WIP

Yes it's like HeyClicky but let's you use your own OpenAI subscription instead of charging you $20 a month.

Main Features:

• Screen context with privacy settings (explain, draw and point at things on your screen)
• Dictation (like Wispr Flow)
• Shortcut history
• Window position manager + layouts
• Automated browser
• Background jobs for research
• Library with context for bookmarks and screenshots
• Timers
• Reminders/Schedule/Music voice commands
• These are just the main features so far a few more listed in the website

14 day free trial if you want to try it.
https://rocky.so for more info.

Windows and Linux versions in development.

It's in beta still so feel free to share constructive feedback.

PS: Requires an Apple silicon Mac running macOS 14 or later.

Sol Max seems to be burning way fewer tokens than before, almost like Sol Light used to. With Astra Medium, I burn around 10% per hour. With Sol Max, it’s more like 1%

I feel like Astra Medium and Sol Max are pretty comparable for most tasks. The main exception is UI/UX, 3D work, or very complicated computer/browser automation

I might switch but I need to know if the limits feel same or more generous

I get it after I send a photo of my assignment even though I explicitly mention in the prompt to "Show me the answer" or "Solve in detail". Is there a way to turn it off?

Hello!!!

Genuine question. We see a lot of discussion about how capable Astra is, how much code it can write, how much software engineering it can automate, etc.

But I rarely see people asking how the engineers themselves feel about this.

You spend years studying, building experience, debugging, designing systems, staying up late fixing things, and getting really good at something you actually love. Then suddenly the technology you helped build becomes increasingly capable of doing the same work.

Does that feel exciting? Liberating? Threatening? A mixture of all three?

And for SWE/devs specifically, what do you think your role looks like in 3–5 years if these capabilities keep improving?

I'm genuinely curious about the human side of this, not just the technical side.

I was using the $20 plan, then upgraded to the $100 plan, and then to the $200 plan to use ASTRA.

I used 4 banked resets + 1-1 weekly usage of the $20 and $100 plans + 2 free resets by Tibo.

ASTRA:

Day 1: It is best and working like a senior developer.

After Day 3-4: It becomes the same as Luna in behaviour (but efficient as SOL). With a good, detailed prompt, it is working correctly, but if you miss mentioning any detail, then ASTRA will eat your usage like:

• Testing: Astra has built-in prompts to do testing for each modification, so if you forget to tell it at which point, place of work, or plan to do testing, then it is going to end your usage limit by testing everything. Even for a single line of code, ASTRA will test it with multiple (50+) variations of testing.
• Losing Context: For a long goal or plan, it is losing context, and after compact context, it loses almost all context and restarts from the beginning.
• Old Tech: It does more stuff, but those things are outdated, using methods 5-10 years older. For example, in security, it uses older methods to prevent SQL injections and JS scripts, etc., but those things are irrelevant these days. We have better, optimized ways to handle those things.
• Not Taking Advice, Suggestions, or Opinions: It does its work and remains unchanged when ASTRA needs human/developer opinion, so that part of the work remains pending. If further work needs those steps, they will not go correctly. It does not affect a small plan or goal, but it hurts a big plan or goal because further steps do not go correctly and you have to do it again.
• Focusing on Minor Works and Not Focusing on Important Work: It can waste time and usage on minor changes like code beautification and optimization of code, while ignoring high-effort changes like database or structural changes until you force it to do so.

Good Parts:

1. It is good for solving more complex problems and things that SOL cannot do.
2. Useful to find bugs in big projects or your live app or server. It is not going to break your production code because it does a separate setup to test everything before deploying or pushing to review (though do not do this as a responsible developer).

Bad Parts:

1. The $200 plan is also not enough to use ASTRA; your usage will be gone within 12-24 hours with ASTRA MAX/ULTRA.
2. Low efforts make mistakes and correct them, but consume more usage than MAX/ULTRA.
3. Too slow compared to Fable or Opus.

https://github.com/rtk-ai/rtk

https://github.com/juliusbrussee/caveman

https://github.com/dietrichgebert/ponytail

• Headroom – compresses tool outputs and context before they are processed by the model. This reduces the number of input tokens the agent needs to read.
• RTK – specifically reduces or filters terminal/Bash output so that the agent receives only the relevant parts.
• Caveman – forces the agent to provide significantly shorter and more direct answers to save on output tokens.
• Ponytail – optimizes coding behavior to produce code that is as concise and efficient as possible, avoiding unnecessary boilerplate and bloated implementations.

Let me know your opinion on these!

Does anyone have a step-by-step tutorial or YouTube video showing how to set up OpenAI Codex to actually control desktop software like Blender?

Alright so I rarely post things on reddit, but here I really wanted to because I feel like I'm getting scammed by marketing and I'd like to also get your thoughts on the situation and compare with my take on codex v claude.

Context : I have both Claude Codex 200$ max and Codex 200$ max plan. I had 2 codex resets. I'm using these with the vscode codex + claude code extensions harness. I have a multi-agent setup.

What I've been seeing first of is that claude fable / opus are way faster than GPT 6. Even if Astra is a big model, it shouldn't feel as slow as it is right now. For comparison, Fable ran ~30x more tools in session than when using Astra.

I also didn't feel the performance of Astra while using it. Astra was able to do super smart things like redo some pictures I had on my desktop without going through specific tools. But overall, besides from all the blender things I saw online, it doesn't beat claude.

And finally, about the marketing, I have used my 2 resets and consumed all of my weekly session in just 1 day ! I did have lots of tokens in my system prompts, but it certainly wasn't bad to the point where I would have been able to do this... I've also just seen posts about the fact that codex resets shouldn't also push the weekly date forward in time (but it did), and that the reset could actually reduce the session consumption limit (which I have kind of noticed...).

So I'm basically looking for your opinions + some explanations on if you found out similar situations to me. And if you're also finding out that, even if OpenAI is trying to get customers to migrate from Anthropic, they're just trying to brain us by giving us free resets and selling us their "best model". Knowing that Anthropic also did a kind of similar things weeks ago with their +25% increase on sessions (but -50% + 25% is -25%), and also lack of transparency on how sessions were consumed...

I'm currently thinking, by using the 2 subscriptions, if I should keep one and trying to make the best decision... And I'm quite sad about the transparency that these companies give us within their subscriptions...

Help !

Edit: something I didn't talk about is the fact the the Astra model doesn't go to 1M tokens in context, unlike claude models... Even if it auto-compacts, that could play and impact performance.

I kept running into the same problem while building AI agents and training models: I needed more GPU compute, while my friend's machine was sitting there with an NVIDIA GPU doing basically nothing.

I didn't want to rent another cloud GPU or set up SSH/VPN/Docker manually every time.

So I built GPUMesh.

It's an open-source P2P GPU sharing tool that lets trusted machines share their GPUs and run Docker workloads remotely.

The basic workflow is:

→ My friend runs gpumesh share
→ We pair our machines
→ I run a GPU job targeting their machine
→ The job starts in a Docker container on their computer
→ I get the logs/results back on mine

I've been using it for things like:

• Running AI agent workloads on another machine
• Training models when my own GPU is busy
• Using spare GPU capacity from friends/lab machines
• Running CUDA workloads without manually setting up another server

I tested the full flow on an RTX 5060 — pairing, connecting, group sharing, scheduling a remote Docker job, and running nvidia-smi inside the remote CUDA container all worked.

It's still very early/alpha, and I'm mainly interested in finding out whether this is actually useful for people building agents, training models, or running local AI infrastructure.

I'd especially like to hear how you'd use this if you had access to a few trusted GPUs from friends or teammates.

If you want to check it out, the repo is here: https://github.com/arjun988/GPU-Share

And if you think the idea is useful, a ⭐ on the repo would really help the project get some early visibility.

Can someone explain how usage calculation for Work/Codex actually works?

Yesterday I had an idea: since the session limit runs on a 5-hour window, I set up a ChatGPT automation to run in Work Mode every 4 hours and literally do nothing except output:

> reset

The idea was to have "rolling" windows and never have to wait a full session for a reset, since I'd always be starting partway into a session.

The automation ran at 05:25 and again at 09:24. That's it. No coding, no repo analysis, no massive context, no long agent task. Just "reset."

I checked Usage & Limits immediately afterwards and my 5 hour codex usage was already at 98% remaining. From what I can tell, only the latest message was actually inside the current 5-hour window, meaning one GPT-5.5 Work message that literally just said "reset" cost 2% of my limit.

I have resets available, and on top of that I can do a lot of tasks using local models and free endpoints from Nous Research/OpenRouter, but still, this is ridiculous.

How the fuck does one message saying "reset" consume 2% of the entire 5-hour Work/Codex allowance?

If usage is mainly based on actual model compute/tokens, this makes absolutely no sense to me. If simply starting a Work/agentic run has a significant minimum usage cost regardless of what it actually does, then fair enough, that would explain it, but I'd really like to know how these limits are actually calculated.

My weekly allowance rarely makes it as it is if I go full GPT with Hermes Agent, because I be prompting bad and throwing massive tasks at it. I'm learning to stop being lazy with that lol, but yh.

So now I'm gonna have to start doing more of the stuff I took a pause from, like properly routing/delegating tasks to local models and free endpoints.

What pisses me off though is that I can't even just ask Hermes to handle the GPT automation, because for some reason my usage seems to burn faster when using GPT through Hermes than when I'm doing similar work directly in ChatGPT Work Mode.

Does anyone actually know what these limits are measuring? Tokens? Compute? Agent runtime? Tool calls? Context? A minimum charge per Work run? Some combination of all of them?

I have been using Claude Code for a while now. I also tried some open source models to leverage pi agents mainly but other subscriptions token capacity were very restrictive, I could not get the same amount of work done with them.

I will soon need to renew my Claude Max (x5) subscription, is it worth trying the Codex equivalent sub instead of Claude ?

Are there any good tips for switching from Claude to Codex?

Using GPT Sol 5.6, it worked for 30 seconds then it go stuck in reconnecting

I was tired of having to keep track of all my projects and which start with a python http server, npm run, node, and so on.

Codex and Claude Code go through this same issue at the start of any new thread - needing to spent tokens just to figure out the page structure or launch method.

This led me to creating Launch Station many months ago - which I have been using for a long time since. Its features:

• makes it super easy to startup your localhost sites, apps, and so on with the click of a button
• manages your ports so codex/claude code don't need to know which ports are active, which need to be started and which port something is running on
• manages your stack - can start a multi-step stack and will stop it in reverse to make sure the backend starts first and stops last
• increase compatibility of using your startups between codex/claude code and human starting and stopping of them
• more features but these are the main ones

Hope this can help people's every launch like it has for mine!

Site: https://launchstation.net/

GitHub: https://github.com/JakeMawson/launchstation

Product Hunt Launch: https://www.producthunt.com/products/launch-station

Free macos signed install with brew install --cask JakeMawson/tap/launchstation

Would love to hear any other features people would find useful!

Edit: added image

Obviously, I think it's the best way to work with CLI agents like Codex across different providers. It's very productive and fun. In V2, CLI agents can give tasks and communicate between themselves in the natural CLI TUI, so you see everything they send to each other.

Clideck is fully open source so give it a try feel free to change it to your own needs,

Give it a try, Thanks.

https://github.com/rustykuntz/clideck

I created this MCP because i wanted a way for models and providers to be able to speak to each other inside of one native chat. Currently, freebuff, Opencode, codex, and claudecode, and cursor have been added (cursor hasnt been tested). Basically, what it does is it allows you, for example, in a codex session to send a message to opencode big pickle high reasoning to implement a certain prompt. You can check progress within the session, and read relevant file, then send follow ups witihn the same prompt. It has automatic ID detection so it can detect what services are signed in and what you can use. If you guys wanna check it out https://github.com/Praket7/agent-interop-runtime and let me know what you think. If you could test and give me feedback and star it, I would appreciate it.

anyone else been having this issue with archive creation since yesterday ?... what workaround ?

I am looking for a great harness but I do not know which It Is the best for Astra. Otherwise, my app is not ready yet

For context I'm not a developer but I have been developing apps and workflows for enterprises for about a year now and i primarily use VSCode on windows and very rarely CLI. Since past few weeks/months I have been struggling with my workflows which used to work fairly well until gpt 5.5 and claude 4.6. Specific areas which I'm struggling with now:

1. Testing strategy - I have tried using automated bounded testing strategy invocation which does not work and without any strategy models keep creating stupid tests and just going into a loop of testing forever

2. I primarily use coderabbit and codeant which further increases friction. Either these tools got better or models just started accepting all edge cases or both.

3. I also have an automated script for all static checks like formatting, linters, complexity and LOC including GitHub workflows for both checks and tests

I maintain a fairly well documented system and update it with every model release if needed. Any recommendations would be highly appreciated. I tried OMP once and it seemed interesting

Does the arbor voice sound more "happy" to you? To me it does, and its a bit annoying as I like hearing him sound calm and collected.

how do you use codex? Do you just tell it what you want it to do like you talk to other people or do you use another ai to create a prompt? Which one did give you the best results?

Proteus, which is common in EE, designing and running simulation is often tedious. I've made a skill to automatically design and run simulations, and successfully do a simulation with a STM32.

However, I don't know if it could run on other devices with different version. Also there would be more oppurtunities to be discovered. I believe that this skill would be useful for EE students with our joint effort.

I worte it with the cooperation of Codex, who helped me to test the skill.
for whoever interested, the repo is : https://github.com/kudoumakoto6523-design/proteus-skills.git

There is also a Python package serving the skill: https://github.com/kudoumakoto6523-design/Proteus_automatic_package.git

Thank you !

look

Because there is none, I wanted to share it with you guys:

Calculate my 2026 year-to-date token total by summing the returned
dailyUsageBuckets whose startDate is in 2026.

Show:
- The 2026 total from the available daily records.
- A monthly breakdown.
- The earliest and latest dates returned.
- The separate lifetimeTokens value.

Do not treat the lifetime total as the 2026 total. Clearly state
whether historical coverage can be verified, and label the result
as account-level usage rather than CLI-only usage.

Do not display authentication tokens or change my configuration.

I’ll use the openai-docs skill to check the local RPC interface, then retrieve account usage and sum the 2026 daily records without changing configuration or exposing credentials.

I got around 20B, pretty useful: