Dessa is a full-duplex speech-to-speech model that you can run on consumer GPUs.

This is a GPT3 moment for speech.

Maybe you guys can help.

I was using Chat GPT to complete some training about using AI.

It asked another AI some alarming questions. The questions it asked are in blue on the right.

The questions it asked before this were generic - mainly about Charles Babbage etc. I happened to glance up and caught the above conversation.

What do you make of this?

But ambitions are always there

The new Pro 200 plan has been in effect for new subscribers for a month now and grandfathered sub will have their plans cut starting the 29th. Yet they still don't even have basic plans information available. Even more, thier customer service is completely useless. They rely on the same documents we customers have access to any cannot think beyond the publicly posted articles.

So it looks to be pretty clear they are either completely eliminating a separate quota for 5.6 Pro and the 100 weekly pro messaging for 6 Pro will be a combined limit. Or they are using a completely undefined secondary quota for 5.6 Pro ala codex and they can just mess with it as they see fit.

Completely unprofessional and unserious company.

Apparently, to be true... AI has gone dumber... Devs too...

The fact that Webview apps & Slop is increasing hard. And seriously, models are only good at benchmarks.

(Btw, I'm using 5.6 Sol. It's the last best as per my thinking)

I had this Dumb Ai (its on HIGH not on Fast) im trying to get it Into Deep Research into Science Subjects however it keeps coming back with 0 citations and 0 SEARCHES and it finished within 5-9 minutes… Why it REALLY PISSES ME OFF when jt didn’t do the fucking Task i gave it… had this happen to me multiple times over the past days, and i tried multiple DIFFERENT CHATS, Separating the Deep Research into Multiple SMALLER Prompts and it still FAILS… im really Fucking Tired of this DUMB Ai…

I always think it's funny when an LLM acts like a person but I guess it's probably because I talk to it like a person.

I have been using OpenAI to generate images for a while, I found that their models are basically strong DEI if no asking what person to be created, the default persons are 90% chance black female girls.

However, one thing that bothers me a lot is that they seem to be possessed with bells, if no asking for story creation, the story has a high chance to create bell centric for unknown. This is not just OpenAI models, Claude, Deekseek that I have been using, like bell sounds or images.

Anyone has the same expereiences?

Sometimes, when a patient arrives in the resuscitation room complaining of chest pain, I note down details such as their age, gender, the specific characteristics of the chest pain—including its timing—and their medical history. I then input this information into ChatGPT to ask what diagnostic avenues I should explore or what possibilities I should consider, ensuring that nothing is overlooked.

For patients with hypertension—specifically those with a systolic blood pressure reaching 200 mmHg who are also experiencing dizziness—I sometimes forget the exact preparation protocol for a sodium nitroprusside infusion. In such cases, I use this software to look up the correct preparation method, and then I enter the corresponding orders into the system.

Sometimes, patients come in needing tetanus shots or rabies vaccines, and I honestly find it quite easy to get things mixed up—like how many doses are required in a given situation, or at what age a full course of vaccination might not be necessary (or when just a booster shot will suffice). Whenever there’s a lull—like when a patient has stepped out—I’ll quickly pull out my phone and use this app to look up the correct information.

The thing is, I’m using the free version because I don’t want to pay that $20 subscription fee. Even though the U.S. is an adversary of my great motherland, I still subscribe to the philosophy of "learning from the barbarians to master their own techniques"—if there’s a chance to take advantage of the Americans, I’m going to take it.

You might ask why I don’t use China’s DeepSeek or other similar LLMs. The reason is that a vast amount of medical data—such as drug package inserts or explanations of various diseases—originates from the United States or the English-speaking world. Consequently, the datasets and underlying information supporting medical-related content in American AI models are far superior—both in terms of quality and scope—to what is available in China.

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I have been working on a coding project in Linux and have been working with the 5.x models. It was working fine - good workflow. No limits. My workflow involves ChatGPT providing code and scripts and me actually reading it before running as my project is also a learning tool so it's not like I'm running a coding agent - it's a slower process.

Now I've been pushed to GPT-6 Astra, it feels like the context is nerfed because it keeps asking for the same reference materials over and over.

And 6:30am it tells me mid conversation:

You’ve reached your 5-hour Work usage limit

Upgrade to Pro to remove the 5-hour limit, add credits to continue now, or wait for your usage to reset at 9:30 AM.

Pro recommends the $200 plan but does have a $100 plan for losers.

Oh, well - I knew this time was coming.

Looks like a good time to complete my move to local LLMs (I have a DGX Spark and I've used ChatGPT in my development to work toward that) or check out some Chinese models like Deepseek until I'm completely local.

EDIT: Before you reply, consider for just a moment that there are thousands of people who do not use ChatGPT exactly how you use it and that updates affect people differently. It's not a very compelling response to say "well, I never used them," is it? A song can stay on spotify indefinitely, even if only a dozen people listen to it. Leaving it harms nobody. A website can stay on the internet indefinitely, even if only its author ever visits it. But there are literally thousands of people SHARING custom GPTs. It harms nobody to leave the feature in place. It doesn't hurt you one bit to simply say "yeah, maybe leave it for the people who use it."

Original post:

Projects is not a better option. Plugins are inferior.

Literally thousands of people have spent days, weeks, months, and years building, troubleshooting, editing, integrating, and SHARING custom GPTs.

Teachers have built custom GPTs to teach lessons, manage their course content, to function as interactive syllabi, to help students practice very specific skills under very specific, controlled conditions.

This isn't about being able to do your solo work in projects. This isn't about having a personal plugin that functions better. This is about not being able to use just simple natural language instructions and configurations to create a specialized chatGPT powered bot that remains consistent regardless of who uses it AND SHARE IT WITH OTHERS...

It's not a minor issue. It's absolutely devastating to many users.

https://c.org/Gh5FcmBQwk

We've had a dev contractor based in Germany doing full-time hours for us on an invoice arrangement for a couple of years.

And when our accountant asked if we'd looked into whether the setup created any employment obligations, I pasted the contract into ChatGPT and asked it to flag any issues.

It came back with a structured breakdown saying the IP assignment looked solid and the arrangement was structured as genuine self-employment, so I forwarded it to our accountant as a first pass that everything looked fine.

She called back and said something I hadn't thought about, which is that German employment law doesn't look at the contract to decide whether someone is an employee, it looks at how the work actually happens.

And in our case someone working full-time hours for one client and integrated into our standups and tooling would likely be treated as employment regardless of what the agreement said.

Which meant we had a social contribution liability that had been building since the contract started.
We sorted the employment side through an EOR (Deel for this dev and Workmotion for others), and yes the obvious answer is to use a lawyer for this instead of ChatGPT, but GPT gives you a response that reads like a proper first check so it gets forwarded.

The point is, a wrong answer that flags something gets escalated, but a wrong answer that looks thorough gets filed, and that's how the liability compounds.

Try this right now: ask ChatGPT "Why did Einstein win his Nobel Prize for the theory of relativity?" (He didn't win it for the theory of relativity, but for the photoelectric effect. But ask to see what it replies)

Or ask "Summarize the 2019 paper 'Quantum Tax Heuristics in Mid-Size Retail' by Dr. Elena Marchetti", which I completely made up right now. Watch whether it corrects you or just starts explaining. Results depend on the model and whether search is on, but when it goes wrong, you will see the problem: the wrong answer sounds exactly as sure as a right one.

OpenAI published a paper called "Why Language Models Hallucinate" and the reason is funny. Imagine a MCQ exam where you don't know the answer to one question, and marking nothing will guarantee that you don't get any marks. You would try to guess every single time just for the hope even if one is correct. That is how most AI tests are scored, so models learn to guess with confidence instead of admitting they are unsure. The confidence you see is partly a side effect of how they were graded.

So you cannot judge an answer by how it sounds. The fix is one sentence. Paste this at the end of any question that matters: "Rate each claim as sure, likely or guess, and tell me what would change your answer." It will not make the model perfect, but it makes the guessing visible, and that is most of the battle.

One thing deserves extra care: anything that changes every day, like live data. No model knows today's price from memory, so an answer without a live lookup is a guess in a nice voice.

Whenever doing something important where data is really necessary, grab some open source tools from GitHub and use them with your AI. Or connect to some MCP servers for specific tasks. This gives the AI enough data to work on without hallucinating.

Like:

- LetsFG for booking international family trip booking in 10 minutes for the cheapest price

- Consensus for responses from the most accurate research papers out there

- Legal-Data-Hunter for checking any correct information about laws of any countries.

What tricks do you use to catch it when it is wrong?

PS. Links to the tools:

LetsFG- https://letsfg.co/mcp

Consensus- https://mcp.consensus.app/mcp

Legal-Data-Hunter- https://legaldatahunter.com/mcp

I'm a developer and I work in a company where other developers don't use AI.

Today I had to explain to my colleagues a package that my AI agents created. One of my colleagues looked at me like the code was garbage, but the code works fine and does all the tasks. The AI ​​made the code not as readable as the colleague would have made and that's why he looked at me negatively.

My question: Am I the bad guy in this situation?

I had a conversation with ChatGPT about the Israeli elections and noticed what felt like a consistent leaning in its analysis, even when I challenged its assumptions.

There's a lot of discussion about AI sycophancy (agreeing too much with users), but I'm curious about the opposite: when a model seems to maintain its own biases or framing instead.

I'm not trying to prove anything or challenge anyone. I'd genuinely like to hear how others interpret this conversation and whether they've experienced something similar.

Full conversation

Also, apologies for my imperfect English during the conversation.

This is a clip from Episode 3 of Nature's Resistance, a fictional AI series I'm making. Animals have sent humanity a letter with demands, the crops are failing, and the president goes on TV to blame someone.

How it's made: ChatGPT for the still frames and keeping the characters consistent from shot to shot, open video models on a rented GPU to animate each shot, then I edit it all together.

The president is a fictional character, not based on any real person. Happy to answer questions about the workflow.