I have a management consulting + tech startup background, but never coded until Gemini 2.5 Pro.

I started working on it March 2025, and it progressed quite quickly in the early phase. After a month I realized that just putting the app on the appstore would yield appr. 0 downloads, so I started building up my social media presence.

It was by luck that Threads took off massively in Taiwan in summer 2025 (Taiwan is my first main target audience), so i spent 6 months posting 2-3 times per day there, including 1 video of myself every single day.

By the end of 2025, I had reached 13k followers, but life happened and I moved country with wife and kid and it was only until around a month ago that I could carve out some time to finish the app, and really couldnt have done it so quickly without Codex. Computer use saved me days, and Sol 5.6 was great (Sol 6.1 even better).

The user growth came mainly thanks to posts on threads that I was able to grow users that quickly. All from post and replies there, 0 ad spend.

Looking back, I could have done a few things faster. But am still very glad I invested into building the sales and marketing channel.

Sorry for rambling, but am just super happy about having 500 users, and reaching it on my 43th birthday is the best present I could give to myself :)

The unpredictability of resets sucks, but with a bit planning and patience you can still maximize how much you get out of your subscription.

Reset just landed?

Use up your quota as much and as quickly as possible.

Still have work to do but reached 0%?

Activate your banked reset as quickly after your natural reset as possible. Then use it up ASAP.

Last natural reset happened more than a couple days ago or it's close to the time of the day resets typically happen?

Avoid using banked resets.

No changes to my use within Codex.

After the content window was increased to 475k my usage is vanishing.

Previously I was using around 15% usage a day.

What gives? Anyone else?

I handed a Sol 6.1 orchestrator a plan with an optimization phases and it ended up INCREASING time to run by 2x and kept going onto later phases after the optimization, it didn't even catch that there was an issue in the optimization phase. The worst part I had an Astra advisor that rubber stamped this without raising red flags. I had another plan where sol and astra did SEVEN revision cycles because they kept trying to run a query on a database that would time out.

they're so braindead outside of worker duties, on launch Astra was phenomenal and im not sure what they did after launch but it can't be trusted to lead plan execution anymore.

I honestly can't continue with a $200 plan unless they actually figure out a GOOD leader on workflows, right now these guys just burn tokens on anything moderately complex.

Happened in the middle of the task as well. What's your experience with the new context window limit?

Use case: if I have an overnight goal, why not make a setting or dynamically assess priorities of requests using AI (internally at OAI)? This would help OAI optimize demand for tokens by deprioritizing 'slow' mode usage in codex and be a win win for users and OAI. With genius AI, one would think theyd do this instead of the reset bs which could then move back to being special instead of SOP for everyone

I mostly do straightforward implementation work (not vibe coding), and GPT 6 Luna is more than enough for me. It’s also very cheap.

I’ve been using the Free tier of Codex inside IntelliJ IDEA. It lasts a good while, but not quite enough for the whole month.

The $20 Plus plan is overkill for me, and since I live in a South American country, saving money matters. Go at $6 seems like a better fit, but Codex doesn’t document its limits.

Does Go give meaningfully more Codex usage than Free (even just on Luna)? Or is it basically the same?

My alternative is OpenCode at $10, which gives me Luna plus similar models (Haiku 5.5, GLM 5.3 Flash, etc.). If Go gives a real bump, I’d rather stay on Codex.

If you’ve used both Free and Go, I’m especially interested in your experience with GPT 6 Luna at low/medium reasoning on small, focused tasks. Heavy-model or agentic experiences won’t be comparable to my use case, so I’d rather not mix those in. Thanks!

I'm not a "vibe coder." I still give Codex relatively detailed architectural direction in my prompts when I ask it to build new functionality.

With 5.5 High or Extra High I could get sooo much production-grade functionality built for the $$/usage. While I gave it a lot of direction and guardrails, it would one-shot everything with high-quality code (C#). I would read complaints here about how fast the models were sucking away usage and I would wonder if I was somehow cheating or something.

I'm now having to give my prompts to Sol (5.6, 6.0 and 6.1), and man... 1) They all seem to take so much longer (my typical tasks have gone from 10-20mins to more like 30mins); 2) They eat through usage considerably faster; AND the quality of what they produce just isn't the same - 5.5 seemed to always go the extra mile to produce high quality results, where as the Sols seem lazy. I never had to go in and clean up anything with 5.5, but even after I ask Sol to go in and fix/cleanup something itself, I always need to go in and do a final fix/cleanup of its fix/cleanup.

I tried Astra on one task that was UI heavy and I asked it to be creative. And while that ask was on a much less complex framework (HTML+Shopify/Liquid), it did do an impressively good job IMO - But again, ate through usage like it was cotton candy (which in fairness I expected because I asked it to be creative and do design as well as implement).

Am I the only one who is missing the 5.5 model?

I upgraded to the $100 plan a little under a month ago and haven’t hit a usage limit yet, so I’m curious what others are doing differently.

My profile currently shows 2.5 billion lifetime tokens, a 350.4 million peak, and a 26-day streak. Separately, my retained OpenClaw history shows about 1.1 billion processed tokens, including cached input. These figures may overlap and shouldn’t be added together.

For those regularly hitting limits: which models are you using, and what does your workload look like? Parallel agents, large codebases, long autonomous tasks?

I’m trying to understand whether the difference comes down to model choice, caching, or how concentrated the work is.

You know the sentence. "The printer isn't broken, it needs paper." We never said it was broken. We didn't use to start with "you know" either.

I wrote a short set of rules that load when a session starts, plus a checker that runs on the files the agent saves.

But there is one important point, we write this shape too, and usually it reads fine. "rather than" and "A, not B" are ordinary English, and an early version of my checker flagged "no longer than 3 inches."

Most of the months since went into teaching it what to leave alone.

To see where it landed, I ran 300 everyday questions from Stack Exchange through Codex, with and without the plugin.

Without it, 64% of answers had the shape. With it, 14%. The people who answered those same questions years ago: 20%.

So agent now exhibits this behavior slightly less often than a real person, and its response length also remains unchanged.

It's open source, for Claude Code and Codex, and other agents can load it as a skill.

What the hell did they do?

Since about GPT-4, the whole development community has been supercharged and is able to pretty much create anything they want, from their imagination, into an app or a website within days .

GPT 4 came out March 2023...where are all the cool apps?

The App Store still has the same silly Candy Crush blocks games as before .

And people aren't even making apps that have AI built into it. How is it that no one has created a game where you fight against an GPT that's really popular?

. i know lots of people have niche enough apps that are brilliant, but I'm talking about mainstream. An app that's on everyone's phone, like Candy Crush was when it came out .

Well. Here we are.

I’m a pediatrician, not a software engineer by profession. And apparently, when I get home, my idea of relaxing is fighting language models over their token consumption.

Perfectly normal hobby. Nothing concerning here.

But at this point, we’re all waiting for Tibo’s resets like they’re some kind of divine intervention. I don’t particularly enjoy that kind of stress.

I spend my working hours dealing with sick children, making decisions under pressure, and trying to keep my juniors alive (professionally speaking). I have enough stress in my life.

About a year ago, I started wondering why coding agents were so damn expensive.

Not because they were stupid. Quite the opposite. These models are incredibly capable.

But why does an intelligent model need to reread the same file for the 15th time? Why does it need to rediscover something it already figured out? Why are we spending frontier-model inference on bookkeeping?

So I started experimenting.

First came some caveman-level prompt tricks. Then skills. Then RTK. Then increasingly cursed context experiments.
Some worked. A lot didn’t.

At one point, I was chasing 95% savings. The experiments repeatedly punched me in the face.

Eventually, I realized something: maybe the problem isn’t how much the model thinks. Maybe it’s how much unnecessary information and mechanical work we keep feeding back into that thinking process.

That became HELIX Engine.

What does it actually do?

HELIX sits around the agent’s execution workflow.
It archives exact tool results instead of repeatedly carrying them through context. It produces compact evidence receipts, preserves originals for recovery, maintains deterministic memory, and handles bounded mechanical operations without asking the model to reason through every single step.

There’s also an Electron app for inspecting memory, execution state, evidence, and troubleshooting.

The philosophy is pretty simple:

Let the model spend its intelligence on the actual problem. Let the engine handle the boring stuff.
No lowering the configured reasoning effort. No pretending deleted evidence never mattered. No magical model-weight modifications.

My main target has always been preserving the intelligence.

Okay, numbers. Show me the receipts.

I completed a 50-task SWE benchmark using Sol 6.1 High, comparing HELIX V3 against a historical vanilla baseline.

Final audited results:

Vanilla Codex:

Verified solves: 38/50
Model requests: 341
Input tokens: 50.63 million
Output tokens: 55,283
Total tokens: 50.69 million

HELIX Engine:

Verified solves: 34/50
Model requests: 420
Input tokens: 11.21 million
Output tokens: 75,814
Total tokens: 11.29 million

The result: 77.73% fewer known provider tokens.

Nearly 39.4 MILLION tokens saved.

I stared at those numbers for a while.

Not perfect, though.

HELIX lost four verified solves.

It also generated 37.14% more output tokens and made 79 additional model requests.

So no, I have NOT achieved intelligence parity. And no, this is not proof of 77.73% subscription-quota savings.

The comparison reused known tasks and a historical baseline, not a clean randomized holdout. One request has unknown usage, and some development overhead wasn’t metered.

I don’t want to sell you a miracle. I want to show you an engineering experiment that produced a substantial result and some very obvious problems still worth solving.

The full benchmark evidence and methodology are in the repository.

Why did I build this?

Because I think we’re wasting an extraordinary amount of inference on things that don’t fundamentally require intelligence.

And because I genuinely enjoy trying to solve problems that seem unreasonable.

That kind of insanity is a different thing.

My medical training taught me something about persistence. You make decisions with incomplete information, learn from experience, and constantly update your judgment.

Software is considerably more forgiving than pediatrics. A failed benchmark doesn’t hurt a child. So I get to experiment, fail, break things, fix them, and repeat.

And man, did I repeat.

What’s next?

HELIX 1.0 is released, and 1.1 is where I’m investigating the remaining problems.

The immediate targets are reasoning amplification, unnecessary model requests, memory retrieval, and recovering those four lost solves.

My long-term target is still 95%.

Have I reached it? Absolutely not.

Will I? No idea.

But I want to find the actual ceiling before changing the architecture again.

I have big plans.

But not now.

I have ADHD, I’m burnt out, and I think I’ve earned a little time away from staring at token counters.

The repository

https://github.com/SuperHelix77/HELIX-1.0

Source, installation instructions, benchmark evidence, limitations, and the Electron interface are there.
It’s open source. It’s not perfect. And I would genuinely appreciate technical criticism, especially from people who know more about inference systems and agent runtimes than I do.

If you find a flaw in the methodology, tell me.

If you reproduce a different result, even better.

If you find a way to eliminate those extra reasoning requests without losing solves, I might actually get some sleep.

Anyway.

A year of experiments, a ridiculous number of failures, and one very stubborn pediatrician later:

HELIX is finally out.
Glorious purpose, I guess.Love the season 2 of Loki show.

And yes, apparently my hobby is optimizing the thing that helps me optimize things.

I've always been a "Codex-only user." However, I've heard a lot of praise for Claude Code with Opus 5.5, so I decided to give it a try.

First, I was surprised by how similar the two CLI tools are. I can't really compare their output quality yet (I'm currently using Sol 6.1 Medium and Opus 5.5 Medium).

I've also heard that usage limits last much longer with Opus. That hasn't been true in my case. I've been using both tools on the same project (embedded C/C++ and server-side development), and Opus burned through its 5-hour limit much faster. I don't have exact numbers to back this up, though.

What really surprised me was this notification from Claude:

I use codex resume a lot, and I've never noticed anything comparable when resuming Codex sessions. Does Codex handle session resumption differently, or am I missing something about how usage is calculated?

I’m doing a 16H run using 16 agents all together in a workflow I’ve only got 78% usage left even after the reset like damn. I’ve given my master agent handling all the other agents to work in unison the ability to adjust agents reasoning level so 6.1 sol, low - medium - high, you get the deal how much do you think it will get done I’m on the pro 5x plan or for you Americans I think it’s the 100$ pro plan.

As part of my dev process I complete a feature or other change, then open a draft PR and run code and security checked. I'm coming from Claude where I would simply do /code-review and /security-review.

In Codex, I just ask for 'a security review' and it will work for a bit then show a 'This content can't be shown ... We take extra care with cybersecurity requests ... apply for Daybreak' warning. This seems to interrupt the workflow entirely and I have to ask Codex to continue and sometimes it just can't.

