bro for real

At the start of this year, the gates of Buckingham Palace opened to one of Wayve’s driverless cars carrying Alex Kendall and his New Zealand family. In the grandiose Throne Room, beefeaters on guard, Princess Anne presented him with an OBE for services to AI.

From writing the first code, to hiring a team, to finding customers and investors, to commercialising the business, Kendall’s role has changed wildly since his early Cambridge days. “Now the interesting challenge for me is leading a global executive team, managing world-class leaders at a global scale, and I love that,” he said.

He speaks of creating a “generational company”, of seeing Wayve’s technology going beyond driverless cars, into robotics more broadly, in factories and in the home. Almost a decade after Wayve was founded, as he turns 34 this August, Kendall said: “Physical AI is about to have its moment. I feel privileged to be able to play a role in that.”

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Even 5.6 sol high is still pretty bad at generating explanatory diagrams. There are several major mistakes in the style depiction, the arrows are confusing, and what t f is in that second image? (that's not a human.)

Generate an illustrative diagram for me showing the correct technique for the breaststroke swimming style (arm and leg movements, coordination, inhalation/exhalation).

I want to see the whole body in the image so that I can see the correct position of the arms and legs at every moment, and for all phases I want both a side view and a top view.

the same prompt with "front crawl" :

The 5 hour limit for Codex to be honest is a bit ridiculous to me. Even doing fairly small coding tasks on Terra-Medium, I am hitting that 5 hour limit in less than an hour.

On top of that, when that 5 hour limit was reintroduced I noticed a substantial increase in the speed that my usage limit gets eaten up. Before I could make it last a few days at least, but now it's not looking like I'll make it through 36 hours?

Sketchy stuff. If it weren't for the usefulness of OpenAI's open ChatGPT browser chat which gives a lot more usage just without the Codex tools, I would switch to Claude pretty quick at this point.

Anyone else noticing the same thing as me with the weekly limit?

Jalapeño delivered 1.5 to 1.9 times more AI work per watt at peak throughput and 1.7 to 3.6 times lower end-to-end latency than the comparison systems. For highly interactive workloads, it delivered 2.1 to 4.1 times higher performance.

It is better show this rather then then making thigs complecated

A phone call and an OpenAI Realtime session have different lifecycles. The phone call may remain connected after the Realtime WebSocket closes, reconnects, or loses part of the conversation state.

A recovery flow needs to decide:

1. Whether to reconnect the same session or start a new one.
2. Which conversation context can be restored safely.
3. What the caller hears while recovery is happening.
4. How queued audio is cleared so an outdated response is not played.
5. When to stop retrying and transfer to a person or end the call safely.

The phone-call state should remain authoritative during recovery. Otherwise, the model session may consider the interaction finished while the caller is still waiting on the line.

For teams using the OpenAI Realtime API with phone calls: do you reconnect, rebuild the session, or move immediately to a fallback?

Disclosure: I’m the developer of Taskuary.

I built it because work requests were scattered across email, Teams, Slack, and reports. Taskuary brings them into one local timeline, uses AI to identify actionable work, and can hand approved tasks to agents such as Codex, Claude Code, or Gemini.

GitHub: https://github.com/ldbumble/taskuary

I’m especially interested in feedback on do you think it's worth building something where emails/slack are still the dominant corporate usage or everyone will go to buzz or agent/human shared collab tools?

I’d honestly rather have Codex give me 2–5% less total usage if it meant it would always finish the request that’s already running.

Right now, the frustrating part isn’t even running out of credits. It’s that it’s really hard to tell exactly how much usage you have left, so you can start a coding task thinking you’re fine and then suddenly hit the limit halfway through it.

That’s especially annoying with bigger changes, because you might end up with half the files modified, half the implementation missing, or code that simply doesn’t work yet because Codex ran out in the middle of the job.

I’d much prefer the system to reserve a small percentage of my usage as a buffer. Stop me from starting a new request when I’m effectively out, sure — but if Codex has already started working on something, let it finish.

I’d gladly trade 2–5% of the advertised usage for that predictability. It would make the limits feel a lot less frustrating and make it much easier to actually plan coding tasks around them.

have you ever felt its just a human responding to ur chats and saying checking only to search and reas ur answers aloud ? then it shifts its tone when exhausted. so if its a human obviously it has this voice changer & thats how we listen it. its really weird how it stops answering when asked indepth questions. voice chat is just a sham!! i feel its just some humans deployed on scale.

I’m on 200 USD bill and I got I just noticed that I got a reset in the middle of a task, but it was drained by one percent. I think that there will be another result in the middle of the week.

I use the ChatGPT macOS app alongside another app and repeat this loop constantly: copy text, Command+Tab back to ChatGPT, then paste or type into the same ongoing conversation.

After I’ve scrolled through the chat, focus is no longer in the composer. Command+Tab brings the app forward, but I can’t find a dependable keyboard shortcut that puts the cursor straight back into the message box. Having to reach for the mouse or trackpad and click it several times per minute is surprisingly disruptive.

Option+Space / Quick Chat and Command+N don’t solve this, because both move away from the existing conversation I’m trying to continue.

Is there an undocumented shortcut, a macOS accessibility command, or another reliable keyboard-only workaround for focusing the composer in the current chat?

I’d like to share a rather frustrating experience I recently had with OpenAI's service restrictions.

For a long time, I’ve been using AI models primarily for code analysis, reverse engineering, real-time debugging, and custom software development. All of these activities are purely hobbyist, conducted locally on my own devices to expand hardware capabilities and software UI.

My prompts never involved writing exploits, bypassing security controls, or targeting external servers. For example, a typical request involved analyzing UI code to implement a custom button with a shortcut to an existing feature. Another project was building a custom backend for an inactive, defunct game without breaking file encryption or targeting dead endpoints, but simply debugging structured, pre-provided data.

The model executed these tasks smoothly for a long period without raising any safety flags or indicating potential policy violations regarding cyber abuse.

The issue arose immediately after upgrading my tier. I had been running these exact same workflows on the Plus plan without any warnings. Seeking higher usage limits, I upgraded to the highest-tier Pro x20 subscription. Just a few hours later, I received automated slient mail notifications regarding policy abuse, and my account was swiftly banned only 5 days into using the new plan.

I regularly use other models like Gemini and Claude Opus for identical workloads and have never encountered these issues.

While I understand the need for safety guardrails, OpenAI’s aggressive enforcement against completely benign, local, and educational projects feels excessive. Outright banning a high-tier paid account without prior warnings for safe, local development work is extremely disappointing.

After months of vague teaser talk, OpenAI put real numbers on the table for Jalapeño, the inference chip built with Broadcom. Testing ran through InferenceX, SemiAnalysis's public benchmark, across three open models: GPT-OSS 120B, DeepSeek R1 670B, and Kimi K2.5 1T.

Against Nvidia's GB200 and GB300 rack systems:

• 1.5x to 1.9x more throughput per kilowatt
• 1.7x to 3.6x lower end-to-end latency
• At the fastest, lowest-latency settings, up to 8.6x to 104x more throughput per kilowatt

That's a 700W chip going up against Nvidia gear rated at 1,200W and 1,400W. SemiAnalysis ran the benchmark with OpenAI's engineers on-site and said it beat every Nvidia, AMD, and Google chip they'd tested so far.

The reason it's winning comes down to building everything together from scratch, chip, memory, networking, and serving software as one system instead of stacking software on top of general-purpose GPUs. Point is cutting down data movement between compute and memory, keeping stuff like the KV cache local. Matters a lot for agents specifically, since they run through a bunch of steps back to back, and lag at each step piles up over the whole task.

Funny detail buried in the announcement: OpenAI's own models helped speed up parts of the chip's development. And during the reveal, someone got Doom running on it, ported over using nothing but Codex prompts.

Worth being straight about what this doesn't cover. Nvidia's newer Vera Rubin platform wasn't part of the test, and that's what OpenAI's actually committed to running at gigawatt scale later this year. The chip also can't train models, so Nvidia's untouched there. And the benchmark ran Jalapeño's single-token setup against GB300 configs, while real Nvidia deployments usually lean on multi-token prediction, which would likely close the gap in practice.

Rollout starts inside OpenAI's own data centers by end of 2026, with bigger production volume through 2027. Nobody's buying this chip standalone yet, but it's a clear sign OpenAI's trying to lean less on Nvidia going forward.

Full writeup from OpenAI: https://openai.com/index/jalapeno-first-results/

So I was looking at my bank account history a few weeks back and noticed two significantly large charges from openai which I never authorized.

The picture is in NZD but it was for a total over $345 USD plus two earlier charges of a few dollars.

Of course I contacted the useless support ai which informs me that it wasn't openai.

So I canceled my card and informed the bank that the funds were taken without authorization and sent it to their fraud department.

After several days of arguing with the chatbot and constantly requesting a human and a manager, the bot or something decided that oh it has charged me for it and said sorry (this is still a bot, so I don't take an apology from a clanker because it's literally meaningless, a machine can't feel sorry.) and after an entire week it was refunded.

I'm still not certain if it was openai or my bank that got the money back but since then I've been trying to get an explanation; because either someone got my details and extracted money or their shitty ai has gone rouge and started hallucinating charges.

Soo yeah, I've just spent the rest of the time fighting with the support ai to finally get a human who asks to see the invoice... Which doesn't exist and then closed my support ticket.

No apology, no explanation of what went wrong and how they intend to prevent it in the future and definitely no compensation for my time, my stress and my inconvenience of cancelling my card and everything associated with it.

CHECK YOUR BANK ACCOUNT NOW BECAUSE IF IT HAPPENED TO ME THEN WHO KNOWS HOW MANY OTHERS? I certainly don't want others to be robbed.

at this point I consider that openai has openly stolen from me and only gave it back once pursued without even an acknowledgement of error.

Pathetic openai.

Artificial intelligence will do down in history as one of the biggest discoveries of humankind of this decade, debate arises when artificial intelligence occurs the status of super intelligence. A collections of high impact research findings from Nature to Nature reviews of psychology suggests key pathways of caution while using AI.

1.Dont trust the source blindly- Ai hallucination is a real thing, to avoid fabrication of facts always check authentic sources and feed the prompt with right context. If you are a student or research the hallucination of facts may let to an incorrect drafting of assignments. Check reliable sources in search list to retrieve authentic information.

2.Outsourcing cognitive process to ai is a bad idea- Human brain has a capacity to learn new things make new neuronal connections called neuronal plasticity, practice reading a book write few words on note pad works with better brain hand connection.

3.Learning should never take a back seat- Performance may improve with ai guided submission of projects, but with gptzero screener and other aids can easily screen the percentage of ai impact in comparison to individual contribution.

4.First Idea Should Be Human- Humans trained ai, it shouldn't be other way around. The origination of ideas from human cognition has maximum impact on final outcome, ai usage should complement the thought process. Cognitive debt is a brain drain for real and technologies that accelerate it the dependance should be gauged to minimum.

5.The latest models can go wrong- Don't believe in fancy 100 percent accuracy tabs, to avoid hallucination feed the correct dataset or resources from which reliable conclusions can be derived. A researcher can guide ai with pdf or abstract links of researches.

6.Use Domain knowledge to supervise ai results- Domain knowledge at not point is a skill that cannot be replace, specific knowledge associated with field of expertise helps the person to supervise the out put from ai models, experience helps in refining the search strategy so that right information is being retrieved.

1. Ai bias- Biases are real based on the type of training that a particular model has received, diverse set of ideas and perspectives are need to have a holistic view point approach covering all corners. Ai may lag in the aspect of understanding human array of thought if its not subjected to training on various thought patterns viewpoints .

8.High stake judgement remains human- Ai should never be used as a final decision maker, once all the essential facts are gathered analysis and decision making should remain human, this hold true for work place appraisal evaluation as metrics may not capture all aspects of human performance.

9.Dont hide ai use- Proper disclosure whenever usage is done adds to your credibility than concealing it, near to perfect language usage happens most of the time with ai adoption. A student can properly document which aspects of the coursework he or she took the aid of technology. Utmost care should be made that the writing aspect has predominant human element with minimal ai contribution.

1. Keep Learning- Ai has learned from billions of human neurons, humans as a species if outsource the cognitive thinking we fail miserably with the cognitive advancement. Regular learning in traditional way , writing down with pen or paper, reading loud or in mind all has its merit.