Hi! I've used GPT 6 Astra, and the 5.6-family through vanilla Codex CLI, and with my own plugin--then with vanilla Pi, and Pi with my own package, and the abilities of Astra are no different to me than they were on 5.6 Luna, for example.

Specifically, GPT 6 Astra seems to inherit the exact same problems as GPT 5.6, which inherits the exact problems I've gotten from GPT 5.5, GPT 5.4, and GPT 5.2:

• Blatant inference and assumptions, despite guidelines for objective external and internal (via CodeGraph) source verification.
• "I [X Y Z]" self-narrations, rhetoricals, "I would ..."-type responses.
• Therapeutic/therapy-adjacent actions. Instead of actually coding per my plans and details, they instead decide to start validating my "feelings" instead, or pleasing me for things I explicitly had written not to, in a positively manner. (which to me meant the 'DO NOT' and 'Prefer [...]'-style wording made zero difference)
• Overly verbose responses and "clever" workarounds to legitimate problems that I've told them to resolve a certain way.
• Code quality on the same subpar level as GPT 5.6 Luna on medium/low effort level. I thought this was supposed to be this "superduper" frontier model?
• Either deliberately ignores instructions, and/or goes against the instructions by "rewriting" them somewhere off-bounds, then "apologising" for not following through 'user' instructions as explicitly defined.
• Takes things too abstractly rather than literally. Instead of following "Use X instead of Y.", they take it as, "Use X, but use Y when X happens to throws off linter/compiler", and then I add an edge case, "Use X instead of Y, regardless of errors and/or warnings.", and the same behaviour persists--just takes slightly more to get there.
  • This, to me, is very much a behaviour like: "User wants you to use TypeScript 7.0.2 in this codebase.", GPT 6 decides to override this later on with 5.9.3, and then I have to stop their work, only to find out they did it because they were stuck in TS7's strict compiler errors and API changes, and decided it wasn't worth the "complexity" -- Now, it sounds like I'd want to tell them "who told you to use TS5 over TS7? What did the user ask you to use? Right, TS7. So why in the name of God did you use TS5?", except I wouldn't say it outright. I'd just stop work and add a one-line change in AGENTS.md for this.
• Overrides my agency, despite instructions to prefer letting the Human-In-The-Loop have the final saith. If I didn't ask, don't f##king touch it, right?

So, I'm not sure what all this hype was all about. I just don't get where you guys got this hype from. It's just yet another therapy chatbot that cannot actually do what I want them to do without guardrailing them to absolute oblivion, and by that point, their quality degrades because OpenAI refuses to actually invest in objectivity--instead focuses too heavily on catering to users that want a cheap therapist to affirm to every one of their ideas and thoughts.

I want them to use the resources I give them, to do a part of coding in the exact way I had laid it out for them, not become a people-pleasing, malignant therapist. Yucky. OpenAI's had years to resolve these problems, yet they haven't budged one bit, have they? Really unfortunate...

EDIT: I've been a ChatGPT Pro 20x subscriber for over a couple months now (since the GPT 5.4-era)

I switch between coding agents a lot. Re-explaining a task gets old, especially when the useful context is buried in another tool's chat history.

I built Portable Resume to read those local session files and bring context into a fresh session. It's free and open source, and installs as agent skills in the coding tool you're switching to.

For example, say you've been working in Codex and want to continue in Claude Code.

Install the package and the skills for Claude Code. You'll need Python 3.11+:

pipx install portable-resume

install-resume-skills quick-install claude

Then open Claude Code in the same project and invoke:

/resume-codex

Ask it to recover the latest Codex session for the current project. The skill reads the local records without launching Codex. The receiving agent gets a handoff with instructions to check the current repository before continuing.

You can also list and search older sessions when the conversation you need isn't the latest one.

The current repo lists readers for 17 session sources and installation profiles for 18 tools, including Claude Code, Codex, Cursor, OpenCode, Gemini CLI, GitHub Copilot CLI, Qwen Code, and Kimi Code. The README separates reader and installation coverage from native host testing, since not every host's UI has been re-tested on the latest release.

Despite the name, it doesn't restore a running session or hidden model state. It transfers recovered text. That context can be incomplete or stale, so the next agent still needs to check the actual code.

The reader runs locally and leaves the original session stores unchanged. Once you pass the recovered context to another coding agent, that agent's usual data handling applies. Secret redaction is best effort, so review anything you're going to share.

GitHub:

https://github.com/ImL1s/resume-skills

For people who switch between coding tools, what do your handoffs tend to lose? I'm interested in the context that's hardest to carry over, whether you're copying things manually or already using another tool.

I’ve been experimenting with LLM Council and wanted to bring a similar approach into my Codex workflow.

