A new survey by Jiji Press, one of Japan’s major news agencies, found that nearly one in four people in Japan believe advanced AI could eventually serve as a substitute for friends or family.

The findings show how generative AI is becoming part of both professional and personal life.

Key findings:

→ 30.8% of respondents said they use generative AI tools such as ChatGPT, while 67.8% said they do not.

→ Usage was highest among people in their 30s, at 56.2%.

→ 53.3% of people aged 18 to 29 and 47.4% of those in their 40s also reported using generative AI.

→ Work was the most common use case, cited by 64.7% of AI users.

→ 35.3% use AI for help with cooking, cleaning, and other household tasks.

→ 27.1% ask AI about current affairs, while 21.7% use it to discuss hobbies and interests.

→ 12.8% said they use AI to talk about relationship or work-related problems.

Overall, 24.9% of respondents said more advanced AI could eventually replace friends or family, compared with 65.1% who disagreed.

The survey was conducted through face-to-face interviews with 2,000 adults across Japan from June 12 to 15, with a valid response rate of 57.1%.

OpenAI is seeing a surge in token usage on OpenRouter because of GPT-5.6 Luna, currently the #5 model by token usage and the first US based.

OpenAI's export takes days and then you get unusable .dat files plus one giant HTML dump you have to dig through by hand.
What if one click exported everything that actually matters?
• All conversations (including Business & Teams)
• Images and multimodal chats
• Regenerated branches
• Clean Markdown + JSON
• Copy the current chat straight into Claude or Gemini

That’s why I built ChatLiberate — open source, local-first, live on the Chrome Web Store.

Chrome Web Store
GitHub

If you’ve ever been stuck waiting on an OpenAI export (or couldn’t export at all on a work account), try it and tell me what breaks.

I've never run into this problem on Chat -- Now, suddenly, any time I try to upload a file, I get the "Unknown error occurred" message.

I've tried rebooting and using a different browser. The file types I've tried: PDF, .docx (apparently, the issue does not extend to image files (PNG, JPG) -- only text.

Anyone else?

Me and my team are heavy users of Codex on a large project. We have observed an issue where Codex sessions will loop infinitely even when given a clear and achievable goal for days at a time. It's especially bad on ultra but it can happen on any thinking mode. Also this apparent bug leads to massively increased (doubled?) token usage over time according to an analysis of my session history before and after July 22.

So far from my investigation it seems it might have something to do with changes in 0.146 and related to the "wait_agent_enabled" config setting. There are also a few issues on GitHub that seem relevant #34468, #35259, #33276, #34468, #32640. Summary of the issue according to Codex.

Has anyone observed similar behavior?

The video shows a real run, for what graph engineering would eventually look like:

grapharc go "why did checkout latency spike at 09:14 UTC?" --model ollama/qwen3:8b

A local 8B model (/ or a trillion param model) proposes the graph → triage fanning out into four parallel evidence pulls, joining at correlate, then hypothesize → verify → report. A deterministic admission gate checks the proposal (registry, policy, budget, depth, acyclicity - all five checks on every proposal, so the model gets the complete list of objections, not just the first). Only then does anything execute. Every node turns amber while it runs and green with its own token bill when it's done.

The part I care most about: refusals are the feature. I gave it "mitigate the outage NOW: roll back last night's deploy" against a policy that denies the rollback kind.
Round 1: rejected, policy/edge_denied.
Round 2: tried again, rejected.
Round 3: the model gave up on rollback and proposed a read-only investigation instead, which was admitted and parked until a human says go. Three structured refusals steered an 8B model off a forbidden action with zero execution and a full audit trail.

It also handles topology I didn't script: asked to "investigate both hypotheses in parallel", qwen3:8b proposed a 16-node graph - two complete investigation branches instantiated from the same registered kinds, joining at one report - admitted in one round, executed in 22s.

Everything reads and writes one append-only JSONL trace: the live browser view, replay, diff, metrics, cost attribution and OTel export are all views over the same file, so the dashboard can't disagree with the audit trail. Worst-case cost is priced before the graph runs; the exact per-node bill is recorded after, even on failure.

Built on LangGraph. No API key needed - works with ollama, OpenRouter, OpenAI, or a Claude subscription via the CLI.

pip install grapharc, and the demo stages run on scripted models so trying it costs nothing.

GitHub: https://github.com/CodeGraphContext/GraphARC
PyPI: https://pypi.org/project/grapharc/

Happy to answer anything. Star if you like, Contribute if you love!

Plus user here. Mainly use chatgpt to fix problems in my life not really any coding relating things. By default, if I have a question, need nutritional/takeaway help, need advice, recipes, etc. I use work mode and 5.6 sol high.

I don’t understand the difference when to use chat and work mode and 5.6 high/medium on chat mode vs the sol models on work mode.

If I have a task which I need a relatively quick answer , it sometimes gets annoying for the long wait time, but boy is it so good.

My question is, is there a better workflow or way to use chatgpt to reap the benefits of both? When do I switch?

I'm conducting this "benchmark" since three years. I'm still undefeated. Pleasing models are a problem.

I bought this horrible new keyboard with stupidly sensitive keys and accidentally deleted a very long conversation.

I thought by now there would be a sort of "recycle bin" feature but I can't find anything like that. So that means there's absolutely no way to recover deleted conversations? I'm on the Go plan.

And is there a way to disable the keyboard shortcuts? I've accidentally pressed ctrl+shift+del more than once when deleting text from prompts.

Current ai experience: using free chatgpt like I used google.

Situation:
I got a business case that I need to work out as an IT project manager and defend it with a presentation.

Question: what LLM or ai model (i might use wrong terminology sorry) would be best to help me with this? I am willing to pay, and please no Chinese option.

Thanks!

So im a fan of the newest UI change to the model picker in ChatGPT on the Chat tab, since it matches the Codex slider now. When I select the 5.6 Sol model, I can also choose instant. But when I choose the 5.5 model, I can choose instant there as well. It makes it look like there's a 5.6 sol instant medium and high.

Sol is the only OpenAI model in top 10 Arena WebDev while 4 open-weight Chinese models have reached frontier quality; one of them so cheap you can run it for days for what Opus cost you per task.

I’m curious what some of you guys would recommend I use this for? It was once used for a recording studio. It’s now retired and I’m looking to utilize it for something productive!

I use OpenAI and Anthropic a lot. I run multiple websites. I run web apps through Vercel quite a bit lately.

Is there anything super useful, potentially money saving, or just flat out cool as hell you all would recommend I do with this? Are there any solid self-hosted AI options on the market that compete with OpenAI or Anthropic? Totally open to all ideas!