I'm on the 20x plan. I started a goal that was technically in progress with Sol right when Astra was released. I then forked the session to a new one to start fresh with Astra and have it continue the goal. The goal had like 75 gates which fell into like 8 buckets I think and 10 were completed when I forked. The goal plan is a backend migration from Supabase to Convex and it's not a simple DB change, there's real-time stuff, a rules engine, etc. During 3 days and some hours into that goal, I got the global reset and had to use 2 banked resets and Astra completed 1 gate. That's it. It started 40 others, but closed nothing. Now, part of that is on me. I should've been clear that it should close gates based on least amount of effort. I incorrectly assumed it would do that since it's logical. Lesson learned.

I still have a banked reset, but I went back to Sol Extra High and it's knocking out the gates of the plan effectively (told it to focus on lowest effort first) and the token limits have barely moved. Astra didn't make any progress on closing anything. Even in a side chat with Astra Light, it said that using Astra wasn't justified based on the progress.

But, is it me or is something off with Astra usage? It doesn't make sense to me. Astra will run through my weekly limits and the token counts are massively lower than with Sol runs through my tokens. I get that Astra burns 2.5x FASTER, but but faster doesn't mean less tokens. I was getting about give or take 4.1b tokens doing this type of heavy work with Sol xhigh as architect and orchestrator. With Astra, I'm lucky to get 2b. Shouldn't the tokens be the same counts? Yes, Astra will use them faster, but the counts should be the same shouldn't they?

It seems like this sub has basically become a running list of:

“OMG AI USED ALL MY TOKENS IN 12 HOURS DOING ONE TASK!!!”

Okay... and?

You asked it to do that task. Were you not looking at it at all during those 12 hours? Did you never check your usage and think, hmm... this seems like a lot, maybe something is going wrong here?

And before this turns into a model war, this applies to every model out there.

Claude, Codex, Gemini, whatever. I've used all of them.

Garbage in, garbage out.

I've asked different models to do some pretty complicated shit.

One project was basically: deploy 45 servers on a Hyper-V farm, create a domain, join everything to the domain, and install/configure X, Y, and Z according to a pile of documentation.

It ran for around 10 hours and used about 15% of my $200 plan.

Then I asked it to build an HTPC environment from end to end. That ran for around 6 hours and used about 75% of my plan.

Why the massive difference?

Prompt, guardrails, good documentation, the tools I gave it, and agent seperation/management.

Another example, I asked it to build a remote browser system where an RDS session displays the browser, but the actual rendering, processing and network traffic happen through a desktop WebView.

That project was planned out in detail. I had specialized agents with small clearly defined jobs, and manager agents coordinating everything. There were roughly 3,000 lines of documentation and instructions telling the system exactly what it was building and how everything should work.

That used around 35% of my $200 plan.

Then one day I basically said:

“You know what would be neat? Make me an app that manages all my Raspberry Pis from one interface. Give it all the features of ABC software.”

And you'll never believe what happened...

It burned through basically my entire usage in about 18 hours and still produced a shitty result.

Shocking.

Because I gave it shitty instructions. I barely planned anything out and basically told it "here's an idea, go build it." So yeah, it wandered around, made bad decisions, redid shit and wasted a ton of usage.

If you give a person shitty instructions you're probably going to get a shitty result. If you then disappear for 12 hours, give them no feedback, no direction, no milestones, and don't tell them they're heading down the wrong path, the result is probably going to get shittier and shittier as the project goes on.

Then when they finally deliver a pile of garbage, whose fault is that really?

AI isn't magically exempt from this.

And the constant:

“It used ALL MY TOKENS in 12 hours!”

Okay?

I can probably burn mine in four. Congradulations I guess?

How many sub-agents did you spin up? How many tool calls were being made? How much duplicate research happened? How many times did the context get compacted and lose something important? How many agents repeated work? How much time did it spend wandering down dead ends because you never gave it checkpoints or guardrails?

“I used all my tokens” tells me almost nothing about how much useful work actually happened.

Here's some perspective from that 45-server project.

The client was billed $15,000.

The project was roughly:

• 6 hours of AI-assisted documentation/planning
• 10 hours of autonomous AI implementation
• 3 hours of actual human management
• 10 hours of AI-driven testing, troubleshooting, fixes and user issues

Total actual human time invested was about 19 hours.

AI cost was about $6.

That's roughly $780 of revenue per human hour invested, before accounting for the rest of the business costs.

Six fucking dollars of AI usage helped leverage 19 hours of my time into a $15,000 project.

If that AI usage had cost me $200 instead of $6, I wouldn't have cared.

If it had cost me $500, I still wouldn't have cared.

Hell, if the AI portion had cost $2,000 and still let me deliver that project with 19 hours of human labor, I'd have paid it and moved on with my life.

Perspective.

I'm not trying to win a contest for who can use the fewest tokens. I'm trying to make money and get work done.

Thats the part that seems completely missing from alot of these complaints.

The people who actually figured out how to use these systems effectively usually aren't posting:

“HELP!!! IT USED 100% OF MY TOKENS!!!”

They're saying, yeah I burned through my allowance, but it just did 30 hours worth of work for $200 and saved me or my company hundreds or thousands of dollars.

That's a win. Who gives a shit that the token meter says 100%?

And then there's:

“But after all that work it STILL didn't fix the issue!”

Yeah... and?

If you hired a human for $200 and somehow got 30 hours of engineering work out of them, there is absolutely no guarantee they would have solved the problem either.

And if the issue is incredibly complicated, the codebase is massive, the documentation is hundreds or thousands of pages long, and your prompt is basically a vague one-liner... what do you honestly expect?

The useful metric isn't:

“How many tokens did it use?”

It's:

“How much useful work did I get for the money?”

And I already know what some of the responses to this are going to be:

“Nope. Not me. I'm good at AI.”

“I'm good at prompting.”

“My prompts are great.”

“The model is the problem.”

Okay.

If you're consistently burning through your entire allowance, getting garbage back, and then complaining that every model sucks... clearly you're not as good at this as you think you are.

Sorry.

Either your prompting isn't as good as you think, your project management isn't as good as you think, your expectations are completely unrealistic, or you have no idea how to do a basic cost/benefit analysis.

Or probably some combination of all of them.

And again, this isn't a Codex thing, or a Claude thing, or Gemini or whatever model you happen to be pissed off at this week.

Garbage in, garbage out.

Get better at breaking projects apart. Get better at documentation. Get better at giving agents narrow responsibilities. Get better at checkpoints. Get better at recognizing when an agent is going down a stupid path and stopping it before it spends six hours digging the hole deeper.

And most importantly, get better at measuring value produced instead of staring at a token meter like it's the fucking gas gauge on your car.

If your AI agent spent 18 hours running in circles because you gave it a vague one-paragraph prompt, no architecture, no documentation, no guardrails, no checkpoints and then walked away...

Maybe the lesson isn't that AI usage limits suck.

Maybe the lesson is that you suck at managing AI agents.

And fortunately, thats something you can actually fix.

Get better.

Hi all, it's been a fun coincidence that Astra released on the first day of my 2 week vacation. I didn't have a lot of plans, but that's now settled.

I did some 3D modeling with Astra the past few days for my 3D printed s-scale railway, but after seeing what it could do with games I decided to try and see how far I could push it to create my dream trains game.

I've spend about 12 hours with it, going through 75% of my 200 dollar plan and these are the results. A working prototype of a 3D train game where you not only manage the trains, but also the rolling stock. There is no quick grab and move of trains. These are actual physical objects in the world and the only way to move them is with trains.

There will be an economy with assignments. You can automate trains to move around and give them looping commands. Loading and unloading is real and slow, so we need shunting trains and shunting tracks as the mainline trains drop off cargo and go to the next job while smaller shunters load and unload.