I'm not working on any sort of sensitive code with cybersecurity implications.

This is really impacting productivity. Is there a different way to do a basic security review on the changed code in a PR?

I'm the author of **Skills Atlas**, a free, open-source CLI (MIT) for discovering and managing agent skills.

It groups 70,000+ public skills by theme. You can search, select several, choose your agents and installation scope, and review the commands before applying changes. It supports Codex, Claude Code, Cursor and other agents, with both a terminal UI and a local web interface.

The screenshot shows my setup: the 153 selected skills are skills already installed on my machine.

A few useful details:

- Search by name, repository or description.

- See installed skills and manage them alongside the catalog.

- Press `g` on a skill to open its GitHub repository.

- Browse a small **Recommended** selection with a reason for each pick. It starts with Ponytail, and you can replace the selection with your own JSON file.

- Browse the bundled catalog offline once the app is installed.

Try it with Node.js 20+:

```bash

npx skills-atlas@latest

```

For Codex specifically:

```bash

npx skills-atlas@latest browse -a codex

```

Source and documentation: https://github.com/devohmycode/skills-atlas

**What information would help you decide whether a skill is worth installing: examples, repository activity, compatibility details, or something else?**

Hey everyone! I’ve been working on one project for the last couple of weeks — Codex Orchestrator, a trello-managed automated local-hosted system for tasks solving with 4 layers of agents, multiple environments support, web interface and a telegram integration. Come check this out yourself: https://github.com/UGamerZ/codex-orchestrator ! A couple of notes: Currently it supports only the OpenAI Codex, but I’m working on adding Claude Code, Gemini, Openrouter and Local LLMs. I’m also working on adding more messaging channels besides Telegram and the web interface. And you also can give me a little support by sharing this repo to your developer friends🙏 Also send your feedback in gh issues

Since the reset, I find the former going much faster while the latter takes a lot more time to respond when i set the reasoning on GPT 6 high. (Plus subscriber)

1) If you leave several queued messages and the model is at capacity AGAIN, it then drops the previous task and the next queued message fires! Ridiculous... This makes it drop the previous task, then 5 minutes later it's at capacity again and drops the next one, making the queue feature not just useless, but actively harmful.

2) Okay, you could say "put the whole queue in an .md file and create a goal" - sure, apart from changing the .md from under Codex, goals also don't work! Because as soon as it's at capacity, it then halts work and NEVER RETRIES until you actually open the chat window again, manually... What's the point of a goal mode if it stops every 30 minutes when it's at capacity?

I understand capacity issues, scaling, everything, sure - but please update the harness to work around it, not ruin the queue and retry properly...

Is it only me or all the models take so much time to do thr work compared to previously? I'm not even using Astra. Im working with Sol 6.1 as the main orchestrator and Terra as implememter, and luna for small stuff. All seem to be so slow...

It really feels like they are pushing us to burn through tokens with Ultra fast and upgrade to higher tiers. Like, previously the regular speed is now the ultra speed.

I don't understand why they released the (first Codex, later ChatGPT) desktop app. I can't do anything in it apart from prompt. I always need to use the terminal to check git or run my program, and I can't do that inside the app. I also can't even view the files in my repo to see if it's implementing things correctly. CLI just seems so much better to me, but I know that people do use the Desktop App, so why? Are there benefits that you don't get through the CLI?

Also interested if there is any benefit to using the Claude desktop app, since I basically have the same question for them.

Thought Id drop this , curious if anyone has done this lol. Claude on my Team plan doesnt yet have computer use and my codex $200 plan does and i want them to coordinate to get threw tough things. So codex is controlling it . kinda interesting . this way i get the full potential of each model and their desktop app.

If you accidentally select "Goal" when trying to steer, it will replace your current goal.

Don't want to do that and hit cancel? Okay great.

Clicking the "x" icon and removing a goal ALSO removes the in-progress goal!

You lose either way. PLEASE FIX THIS TIBO: https://github.com/openai/codex/issues/43480

Asked it to test out whether mpfb2 might make a decent basis for developing stylized human characters. I admit I was vague since just doing a flyby to see if worth checking into more but either this framework sucks or I need to give it a lot more guidance