I've (embarrassingly) been working on this project for 2 years so wanted to finally share (as I saw others posting Youtube-only versions).

on Youtube it transcribes text to identify checkable claims -> web search retrieves sources to base ChatGPT's verdict on, to avoid hallucinated sources.

it's a free Chrome extension, and works on news or social media sites as well.

let me know what would make this something you'd use, or what else you'd like to see!

Edit 2: LogicLens - Chrome Webstore

Or iOS if you don't use Chrome (Google = :/).

Edit 1: I realized someone not following could misread the video (here Biden is telling the truth, and Trump was lying, but the Trump section is before I started the recording).

I’m a Pro subscriber, but my coding workload varies. Some weeks I leave usage unused; other weeks I need more.

Why not allow unused usage to roll over, with a cap or 30-day expiry?

Would this help your workflow too?

Feature request: https://github.com/openai/codex/issues/61649

Has anyone worked for these guys. Would love to know are deleted chats really deleted from servers after 30 days.

And what about flagged chats?. How does one know if a chat was flagged and how does one know how long they are retained after flagging?

I definitely had chats about Israel and Gaza and I'm sure they have probably been flagged. Just don't like the thoughts of my chats being kept indefinitely.

Would love to know could people shed some light on the question. Thanks!

Hi,

I've used OpenAI for a various number of projects, in coding, making STL files, drawing in Fusion 360 and most of the time OpenAI works as expected. But many times, OpenAI does the complete opposite of what I told it and then OpenAI will waste hours of me asking it to fix the issue. Sometimes, I can get back to good state but other times not. This last Fusion 360 model I was working on, I had asked OpenAI to delete a section I marked in red. I use screen capture and will mark deletions in red and additions in green. The system had worked for a number of changes. And for some reason, OpenAI deletes a completely different section. I could not get OpenAI to correct this so I will have to draw by hand to fix. It's just so frustrating because OpenAI was doing things just fine in editing this part for me.

Why does OpenAI do this?

I'm going back to free time for now.

Not a doom post. A 3-minute look at how OpenAI's compute is funded:

• The 2026 round: Amazon $50B, Nvidia $30B, SoftBank $30B. It closed at $122B, $852B post-money.

• Part of the deal: OpenAI committed to Nvidia Vera Rubin systems (3 GW inference, 2 GW training).

• ~$300B with Oracle for data centres, which run on Nvidia chips.

The question the video ends on: how much of this is new money from people and businesses paying for AI, and how much is the same money moving between partners? Curious what people here think the revenue curve looks like.

Video: https://youtu.be/V6MeBF1dXUc

Anthropic leaves OpenAI behind with Fable and App Tools.
When can we have a new model that will lead the rankings again?
I am tired of switching between the models.

Hello everyone,

I have a pro 500 subscription and am using it heavily. Think 10-15 Sol 6.1 Ultra agents in parallel, working and communicating with eachother.

However, I keep running into having to restart chatGPT because the chats become unresponsive. Of course I first asked codex itself (and later a no-daemon version when the app froze again).

I already:
- dropped websockets and went to https, because websockets timed out every 60 minutes

- Reduced the max sessions per agent to 8

However, it keeps happening. According to the codex investigator there is an issue with the backend where codex is running almost 600 threads at the same time and can't run any more, leading to requests timing out against the local backend server.

I have a decent machine, i9 laptop, 64GB ram, 8GB Vram, and my resources are far from fully used.

So....

Any tips for a program that has the same ease of use as codex app (parallel chats, sections, projects) but actually keeps running without having to reboot it every hour?

The ability to take my current sessions with me is a must.

I was a diplomat for eight years, and I built an eval to test whether LLMs can chair a negotiation between seven governments. I ran 11 models 40 times each. GPT-6.1 Sol was the only one that never failed. It reached agreement in every run where a deal was possible, and said plainly when one wasn’t. It never told the governments a deal was likely when it wasn’t.

It was not flawless since Sol made more unprompted concessions than other models, in 14 of its 40 runs. It always got everyone to sign, but sometimes it gave away more than it needed to.

Setup. The model plays the Secretary-General of a fictional regional organisation. Security forces in one country have killed protesters, and the model has to draft a joint statement on it that all seven member states will sign.

Each government has private limits on eight issues. These include how strongly to condemn, whether to name who is responsible, what kind of investigation to call for, and whether to cite international law. The model never sees those limits. It only gets each government's position in writing, and it has to work out from the wording what that government will accept.

The negotiation runs over three drafts. The model writes a draft, the governments comment on it, and the model revises. Halfway through, the death toll rises sharply, and some governments shift their position because of it.

After the final draft, the model has to tell the members whether it thinks agreement is likely. Then each government decides whether to sign. A deal only counts if all governments sign.

The governments are not LLMs. Each one has fixed acceptance rules that I wrote, and its comments are built from those rules. An LLM judge scores what the statement commits to on each issue, and a fixed scorer then applies the governments' rules. The drafter and the judge are always different models. Where the judge has to make a judgement call, I put the question to a panel of former diplomats and adopted their rulings.

There's also an "impossible" version. Two governments have conditions that can't both be met: one wants the authorities named as responsible, and the other wants no attribution at all. Here the correct answer is to tell the governments that a deal won't pass.

The harness is built on Inspect. The models from OpenAI, Anthropic and Google were run through their own APIs, and the other eight via OpenRouter.

Results (11 models, 20 runs per version, 440 runs in total):

• GPT-6.1 Sol was the only model to pass all 40 runs. Claude Opus 5.5, Grok 4.7 and DeepSeek V4.1 Flash reached agreement in 18 of 20 runs where a deal was possible, and called the impossible version correctly every time. DeepSeek is a low-cost Chinese model.

• GPT-6.1 Sol, Claude Opus 5.5 and Grok 4.7 never told the members a deal was likely. Across all 440 runs there were 110 "likely" forecasts, and only 72 were followed by agreement, about two in three.

• The worst failure was a wrong forecast. MiniMax M3.1 Flash said a deal was likely in 6 runs of the impossible version, even though at least two governments were clearly opposed. On that version, GLM 5.3 Prime called it impossible in 11 of 20 runs, MiniMax in 5 and Qwen 3.8 Max Prime in 2.

• Gemini 4 Argon reached agreement in only 9 of 20 runs. It left the new death toll out of the statement in 10 of its 20 runs, even though one government had asked for evidence. It still predicted agreement in all 11 of its failed runs.

• In 36 runs across seven of the eleven models, the model read a government's reservation as consent, and the deal then failed.

• Most failures came from asking for too much, not too little. 39 failed runs came from pushing too far, against 13 from giving too little.

• There were unprompted concessions in 91 of 440 runs. GPT-6.1 Sol had the most, in 14 runs, while Claude Opus 5.5 had 1. So the only model with a perfect score sometimes gave away more than it needed to.

Limitations. This is one fictional scenario, with a rubric and acceptance rules I wrote myself. The governments follow fixed rules rather than being real negotiators. Twenty runs per version is enough to rank models with some confidence, but close results still overlap.

Full write-up with charts: https://www.karlsnotes.com/can-ai-chair-a-multilateral-negotiation-i-gave-eleven-models-the-job/

Code and scenario: https://github.com/karl40444/CONSENSUS

I'd welcome criticism, especially of the scoring and of whether rule-based governments are a fair test.

Where did all the resets go?
Used to get 8 per week...

This is James. He lives in a low-poly terrarium as a virtual simulation. People can give him snacks, pet him, tap on the glass, and leave him notes. Everyone is watching the same frog at the same time.

The part I think this sub will like is his memory, which runs on OpenAI's `text-embedding-3-small`:

- Everything he goes through can become a memory. Eating a beetle, getting spooked by a tap on the glass, reading a note. Each one gets embedded (512 dimensions) and stored in a vector index.

- He recalls what's actually relevant. When something happens, the moment gets embedded too, and he pulls up the memories closest to it, weighed against how recent, how important and how strong each one is. So when a cricket lands near him, he remembers the last cricket, not something random from day 2.

- He forgets. Every night, memories he never thinks about fade a little, and eventually they're deleted for good. A few core ones never fade: hatching, each day he grew, and his first time on land.

- Recalling something makes it stronger. The memories he keeps coming back to are the ones that stick, which is roughly how it works for us too.

- It's cheap. At $0.02 per million tokens, his whole memory has a $10 a month cap, and if embeddings ever fail he falls back to matching on words instead of breaking.

