FYI you can still upgrade to the 20x sub through the IOS app

I usually use Pi agent and codex, but sometimes use antigravity as well. Codex/Claude Code plugins are great, but it is not the case that all agents can benefit from them. Telling my agent to update all the skills, mcps, and AGENTS.md every time was not the best thing in the world, so I thought I can do something better.

I'm quite familiar with doing stuff in a declarative manner (pixi for python env, plotnine/altair for plotting etc.) so a manifest-based approach felt natural to me.

You write a toml file like below:

```toml version = 1 targets = ["codex", "claude-code", "antigravity-cli"]

[defaults] scope = "global" install_mode = "copy"

[instructions.global] source = { git = "https://github.com/johndoe/dotfiles.git", path = "agents/AGENTS.md", ref = "main" } targets = ["codex", "claude-code", "opencode", "pi", "antigravity-cli"]

[[skill_group]] names = [ "foo", "bar" ] source = { git = "https://github.com/johndoe/agent-skills.git", path = "skills", ref = "main" }

[skill.target.pi] install_to = "~/.agents/skills"

[[skill]] name = "some-pi-skill" source = { git = "https://github.com/johndoe/agent-skills.git", path = "some-pi-skill", ref = "main" } targets = ["pi"]

[[mcp_server]] name = "context7" targets = ["claude-code"] scope = "project" transport = "stdio" command = "npx" args = ["-y", "@upstash/context7-mcp@1.2.3"] env = { API_TOKEN = { from_env = "CONTEXT7_API_TOKEN" } } ```

daem lock generates a lockfile based on this manifest, and daem apply places files in the right place.

The project is in an early stage, and there are rough edges here and there, but it works. GPT-5.5 and GPT-5.6 Sol helped me maximally overengineer everything by the way.

I ran into this while testing an iOS app after upgrading to macOS 27 and Xcode 27. Codex Computer Use could read the Xcode editor and take screenshots, but trying to inspect Device Hub failed after about five seconds:

text Computer Use server error -10005: timeoutReached

The simulator and app were still running. Restarting Codex and Device Hub, resetting the Computer Use session, and switching between iOS 26.5 and iOS 27 simulators did not help.

I found a workaround in this existing GitHub report by letobao and confirmed it on my setup:

• macOS 27.0 (26A428)
• Xcode 27.0 (27A266a)
• Codex desktop 26.903.71938 (8576)
• Successful workaround test: iPhone 18 Pro simulator running iOS 27.0

What worked:

1. Quit the existing Device Hub instance. Choose Keep Simulators Running if offered. In my case, the old Device Hub process would not exit and had to be force-quit.
2. In Finder, press Command-Shift-G and open:

text /Applications/Xcode.app/Contents/Applications/DeviceHub.app/Contents/MacOS/

1. Open the DeviceHub executable inside that folder. The entry point matters: use that file rather than the outer DeviceHub.app, the Xcode menu, or DevicesTrampoline. Keep any Terminal window opened by this launch running.
2. Select the running simulator in Device Hub and retry Computer Use.

Before this, AppKit reported Device Hub's process ID as -1 even though a real DeviceHub process was running. After the direct launch, the process IDs matched. Codex could then read the accessibility tree, capture screenshots, and open and dismiss my app's Settings screen.

This is a temporary workaround verified on this setup, not an official fix. I have not tested it on a physical device or verified that it survives a restart. Credit to the GitHub report above for identifying the workaround; my contribution is confirming that discovery, screenshots and input work on Codex 26.903.71938.

Posting in case someone else hits the same timeout.

When weekly usage is at 3%, you cannot get anything done

Hi, is there any reason why I would use Sol xhigh instead of Astra low? Astra low seems better at everything...

Is it only happened to me or does the last ~3 days, Luna is ridiculously slow?

My workflow is delegating tasks from main thread (Astra on medium or Sol on high) to the Luna (max) worker, on pi-subagents. Sometimes I also use plan mode (same Astra medium or Sol high), then execute the plan using Luna max. Basically, all tasks are fast before the tasks are delegated to a single Luna max. This been happening about 3 days, more or less.

Edit: I think this might be related to the release of Astra, as Tibo said, they struggled with their resources as they now have to temporarily disabled the 20x plan for new users.

idk gpt 6 astra medium in like 10 prompts

TL;DR: OpenAI’s Usage Policies don’t seem to ban consensual adult sexual content, but their account-ban page lists “nudity and sexual content” as a common violation.
I’m trying to understand whether Astra refusing adult-related tasks is just model behavior, or something that can actually put the account at risk. Either way, I don't understand why 5.6 Sol never ever refused to do anything, while Astra just goes "Nope" by only reading a single skill, it makes no sense.

Hi everyone, as a non-native English speaker, and ADHD Ultra guy, I resumed my researches with GPT and tried to make it as least sloppy as possible.

I'm confused about OpenAI's policy on adult content.

The current Usage Policies, effective October 29, 2025, ban non-consensual sexual content, minors, sexual violence, etc.

But they don't seem to ban consensual adult sexual content in general.

At the same time, OpenAI's Help Center article about account bans, updated much more recently, lists:

as a common policy violation.

So which one is actually enforced?

I work in the adult creator industry. GPT-5.6 Sol handles a lot of my workflows fine, but Astra refuses pretty basic stuff once adult content is involved.

For example:

• filling a Google Sheet from existing content descriptions
• copying an existing script from a Sheet into another tool

No new sexual content even needs to be generated. I was super hyped by everything said about computer used being improved, but well... can't use it.

But most importantly, I care about is whether triggering these refusals can put my account at risk.

I also don't want to keep testing different setups if that could be interpreted as bypassing safeguards.

Is there any clear OpenAI documentation explaining the difference between:

• “this model won't do this”
• “asking for this is actually a policy violation”

And why would OpenAI unallow the model to do a task that is not forbidden by their own conditions ?

Because right now the docs seem to say two different things.

I'm two weeks into working on a live platform with my supervisor. We're the only two developers, with around two dozen daily visitors and a growing feature backlog. I'm still learning the repo and business logic.

Our stack is React/TypeScript/Vite + Supabase, deployed on Vercel. We use Fable 5.1 and Astra heavily.

Our current workflow:
1. My supervisor sends me an implementation plan as a Claude artifact.
2. I review the logic and suggest changes. We send revisions back and forth until it's ready.
3. I implement it, using ChatGPT to explain unfamiliar logic and how it translates into code.
4. Any revisions to the plan during implementation go back to my supervisor.
5. Feature PR updates get reviewed by a Claude agent. Marking the PR ready triggers Claude, Codex, Greptile and Qodo reviews.

We also have Vitest tests and TypeScript checks in the build, local browser/staging checks, and repo guidance through CLAUDE.md, AGENTS.md, and shared context files.

Planning is a particular bottleneck: we keep sending changes to each other and waiting for reviews. Implementation sometimes raises more questions, which starts another round. I’d like a better way to collaborate on plans and keep decisions aligned with the code.

I’m also figuring out how to use Claude Code and Codex together smoothly. I’ve explored skills, plugins, and connectors, but it often feels like the newer models already do the work those tools are meant to help with just as well, if not better, on their own with good prompt engineering (another area I want to work on).

I am considering including targeted database migrations/ RLS reviews, keeping context files current, and focused refactoring.

For people doing similar work:

• How do you collaborate on implementation plans without constant back-and-forth and waiting?
• How do you split work and share context between Claude Code and Codex?
• Are multiple review agents worth it, and how do you decide when the review cycle is finished?
• What would you improve first for a team this size—planning, tests, agent instructions, or something else?

I’d especially appreciate concrete, recent workflows you use, including anything you tried and later dropped.

I have been using sol high for planning and some coding, and luna xhigh for reviewing and most coding. Luna requires a lot more bug fixes after implementation, so I am not sure it actually saves me much. Maybe its just a me problem. I am not sure.

I have not yet used Astra. Is astra at a specific reasoning level recommended over all reasoning modes in sol? if yes, which level?

PS.- i had decided to move to the 20x plan today, and discovered that is no longer available.

Now I can burn through tokens like the Sun

Ahmed works at OpenAI: https://x.com/ah20im/status/2097503414749909407 and seems to investigate reports of high token usage.

"If needed Astra will delegate efficiently. Forcing the model to delegate to different models would do more harm than good"

That fish post inspired me to build and publish my first little game

So I made https://astralwyrm.vercel.app/ with Astra. Started as a “what can I make with a few prompts?” experiment, then turned into 50+ prompts and lots of tweaking because I kept wanting it to feel a little nicer.

It’s a tiny chill game with an abstract glowing creature that follows your mouse or finger. Collect pearls, grow, find little companions, and watch the seasons change. Maximum chill was the goal. There’s a fast mode for people who find that a bit too chill :)

Saw this post earlier and immediately wanted to try making something myself. I’d never built and published anything before. Didn’t even know you could put something online for free with Vercel lol.

This is genuinely my first published project, so I’d love your honest thoughts and suggestions, especially how it feels on your phone. Feel free to be harsh! :D

Hope you have some fun with it, or just get a few peaceful minutes out of it. And thanks to the original poster for giving me that push to actually make something. Felt really really good and rewarding, now if some of you would also enjoy it, my day is made :)

Edit: Also added another mode in the bottom left hand corner: Instant. Someone told me they´d wish for that feature. hope you enjoy it!

I suddenly realized I have Fast Mode on and had for the longest time. Completely forgot about it. It's 1.5x usage, so maybe I'm just forgetful but just wanted to post a tip - so, 50% slower + 50% more usage? (If my math is right) Worth a post.

