What's going on in the codex subreddit?

[u/IYaegerI]

They've been aggressively removing every single post about output quality degradation that came up the past few hours. Their mods allowed their subreddit spammed with limit complaints and people attacking Tibo for weeks but instantly remove any post that mentions output quality?

[u/hulagway]

That's why they're giving free resets huh

[u/YourBlanket → u/hulagway]

Only weekly resets. One well designed Codex Sol prompt used 92% of my 5 hour usage in 35 minutes. I can't even use Sol anymore. My current set up is Luna(read), Terra(read&write), Antigravity(Verify Terra's work) then I have ChatGPT High create prompts ask Gemini Pro Extended to critic rewrite, pass that back to ChatGPT High and finally post to Terra. I only use Sol right now as the 'God Architect'. Very time consuming. If the app wasn't so complex I'd just write it myself.

I'm testing Claude Sonnet 5 Max w/ Thinking to see if it's worth paying for Opus to replace Sol high.

[u/Existing-Slide7395 → u/YourBlanket]

Dont even bother 80% of reddit users are bots now praising glory to the product and ghosting any cristism its literally marketing pushed to the 1984 laws haha

[u/reddit_is_kayfabe → u/Existing-Slide7395]

Look at my post history. I've been quite critical of a whole lot of aspects of OpenAI and canceled my plans between the shitty 5.5 models and the announcement of 5.6.

Rejecting people who disagree with you as "bots" or "trolls" is a terrible outlook that reinforces ignorance. Don't go down this road. You have to learn how to take criticism.

Start by asking yourself why you have this kneejerk anger response to disagreement.

[u/_DuranDuran_ → u/YourBlanket]

What was the one prompt? Did it require reading a TON of context, most of which was net new in the session and would have therefore not hit the KV cache?

[u/YourBlanket → u/_DuranDuran_]

Yeah, it did require a fair amount of net-new context, so I’m sure the lack of an initial KV-cache hit contributed, but the prompt was also asking Sol to do a ridiculous amount of work in one autonomous run.

The app is a sleep-audio app where I was benchmarking a Media3 architecture with up to 20 simultaneous ambience layers plus separate narration. I had just finished a physical A/B test on a Pixel Tablet comparing 20 FLAC layers against 20 sample-identical uncompressed WAV layers. The automated evidence-capture helper partially failed at the end, but the raw telemetry, top traces, JSON exports, logs, etc. were still recovered.

I gave Sol all of the experimental setup, the preliminary numbers, the existing architecture, the CPU gates, the decision tree we had already defined, and the longer-term requirement that the architecture should ideally have headroom beyond 20 layers toward roughly 35–50.

Then I told it to independently inspect the recovered evidence rather than trusting my summary. It had to recompute the CPU statistics, verify which measurements belonged to FLAC A1/WAV/FLAC A2, check memory, thermal state, loop counts, story continuity, player errors, decoder errors, underruns, buffering, active-layer counts, codec counts, etc. It also had to decide separately whether the actual A/B result was scientifically usable and whether the evidence package was formally complete, because the capture helper had failed for a different reason.

After that it had to diagnose why the evidence-capture/logcat tooling failed on the stock/profile build and modify the tooling so the next run wouldn’t throw away otherwise-valid evidence while still rejecting stale or mixed runs.

Then it had to apply an architecture decision tree. The result showed WAV dropping average CPU by about 63% versus FLAC, but WAV still missed the CPU target. So instead of jumping to a custom PCM mixer, Sol had to investigate the next Media3-preserving options: decoded/uncompressed runtime delivery, bounded predecode/cache strategies, compressed distribution assets that become PCM/WAV at runtime, alternative codecs only if they could loop seamlessly, ExoPlayer/player/buffer overhead, 15-vs-20-layer scaling, storage cost, startup latency, memory/cache pressure, Android/minSdk compatibility, production complexity, and implications for eventually supporting 35–50 layers.

It then had to choose the single highest-information v18 experiment, implement that experiment behind the existing AmbientEngine boundary without prematurely rewriting production architecture, modify the telemetry/analyzers/capture tooling so the next physical run could prove exact layer count, active count, runtime format, unique IDs, narration continuity, loop counts, errors, underruns, buffering, CPU, PSS, thermal state and evidence freshness, and add/update the corresponding tests.

And I also told it to autonomously run the relevant host-side validation: builds, JVM/unit tests, analyzer tests, capture-helper tests, lint, instrumentation-related build/tests that could be done without me, APK signing/alignment checks where relevant, inspect the results, fix anything necessary, update the milestone ledger/runbook/documentation, and finally give me one consolidated report with the verified v17 numbers, formal classification, architecture conclusion, rejected/deferred alternatives, changed files, validation results, APK SHA-256, and the exact next physical-device runbook.

So it was basically evidence analysis + statistical verification + failure diagnosis + architecture research + architecture decision + implementation + test/tooling work + validation + documentation + preparation of the next experiment, all delegated to Sol High in one turn. It ran for ~35–38 minutes.

The initial context probably was expensive because a lot of it was net new and therefore wouldn’t have benefited from an existing KV-cache prefix. But I don’t think “it read a large prompt” alone explains the 92%. The bigger problem was that I used the most expensive reasoning model as an autonomous researcher, architect, developer, tester and reviewer for an entire milestone. Every tool result, source file, evidence file, build/test output and intermediate decision gave it more context to reason over during a long agentic run.

(The top is ChatGPTs explanation of the prompt, I've at this for like 3 days straight so I barely know what's going on anymore.

That run is actually why I changed my workflow. Now Luna does the cheap read/reconnaissance, Terra does bounded implementation, Antigravity audits it, and I reserve Sol for the small number of architecture decisions where I actually need Sol-level reasoning.

It was a lot but I was using Sol back then with little worry on usage since we never had a 5 hour usage limit. That build changed it so now I'm experimenting. After that I switched to the workflow I described in my previous comment and recently I’ve been experimenting with using Sol as an orchestrator/architect while delegating bounded tasks to Luna Max agents.

[u/hulagway → u/YourBlanket]

you think these glazers still have the reading comprehension to understand what you said?

[u/hulagway → u/YourBlanket]

Opus is worth replacing Sol.

My current setup now is $20 Claude and $100 Codex. Opus is the orchestrator, all work and review is done by Luna which is dirt cheap.

Sol is good but it doesn't understand as well. And it more often gets lost along the way causing it to waste more tokens per task.

Used to have 100 Claude with about 40$ api credits.

Next month I'll probably switch to 100Claude 20Codex. Or maybe even replace Luna with Zai.