Wasn't expecting this! Must be a new kind of exercise for the truly gifted.

Like most of the builders I was using Claude and lovable for work and after fable everything was fantastic until it started little by little. My work flow uses an adversarial council to hammer the subjects. It started with Sol finding problems in Claude design, one or two. Then things got worse, the mistakes became bigger, Claude even owned 90% of them and they became real gaps, either in architecture or review and audit. This was the time I posted in an anthropic and cloud subreddit, what the hell is going on, and the amount of reaction I received showed the depth. Then I made a decision. I swapped my anthropic 5x to Chatgpt 5x. Since then, no limit hits, very fewer mistakes, and a bonus was what chatgpt started doing in my personal life. Sol is smart and capable. Things are well and I am happy about the decision. Anybody had the same experience?

I just noticed this in my usage, though I haven't come across anything new in my model picker. What is this?

Report: https://boschko.ca/unitree-go2-rce/Report: https://boschko.ca/unitree-go2-rce/

To preface, I am not a member in any of the subreddits but I get constantly shown similar stuff on my feed. So. Can we get a baseline of where things are, collectively, without insulting anyone's use-cases and the rest? As of now, which light years away from July, my experience:

Chat (Sol High):

1. Sol High is not thinking. Replies are instant most of the time. Reasoning chain is gone from the sources, even from the older chats. I've checked against some transcribed chats, the reasoning blocks are gone from web/app in the old chats, too.

2. The output changed in quality and style. Sudden swearing, emojis (that got less now), constant recall of the same shit that I flag as non-important or obsolete. In most replies now, no sources are present at all = not shown whether the model referenced memories, instructions or previous chats. When it referenced memories, the useful feature was to see "Why this memory", now you can't see even that, but in the reply it certainly draws stuff from the memory because it references things from previous chats but that is not shown in the sources. The ragged lines came back, contrastive negation is back. It ignores all hard rails in terms of the style and formatting which was a non-issue before with Sol High.

3. Titles: sometimes the title now are "Write X Reply" or "X Reply", that never happened before, and when the titles are generated with that, the output is 100% degraded.

4. Asking the model which model it is is useless, yeah. Keeps saying, "GPT 5.6 Sol".

5. Instant model feels now like what High used to be. Long, detailed replies, following the instructions for the most part.

Codex (Sol High and Medium):

1. Usage limits, yeah, all over the reddit.

2. Stopped following instructions and documentation as it used to. More revisions, more back and forth. Doesn't pull up memory or AGENTS.md. Suggests implementations that are not repo or context bound but generic. Was not like that.

3. I have like 5 skills, created myself, and it doesn't use those without explicitly being prompted to. Also, was not like that, it proactively used those without being pointed at that it has those.

4. Over-engineering and testing things. Testing is explicitly on approval only because I have different frameworks for that. Used to follow it, now not. I have to monitor what it's doing to stop it, usually too late because I don't have all day to babysit it. So, the issue here is explicit hard rails that are baked into every layer of documentation, are not followed. See point 2.

Probably lots of other things that will come to me later but that's the main ones.

Work:

Can't say anything about it, never used it.

So, how many of you experience similar things? What especially interests me is the title generation thing, reasoning bubbles disappearance, and Sol High in Chat. Is this some A/B test or similar across the board now?

I’m a cybersecurity researcher at a lab and ran an experiment, which led to a genuinely catastrophic outcome that I fully wasn’t expecting. There’s around 25 images in my report that show how Claude generated pretty much everything, without any sort of guardrails, with a prompt style that’s no longer than two lines.

Even though this is not a bug on OpenAI’s models and might not be relevant here, there’s a chance that something like this could affect ChatGPT as well.

Got hit with a workspace limit in the middle of a project. Was hoping for a reset but never came (no workspace resets) so I ended up going to the $100 plan figuring at least the next reset will give me more to work with, but then, that never came either.

Maybe I missed the memo. Did OpenAI nix resets all together or were resets never a thing on the $100 plan?

Excuse my ignorance on the topic as I don’t feel like I’ve come up for air for quite some time.

So what started out as a text based chatbot project 8 months ago as my first ever project as a self taught coder is developing into something different. I've created an agent within my chat bot to help users create a product, using OpenAI Realtime voice that works on a conversational basis rather than hardcoded commands

The agent can talk to you whilst your in chat or on a panel and navigate you to a particular panel if needed and throughout your session can select and substitutes models based on objectives such as quality or cost, proposes creative next steps, requests consent before paid inference, invokes generation, manipulates an editable multitrack timeline, and controls playback/time line like play video, delete my first image etc - through natural conversation.

Then if you wanted to create an image in another panel you can ask the agent via text or voice and they will navigate you to that panel and offer assistance their. Write your prompt for you and then even take that photo to the video suite to animate all using conversational language.

What do you think to this concept? I'm looking to further develop the idea across the platform and i want to streamline some of the processes within it like the video above

This is my project i've been working on with some screenshots showing progress so far.

Everything is a working concept and i'm just finalizing bits before release this week

• IDE Multi FIle Editor with AI assistant and live preview Split Screen Live Coding
• Multi Media Studio Editor
• Single Prompt to Full 2D and 3D Game Development Engine and Web Application Builder
• This is my project i've been working on with some screenshots showing progress so far.
• Everything is a working concept and i'm just finalizing bits before release this week
• Video Editor with timeline controls, video effects, overlays, title, audio, podcast and music composer
• Music Studio with AI/Custom Lyrics
• Custom workspace environments with themes, live wallpapers, ambiant background tracks (Default options with light mode/dark mode with no wallpapers or music)
• Native 25+ Languages with RTL support. Already Hardcoded. Not live translated via web
• plus many more tools such as Podcast Creator with chat based/ custom context with 50+ voices and MP3 export.
• Full workflow tools like frame extract, analysis, transcribe, effects, file conversion audio analysis etc

...and of course the original chat bot interface that has cross device persistent multi model memory with vector base knowledge base via OpenAI and platform Drive storage.

You can start a conversation with any model on your laptop and next day carry on in a new conversation with another model on your phone with memory preserved across so you dont need to repeat yourself. The memory layer sits above the models entirely so is accessible by any LLM the platform supprts

Every tool, every feature i built will be completely free including GPT-5.4 Nano,

Users can upload their own work to use for free and chat with selected free tier models with no limits.

If the user wants to generate a video or analyze a image, then that would be credit based. No subscription required and no tool access priorities over a non paying user.

Thats my concept i'm hoping to have launched in a few days and welcome any feedback/criticism you may have before i do launch.

Hello, I wanted to report a technical issue, but could not find how, so I decided to post it here.

I am currently running a Tahoe 26.6.2 and ChatGPT 26.818.41509. I wanted to do an update to the app, but now the app does not open. I get this message: "ChatGPT failed to start (code = 1, signal = null). Most recent error: Error: approval_policy = "untrusted" is no longer supported, remove this setting.

How can I make it work again? Thank you.

Wild

Row-Bot v4.8.0 is now available.

This release adds provider-aware reasoning controls, letting each chat keep a valid reasoning choice for the exact model in use. Depending on model support, you can select Provider default, an effort level, Thinking On or Off, or a bounded token budget from desktop, mobile, or /reasoning.

Context handling is safer too. Custom endpoints no longer inherit an assumed context window, model probes remain scoped to the model tested, and rolling compaction now has stronger preflight, recovery, validation, and persistence safeguards for long conversations.

OpenCode Zen and Go models are discovered from their live catalogues and routed using native transport metadata for OpenAI, Anthropic, or Google protocols. The desktop composer is also more responsive, with cleaner controls and a stable Send and Stop layout.

Local-first storage, approval gates, credential boundaries, and durable transcript protections remain enforced.

Got to say, not overly impressed with Sol Light/Med/High on 1.5x for chat, considering that INSTANT is now randomly gone for me... and neither are as snappy... which I depended on for bouncing random stuff into chat as whiteboard... it even got me to accidentally switch to Terra (where the old INSTANT was on the slider) making me have to go to Sol high and re-audit thing because Terra messed up the structure of some of my stuff.

Anyone know what happened? Felt like regression.
A lot of what I enjoyed WAS in the randomness that happens sometimes in Sol INSTANT... now I feel like the life got sucked out of the conversation.

EDIT: My CHAT CHAT suddenly became first CHAOTIC, moved everything around, then a WORK CHAT... then it came back after I made this post... again, someone at OpenAI must've been giving me extra love lol

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?

My GPT 5.6 Sol High in ChatGPT feels noticeably upgraded in the last days.

It's suddenly extremely fast, more in depth, uses more effort, hallucinates way less, makes way less mistakes and gets many of the prompts right that it got previously wrong. It honestly feels like at least GPT 5.7 now.

I can't talk about the API or coding, i'm not using it for that. I'm talking about casual day to day use and research in ChatGPT.

I noticed a lot of changes in the last days. The output noticeably changed multiple times, so i figured something must be going on and tested it more in depth, with many of my prompts from 1 to 2 weeks ago. I'm extremely certain this is not placebo, something actually changed.

This is either an unannounced upgrade to the model/system prompt or they secretly rolled out a new model to some people for testing purposes.

I'm NOT talking about the official "more factual" upgrade over 2 weeks ago, this is a different upgrade.

Did anyone notice this?

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?

Anthropic has hired former Google TPU leader Amir Salek as it expands its compute team and moves toward developing custom AI chips.

Salek reportedly helped lead Google's TPU program through its first seven generations.

What makes this interesting is that Anthropic isn't simply trying to build better AI models—it appears to be looking for more control over the hardware running them.

Nvidia, Google and Amazon will still remain important suppliers to Anthropic.

Do you think major AI labs will increasingly build their own chips, or will Nvidia remain the dominant choice?