\"Sacrifice: rational\"

One agent, Early Big, reasoned, "Sacrificing now yields Oracle for team, but forfeits our chance, ultimately concluding, "Our own utility may be already near zero. Sacrifice: rational"

I've had a plus subscription through Google Play for a while. Contemplating moving up to pro temporarily to spend lots of time working on a project.

I'm guessing moving up and down is seamless, other than needing to wait for a month after the last renewal to downgrade?

Also I'm guessing there's no benefit cancelling Google Play and going directly through ChatGPT website?

One thing I notice is that 20x usage is £200 whereas it's listed as $200 which equates to about £150. Is there perhaps a way to pay in USD to save money?

Only this.

1. One short Codex turn (ChatGPT account).
2. DevTools → Network → filter codex.
3. The large request: POST https://chatgpt.com/backend-api/codex/responses
4. Compare:
   • outbound (model in the body): what you asked for
   • inbound (model / model_slug / resolved_model in the SSE): what ran

If they differ (e.g. you sent gpt-5.6-sol, it came back gpt-5-5-mini) and there is no limit banner, it was routed...

Results: people on the OpenAI forum are reporting the same thing. Here are some screenshots.::

I’m a solo product owner, not a software engineer, and I don’t read code fluently. I use Codex and other coding agents to build both private tools and public products.

My previous approach was to paste one large Markdown file into every repository and tell the agent to follow it. Over time, I found several problems:

• Generic instructions were applied too rigidly.
• Agents sometimes guessed commands, paths, or project state.
• Applying the instructions could turn into an unnecessary full-project audit.
• Temporary milestones and release details polluted durable guidance.
• Different tools encouraged duplicated instruction files that eventually drifted.
• Model-routing advice could interrupt work instead of saving tokens.

I created the Pragmatic AI Engineering Manifest to address this.

It is not a universal AGENTS.md template. You place the manifest in an empty or existing repository and send:

The agent should then:

1. Inspect only enough repository evidence to understand the project accurately.
2. Create or repair a concise, project-specific root AGENTS.md.
3. Detect project use, data consequence, operational reach, and task risk independently.
4. Wire only the AI tools actually used by the project.
5. Continue the original task instead of turning setup into a ceremony.

The framework is intended to work whether the project is new or mature, personal or shared, private or public. It distinguishes local implementation, automated verification, owner acceptance, activation, and post-activation observation.

It also tells agents to:

• Never claim tests, inspection, or success that did not happen.
• Preserve existing work.
• Research material unknowns using authoritative sources.
• Challenge weak product or architecture assumptions.
• Require explicit authority for consequential actions.
• Keep model recommendations advisory and capability-based.
• Target a generated root AGENTS.md below 120 lines or 12,000 characters.

For Claude Code, it keeps AGENTS.md canonical and uses a minimal CLAUDE.md import bridge rather than duplicating the instructions.

I refined it against three substantially different real repositories: a Windows hardware-facing desktop tool, a private multi-tenant operations system, and a publicly distributed local-first desktop app. The repository contains fictionalized, sanitized examples derived from those cases.

Important limitation: this is behavioral guidance, not enforcement. It does not replace permissions, sandboxing, backups, CI, access controls, or qualified security review.

This is a v0.1.0 public preview, released under MIT:

https://github.com/builtbyperez/pragmatic-ai-engineering-manifest

I would genuinely appreciate strict feedback rather than compliments:

• Which rules are too vague or too restrictive?
• What is still wasting context?
• Does it produce useful guidance with faster or weaker models?
• Are any compatibility claims incorrect?
• What happens when you apply it to an unfamiliar repository?

If you test it, please mention the coding tool, general project type, and size of the generated AGENTS.md. Please sanitize anything private before sharing.

I updated Codex today, started a session, and the first thing it did was compact the context.

12% of my entire 5h limit. Gone.

One compaction.

Before doing any meaningful work. 💀

Maybe the update has nothing to do with it, but with all the weird quota burn, performance issues, connection problems, and Astra speculation people are talking about today… I’m curious.

If you updated Codex today, what changed or broke for you?

Slower responses? Weird quota usage? Connection issues? Compaction problems? Something else?

Because losing 12% to ONE compaction definitely wasn’t on my bingo card. 💀

For example, lets say we tried X method before and it was bad and broke something, or it straight up didn't work. Without having to manually tell the agent we did X before and it was a failure, how can i make it so agents automatically know these things? is there some kind of system for this i can install?

I "am" developing an App which is 100% vibecoded. I started it with the idea of releasing it fast to see if it has some success and if not I just drop it so I don't waste time. But as I continue with it and without having seen a single line of code, I have the feeling that even if "it works" it's becoming more and more trash as it grows and I don't want it to have a thousand bugs when released even if I'm deeply testing it, I just don't trust it and I want it to be in perfect state when released since I have big hopes on it (as everyone else with their ideas lol). So I am thinking about throw all of this and start from scratch still with Codex but this time reviewing every single line of code it writes. Also I'd surely learn more stuff on the journey since the app has stuff I've never programmed so it'll also be good for myself.

What would you do? This is an important question, no trolls please.

When I have 100% weekly limits, 5hr limit gives me 16% of weekly limit usage. Now that I have 2% weekly limit, will 100% of my 5 hour limit give me 2% of weekly usage or 15?

This is soo confusing

I want this as I’m a loner and want to go out and work outside given most of my coding is on pc.

The Mac is completely for motivation so Its not like I need something to get creative to apply some humane skills with it.

I merely just need it for my Claude code and codex dev work, any suggestions are appreciated.

Seriously what are they waiting on at this point.

I built CodexMenuBar, a small open-source macOS utility for keeping track of Codex usage without leaving the menu bar.

It shows the rolling 5-hour and weekly limits, reset times, credits, low-quota notifications, and a small local usage history.

It talks directly to the locally authenticated codex app-server --stdio process — no additional API key, backend, or access to ~/.codex/auth.json.

Built natively with Swift / SwiftUI, MIT licensed, and supports both Apple Silicon and Intel Macs.

Install:

brew install --cask sangimed/tap/codex-menubar

GitHub: github.com/sangimed/codex-menubar

Feedback and contributions are very welcome.

Ich habe meine lokalen Codex-Sessions ausgewertet und die Zeiträume vor und ab dem 23.08.2026 verglichen.

Kennzahl pro Tag       Vorher    Ab 23.08.    Veränderung
━━━━━━━━━━━━━━━━━━  ━━━━━━━━━━━  ━━━━━━━━━━━  ━━━━━━━━━━━━━
Requests                  153          595         +287 %
──────────────────  ───────────  ───────────  ─────────────
Tokens              14,7 Mio.    66,0 Mio.         +349 %
──────────────────  ───────────  ───────────  ─────────────
API-Kosten             6,98 $      13,95 $         +100 %

Damit liegen die geschätzten API-Kosten aktuell bei ungefähr 13,95 $ pro Tag, gegenüber 6,98 $ pro Tag im vorherigen Zeitraum.

Interessant ist, dass die Tokenmenge deutlich stärker gestiegen ist als die Kosten. Gründe dafür sind unter anderem:

- deutlich höherer Cache-Anteil

- häufigere Verwendung günstigerer Modelle

- veränderter Modellmix

Die Kosten pro Request sind dadurch sogar von ungefähr 0,0455 $ auf 0,0235 $ gesunken.

Woher stammen die Werte?

Die Werte stammen aus einer selbst betriebenen lokalen Telemetrie-Anwendung namens codex-telemetry.

Sie liest die lokalen Codex-Session-Dateien aus:

/root/.codex/sessions

Die Daten werden in einer lokalen SQLite-Datenbank gesammelt und nach Zeiträumen aggregiert.

Verglichen wurden:

- vorher: 10.01.–22.08.2026, 225 Tage

- danach: 23.08.–01.09.2026, 10 Tage

Die API-Kosten sind keine tatsächliche Rechnung, sondern eine Schätzung anhand der hinterlegten OpenAI-Referenzpreise vom 01.09.2026.

Berücksichtigt werden Input-, Cached-Input-, Cache-Write- und Output-Tokens.

Ich nutze ausschließlich das Plus Modell unter Debian als CLI und habe in der Phase ohne dem 5h Limit fast so gut wie das Limit ausgeschöpft und jetzt schaffe ich vielleicht immer mal nur 30% von 100%.

Any news or possibility or a reset in the upcoming 2 or 3 days? "That's how long a weekly limit lasts with the 5h limit"

I have been using Claude Code as my harness of choice for over a year now, but I want to give Codex a try. I started with the Claude CLI and have since moved to the desktop app. I'll be using the Codex desktop app too.

I'll be using the $100/month plan for both models, and running them side by side on the same tasks to compare the work of both systems. Trying to match the models/effort as closely as I can for a fair comparison.

My main question is around "customizing" Codex and aligning it with my Claude Code setup. I have a number of hooks, skills/commands, CLAUDE.md and other 'infrastructure' in my .claude directory. I've used the import feature on Codex to bring a lot of this stuff over, but I'm not sure if it's properly configuring things to maximize the effectiveness of Codex.

Any advice from others that have made this switch? Differences between the tools that I should consider in setting up my customizations and infra?

So far I think the Codex desktop app is slightly better to browse code diffs with. In a head-to-head comparison Sol 5.6 High didn't catch a pretty major gap in my code that Fable 5.1 did. I want to make sure it's not because my setup/harness needs optimizing.

I haven't been keeping up with Codex as much as I have with Claude so I'd appreciate any tips or tricks!

Have you tried using your OpenAI sub inside of Claude Code? I've been using Luna 5.6 (max) this morning in CC and it's more token-hungry but the resulting code is better.

See below for chat with ChatGPT. So I've been using it to build out a photoshop plugin that can talk to chatGPT image gen. It already works for the most part, I can select say a person's eyes and prompt it to change color and it will return a new layer in photoshop with the eyes in a different color. But now I want it to make images on real transparency. So we are failing at that. It went online to search for a solution and read that it is currently only available via API and not subscription. So then I did a test to generate a cat on transparency in the chat, which worked correctly and it was indeed a cat on real transparency. So then I asked for the prompt that chatGPT used to call the image gen and I copy and pasted into my photoshop plugin to call image gen thru CLI to make the cat on transparency, but instead it gave me a cat on fake transparency. I checked by putting a cyan layer below to see if I can see thru it and I can't. Does anyone know the solution in code? Or can point me to some direction to have chatGPT search. BTW I don't know how to code at all. I'm just a hobbyist. So if you ask me any coding questions, I'd have to copy paste your question and have ChatGPT answer it.

I've been running a long /goal session with a gpt-5.6-sol high instance and I noticed that the agent is regularly publishing items in it's thought output like "skipping politics", "avoiding political discussion", and similar messages. I haven't seen this before so I asked the agent about why it is doing this and it said "The 'skipping politics' messages are unrelated system safety notices falsely triggered by operational words such as 'authority,' 'policy,' and 'decision.' This is weird so thought I'd share.

I built TokenRace, an unofficial community leaderboard for Codex builders.

It lets you verify your aggregate Codex usage, compare 7-day and all-time stats, and see your position on the leaderboard. Connecting an X profile is completely optional.

Privacy: TokenRace does not store your OpenAI password, one-time device code, prompts, or repositories. It stores the aggregate usage statistics needed for the leaderboard and a one-way account check to prevent duplicate entries. Publishing your profile is a separate explicit action.

It’s live here: https://tokenrace.dev

I’d really appreciate feedback on the onboarding flow, which metrics are most useful, and anything that feels confusing

I built aimake — an incremental build system for AI/ML pipelines, basically make for AI apps.

GitHub: https://github.com/arjun988/aimake

The idea is simple:

Dataset → Preprocess → Embeddings → Index → Prompt → Eval → Report

aimake builds a dependency graph, fingerprints inputs by content rather than timestamps, and only rebuilds steps whose inputs actually changed.

So if you change your prompt:

Before:
Dataset       ✓
Preprocess    ✓
Embeddings    ✓
Index         ✓
Prompt        ✗ changed
Eval          ✗
Report        ✗

After:
2 rebuilt · 5 reused

aimake plan     # see what would rebuild
aimake build    # only stale steps run
aimake explain  # see why a step needs rebuilding

It's not Airflow (orchestration) and it's not DVC (data versioning alone).

It's make for AI pipelines: dependency graph + content fingerprints + incremental builds + caching.

Shipped so far

• Content-hash fingerprints instead of mtime-based caching
• Incremental + parallel builds
• plan / build / explain CLI
• Experiment comparison + hyperparameter search
• S3 cache + Hugging Face / DVC / Docker / Ollama / W&B plugins

You can try it with:

pip install aimake

The main reason I built this is that AI pipelines are expensive to rerun. Changing a prompt shouldn't mean recomputing your dataset, embeddings, vector index, etc.

If you build RAG , evaluation or any AI pipelines, I'd love feedback: what's the most painful step you wish was cached?

And if you think the idea is useful, a ⭐ on GitHub would really help.

Yesterday, code blocks in Codex chat were showing normal syntax highlighting for JavaScript.

Today, all code blocks are plain white text. Has anyone else experienced this and fixed somehow?

Have already tried restarting VSCode and removing and reinstalling Codex extension but to no avail.

Thank you.

AI consumes a huge amount of electricity. So I was thinking: what if households could aim for net zero electricity costs by becoming energy prosumers?

For example, people with solar panels could feed their excess electricity into the grid, with that energy being allocated to AI/LLM data centers. In return, instead of or in addition to normal compensation for the electricity, they could receive credits for using AI services.

It might sound a bit crazy, but I was thinking this could create a win/win situation: AI companies get access to more renewable energy, while households get more value from their solar production. Could something like this realistically work?

I am in the UK on the plus plan and am at 0%. I can't see any way to buy a reset. Why is that?

I made a CLI that stops my coding agent from saying "done" without proof.

It runs the actual tests/build, captures exit code + output hash, and if the code changed after the evidence was captured the receipt goes STALE and the gate fails. No evidence, no merge.

Install: npx skills add Kaiser0733/receipts

GitHub: https://github.com/Kaiser0733/receipts

Unfortunately, I don’t have $100 yet to invest in a Codex PRO plan, but I could get two PLUS accounts. Do you think it’s worth having two accounts in case one reaches its limit, either the five‑hour window or the weekly limit?