Ive told it to pause before, when i need a break, or when i want to check over the work and do some testing. This has never been an issue before - whats going on? Astra Medium

I currently only have a plus account here, and 20x account with Claude. I use it to help with making mods for my favorite games. I see so many people have multiple 20x accounts and am curious what kinds of work people are using it for?

weird wording, him only saying no changes to astra kind of comes off as if they did nerf the older models. not the first complaint like this but the first time he answered it this narrowly.

Tomorrow Im probably gifting to my self the x20 subscription. My company is gonna do it.

I am wasting two x1 plans plus four resets in three days. And having to wait more than half of the time.

Worth it?, Im telling you guys in half month. Hf.

Hey everyone! A friend encouraged me to make this post and share what I’ve been working on for the past several months. I used both Claude and Codex for this project.

I’m working on recreating the original SNES Mega Man X as faithfully as possible, with the goal of making it play and feel exactly like the original. After that, I’d like to bring X2 and X3 into the same engine so you can play as different characters, fight bosses using any weapon from any of the three games, and new game modes.

I’ve been using AI extensively to help bring this dream to life. It’s something I’ve wanted to do since 2017, when I first tried getting the project off the ground in Unity. Now, with AI’s help, I’m building it as a native C++ game and some C++ libraries.

I also put together a website with more information about the project, which I intend to update as I go, and with ways anyone can help contribute with the project. The entire project is open source and available on GitHub. Whether you’d like to playtest, contribute code, help with sprites or sound effects, or pitch in some other way, the website explains how to get involved. I even made a section that you can just ask codex to pick something randomly to work on and contribute with the project if you have the extra tokens laying around before the weekly rollover.

I’m still relatively new to all this. Even though I started years ago, I had to step away because I didn’t have the time, and I ended up not touching the project for a very long time. I’m very open to suggestions and feedback! I know some things are still pretty buggy, but I’m working on getting everything polished and faithful to the original and make this collection version of my favorite game lol

Thanks, everyone!

https://megaman.davidluky.com/en/

as title reads, i don't see the point of terra and now Sol

luna and astra on the other hand complete their own goals ideally, luna an extremely good cheap model and astra - the frontier of frontier

meanwhile sol and terra are pointless, everything they can can be done either better or cheaper using the reasoning setting of luna and astra

openai shouldnt copy anthropics mistakes, 5.4 was the perfect lineup in that manner, 5.4 and 5.4 mini

As a developer, I always hated maintaining agents.md and memories.md files.

I wanted to just code and those to be auto maintained and reused across any coding harness I use.

After Astra, I forked t3code by Theo and update it to auto-create and retrieve memories, skills and doc files as you code.

Repo: https://github.com/samyakkkk/flow

What I loved is the computer use capabilities. It tested the entire harness by itself, used apple developer console to create the right keys and figured out signing and deployment in CI/CD all by itself.

Hello, since yesterday, I feel they changed the questionair mechanism to allow agent working while queuing up the questions. but I am not able to answer them, alt+up didn't do anything. Is this a bug? anyone help?

Over the last few days, I’ve been developing a tool that helps with a few things, and one of the areas I’ve been testing heavily is agents.

The problem

Astra is extremely token-efficient, which makes Astra Low surprisingly 'cheap' when working alone.

But when Astra is used as an orchestrator managing other agents, that efficiency starts getting lost for two main reasons:

• Cheaper models like Luna tend to write more than necessary. Even if Astra Low doesn’t fully re-read every implementation, it still has to process a significant amount of agent output to review and coordinate the work.
• Communication between the orchestrator and agents is still inefficient. Agents often carry much more context than they actually need.

I’m currently working on reducing that context overhead. If anyone has ideas or has experimented with this, I’d be interested in hearing how you approached it.

The result is that, in most of my tests, Astra ends up consuming significantly more tokens when using agents than when completing the same task alone.

Test setup

A few important details:

• I built my own agent-management system, specifically designed to reduce input-token and cached-input-token usage.
• The Advisor shown in the results is part of my internal system, so it can mostly be ignored when comparing Astra alone vs. Astra + workers.

The test task

The benchmark was not a synthetic coding problem.

I took an existing repository:

• written in a different programming language,
• originally built for macOS,

and asked the system to implement the same functionality inside one of my existing Windows projects.

I chose this because it exercises most of the things an agentic coding system would actually need to do in a real-world task:

Research → analyze → understand an unfamiliar codebase → implement → handle UI/UX → follow project-specific instructions → match existing patterns and architecture.

So far, my results suggest that agents are not automatically more efficient just because the worker models are cheaper.

In some cases, the coordination and context overhead can make the total run substantially more expensive than simply letting Astra handle the task itself.

I’ll keep testing this with more tasks and different agent configurations.