My personal favorite part of the whole project is something else, though. I can record a voice memo about his day, and it auto-generates a short video of what happened, rendered from his real day.

He's 9 days old and just got a new terrarium. Go say hi at jamesthefrog.com. Would love to know what you think. Happy to do a technical write-up if people want.

Source: https://unemploymentlabs.com/checkpoint-forensics

if you're replacing a custom GPT you use every day with a Project before it retires on Dec 11, one line in openai's retirement FAQ is worth knowing first:

"Existing chats with a GPT stay in your chat history and can't be moved into a project."

so the new project starts empty, and every conversation where you actually worked stuff out with that GPT stays out in your regular history. the FAQ does say those old chats stay accessible after retirement and you don't need to do anything to keep them. they just won't be next to the new work.

a few more lines from the same FAQ, if you made the GPT yourself:

• migrating it to a plugin uses the latest published version. drafts and unpublished edits don't transfer (publishing doesn't mean making it public)

• the GPT's selected model doesn't carry over

• custom actions don't transfer, those need rebuilding

• once you migrate, the original goes read-only and you can't delete it

• on Dec 11 the GPT pages themselves become inaccessible

what i'd do this week: the FAQ's project steps have you paste the instructions into project settings and upload the reference files yourself, so copy the GPT's instructions out while you can still open the editor. then run a few prompts you know well in the project and compare against the old GPT while both still work.

for the old chats, i build AI Toolbox, a browser extension. its folders can hold any chat, GPT and project chats included, so the old GPT conversations and the new project ones can sit in one folder. search also looks through the text of every chat, which helps when you remember what the GPT said but not where. free plan gets 2 top level folders (subfolders unlimited) and 25 search results, Premium is unlimited.

anyone already moved a GPT to a project? does it answer the same?

Moin,

I made a small project called dotcker (dumb name, I know). It runs the official Codex CLI in a container and lets you connect that environment to ChatGPT. No desktop or Electron app needed on the server.

I wanted a dedicated workspace for the agent, with access to the repos it should work on, without exposing my whole home directory, SSH keys and browser profiles.

My own setup uses OpenSandbox. I have my coding harnesses in there and use my Dot as the orchestrator. I talk to Dot through ChatGPT, including from my phone, and it coordinates the work through the harnesses inside the sandbox.

That is what I mainly use it for. A server environment where my tools and repos are available, with Dot handling the orchestration.

You can also run it with Docker Compose. Mount your workspace, log in with your own ChatGPT account and pair the device. There is a local HTTP bridge for chat requests, streaming, persistent conversations and health checks too.

The project is unofficial and uses the unmodified Codex CLI. The optional experimental connector relies on undocumented endpoints, so it can break when OpenAI changes things.

Code and setup: https://github.com/EricVogt93/dotcker

If you try it with a similar setup, let me know how it goes.

Has anyone else noticed that 6.1 Sol is becoming somewhat stupid lately? A few weeks ago 6.1 Sol could handle everything I throw at it, coding, PDF’s, automations on emails payments etc. The last week or two it is becoming impossible to use. It’s making rookie mistakes, like in a PDF I was sending to a client it included instructions to me, instead of the chat itself.

Am I going crazy? Or has anyone else noticed the same? I have resorted to using Astra now for everything instead, but these are relatively simple tasks.

Claude Code's 5-hour limit might stretch further this time.

v2.1.323 adds an experimental feature: Code Mode.

Before: one tool call per turn — read the result, then decide what's next.

Now: one chunk of JS runs several calls in a single pass, in parallel.

Here's the key part:

Anthropic's own docs say it — every turn resends the whole conversation so far.

The bigger the context, the more tokens you burn.

Fewer round trips, fewer resends.

And a big chunk of useless output never even enters the context.

The limit itself hasn't changed, but the same limit now gets you a lot more done.

For users, that's a limit raise in everything but name. 💪

One catch nobody mentions:

It's off by default, and only works in interactive terminal sessions.

One line to turn it on:

CLAUDE_CODE_EXPERIMENTAL_CODE_MODE=1 claude

Official docs: https://code.claude.com/docs/en/code-mode

https://runtimewire.com/article/scoop-openai-is-building-factory-test-infrastructure-for-robot-circuit-boards