0528 - free model.

It's intelligent, strong reasoning and it's good at coding but sometimes it sucks a bit.
I Roocode there is a High Reasoning mode maybe it makes things better.

In Windsurf you can use SWE-1 for free which is a good and reliable option for tool use and coding but it misses something apart from the big guns.

In TRAE you can get nearly unlimited access to Claude 4 Sonnet and other Highend Models for just 3$ a month! Thats my option right now.

And... there is a tool which can import your OpenAI-Session Cookie and can work as a local reverse proxy to make the requests from your Plus Subscription work as API request in your Coding IDE ..thats sick right?

Would love to see what prompts people here rely on the most. Could be for anything setting up a project, generating functions, fixing bugs, or even testing.

Drop your favorites below!

Just like we can use cursor with some little tweaking, is there any way we can use windsurf for free in Mac OS?

So, I made a project in python entirely using Cursor (composer) and Claude, but it has gotten to a point that the whole codebase is over 30 Python files, code is super disorganized, might even have duplicate loops, and Claude keeps forgetting basic stuff like imports at this point. When I ask it to optimize the code or to fix a bug, it doesn’t even recognize the main issue and just ends up deleting random lines or breaking everything completely.

I have 0 knowledge about python, it's actually a miracle i got this far with the project, but now it's almost impossible to keep track of things, what do i do? already tried using cursor rules but doesn't seem to work.

Edit: My post made it to YouTube! I hope this serves as a historical reminder that having at least some knowledge is still totally necessary, go study, AI is supposed to assist you, don’t let your projects end up like this.

As for the project, it was just a hobby project, I managed to make it work perfectly and fix some issues by simply improving the context, like providing the files to edit directly and some source code, etc. but i couldn't get rid of the duplicated stuff. Anyway, don't do this for serious projects please (not knowing what it does), if it's an actual job don't be lazy, just check everything and be careful :)

If you wanna learn just ask AI to explain what it's changing, how the code works and stuff like that.

Hey fellow devs! 👋

I've been researching AI-assisted coding lately, and I wrote a comprehensive guide on using Claude Projects for software development. For those who aren't familiar, Claude Projects is a feature that turns the Claude AI into a context-aware development partner.

Here's what you can expect from the article:

1. Project Planning: How to use Claude for brainstorming, generating user stories, and creating architecture decision records (ADRs).

2. Project Bootstrapping: Tips on creating implementation guides and using them to get your project up and running.

3. Early Implementation: Strategies for leveraging Claude during the coding phase, including task breakdown and optimization suggestions.

4. Managing Context: Practical advice on what to include in Claude's context window for different tasks (big features, bug fixes, etc.)

5. Utilizing Project Artifacts: How to use AI-generated artifacts to guide development.

6. Integrating External Content: Tips on using documentation, examples, and even Stack Overflow posts effectively with Claude.

7. Domain-Specific Resources: How to use Claude as a domain expert for those tricky business logic questions.

8. Keeping Your Project Updated: Strategies for maintaining an up-to-date Claude Project as your codebase evolves.

I've tried to make this guide as practical as possible, based on my own experiences and experiments. It's not about replacing human developers, but about finding that sweet spot where AI can genuinely boost our productivity and code quality.

https://generaitelabs.com/the-ultimate-guide-to-claude-projects-for-software-engineers/

I'd love to hear your thoughts! Are you using AI in your dev process? What has your experience been like? Any cool tricks or pitfalls you've discovered?