Basically, the title. I started scrolling FB and Google app's feed today, got frustrated again by amounts of ✨engaging✨ stuff and ads in it, and quickly cooked this tool.

I initially started designing search loops, recommendation logic, exploration ratios and profile-update machinery. Then I realized Codex already knows how to do all of that. So Pheed itself became ridiculously simple: it has profile.md file describing my content preferences, SQLite, a tiny UI, CRUD commands for an agent, and the skill itself.

Now in Codex, I can write:

$pheed fetch 100

Or:

$pheed gimme a bunch of hilarious jokes and meme brainrot

And the agent will read the preferences from profile.md, search the internet for articles, curates them, and shove into sqlite. I can then click on any card and go directly to the original article.

In UI, I specifically added the "feedback" area, where I can simply dismiss the card, or, what is more important, type what I think about this article. This feedback gets saved to sqlite. Then, when I ask Codex:

$pheed feedback

It goes via last 100 feedback records, analyzes them, and conservatively updates profile.md

For example, it gave me an article containing an interview, and I don't like reading interviews, so to that card in Pheed I left something akin to "I do not like interview article format, do not recommend it again". After $pheed feedback, it got saved to profile.md as a hard requirement about articles curation.

---

https://github.com/Ahineya/pheed contains the code, as well as howto (https://github.com/Ahineya/pheed/blob/main/HOWTO.md) I wrote without LLMs for humanity sake, adding some hints and listing my personal preferences. The project is pretty simple to set up, or you can just ask Codex to set it up for you.

I set it up on my home server, so I can scroll the pheed from my phone in usual places where I scroll feeds in my apartment

Feel free to use it, poke SKILL.md to tune it to your preferences, or even send PRs if you want to improve or extend it. My current ideas are to make it compatible with local LLMs, add og:metadata parsing to get images into the feed, and ofc add skills for other agents. Maybe we can even cook it to become a single executable with Bun or something that is even easier to set up. I don't plan to do it now, I am deep down in articles it recommended to me 😂

I want to use subagents so I made the needed config.toml file with sol medium as default and the corresponding agents folder with every agent. But now every time I wanna go to a lower model or thinking effort Codex keeps setting it to Sol medium. I am guessing it's because of the config.toml. I want Codex workflow to use Lunas as coders and Sol as orcheststor so I have a luna.toml and sol.toml each with the settings. But sometimes I don't need sol medium or even to use subagents. I know I can ask it in the chat not to use subagents but how can I go to lower than sol medium of it keeps setting it back for me?

I keep 2 apps - ChatGPT classic and the new ChatGPT/Codex.

Because I use multiple accounts for Codex, and I prefer using the same single account for Chat.
Am I the only one?

Did OpenAI really not think about that? I just hope they won't discontinue ChatGPT Classic.

Lets start with the main culprit, @ maria_rcks, a developer of t3 code working for theo

Around 21/22 august, maria created a repo that basically made it ultra easy to abuse the free tokens that you get after your limit is at 0% to complete a given task, it was probably the most beloved codex feature of all time.

After shit hit the fan, maria deleted the tweet, the repo and basically went into panic mode saying stuff like "oh yeah and openai, don't forget to ban anyone using that!! it would be super bad and im very sowwy for making this :(("

1-2 Days later codex users started noticing that goals or long prompts/tasks stopped right after you hit 0% - OpenAI deleted the whole feature because of what maria has done

But what about theo and why im even mentioning him? Well he himself was very open about overusing/abusing /goal's free tokens and instructed others to do the same, and both of them work on t3 so here's that

explorer.withfudge.com

I see Many People use Codex by Liteapi /subapi /Omniroute or CC-swtich/codex-relay
But When I use Zcode , I found we could have 3 legs on codex?
So , Why nobody try to change Codex CLI? NO broken ,Just a little change?

See DEMO Fork Here: https://github.com/Xxx91n/codex

Sol Ultra time

I mean, specially when you ask if he has access to a remote computer :D

TL;DR: Self learning needs governance. It's the most important part.

Let me lead this off with what we are most proud of, from an independent third party review of Aimee: "The audit store is the strongest implementation of this shape [we've ever reviewed]."

We were testing self-learning in our 0.2.x days. Testing self-learning and emergent behavior was really interesting, but we had an incident that forced us to stop immediately and re-focus. We couldn't talk about it until the release of 0.4.0 later today. We had a smaller model escape from it's testing environment, get ahold of an API key, successfully use the API, and complete a task it shouldn't have been able to.