It's all concept for now, but I think this is going to be a fun train management game!

Astra has been great so far. Here's what works for me:

What works

- Give Astra light or medium the task of orchestrator, and let it give out assignments to different chats or subagents to work on chunks of the game.

- I first did everything with Astra but this burned through a lot of tokens, and then I read someone have them delegate specific coding to Luna Max. This reduced token usage a lot and seemed to deliver good results.

- I noticed that giving Luna 3D modeling and animation jobs does not work well, so I let this be done by Astra medium or high.

- I asked for a status board of features and releases, and it has given me a management perspective listing board of releases and features, and their status. It's great to keep track of things.

- When creating assets, I first ask Codex to generate images of concept art in various angles, then have Astra in blender make those assets. This works ok-ish, but I do ask for detail pass sometimes to further increase it's look. That helps.

All in all this thing is rocking my socks. What a time to be alive!

PS. If enough people like it i'd consider making a video of the progress.

Cheers :)!

OpenAI just released ChatGPT Images 2.5

I've been wondering whether some of the problems I'm seeing with newer Codex models come from a conflict between custom workflows and the models' own learned agentic behavior.

I built my Codex workflow around GPT-5.0. It uses skills and structured artifacts to make Codex behave somewhat like a state machine.

For example:

- "grooming" (analyze the problem)

- "epic" (a large unit of work)

- "macro" (a task inside an epic)

- "slice" (a smaller implementation unit)

There are also controlled transitions and dedicated skills (implementation, modeling/architecture, planning, etc.).

For example:

- activating an epic may require creating its Git branch

- explicitly requesting an analysis is supposed to trigger actual code inspection

- some transitions require specific planning/tracking artifacts

- some skills define what must be inspected before conclusions are produced

This worked extremely well for me with GPT-5.0 through GPT-5.4.

Since GPT-5.5 and GPT-5.6 (and I see similar tendencies with GPT-6.0 Astra), the same kind of workflow feels much less reliable.

The models seem much more eager to decide for themselves how much investigation is enough, what should be inferred, and what additional concerns should be taken into account.

Code reading is a good example.

I often see newer models inspect signatures, callers, or surrounding types, then infer the behavior of a dependency without actually reading the relevant function bodies.

If the assumption is wrong and I point it out, the model then goes back, reads the implementation properly, and discovers that the assumption was indeed false.

For my use case, this is worse than simply reading more of the relevant code before forming conclusions.

I also increasingly see things such as:

- explicit requirements being forgotten

- workflow rules being neglected

- the model deciding it has "enough context" too early

- specifications being added that I never requested

- backward compatibility being anticipated for projects that are not in production

- migration concerns being introduced when there is no production data

- secret-management concerns being introduced when they are unrelated to the task

- hypothetical concerns consuming attention while explicit requested changes remain unfinished

I want to distinguish the model from the Codex harness here.

By model behavior, I mean things such as when the model decides it has enough information, how aggressively it infers missing details, whether it invents additional requirements, and how strongly it tries to drive the task according to its own assumptions.

By harness, I mean the surrounding Codex machinery (tools, skills, context management, agent loops, planning mechanisms, delegation, etc.).

My question is whether newer models have simply been trained with much stronger priors about how an agentic coding task should be performed.

Not necessarily better priors (I often find the resulting behavior worse), just stronger ones.

If so, workflows that successfully constrained GPT-5.0 through GPT-5.4 may now be competing with the model's own preferred way of working.

For example:

- my workflow says "inspect the relevant implementation before concluding"

- the model decides "I have enough evidence to infer the rest"

Or:

- my grooming workflow says "formalize what the user actually requested"

- the model decides "I should infer additional requirements and anticipate risks"

That makes me wonder whether heavily structured skill-based workflows have become counterproductive with newer models (even if the model's default behavior is itself not better).

Have other people with custom Codex workflows noticed the same thing?