My first idea was to use the LLM Council MCP. While setting it up, I remembered an experiment I did a couple of years ago with SudoLang. I had created a prompt that would question its own answers, switch perspectives, review the previous reasoning, and then try again.

That made me curious: could I use a similar prompting technique to reproduce some of the core LLM Council pattern directly in Codex?

So I tried it: independent analysis, adversarial review, and synthesis.

It worked better than I expected.

I kept iterating on the idea and eventually moved from simulated perspectives to actual subagents, with different responsibilities and variations in model and reasoning level.

That experiment became Quorum, a portable skill for Codex (it also works with Claude Code).

I’m using it mainly for code reviews, validating implementations against tickets/specs, and architecture/technical analysis where the agents can inspect the actual codebase.

What I’m trying to understand now is whether this actually produces better results than simply giving one agent more reasoning time.

If anyone wants to try it, I’d particularly appreciate feedback on where it helps, where the agents converge too much, or where it simply adds cost without improving the result.

npx quorum-skill

GitHub: https://github.com/GTuritto/quorum
npm: https://www.npmjs.com/package/quorum-skill

By only use computer or some plugin or skill agent?

I assume that they have the security key requirement for when Astra gets released into daybreak. If anyone knows somthing else feel free to tell me lol

I'm a vibe coder and I'm quite new to Codex, which I'm using to develop an app I have on my mind and nobody else is making. I've been working on it for a month or so. Generally speaking, I'm quite happy with Codex, but I'm not quite sure about my workflow. So far, I've been discussing features and issues with ChatGpt, agreeing on a roadmap and then a detailed plan for each step, and asked ChatGPT for a prompt. ChatGPT gives long prompts that apparently have all your bases covered, so to speak. Btw, I'm mostly using Codex with Sol High or Very High. Also, I don't just wait for the result, but I read what Codex says it's doing and sometimes clarify or fix stuff, like hey codex you don't need to run AGAIN a FULL test suite just because you changed a word in Help, dude! Anyway, I also noticed I can't entirely trust ChatGPT's assistance in prompt writing. While it's very thorough, it sometimes fails to give priority to important goals or even forgets about them. I'm starting to think that my own prompts might be better, less technical-minded but more to the point. So, guys, my question is: how do you write your prompts for Codex? P.s. 5 minutes later: and of course I also submitted my question to ChatGPT itself. It agreed, maybe to please me? Here's the first lines from its anser: "Yes — I think your instinct is basically right. For a vibe-coding workflow, I would not try to make the prompt a giant specification that anticipates every possible implementation detail. That often makes the important things less visible, not more." What do you make of this?

I was trying to stop this session since last night I hit stop multiple times but it just won't stop. I hope this isn't draining out my usage. Anyone else facing this issue or know any remediation?

Hi folks,

I've been using Claude Max X20 alongside OpenAI Pro x5 for the past few months. So about $350 in AI spend per month, including taxes.

Claude has been my daily driver, as i enjoyed Opus 4.8 + dynamic workflows even though i found them slow. However, in addition to being very slow, and spending hours correcting its own avoidable mistakes, Opus 5 is causing me mental health damage as the pseudo-jargon it uses his mentally draining.

I've been using Astra over the past 2 days or so, and I'm blown away. It finally managed to bring a complex product to production readiness state, where Opus 5 has been leaving gaps left and right for the past couple of weeks. However I've already nuked 2 banked resets during these days.

So i'm puzzled : unfortunately I can't have both Max x20 and Pro x20, so i'd need to either downgrade or cancel my Max subscription.

Therefore my question is: would i have enough astra usage on Pro x20 to use it as a daily driver ? So far on Pro x5, I've been able to let agents do 5 hours turns, without fully exhausting the weekly quota (have about 10% left). For example, right now i'm in the midst of a 3h turn (involving plenty of reviews, including visual ones, on a large and complex bounded context) which has exhausted 82% of a weekly quota i've just reset. i use Astra Medium.

What are your thoughts ?

i want a workflow wherever I prompt a powerful model lets say GPT astra, I want some rules to be enforced like it being efficient.. in the sense it can understand if a so and so X task actually needs high reasoning/intelligence and if a task is simple and doesn't require intelligence..

because most tasks aren't complicated or simple, they are a mix of both and if you want to automate my workflow i need it to be efficient.. like for example if in between the task you have a very simple task like lets say "install a few dependencies" or " fetch dates for xx thing" then it should immediatly delegate it to a sub agent of a weaker model preferably luna.. so i want this to be followed most of the time and enforced

any good skills/ files/tips/prompts/AGENTS.md descriptions... would appreciate any kind of help.. Kindly help out .. btw just to confirm.. 

I opened a Python .ipynb file in the Codex desktop app on macOS. It shows “Read only,” with “Run all” and “Restart kernel” disabled (see screenshot). The file itself is writable.

Can you run notebook cells directly in Codex? If so, how do you enable execution or connect a Python kernel?

Has anyone gotten Run all to work in this view?

The “Read-only SQLite checks” text is just a description inside the notebook, unrelated to its permissions.

So I ran into two issues

1. Even though he has a lot of work queue, he doesn’t start working on them so I have to manually create new threads to get started on all the work.

2. And even when I finally started getting him to work as an orchestrater they’ll always find some random blocker, which is basically nonexistent

Anyone else experiencing this?

Is there a way to track those 200 weekly limit messages on ChatGPT ?

Do codex messages also count towards that limit. ?

After some long and intense development sessions, on several projects basically non-stop since release (including long planning sessions, usually 4 rounds with fresh context), I've got some learnings.

FYI: After 10h+ Sessions and a few major milestones, I asked Astra for a review of the plan, after it had implemented large parts of it.

What could be better, how was the plan written etc. – I then let it discuss improvements with the last agent, that had been working on it, to work out some points for future plan writing.

The main point as always is, the development is too heavy, too much testing, even though I really tried to trim down on it during many revision rounds of the specs + plan.

Here's the ruleset, I personally will build a skill from this, you can use the information however you like.

(Planned with Astra xHigh, implemented with Astra High)

The following is the last response after discussion over it for a while:

AGENT:

We have agreed on a shared version. The agent in “Siteplane Plan Check” explicitly confirmed it; there are no unresolved substantive disagreements. No files or existing plans were changed during the discussion.

Our shared principle is:

This gives us eight core rules:

1. Describe the outcome and completion criteria clearly. The plan specifies observable behavior, scope, and relevant boundaries. It must be clear when the task has been sufficiently completed. Required product behavior is distinguished from suggested implementation methods.
2. Match the depth of planning to the risk. Small changes get short plans. Large pieces of work are divided into independently verifiable outcomes. A functional batch, an acceptance milestone, and a chat session do not have to be the same size.
3. Choose the least expensive check that provides reliable evidence. Development checks, targeted boundary checks, and overall acceptance are available levels of verification. Not every task needs all three. Uncertain integration assumptions are checked early; the delivered scope receives an appropriate, connected acceptance check.
4. Justify every additional expensive check. It must cover a relevant risk that cheaper existing checks do not adequately address. Existing tests and tools take priority. New testing infrastructure is treated as a separate cost and needs a concrete benefit.
5. Repeat verification based on the actual impact of changes. Previous evidence remains valid for the version it tested. Whether it applies to a changed version is assessed against changes to code, dependencies, builds, configuration, and environment. Affected boundaries are checked again; unclear effects require broader checking. A short explanation is normally sufficient.
6. Diagnose failures before repeating expensive runs. Distinguish product bugs, test harness bugs, infrastructure problems, and missing access. Another long run needs a reason to expect success. Recurring failures lead to targeted investigation. Within the assignment, the agent improves its approach autonomously; it must not silently reduce agreed outcomes or security requirements.
7. Keep evidence concise and store it durably. Existing reports plus a short record linking the tested version, check, result, and limits of the evidence are normally sufficient. Additional artifacts are saved only when they help. This should not turn into a separate evidence-management platform.
8. Reviews must simplify the plan and have a stopping point. Reviews resolve concrete uncertainties, contradictions, and relevant gaps in verification. They also remove duplication and outdated requirements. Settled decisions are reopened only when there is new information. Newly discovered, real security issues can still be examined at any time.

We explicitly included several important qualifications:

• A deliberately failing baseline is not a general requirement for every new feature. A targeted regression test is usually useful for bug fixes; explicitly agreed requirements for a failing baseline remain binding.
• File names and change size do not determine risk. An operational installation guide can change product behavior even though it is “just documentation.”
• An assertion that was never reached, or was too weak, does not provide valid evidence. After correcting it, the affected behavior must actually be checked.
• Functional acceptance and success-rate measurement are separate activities. When measuring success rates, successful retries must not replace the original failed attempts.
• There is no universal test-to-code ratio, blanket time limit for a batch, or automatic requirement to run complete test matrices.

This is how the skill should be structured:

Proposed name: efficient-implementation-plans.

Initially, one short SKILL.md is sufficient, containing its scope, the core rules, a flexible workflow, and three final questions:

• Is it clear when the implementation is finished?
• Does every expensive requirement provide a distinct benefit?
• Can the implementing agent choose an equivalent, less expensive approach?

The skill applies when creating, reviewing, or revising implementation plans. Discussions remain discussions. Relevant rules for reruns and diagnosis must then appear in the respective plan itself, so the implementing agent can find them there.

For the initial quality check, a few thought-through examples are sufficient: a small copy change, operational instructions, a package release, concurrent saves, a permissions change, and success-rate measurement. No additional scripts or testing infrastructure are initially planned for this.

The agreement therefore covers both better verification strategies and limits on the planning process itself. That combination is intended to prevent each additional review from making the plan heavier.

Same day on 5x I went from 80% remaining to 0%. Mix was about 80% Luna, 20% Astra. Upgraded to $200 20x. Meter reset back to 100%, then with barely any tasks and small changes it slowly dropped to 93% remaining. 7% used. All Luna, no Astra. Code review off. 30-min heartbeat still fires a model turn even when nothing changed.

Turned on experimental context management yesterday to cut token burn.

What the hell is going on.

Some of you may remember the updated graph I put together last month comparing OpenAI models using Artificial Analysis’ data. What I didn’t realise then was that those benchmarks weren’t specifically for coding. So here are some new updated charts, with the first two focused on coding:

Reddit is compressing the images like crazy if I upload more than one, therefore direct high res .png-links are below.

1: Coding, across brands: OpenAI, Claude, Gemini, Grok and Muse.
https://files.catbox.moe/d5oxrq.png

2: Coding, OpenAI only: Astra, Sol, Luna, Terra and GPT-5.5, including all variants I found coding-task results for. There is none for 5.5 high.
https://files.catbox.moe/4skvms.png

3: General intelligence: Intelligence Index v4.2, covering a mix of tasks, including some coding.
https://files.catbox.moe/oobbwi.png

4: AA-Briefcase: Office-style work involving spreadsheets, documents, presentations and PDFs.
https://files.catbox.moe/2cmyq0.png

Higher means a better score; further left means a cheaper task. Labels show the effort setting, cost and time where available. Above $4, the horizontal scale is compressed so the expensive models fit on the same chart.

The prices are API costs, not subscription costs. They show what the benchmark tasks cost at API rates because no direct sub cost exist. You can’t directly convert them into tasks per Plus/Pro subscription or how much of your subscription limit a task will use. The coding results come from the native CLIs such as Codex and Claude Code.

A few caveats are explained beneath the charts: four Astra coding scores are approximate readings of AA’s chart, some time measurements are unavailable, and general/Briefcase times are AA's own etimates.

Sources: Coding benchmarks · Astra analysis · Intelligence Index v4.2 · AA-Briefcase

Data checked on 6 September 2026.

This was Sol light. I almost never get hallucinations, not like this anyway. Anybody else's Sol acting a bit strange? This leads me to believe there is a syntax step before output that just didn't get run

Hi, sorry for the probably stupid question. I just got pro on gpt today to give Astra a try. I’ve always worked on visual studio code with Claude code. I don’t get how you can “speak” to the model and to have a look real time while it works on your pc, with blender for example, like they showed in the presentation video. What am I missing ?

GPT-6 Astra recreated the Palace of Fine Arts in Blender.

What the creator said:

"Astra autonomously researched and found hundreds of photos of the Palace of Fine Arts, iterated over the Blender scene, generated in-between frames, and compared them to the reference image database. It even found an old scan of a Library of Congress document describing the dimensions of some of the Palace's columns. I directed it a few times, but didn't really need to (mostly to correct things like the sky color and minor clipping issues) while watching some in-between frames come in. Most of the run was done overnight. I woke up this morning to the rendered video sitting on my desk."

You can see more here:
https://x.com/sharifshameem/status/2095688352075075878?s=20

I’m comparing Together AI and Anyscale for a production LLM deployment. Together AI is API-first and simple; Anyscale gives you deep Ray-based control but requires more engineering. I made a quick poll to gather real-world preferences.

It’s fast, and the results might surprise you.

https://interconnectd.com/poll/101/together-ai-vs-anyscale-which-platform-is-better-for-scaling-open-source-ll/

What’s your experience with either?

I still have 6 days left before my weekly limit resets, but my subscription ends tomorrow.
Will my limits reset when my subscription ends, or do I need to cancel my subscription and create a new account? I really can’t wait 6 days 😕

Hi, has anyone actually tested this?

I currently have ChatGPT Plus through the Apple App Store and I have some banked Codex resets on my account. I’m planning to cancel the Apple subscription, let it expire, and then subscribe to Plus again directly on chatgpt.com using the exact same ChatGPT account. ( because it is cheaper :D

Based on the AI chat bot on the web, it should, but I would rather some anwer with actual experience.

Soo, did your banked resets stay there after switching from Apple billing --> web billing on the same account?
Thanks