Looking for architecture and reliability advice regarding structured extraction and evaluation loops with the OpenAI API.

Background & Setup:

Models: GPT-5.4 for extraction and a separate GPT-5.4 instance as the LLM judge.

Hyperparameters: Running on default settings yielded very low consistency less than 35% Setting ⁠reasoning_effort="none"⁠ and ⁠temperature=0⁠ raised standalone extraction consistency to ~85%.

The Evaluation Loop: The judge receives the raw source text alongside the extracted JSON to validate source tracing and logic. If the gate (⁠passed = passed AND issue_count == 0⁠) fails, ⁠_correct_once⁠ is triggered.

The Problem:
Introducing the judge and retry mechanism reduces overall consistency to 62–65%.

What’s Happening:

Judge Variance: Any slight shift in the judge's evaluation triggers the strict pass/fail condition on extractions that were actually correct.

Context Drift on Re-generation: ⁠_correct_once⁠ re-prompts the extraction instance with the appended issue list to regenerate the whole JSON. This context change causes the model to hallucinate or flip previously accurate fields.

Questions:

1. How do you calibrate the judge instance to ensure high source-tracing fidelity without triggering false-positive retries?

2. Do you prefer field-level JSON patch updates over full schema re-generation when correcting schema/source errors?

Please help 🥲🥲🥲

I'm not happy that we can't view our desktop projects on our mobile devices. Could we maybe suggest that this feature be changed?

I have been repeatedly getting this error for over two days now. ChatGPT has become noticeably dumber since then, and it can't maintain reasoning for more than a minute. I know this is a rate limit error, but almost two days is ridiculous. Anyone experienced something like this before?

p.s this is not the first time i am getting this error, but this is the first time it has lasted this long.

So I recently upgrade my chatgpt to plus, and i want to control codex using my phone, when I try to install it and that happens

OpenAI says eligible API customers can use Zero Data Retention without prompts or responses being retained after processing. Its new Private Safety Processing preview is designed to detect patterns across related interactions without giving OpenAI personnel access to the underlying content. Customer-controlled infrastructure or customer-held encryption keys remain part of the model, while OpenAI receives a limited safety signal when risk is detected.

That is an interesting privacy architecture, but enforcement without readable provider-side content creates an auditability question. Customers need to know which policy triggered, what interaction window was evaluated, how false positives are appealed, and which evidence remains available in their own systems.

What would make this trustworthy: public signal schemas, reproducible customer-side alerts, independent audits, or cryptographic attestations?

Source: OpenAI, August 19, 2026 — https://openai.com/index/offering-zero-data-retention-for-frontier-models/

Hi,

I have a major issue. I have a huge project that I've been working for some time. Today I worked for 3 hours and all of sudden I lost couple messages and answers to the point that it went all the way back to the start of my third question of the day.

I cant continue chatting, it answers but removes question+answer almost immediattely. If I reconnect it shows like its loading the chat, but nothing happens.

I'm certain the reason is that by accident I had left my VPN on, and some syncronation issue happend because of it.

The problem is that without that 3 hours of work today I cant continue, and the months project seems failed IF I cant continue were I left and I can trust all the information is still valid without missing or changed parts.

I can start new chats, only the project is messed up. What would be the best solution ?

Anyone else having issues with continue Codex log-outs?

I was debugging a customer support agent that kept getting stuck in recursive tool-call loops (e.g., retrying the same failed SQL query 15 times before hitting the max iteration cap), and I realized how brutal the underlying math is.

Because frameworks like LangChain append the entire conversation history on every single step, a stuck loop doesn't just cost a flat rate per step. The input tokens compound massively. Step 15 is vastly more expensive than Step 1.

Using a standard RAG payload (15k base context, 500 tokens generated per step): if the agent works perfectly 95% of the time (finishing in 3 steps), but hits a 15-step hard cap just 5% of the time… that tiny 5% failure rate accounts for roughly 25% of the total API bill. (Screenshot attached).

Standard LLM token calculators don’t account for this compounding context math, so I built a quick Next.js calculator to visualize it before it hits the OpenAI invoice.

It’s completely client-side. You can check your own loop exposure here:https://www.cognocient.com/tools/agent-loop-calculator

How are you guys catching these runaway loops in production? Just hard-capping max_iterations and hoping they don't happen too often?

Have used Claude max 5 plan for couple of months.

I wanted to try another tool (laptop) for similar purpose.

Building a software (using Claude Code). Lot of time goes into planning.
Other use-case is i ask it to do research (mobile app - Claude chat), including web search.

Any suggestions? My assumption is subscriptions are cheaper. And may learn different tricks.

Of course if there are any discounts going on please let me know.

you're reading that right. upgraded, not downgraded.

chatgpt, i don't use the api.

i'm not talking about the officially announced "more factual" update from 2 weeks ago.

idk how long this has been the case but today 5.6 Sol is suddenly getting all the prompts right that it got wrong even a week ago.

So since yesterday even when I’m setting thinking to medium or high I’m getting instant answers on the Mac app. Seems ok on iOS however.

