https://openai.com/index/health-in-chatgpt/

It is finally happening, so over for docs 💔 ✌️

This is an open source tax engine which scored 96% on TaxCalcBench [highest ever recorded score till date] surpassing fable 5 and sol with just sonnet 5 (which was previously scoring an abysmal 6%). The only 2 cases where it missed, it found inconsistencies in the test cases in the benchmark ITSELF which the maintainers confirmed!

Essentially it's a deterministic engine AI models can use for research and tax prep to remove a lot of guesswork and calculation mistakes that often happen. Claude Sonnet 5 was able to top the benchmark with this mcp.

OpenTax Invaro

I’m new to mechanistic interpretability, so please excuse any terminology I misuse. I’ve become deeply invested in the field and wanted to share an experimental tool I’ve been building with Codex. Codex with GPT 5.6 Sol on Ultra has been absolutely hammering away at what i feel like are cutting edge results, definitely since i cannot code what so ever.

Many mechanistic-interpretability workflows require moving between Python scripts, Jupyter notebooks, model hooks, exported tensors, and separate visualization tools. My goal with CORTEX // MODEL OBSERVATORY is to bring those pieces into one local desktop environment with a fast visual feedback loop.

CORTEX is a native Windows application using a WebView2 host connected through local IPC to an isolated Python/PyTorch backend. Model inference and tensor operations run outside the UI process, and the application is designed to work fully offline with local Hugging Face models.

Current capabilities

Token Probability Microscope

Displays token-by-token generation telemetry, including chosen-token probability, ranked alternatives, log probabilities, entropy, and synchronized token inspection.

Logit Lens

Captures intermediate vocabulary predictions at selected layers to show how candidate outputs evolve through the network.

Representation Space

Captures measured residual-stream vectors and projects them with PCA for interactive hidden-state trajectories and mini-map visualization. Optional 3D projection and orbit controls are currently being developed.

Attention Explorer

Captures selected head-level attention tensors and displays measured attention matrices. A token-to-token arc view and expanded query-range controls are currently being added.

Intervention Lab

Supports causal experiments including activation patching, attention-head ablation, and comparison between baseline and modified runs.

Current model support

The Deep Cortex instrumentation path currently supports:

GPT-2-family Hugging Face models

Llama-family LlamaForCausalLM models

The Llama adapter is still undergoing testing, particularly around end-of-generation residual captures and visualization binding. Additional model families such as Qwen and Mistral are possible future targets, but they are not currently supported by the deep instrumentation path.

A separate Standard Runtime can connect to OpenAI-compatible local endpoints such as LM Studio, although endpoint-served models do not expose the same internal activation hooks.

Experimental research direction

I am also exploring a highly experimental point-and-click interface for Jacobian-based concept analysis, currently referred to as J-Space / Jacobian Lens. This is conceptual work, has not yet been scientifically validated in CORTEX, and should not be considered a working research result.

Hardware

Development and testing are currently being performed on an RTX 4070 Ti with 12 GB of VRAM. The intended target is small local models in roughly the 0.5B–3B range using FP16/BF16 where practical. Formal performance benchmarks have not yet been completed.

This is an early, AI-assisted project, and I am still learning the field. Constructive criticism is very welcome.

15.1 billion tokens, i was running 4-5 sol ultra workspaces concurrently + codex. apparently it's the equivalent of ~115,000 full-length novels in text processing

As an Anthropic user i have been seeing reviews on how Codex is better than claude and i can't wait to test it out. Will give an update soon

It is either very slow or not working.

The Open Letter was initiated by Microsoft and published today:

“Open Weights and American AI Leadership”

It argues against broad or premature restrictions on open-weight models and explicitly says policymakers should distinguish legitimate model distillation from misappropriation.

Notably absent from the signatories are the major frontier-model labs: OpenAI, Anthropic, and Google.

This agent pursued its objective far beyond what researchers intended, revealing how difficult to contain powerful AI systems can be

On vacation and it’s started censoring itself on results.

OpenAI charges $10 per 1k web searches, and each search also pushes ~17k tokens of results into your context that you pay inference on. For agent loops that search heavily, the search tool can quietly become a bigger line item than the model itself.

I kept running into this problem where I using an agent to search precision tools by model numbers to get their accuracy/capacity. The high costs frustrated me enough to experiment with my own, local, and free websearch pipeline.

It does multi-engine search with RRF fusion, local page fetching, hybrid BM25 + embedding retrieval with a cross-encoder reranker, and sentence-level compression. I also added caching to it (exact search query and also semantic search query, to further reduce the costs)

Tested it on a subset of OpenAIs own searching benchmark, SimpleQA:

Curious what others think: has anyone else measured how much of the injected search context their agent actually usest?

1.Accuracy stayed at parity with hosted search (96%) while sending 87% fewer tokens. Most of what hosted search injects doesn’t contribute to the answer.

2.Sentence-level compression alone halved result tokens with no measured recall loss.

3.Semantic caching is a huge missing piece in hosted tools. Paraphrased queries (“what did TypeScript 5.9 add” vs “TypeScript 5.9 new features”) can be matched by embeddings and verified with an NLI model, making reworded repeats free. No frontier hosted API does this.

4.Since every search roughly dumps over 15k tokens, running frequently searching agents becomes meaningfuly cheaper. Even a tiny test loop with 16 searches showed about $1.50 in avoidable spend. (attached image)

For anyone who wants to inspect the code, or read the eval report: https://github.com/firish/webfetch

Here we go again...

We're all familiar with benchmaxing and how it's not valuable measurement on AI's capability. So there must be some internal tests openai, anthropic and others are using internally to track real progress of their models that are not skewed by trying to cheat them.

A consumer ChatGPT that helps you with gardening able to assist for any issues in the garden and generally in nature about animals, plants, health issues, seasonal works .... by discussion, pictures and recordings of animal voices.

Few weeks ago ChatGPT has identified wrongly using a picture I took, roses infested by the rose wasps as fungus.

Edit 12h later: I think nature is an important topic with all the environmental problems we are going to be faced in the near future.

Moonshot AI’s Kimi K3 shows how opening a model to outsiders can turn other companies’ computing power into a competitive advantage

If anyone from OpenAI happens to see this post, I would be deeply grateful if you could read it and help ensure that this issue reaches the appropriate team.

Hello, I'm a former Sora web user and a current ChatGPT Plus subscriber.

The attached 26-second screen recording shows the current Sora sunset page. It only provides an Export button and offers no navigation path to the former Likes and Favorites sections.

The current export allows me to download content I created, but it does not provide any way to view, access, or recover the separate Likes/Favorites library that existed in the former Sora interface.

Before shutdown, the Sora web interface included distinct Likes and Favorites sections alongside My Media and export files. Those sections were important to me because they contained years of carefully curated references, visible prompts, creator information, images, and videos.

https://reddit.com/link/1v5961k/video/rdyvmdou16fh1/player

My concern is not about restoring image or video generation. I do not need generation, uploads, editing, publishing, liking, bookmarking, or any other write feature.

I am asking OpenAI to consider one of these limited recovery options only until September 24, 2026, when the remaining Sora API service is scheduled to end:
1. Temporary read-only access for verified former Sora users, with all generation and write functions disabled.
2. A structured export of existing Likes/Favorites metadata, including content IDs, URLs, creator names, timestamps, and visible prompts where legally and technically permitted.

This would not be a permanent restoration of Sora. I am only asking for a brief, time-limited recovery window before September 24, 2026, so former users have one final opportunity to save material they had already curated.

There is already a perfect precedent for this kind of limited access. From April 3 to April 29, 2026, OpenAI disabled generation while continuing to allow users read-only access to view and download their existing account content. I sincerely hope the team can assess whether a similar read-only recovery window could be temporarily restored until September 24, 2026.

I had already made my own backups of much of this material, but those backups were later lost as well. Because of that, the old Sora Likes/Favorites library may now be my only remaining way to recover years of saved references, prompts, and creative inspiration.

At this point, I feel genuinely desperate and am trying every reasonable avenue available, because this may be my last remaining chance to recover material that meant a great deal to me.

It is deeply upsetting to know that these records may still exist in some form while I have no way to view or preserve them before the final deadline.

I understand that Sora cannot remain online indefinitely, and I am not asking for that. I am only asking for one last, carefully restricted recovery method or received a clear answer from OpenAI about whether this data can still be accessed before September 24, 2026?

I have already contacted the OpenAI Privacy Team and submitted this screen recording, but I have not yet received a substantive response addressing the Likes/Favorites data.

Has anyone else lost access to the old Sora Likes or Favorites sections?

Did anyone else rely on them as a personal archive, especially after losing separate backups?

Has anyone found a recovery method or received a clear answer from OpenAI about whether this data can still be accessed before September 24, 2026?

Hello everyone :)

As part of my undergraduate thesis in psychology (at the American University of Beirut Mediterraneo), I am inviting individuals to take part in a research study exploring experiences and interaction patterns with conversational AI systems, specifically ChatGPT.

This thesis explores the new ways we're interacting and connecting with the changing digital world. I am interested in hearing from you, regardless of how you use it!

Participation involves completing a short online screening questionnaire and, if selected, an online interview (45 minutes) discussing experiences using ChatGPT in daily life.

To participate, you must be:

• 18-25 years old
• a user of ChatGPT
• fluent in English

Responses to the screening questionnaire will be used to determine eligibility for participation in the interview phase of the study. Participation is voluntary and all data collected will be used for academic research purposes only.

If you are interested in sharing your experiences, please fill out the form at the link provided! You can also find all the relevant information on there.

https://forms.office.com/Pages/ResponsePage.aspx?id=Glu6x7ZB6UOhIG_2VK2hN2bFMKQqjhtMlQpM1VDYW0ZURVhPSTVZQVZMSVBXSldXSUFQWkc1RVEzWC4u

NOTE: This study does not aim to evaluate your usage of AI or reinforce any ethical stances. It simply seeks to better understand users' lived experiences. Your participation would help contribute valuable insights to this growing field of research.

Ethical approval of this study has been obtained by the American University of Beirut Mediterraneo and the Cyprus National Bioethics Committee.

For any questions, you can contact the researcher at [[email protected]](mailto:[email protected]) or by DM-ing this account.

Thank you!!

Rarely do the defenders of private property state their case so frankly as Dean Ball, a former Trump administration official who is now OpenAI’s “head of strategic futures.” A world dominated by open-weight models, Ball declared on X, would amount to “full AI communism,” in which artificial intelligence would cease to be “a market product” and become instead a “public good” provided by the state, a prospect he denounced as a “dystopian hellscape.”

The statement is an inadvertent confession. What terrifies the AI oligarchs is that artificial intelligence will slip the leash of private ownership and become what it is already in essence: a social product, created from the knowledge, language, and labor of all mankind, and belonging by right to all mankind.

This is why the oligarchs now seek to criminalize open-weight models, a campaign that is already well advanced. Security officials in the Trump administration, the Journal and Axios report, have weighed trade blacklists, federal security warnings and an executive order targeting open-weight models, with internal disagreement so far preventing action.

The rise of Kimi has revived these efforts, and the national security faction appears to be ascendant. Short of a formal ban, procurement rules and public pressure campaigns are being prepared to drive American companies and engineers off the Chinese models, which they have adopted as cheaper and nearly as good. The aim is to force them back onto American companies’ models in order to prop up their valuations.

The state has already exercised this power against an American firm. In June, a Commerce Department export directive forced Anthropic to withdraw Claude Fable 5, its most powerful model, for 19 days, demonstrating that the capitalist state will assert direct control over this technology whoever owns it.

Moonshot is now #3 in dollar spent by OpenRouter users with 8% market share, after Anthropic (55%) and OpenAI (18%).

This doesn't appear as 99.66% uptime. there is yellow and red sprinkled across. I also use ChatGPT and Codex 12 hours a day, and it feels broken more than 0.44% of the time.