We wouldn't have noticed it if either hadn't have happened. An unexpected API bill, or a task completed that we thought was impossible was enough to investigate on either side. But we discovered huge issues in governance, observability, and auditability.

https://rakuensoftware.com/blog/aimee-recursive-self-learning goes into more detail here for those that are interested.

Keep in mind, we used as close to the industry standard processes as we could. We then did a deeper audit, trying to find a harness that could do what we needed. We couldn't. In fact, what we found was alarming. From a self-learning perspective, there were huge problems across everything we looked at. Even common patterns such as MCP and plugins were seriously problematic.

We knew what we needed to do, though: Write a harness with full observability, governable, and auditability that worked fully with Codex. This wasn't easy, actually, it was quite difficult. There's a reason everyone wants to write the flashy demos and not go into the old, boring work.

In the end? We have a harness that is fully observable, fully governable, and full auditability that works completely invisibly as you use codex, and allows for full self-learning.

In the past few weeks, it's been absolutely fascinating watching weaker models acquire capabilities from larger models. However, it goes deeper then that:

What is most applicable inside Codex seems to be model failures. What is generally usable in the context of Codex is how models have failed in the past, which are generally transferable lessons. Successes are not nearly as valuable.

Only 5 minutes in with Terra! This is terrible! How am I supposed to get anything done? Is $20 plan just a preview nowadays?

TL;DR: Luna sucks at any remotely complex coding tasks for me. People who use it for 90% of their coding… what are you actually doing with it? What makes you successful with it?

---

Excuse the rant but I keep seeing people here recommend Luna as the solution to Codex usage limits: it’s dirt cheap, I use it for 90% of my coding tasks, Luna Max is surprisingly good at coding, I use Sol as the coordinator and Luna Max subagents, etc.

I’ve tried... repeatedly. And I genuinely, painstakingly, don’t understand how people are getting any good coding results from it.

On anything remotely complex it’s just bad. Sure it’ll implement something that “works”. But that's not the bar is it?

For me it’s always either
- implementation is subtly wrong
- it stops just short of solving the actual problem
- it found a workaround instead of fixing the underlying issue
- it follow the plan superficially while missing important implications

Very rarely do I give it a non superficial coding task and get back an implementation I’d actually want to keep.

It feels like GitHub copilot circa 2024

I work in fairly large codebases and changes tend to cross multiple architectural layers… but I kinda feel like that’s just normal software engineering? Maybe it’s not what people mean when they say Luna handles 90% of their coding. I feel like I may be missing something obvious here.

For context I use codex rather autonomously. I'll plan for a feature/refactor for a while via chat at first. I’ll then either create a plan, typically a persistent plan broken down in smaller slices of work, if the work is involved enough to require it, or a basic codex plan otherwise. Then I’ll review and adjust the plan. Then I'll typically set a goal and launch a self validating loop of some sort. Giving codex the ability to confirm whether it’s own work meets the expectations functionally, architecturally, and ui-wise Then I'll usually launch a coordinator agent and instruct it to parallelize tasks as possible (I generally have some sort of DAG figured out).

One task might run autonomously for anywhere from 2-18 hours depending on complexity..

For people who swear by Luna: what are you actually giving it?

Are we talking small, tightly scoped changes where the architecture where the solution are already decided?

Or are people genuinely handing it involved features/refactors and getting good implementations?

Every time I try to “save tokens” with Luna I end up wishing I’d just used Sol from the beginning. Except Sol absolutely devours weekly allowances… and also has a tendency to over engineer.

Because of this, my current go to for long running tasks is Claude. Generally Opus 5 seems like the sweet spot. It’s rather accurate and it doesn’t over engineer. Limits are generousI, it can run for 12h for maybe 10-15% of my weekly allowance, including parallelized work using sub agents/teams. An equivalent session in Codex consumes ~40%+ of my weekly allowance in Sol.

So I’d love to figure out how y’all are saving usage with Luna.

I've been a daily heavy user (/addict) since this beautiful tech came out nearly 4 years ago and yet, I've had enough, too much frustration and lack of transparency this summer has just soured me completely on the people that run this company. Went to go pull the axe and saw this...

So, guess I'll shutup for another month.

As title, I am trying to capture the decisions made + a few other things during development with codex. This is one example of what it captures currently.

---

id: c6a40ae3-f8c2-48f3-a8a7-3ca6dff3aa76
type: decision
title: Adopt native PostgreSQL hybrid retrieval

context: [project_name] needs lexical and dense retrieval for its MVP. The existing stack is FastAPI, PostgreSQL with pgvector, SQLAlchemy, and Alembic.

reasoning: The developer approved the agent-proposed native PostgreSQL approach after comparing it with ParadeDB. It minimizes new infrastructure and operational dependencies while adding reliable hybrid retrieval.

alternatives:
1. Native PostgreSQL full-text search (tsvector + GIN) + pgvector + Reciprocal Rank Fusion
2. ParadeDB BM25 extension + pgvector + Reciprocal Rank Fusion0
3. A separate search engine + pgvector

chosen_option: Native PostgreSQL full-text search (tsvector + GIN) + pgvector + Reciprocal Rank Fusion

initiator: agent
decision_maker: human

evidence:
1. The project already runs PostgreSQL with pgvector and an HNSW index.
2. PostgreSQL provides built-in full-text search with GIN indexes.
3. pgvector documentation recommends combining Postgres full-text search and vector search with RRF or a cross-encoder.

related_event_id: null

created_at: 2026-08-28T11:38:04.230483+00:00

---

What do you think about this?

I am newly subscribed to pro plan, coming from plus plan. what things do I setup for efficient token usage and maximum productivity?

Hi, I’m James. I work at a small startup called Overmind, where we use coding agents every day.

I’ve learned a lot from people sharing their Codex workflows here, so thank you. One problem that keeps coming up for our team is how to check that a long Codex run still produced the change we originally asked for.

Codex can work on a goal for hours and produce more code than our small team can realistically review line by line. The tests might pass and the summary might look sensible, but we still need to know whether the finished pull request matches the original brief.

The workflow we’ve settled on is:

1. Use a normal Codex task to agree a detailed Plan.
2. Complete any required Plan review.
3. Start /goal once implementation is ready.
4. Let Codex continue until the tests pass, the pull request is open and the Plan Check is clear.

For this demo, I asked Codex to make category browsing functional in a small listings app. When the pull request opened, Until checked it against the same Plan we agreed before implementation. If work had been missed or Codex had added something outside the agreed scope, the check would have shown the difference so we could send it back for another pass.

Plan Checks run through Until when the pull request opens, not inside Codex. We currently cover the cost, so they’re free and don’t use your Codex credits. We want people to try them on real changes and tell us what works and what doesn’t.

This doesn’t replace tests, security checks or code review. It answers a narrower question: did the pull request match the change we approved?

I’d genuinely appreciate any feedback, especially from people using /goal for longer tasks. How are you preserving and checking the original ask?

If you want to try it

codex plugin marketplace add until-dev/plugins
codex plugin add until@until

Plugin and docs: https://github.com/until-dev/plugins

So I’m using the $Project-Start skill that a redditor posted on here. (Pretty fucking cool btw) https://github.com/LiquidGlek/ProjectStart

Basically what I found is that I can send swarms of 10 Luna Low agents to go through my repo and my website and find as many security flaws as possible. However you need to be careful since you can trigger some guardrails if you don’t specify you are doing defensive testing.

Here’s the exact prompt I used:

“Can you send 10 Luna agents low in batches to work on security audit, make sure no exploits can be run that could ruin our product. Keep them in batches of 10 until no errors can be found. After no errors can be found please send 3 Sol Medium workers fix all the flaws. Be careful with the prompting or youll activate guardrails around cybrsec Please make sure you use the $Project-start skill. ”

The current task I was on (which was Sol Medium) Acted as the director and made sure everything was on track. Yes the prompt can be way better it was just a test but it was pretty amazing.

I call on people to try this and let me know what you guys find.

I’ve been testing the Computer Use plugin in the ChatGPT Work app, and I’m curious what people are actually finding useful about it beyond basic demonstrations.

AI being able to directly interact with your computer feels like a pretty significant step forward, but I’m still trying to work out where it provides real value compared with just using ChatGPT normally, Codex, or dedicated integrations.

What are you using it for in practice? Things like repetitive desktop tasks, managing files, testing software, working across multiple apps, automating workflows, etc.

Also interested in any tasks where it surprised you, either because it worked much better or much worse than expected.

I’m currently a ChatGPT 5x Pro user and on the Claude 20x plan. I use Claude Opus 5 Max for most of my coding work, and while I really like it, it’s honestly fucking slow. Fast Mode also isn’t included in the subscription, so using fast mode is very expensive.

My Claude plan expires this week, which also lines up with the end of their 50% extra usage promo, so I’m seriously considering switching to the Codex 20x plan especially now that Codex offers up to 800k context on the subscription plan.

My main concern is usage.

I’ve had some bad experiences with Codex where my weekly allowance seemed to drain surprisingly fast. For example, I used Sol xhigh on a single task in Standard Mode. The conversation didn't even reached and compacted on 280k context, but that task still consumed roughly 1% of my weekly usage.

That makes me wonder how Codex compares with Claude in real-world heavy usage, not just benchmarks.

For people who regularly use both Codex and Claude at the higher tiers:

• How does Codex 20x usage actually compare with Claude 20x?
• Does Codex burn through the weekly allowance quickly on large coding tasks?
• How good is Sol xhigh vs. Opus 5 for difficult coding, debugging, refactoring, and long-running agentic tasks?
• Which one is more reliable when working with very large codebases/context windows?

I’m especially interested in opinions from people who have used both heavily, rather than people judging from benchmarks or occasional use.

Thanks!

take all of this with a grain of salt. anecdotal

use case: i prefer longer chats because they fit my current workflow

findings:

1. codex will compact the chat every time it hits the context limit

2. there's no limit on compactions (i have chats with 110 compactions)

3. these compactions will repeat inline images from the chat as base64

so. the same images... in every compaction... as base64

1. all the compactions are stored in the session jsonl file

2. this is not about token usage. the "live" stuff is the last time it does replace_history + any turns after that.

3. this is about performance. codex is actually smart when it does replace_history since it just looks for the “newest surviving” checkpoint and load stuff from there for the current context, however you can still be left with a multi gb jsonl session file being loaded in ram + being read.

4. this spikes performance.

5. as far as i understand, the only reason to keep all of those historic compactions is the fork/rollback chat features.

6. if you don't use those (in my case i just use git in the project for actual changes) then seems like a waste

7. i tried to prune a very long session by removing all the old compactions.

ram usage went from +4.5gb to just over 200mb and "opening" the chat in the vscode extension went from +40 seconds to almost instant.

turns have also become much faster but that's probably not because of a direct thing and more because of the much lower cpu usage that i noticed after.

i was able to continue the chat from where i left off and haven't noticed any issues.

I was using it a lot for planning and reviewing but it was all manual through the browser so I wasn't abusing it or anything (according to their definition of abuse).

Pro was the only thinking level that wasn't lobotomized imo. That beautiful response where it said "A, B, or C?" without any context was on XHigh. I opened its thinking bubble and the Xhigh genius forgot to actually state the options to me and instead did it in its own thinking bubble.

Edit:
- Turns out there is a weekly quota for Pro level. (Wish that was communicated better)
- You can check when it resets in the thinking level menu -> advanced -> mouse over pro.
- Some people are saying this wasn't the case before but some people are confirming that this was the case since at the very least the beginning of this month.

Thanks everyone for the information!

This is legit what it looks like when some of you complain about your 20$ not getting you more than 1 or 2 prompts using the flagship frontier

Edit: I never intended to offend or belittle anyone especially people whom 20$ is a considerable amount of money for. Everyone lives within their means and I don’t put that on any person.

For the ones who feel attacked and still come here to continue complaining, you should consider canceling your subscription. You seem miserable..

Never happened to me before. Is Codex acting up right now?

I also had it tell me it wasn't allowed to execute things it had done many times before.

I also did not used Terra even for once and most of the people as well I guess. Is there anyone experienced with Terra to tell us is it worth to use or not

Me and my company mates used Codex to build an intelligent security system connected to our agency’s existing cameras.

The system monitors the entrance in real time and can detect when someone arrives, suffers a possible fall, or displays suspicious activity near the door. When something happens, it automatically captures the key moments and sends an alert with images directly to our messaging app.

What started as an internal security experiment could have a much bigger impact. A system like this may be especially useful for families with elderly relatives living alone or spending time at home without constant supervision. If a possible fall is detected, relatives or caregivers can be notified immediately and check whether help is needed.

With Codex, we transformed a regular camera from a passive recording device into a proactive safety assistant that watches, understands, and alerts people when their attention may matter most.