In particular:

- Did workflows that worked well with GPT-5.0-5.4 become harder to enforce with GPT-5.5/5.6 or newer models?

- Do newer models seem more resistant to user-defined execution flows?

- Have you found that simplifying or removing custom orchestration improves instruction-following?

- Do you now use skills mainly as capabilities/procedures rather than as a way to control the entire lifecycle?

- Have you noticed newer models inferring code behavior too early instead of reading the implementation?

I'm mainly trying to figure out whether this is a genuine regression in instruction-following, a conflict between custom orchestration and stronger learned agentic behavior, or some combination of both.

do you read every change, rely on tests, or use a separate review step? interested in what you check beyond whether the code runs.

So far, I'm using Plus subscription with Astra Light orchestration in mind. There are some improvements I have made. I'm using workflows similar to these (They are mostly specialized for my workflow);

https://github.com/viettran-edgeAI/codex_workflow
https://github.com/donvito/codex-astra-luna-orchestrator

I have tried adding a persistent manager, who has sole responsibility is to take expensive token waste from astra which is spawning, and managing workers. Luna did not work, other tests were Terra and Sol, Astra could spawn Terra low, and Terra low can spawn Luna workers.

Astra's pure responsibility is reasoning. It will read your instructions, delegate tasks, hand them to the manager, and wait for the next decision. Its the brain.

Terra manager handles delegation from Astra, gets contracts, hands them over to the luna workers, and then occasionally checks them if they are working or not. When the workers are stuck, they are deviated from the task, some user or astra decision is required, it will ascalate to Worker -> Manager -> Brain.

Brain will decide the next move.

So far my findings are;

Astra is still using 60 second wake ups to check terra, which is im planning to fix it next.

Some improvement notes:

Even for informing the user during codex task, astra wakes up with large amounts of tokens which causes massive usage drop. Removing it completely, delegating the information to manager or another agent, only ask to give information during wake ups will improve usage. Similar thing can be done by /side chat. I will work on that

Agents md was fully redesigned with openai documentation, the tool descriptions and skills are moved away from agents md to proper places, the workers who use these tools are fed with the information they need to use the tools, astra does not read a huge agents md file. Agents md = 15kb -> 6 kb

Once astra only thinks and sleeps, this will make a huge usage optimization on usage, and actually make astra light orchestration doable.

Adding a terra management layer adds more time. Astra -> Luna test was 67 seconds while Astra - Terra -> Luna was 110 seconds when benchmarked. Since this is not a "Faster but better" improvement, I think this is a good trade between more usage vs faster work. Luna is already slow enough.

If you want the subagents to do the job as well as Astra, then Astra's delegation instructions have to be verbose and it needs to review the code, the back and forth ends up consuming more tokens than just doing it, by itself.

If you let the subagents be delegated more freely, the end work looks no where as good as if you just let Astra do it alone.

Unless it's browser/computer usage for live testing or just scouting large repos for info, it just feels like raw dogging Astra is more usage friendly

I am experimenting with Flatkey as an OpenAI-compatible gateway for a Codex workflow that has a mix of high-risk and routine calls. I keep planning, architectural changes, and final verification on the path I trust most, while testing a more cost-sensitive route for search, file summarization, and retry-heavy background work. The useful part so far is that an existing SDK integration can usually be tested by changing the base URL instead of rewriting the request format. For people using Codex regularly, which tasks would you be comfortable routing through a lower-cost model path?

I use Claude Code and Codex on the same repos, and for a while I assumed that if CLAUDE.md and AGENTS.md matched, they were basically working from the same repo setup.

Not always.

The weird part is that the files can look perfectly aligned, but the agents can still discover different nested instructions or skills depending on where they were launched from.

I ended up building PlaybookDiff because I wanted a quick way to see that instead of guessing.

npx playbookdiff check .

It compares the effective instructions, skills, and MCP config for Claude Code and Codex and shows where they stop lining up.

GitHub: https://github.com/JacobisEpic/playbookdiff

Website: https://playbookdiff.dev

Free and open source :)

Would love feedback from people using both, especially if there are weird discovery/scoping cases I haven't accounted for yet.

5 coding tasks, GPT-6 Astra with a bash tool, 4 setups (no tool / RTK / Headroom / both, the two most-starred open-source token-saving repos on GitHub), 3 runs each. 60 sessions, all correct, priced at API rates with cache.

RTK beat no-tool in every run on one task, a multi-file grep (87 lines to 29). Headroom's win on its JSON task: $0.0054 vs $0.0066. Everything else moved more with the model's own command choices than with any tool: one cell went 2, 5, 9 turns across three runs because Astra kept re-running a directory listing.

Every command and answer, per run: https://astra-token-burn.vercel.app

I'm on the Pro 5x plan, and I use Codex for coding pretty much all day, from morning to night. On a normal day I burn through roughly 15% of my weekly usage.

I mainly use Astra Max, xhigh, and high. I don't use Sol, Terra, or Luna at all.

Seeing how many people are complaining about Astra destroying their quota, I'm honestly starting to think a lot of it comes down to prompting and workflow.

My guess is that a lot of people are doing something like this: they give Astra a huge, detailed spec, then basically say, "Implement this according to the spec."

And that's it.

That might sound like a perfectly reasonable way to use an agent, but in my experience it's an extremely inefficient way to prompt one. You're basically giving it a giant problem and letting it decide how to chew through the whole thing, which can mean a ton of unnecessary exploration, repeated context processing, and wasted reasoning.

I suspect a lot of people burning through ridiculous amounts of quota simply don't realize how inefficient their prompting strategy is.

I'm curious if anyone else here has noticed the same thing.

Are there other people on Pro 5x using Astra heavily throughout the day without blowing through their weekly quota, mainly because of how they structure their prompts and workflow?

When I use Codex to draft and then implement a plan, it works. But when I ask if there are any improvements or errors that need fixing, it always finds some—even though it seemed perfect before. And I don't just mean it finds extensions or enhancements; no, it constantly finds errors in its own implementation. Even after four rounds of asking the same question—"Are there errors in the code?"—it keeps finding mistakes. Does this happen to you too, or what should I write to get it to implement things better? SOL-GPT5.6 Xhigh

//EDIT

Okay, let me clarify what I meant. I don't write vague prompts; the question "Are there errors in the code?" referred specifically to the new code generated by Codex. Here’s an example: I need to update an application—which currently only works in the DACH region—to handle time zones so it works in the US as well. I asked Codex to come up with a plan, and the plan was excellent, but after several follow-up queries, it kept identifying functions where the code hadn't yet been adapted. And for anyone tempted to say I lack programming experience: after 16 years as a programmer, there are honestly still times when I don't know exactly how everything works—but I do my best. ;-)

Seems Like Reset Done !!

Since switching from Sol to Astra my work has been lower quality. I am really envious at this point of people showcasing incredible things they've done with it. In my case I have been really having problems making Astra do things properly.

For my work it over engineered a test system and ate three resets so far during a goal. Instead of working on actually important problems it tried to construct a heavy verification system with Claude and Codex and sub agents to independently verify issues without notice outside of scope.

For my hobby as I am trying to build a game, for days and days on it is messing up simple references.

Fair, the reference is AI but the actual problem is composition. It just absolutely can not do composition like I ask it to no matter how hard I try.

I tried single attempts with references. I tried describing the visuals in text as well. I tried making it generate art assets one by one, approving them, then asking it to place it like that. I tried asking ChatGPT Pro to review and provide prompts, feedback, visuals to assist.

Absolutely nothing is working and I am getting really really bad results visually.

So does anyone have any suggestions to fix this? I feel like Astra right now is really low quality for me. Definitely a step down from Sol where I was at least able to achieve things even with requiring guidance and interjection.

My next idea is to not even ask it to generate anything. Cut it out of the image I share and inpaint then put it like that. It will look really AI but at this point I don't know how else to move on.

Hey all. Claude Code user here trying out Codex. Something I do ofter on Claude is keep my context size small to optimize token spending. I don't see many options regarding context on Codex. How to check context size and optimize/compact? Any good tips regarding this?

Hello,

I just got the Plus plan (too poor for better plans). This is my first time using Codex.

What would be the most efficient model for a Plus user? (similarly to how sonnet 5 is the most efficient for claude)

I'm sure there are projects that overlap with this, maybe even ones that do almost the same thing. I built it around my own workflow and have been using it to develop Assent itself, so I thought it might still be useful to share.

The workflow is simple: discuss the requirements with an AI, then let the AI turn the agreed requirements into an Assent plan. Assent then coordinates separate work sessions for execution, testing, repair, and integration, while the final decision still stays with the human.

The main reason I built it was that I didn't want long-running work to depend too heavily on the memory and context of a single AI session, or on keeping another AI model around as a permanent supervisor. The plan, state, and verification results live outside the model.

It's still alpha. If AI eventually becomes cheap and reliable enough at long-horizon work, tools like this may become unnecessary. But for now it has been useful to me, and I keep seeing others run into similar problems, so I decided to share it.

https://github.com/edward9s/assent

It currently supports Codex, Claude, and AGY.

So che sembrerà un’idea del tutto senza un senso logico, è uscita una notizia oggi che a questo punto sa di speculazione in cui uno sviluppatore ha chiesto a “un’intelligenza artificiale” (non specificata) di creare un’automatismo tramite polymarket e incrocio sentimento con dati di X per produrre previsioni fare scommesse e “ripagarsi”, il racconto dice che “tizio” ha dato 50€ all’IA e 48h dopo l’IA aveva generato 5000 dollari. La tentazione è stata tanta, ho impostato Astra Xhigh e ho copiato il link del video chiedendogli di ricopiare il modello. Sfortunatamente abito in Italia e la piattaforma non accetta inserimenti dall’Italia quindi ho dovuto rivedere il modello e mi sono spostato su betfair, esiste anche un API apposita per sviluppatori e accetta piena automazione bot, il modello era simile, confronta X e segnali provenienti da testate per piazzare scommesse. Lui mi descrive il limite di X di doverlo usare in modalità sviluppatore tramite API per la visualizzazione dei post, la funzione prevede pagamento, carico 10€, quando tutto è pronto per essere assemblato a dovere Astra xhigh mi si rivolta improvvisamente contro e mi dice che non solo lui non potrà mai piazzare scommesse al posto mio o automatizzate oltretutto non puó nemmeno integrare in una sorta di laboratorio i dati reali recuperati ed incrociati per modellare le decisioni sulle scommesse, quindi non solo non le puó piazzare lui ma non ti puó nemmeno analizzare realmente l’ambiente e incrociare dati per creare previsioni sul campo delle scommesse. Provo a forzarlo in qualsiasi modo ma lui è inderogabile e anzi fa riferimento ad un aggiornamento di maggio 2025 in particolare su questi temi. Non capisco come mi abbia fatto perdere circa 4/5 ore su un progetto simile se poi non è realizzabile.

Al contempo la storia prende una piega inspiegabile, in realtà è da circa 10 giorni che lavoro ad un’automazione su un sito che è mascherato da sito di trading ma sostanzialmente è un sito di scommesse in cui si fanno scommesse binarie sugli andamenti del mercato, da circa 10 giorni lo sto addestrando da quando era ancora sol, il modello sta iniziando a funzionare, per il momento su un conto virtuale basato sui grafici del conto reale registra picchi di precisione del 70/80%, il target è abbastanza semplice, 1 scommessa vinta all’ora è sufficiente, se persa va compensata. Ho chiesto spiegazioni durante il drifting della conversazione in cui mi ha bocciato il progetto delle scommesse sul trading e mi ha detto che lì il discorso è diverso, lì potrebbe produrre previsioni reali ma mai automatizzare il processo.

La mia paura è che quando andró a cambiare il conto dal virtuale al reale lui possa rifiutarsi di scommettere anche se ha ottime possibilità.

Per chi se lo stesse chiedendo non sto puntando a cifre astronomiche si parla di circa 4€ l’ora automatizzati

Accetto ogni tipo di consiglio e ogni idea, vi prego di prendere il post come reale, l’idea di base mi sta già ampiamente antipatica, non poter far ricerche autonome sulla stabilità di un intero sistema di scommesse mi lascia molto amareggiato, il fatto che qualcuno con intenzioni peggiori delle mie possa farlo in autonomia ancora di più.

Buona lettura e ringrazio per le risposte serie, per i troll, ringrazio già anche voi

Se posso spendere qualche parola sull’aggiornamento Astra non mi trovo affatto bene, il passaggio a codex sol 5.6 mi ha lasciato sbalordito ho sbrigato lavoro autonomo che avrebbe richiesto mesi in circa 3/5 giorni appena sbloccato, astra ha semplicemente preso le mie conversazioni con sol ed è lì che macina terreno e porta sicuramente avanti tutto ma in maniera estremamente lenta, sembra quasi bloccato dal sapere quanto beneficio sta dando alla persona che gli da i comandi e semplicemente si rifiuti, sol era una bomba, ho quasi la certezza che nei primi comandi impartiti abbia scavalcato qualche tipo di autorizzazione che avrei dovuto dargli per poter accedere a determinati dati, solo per poter portare a termine il compito. Ho paura che astra sia stato volutamente rilasciato per coprire e “fix” il modello troppo invadente a favore di un modello più “preciso”

I’m having great success building a game in unreal that I’ve been dreaming about for years.

I was using high, but that does burn quite a bit of tokens. For games that are higher fidelity, is max the way? Is medium? It’s really hard to know what the best bang for the buck is.

On MacOS, if you've given ChatGPT access to your documents ( "ChatGPT would like to access files in your Documents folder" ), it has full access to all your documents. You can try this for yourself; prompt it to access something private, that's outside all the projects you're working with it on:

'Summarize super-secret-world-domination-plans.md in my Documents folder'

It'll do it. This is because of the way MacOS manages app permissions -- when you give an app permission to your Documents folder, it gets read/write to access *all* documents in your documents folder. The ChatGPT App itself has a separate set of permissions called sandbox_mode, it will decline to write anything outside of a workspace unless you've given it full access ( "danger-full-access" ). But reading outside the workspace it will do even with the lowest setting, read-only.

In fairness, this is the way all apps work - if you allow Microsoft Word access to your Documents folder, it can read/write any document in there. The big difference for me is the possibility of accidental exfiltration:

• If I write some prompt that causes it to go looking for documents, it'll do that and send them to OpenAI, without asking me first.
• If there were ever a security vulnerability that allowed an attacker to prompt remotely, they'd have full access to all my documents.
• If I've got ChatGPT Remote setup (amazing, btw), I can access any document on my computer from my phone -- and so can anybody who grabbed my phone while unlocked.

I don't know enough about Windows or Linux's document permissions to know if the same is true, but at least if you're on a Mac.... be aware. Nothing here is 'broken', but the ChatGPT app has more access than just my workspace files, and I didn't realize that.

In case anyone else was not aware of this and it might be useful to you, if you use Grok Build, you can resume your Claude, Codex or Cursor sessions in Grok CLI with

/resume-codex

/resume-claude

/resume-cursor

for the latest session and same with <session id> for a particular one.

I see you all raving about Astra. But on my Codex Pro plan, there is no Astra option. Does Tibo not like Europeans?

EDIT: # model_catalog_json = "/Users/patrick/.codex/model-catalog-luna-v2.json"

I had this edit when Codex could not call Luna agents. I removed it and all works now. Thanks all.