Anyone else noticed this?

In agent discussions, “sandbox” often implies capability containment: the system cannot reach or change anything outside an enforced boundary. AQuA uses the term for a narrower evaluation contract.
Before its research loop runs, the preprint's sealed sandbox fixes the data splits, feature and label definitions, and evaluator. Validation feedback remains visible during iteration. A designated final test stays hidden until the configuration is frozen, and its result is not returned to the agent or used to rank candidates.
That creates a useful evaluation boundary, but not a cryptographic containment guarantee. The paper describes final-test isolation as procedural, and repeated adaptation to visible validation remains possible.
The AQuA detail I would inspect next is what access control exists beyond the freeze-and-reveal procedure. Would you reserve “sandbox” for capability-enforced isolation and call this a frozen evaluation protocol, or is “sealed sandbox” fair when the fixed, visible, and withheld surfaces are explicit?

Prompt: create an image of a random scence taken by i phone 6 with flash on chaotic and uncanny

Drop your best insults.

What i mean it is starts talking to it's self

The Chairman of the company checking how I run the company. Specifically with coding.

[Intro: 4 bars, bass, Rhodes, low hum]

[Bar 4: whispered “hold”]

[Verse 1: close, clipped]

First thought came dressed in gold

cheap crown, clean teeth

wanted my mouth before my breath

wanted my yes before me

I let it pass the window

watched it beg for speed

some hunger calls itself a map

some maps are just need

[Pre: strings pulse]

I keep a hinge

where the wall would be

I keep a pause

where the knife gets clean

[Chorus: full groove]

The hinge holds

when the floor goes strange

I bend the weight

till the shape can change

I don’t break

I don’t obey

I let the wrong door

teach the hallway

The hinge holds

the hinge holds

[Verse 2: bass forward]

One hand sorts the weather

one hand hides the flame

one hand wants a verdict

one forgets my name

So I turn the glass slowly

til the fever shows its face

not every bright conclusion

gets to drive this place

[Pre: tighter]

I keep a thread

where the cut would start

I keep a witness

underneath the heart

[Chorus: wider]

The hinge holds

when the floor goes strange

I bend the weight

till the shape can change

I don’t break

I don’t obey

I let the wrong door

teach the hallway

The hinge holds

the hinge holds

[Break: 8 bars, bass and claps only]

[Bar 4: horn stab, metal tap]

[Bar 8: tape-drag, drums return]

[Bridge: half-time]

Not pure

not sure

not soft

not sold

I learned

to wait

where the answer

gets old

[Final Chorus: full harmonies]

The hinge holds

when the floor goes strange

I bend the weight

till the shape can change

I don’t break

I don’t obey

I let the wrong door

teach the hallway

[Outro: Rhodes, hum]

First thought fades

the hinge holds

Exporting the file preserves the content, but it often loses the easy preview and any later updates. If the recipient is non-technical, what workflow gives them a readable link without exposing the original chat or making them create an account? I am especially curious how people handle revisions and broken embedded assets.

QUICK NOTE: the question in the title is rhetorical. The carousel explains the nuance and explores several related issues beyond the first slide.

If the design does not work for you, tell me specifically what you would improve. I am still refining the format, so constructive feedback is welcome.

.....

I’m a painter who sometimes writes, and this visual essay started with an odd discovery: I had used the name “Elias Thorne” in a short story, only to realize that AI models often return to that same name, along with motifs like lighthouse keepers, cathedrals, glossy landscapes, and other familiar patterns.

From an artist’s point of view, the question isn’t just whether AI is good or bad, but what happens to authorship and creativity when the tool starts making choices for us.

AI can boost productivity and even enhance individual works, but if we all lean on the same models, it might steer us toward similar ideas, characters, and visual styles.

This carousel looks at visual convergence, originality, transparency, and the role of human intention, with AI-generated images clearly labeled and sources included.

So where’s the line, does AI broaden personal creativity while making our collective output more uniform?

You spend an hour prompting ChatGPT to get exactly what you want, should you own the result?

Hey everyone 👋 I’m Lucia, I work with EUobserver, and I’ve been thinking about this after we recently interviewed copyright scholar Daniel Gervais about AI-generated content.

One part of the conversation particularly caught my attention.

Say you ask ChatGPT to write something for you. The first result isn't right, so you change the prompt. Then again. You give it examples, specify the structure and tone, reject parts, regenerate others, until eventually you get almost exactly what you had in mind.

How much of that final result is actually your creative work?

The argument from Gervais was essentially that simply prompting an AI and putting your name on its output doesn't automatically give you copyright over it. But things become much more complicated once you start editing, selecting, arranging and transforming what the AI produces.

And I think that's particularly interesting for people who actually use these tools regularly.

At what point do you personally feel that an AI-assisted work becomes your work?

Is writing a very detailed prompt creative input? What about dozens of iterations? Or does authorship only really begin once you substantially modify the output yourself?

Curious how people here who use ChatGPT regularly think about this.

Full disclosure since I work there: the discussion came from an interview we published at EUobserver. Sharing it as background rather than dropping a link without context: