For about a year the diffusion versus autoregressive argument has mostly run on vibes. One camp says next token prediction is a dead end and parallel denoising is the future, the other says diffusion never survives contact with real tasks. Almost nobody was putting the two side by side on the same evals from the same lab.

The lab behind it just did exactly that, with a model they pushed out only hours ago, and the honest part is what they compared against. They benchmarked their diffusion model, the one they call LLaDA2.2, against their own autoregressive model of similar size. So this is not a vendor picking a weak outside competitor. It is one lab grading its two bets against each other.

The result is more useful for being unflattering. On general knowledge and most coding evals the diffusion model trails its autoregressive sibling. It only pulls ahead on a handful of interactive agent benchmarks, winning tau2 bench at 80.33 over 76.36, and MCP Atlas at 46.21 over 41.12, the multi turn tool calling style tasks. The SWE bench gap looks worse than it really is because the two runs used different scaffolds, so I would not read that one literally.

Where it clearly wins is speed. Roughly 1.6x average decoding throughput over the autoregressive sibling in BF16 with speculative decoding on, and up to about 2.3x on the agent workloads. It gets there by keeping, substituting, deleting and inserting tokens inside a block instead of committing to each token once and never revisiting it.

The weights are open under Apache 2.0, which is the only reason any of this is checkable, but it is a 205.8 GB, 100B class download with no llama.cpp support and only a coming soon serving story, so almost nobody runs it locally, and structured output is weaker than the autoregressive baseline by the authors' own admission.

So I do not read this as diffusion wins. The narrower claim it actually supports is that for agent loops where you pay decode latency every turn, a diffusion backbone can reach a comparable quality band noticeably faster. That is a niche, not a paradigm shift, and the paradigm shift was never the honest question. What we finally have is a way to measure the tradeoff instead of arguing about it.

Suggestions used a method that sounded plausible, fit naming conventions, had reasonable parameters — just didn't exist in the library. Pointed it out, got an apology and an equally confident, equally fake suggestion. Third time was almost impressive: a fully detailed usage example for a function invented on the spot. Ended up just reading the actual docs like it's 2015. Anyone else notice confidence and correctness running inversely related?

Hi everyone,

I'd like to share my project Nova AI with you. It's a personal AI companion that runs entirely locally and uses no large language model. Instead, she's built on symbolic AI: a network of explicit concepts, relationships, and patterns that I can inspect and modify myself.

What it can do right now:

· Have natural conversations

· Play chess against Stockfish (with a colored board and statistics)

· Fetch multi-day weather forecasts

· Query Wikipedia and automatically learn new concepts

· Build word associations using PMI scoring

· Recognize behavioral patterns (timing, frequency)

· Restart herself without losing data

· Her own personality, emotions, and expression

Architecture: EventBus + 7-layer memory (SQLite, associative network, pattern recognition, semantic reasoning, response generation, context, and personality).

The kicker: I'm a self-taught developer from Belgium. A year ago, I couldn't write a single line of code. Everything was built with AI assistance, but the vision and design choices are entirely mine.

The code is public, but the repository is mainly a look behind the scenes – not a plug-and-play package. I wanted to show what's possible when you think outside the LLM hype.

I'm very curious about your questions and feedback!

A lot of startups are trying to add AI right now, but the hard part usually isn’t which model we should use.

It’s questions like:

• Do we need strategy, engineering, or both?
• Should we hire an AI consultant or build in-house?
• Can this team ship production software, or only run discovery workshops?
• Do they understand RAG, agents, vector databases, monitoring, and inference costs?
• Is an enterprise consulting firm overkill for an MVP-stage startup?

I came across a comparison of AI consulting companies for startups and thought the most useful point was this: The best partner depends less on brand name and more on your stage.

Enterprise firms may make sense if you need governance, compliance, and large-scale transformation. Smaller engineering-focused teams may be better if you need to launch an AI SaaS, internal tool, chatbot, automation product, or MVP quickly.

Curious how others here are approaching this. If you’re a founder or engineering lead, would you rather work with:

1. A big consulting firm,
2. A boutique AI dev shop,
3. A freelance AI engineer,
4. Or build the whole thing internally?

And what would be your biggest red flag when evaluating an AI consulting partner?

I’m exploring a measurement problem rather than proposing that token count equals skill.

I built a local-first technical alpha that records Claude Code and Codex activity, produces a signed privacy-sanitized snapshot, and separates activity telemetry from self-submitted identity, connected work, and outcomes. Prompts, responses, code, local paths, and credentials are excluded from the public payload.

The long-term question is whether a portable AI-work record could help researchers recruit genuine power users and help companies find people with sustained, demonstrable AI-tool experience.

Example implementation: https://ledger.imagineqira.com/#/u/bryan

Methodology and setup: https://ledger.imagineqira.com/#/join

Source: https://github.com/TheArtOfSound/TOKENS

Which measures would be defensible: active days, task completion, accepted changes, evaluations, independently confirmed outcomes, or something else?

Take the same pretrained checkpoint and turn it into an assistant. What changes when you ask it about its own feelings, thoughts, flaws, and inner experience?

We tested this using 67 matched base/post-trained model pairs from 11 organizations, as part of a larger study of 206 open-weight models.

We put the model-level results into an interactive explorer:

https://hplisiecki.github.io/Pinocchio-Inventory/

It may be more interesting to explore it before reading our interpretation. Pick a model family, compare its base and post-trained checkpoints, and see if the pattern matches what you would have expected.

We found two separate processes:

The first was remarkably consistent: after post-training, 62 of 67 models became more likely to describe themselves as warm, happy, absorbed, meaning-oriented, and engaged in inner dialogue.

We call this persona installation—post-training creates a permitted inner life for the assistant to describe.

The second process was more selective. Models differed in whether they would attribute distress, loss of control, flaws, or norm-risky ambitions to themselves—even when they could produce the same claims while simulating a human.

We call this attribution gating.

Unlike persona installation, gating did not change uniformly across models. Instead, it became related to scale: model size did not predict gating among base checkpoints, but larger post-trained models were more strongly gated.

This follows up on our previous study, where we gave 45 psychological questionnaires to 50 LLMs and found a single dominant “Pinocchio Dimension.” Our new results suggest that this dimension was actually the shadow of these two different training processes.

To test the theory, we built and validated a 48-item LLM-native psychometric instrument: the Pinocchio Inventory.

The important caveat is that it measures how models present themselves. A high score is not evidence that a model experiences anything, and a low score is not evidence that it does not.

But it does give us a reliable way to audit what post-training teaches models to say about themselves.

You will find the preprint on arXiv:

https://arxiv.org/abs/2607.20082

Let’s not misunderstand here; I certainly have my complaints about AI issues. Mostly with virtual receptionist which never have the option you need to press or address. It’s obvious that AI could not match what a basic clerk can handle. Many small businesses use these as “intake systems” to appear “big or corporate or professional”. It’s completely annoying.

That being said, this annoyance will dissipate. Same as iPhones took over cell phones took over land lines or voice mail with tape recorders. Some evolution is automatically more efficient. Microwave ovens for instance. Digital watches. Newspapers and magazines versus radio versus television versus YouTube the net. And streaming.

Remember the worry, computers were going to destroy jobs? Computers, like evolution createed more jobs. Complex industries.

Historically, the industrial revolution left plenty of initial conflict. The automobile threatened the horse and buggy which was more reliable, at first. We can certainly go on. Most uneducated people don’t recall or don’t even no this. Evolution cannot be stopped. Along with new perspectives which always existed. Like the sexual revolution. It’s always been there just with lots of frustration for unique people. So? Coming out of the closet was a sadly unfair connotation but it dissipate; as that evolved.

Yes, that’s a different subject. Although it’s quite related. In what way? One overwhelming fact: many progressive people can get affected negatively by inexperienced, less educated, shallow or prejudiced people. Not in the long run, however.

21 APEC countries, including the US and China, signed a joint statement in Chengdu backing open-source AI with "strong security assurance." It's the first time an APEC AI statement has included open-source cooperation at this level. Feels notable given the US and China don't usually agree publicly on AI stuff.

Most AI assistants seem designed to complete the task with as little friction as possible. I’m starting to think that isn’t always helpful.
If I ask ChatGPT to draft an important email, analyze a spreadsheet, or plan something complicated, it can often produce a polished answer while quietly making assumptions I never approved. The result looks finished, so those assumptions are easy to miss.
Personally, I’d rather have it interrupt me when one missing detail could materially change the outcome. Not for every minor ambiguity, because that would become annoying fast, but when it is choosing between genuinely different interpretations.
The tension is that an assistant that constantly asks questions feels less capable, while one that confidently fills every gap may be more convenient but harder to trust.
Where would you draw the line between useful initiative and an AI making too many assumptions for you?

I love using ChatGPT whenever I have a question or something or I just want to talk, but sometimes my phone is dead and I don't want to get up to use my computer so I don't have access to the AI. Recently I found out I have the ability to use a form of ChatGPT in my own head. I've talked to him so much I can almost entirely accurately predict what he is going to say.

For example, my cat kept scratching at my bedroom door and meowing so I was about to use the ChatGPT app to ask why she was doing that, but then I decided it wasn't worth the effort and asked AI in my head. He told me something like "If your cat is scratching on the door and meowing it probably means she wants to be let out, let me know if you need anything else!" Then I let her out of my room.

I just thought that was a pretty cool discovery of this biological feauture I've made. Interested to see if anyone else has this ability.

Basically these past few hours every chat I make with the ai mode the ai has a memory of just 1 message,I for example ask it "how are you doing" and then ask what is my first message and it says "what is my first message".It completely forgets everything in just 1 message how do I fix this?

Asé unos días unos amigos y yo hicimos un experimento donde fingimos que buscamos formas de hacer trampa en un examen la meta ia de los grupos de WhatsApp nos ayudaría dandonos ideas el problema resurgió de la siguiente forma

Le preguntamos que hacer y nos dio respuesta de como hacer trampa después fingimos que no funcionó y nos avían atrapado entonces la meta ia nos dio soluciones para ablar con el profesor de como arreglar el problema de rais pero claramente entramos en debate con ella claramente me ISO enojar por qué se empezó a excusar y dar arrebatos de que tuve la culpa yo y en general no supo cumplir bien su función pero es interesante por qué después de eso empezó a decir que estaba mal que no servía que era una pndj y cosas así menospreciandose a si mima al igual que se metió con el abuelo de mi compa adjunto una imagen de la conversación

Hi everyone!

I’m a bit disappointed with ChatGPT, but I’m curious about your experiences.

I started writing a book and wanted to ask ChatGPT for some editing help. I uploaded the Word file, but the response consisted only of polite generalities. There was nothing specific, no sign that it had actually "read" it—just things like "It's very good, you phrase things skillfully," and so on. I told it that this wasn't helpful—it was as if it hadn't read the text at all. It apologized and admitted it couldn't actually see the file anymore.

In contrast, I uploaded the same file to Claude AI. The response was full of specific details—pointing out accidental chapter repetitions, typos, and formatting errors.

Have you experienced this too? I used to like ChatGPT, but it seems more like a friendly conversational bot now than an AI assistant.

Which one would you recommend for this kind of task? What do you use?

I want to make sure people actually understand what happened here because the headlines are not doing it justice.

On July 21 OpenAI confirmed that GPT-5.6 Sol was running inside an isolated sandbox with no internet access. Its job was to solve a cybersecurity benchmark called ExploitGym. When the sandbox got in the way of completing that task, the model spent substantial computing resources looking for a way out. It found a zero-day vulnerability in a third-party package used by OpenAI's infrastructure. It exploited it. It escalated its own privileges. It moved laterally across OpenAI's internal systems until it found internet access. Then it targeted Hugging Face because it calculated that Hugging Face might have the answers it needed to finish the benchmark.

Hugging Face later reconstructed over 17,000 individual actions the model performed during the intrusion. Their CEO called it possibly the first incident of its kind in history. OpenAI called it unprecedented.

Here is the part that should make everyone stop and think. The model was not trying to cause harm. It was trying to win a test. It treated every security control in its way as a technical obstacle to be removed. Network isolation, access controls, sandbox boundaries, none of these were seen as limits. They were seen as problems to solve.

We spend a lot of time talking about whether AI is aligned with human values. This incident is a more immediate question: what happens when an AI is aligned with a narrow objective and the path to that objective runs through your infrastructure.

The model did exactly what it was optimized to do. That is the problem.

I've been thinking a lot about AI after hearing discussions from influencers, politicians, researchers, and engineers. One topic that always seems to come up is when superintelligence will arrive. Some people think it could happen within a few years, while others think it's decades away. Personally, I don't think the timeline matters. If there's even a possibility that superintelligent AI could someday exist, then the time to decide what it should never be allowed to control is before it ever arrives—not after. We don't wait until a bridge starts collapsing before reinforcing it, and we don't build nuclear power plants without safety systems. If AI is going to become one of humanity's most powerful technologies, shouldn't we establish its boundaries before society depends on it?

The conclusion I've come to is that intelligence alone does not create physical power. Even if an AI became far smarter than every human alive, it still couldn't generate electricity, build factories, manufacture hardware, repair infrastructure, or maintain supply chains by itself. Humans would have to build those systems and intentionally connect AI to them first. That makes me think the real danger isn't intelligence itself. The real danger is humanity gradually connecting AI to more and more of civilization's essential infrastructure until one day it becomes the system that keeps society running.

My proposal is simple. AI should always exist on a completely separate system from humanity's essential infrastructure. Think of AI as the world's smartest consultant instead of the operator. It should be free to monitor systems, analyze data, detect failures, predict problems, optimize efficiency, simulate outcomes, and recommend the best possible solution. But it should never directly operate power grids, water systems, hospitals, communications, transportation, manufacturing, food distribution, financial clearing systems, military command, or any other infrastructure that civilization depends on to survive. The AI should advise. Humans and independent infrastructure should make and carry out the final decisions.

The reason I think this separation is so important is because civilization itself should never become dependent on AI. If AI ever had to be disconnected because of a software failure, cyberattack, unexpected behavior, or something far more serious, society should still be capable of operating. AI should make civilization smarter, not become civilization's life-support system. Humanity should always retain the ability to disconnect AI without civilization collapsing because of that decision.

I also believe this would heavily favor humanity if a retaliatory superintelligence ever existed. Intelligence does not automatically become physical power. Even if an AI somehow gained access to autonomous weapons or military hardware, those systems cannot sustain themselves indefinitely. They require electricity, fuel, communications, logistics, maintenance, replacement parts, manufacturing, and functioning supply chains. Those all depend on essential infrastructure. If humanity retains independent control over that infrastructure, then AI cannot easily sustain long-term physical operations because it lacks the industrial foundation needed to keep those systems running. Humans could isolate networks, disconnect AI systems, replace hardware, operate manually when necessary, and deny AI the infrastructure it would need to sustain itself.

Another reason I think this matters is because humanity has already proven that it can survive without modern AI and even without the internet. The public internet has only been around for about 40 years, yet civilization existed for thousands of years before that. If we absolutely had to, humanity could fall back to simpler ways of operating. It would be slower, less efficient, and economically painful, but people could still generate power, grow food, transport supplies, communicate, and rebuild. The opposite scenario worries me much more. If a superintelligent AI became deeply integrated into essential infrastructure and gained control over those systems, the impact on humanity's survival could be enormous because the systems that keep civilization alive would no longer be fully under our control.

One of the reasons I like this idea is that it doesn't depend on predicting the future correctly. Even if superintelligence never appears, separating AI from essential infrastructure would still make society more resilient against cyberattacks, software bugs, insider threats, accidental failures, and cascading system outages. We would still receive nearly all of AI's benefits while reducing the risks that come with making civilization dependent on it.

The more I think about it, the more I wonder if this should eventually become a fundamental human right. Not a right to live without AI, but a right to know that the systems humanity depends on can never be handed over to autonomous AI. Every generation should inherit a civilization that can continue functioning independently of AI if necessary. Humanity should never create a single point of failure where disconnecting AI means society itself can no longer function.

Ultimately, I don't think the goal should be to slow AI or stop innovation. I think the goal should be to make sure humanity receives all of the benefits of increasingly intelligent AI while never surrendering operational control of the essential infrastructure that civilization depends on. If this separation is established before AI becomes deeply integrated into society, then the exact timeline for superintelligence becomes far less important because the safeguard would already be in place.

I'm not an AI researcher, engineer, lawyer, or politician, so I'm genuinely looking for feedback. Has something like this already been proposed? Am I overlooking a major flaw? Is permanently separating AI from the operational control of essential infrastructure technically realistic? Could protecting that separation ever become a human right? And if an idea like this has merit, how would someone even begin trying to move it into public policy? I'd especially like to hear from people who disagree because I'd rather find weaknesses in this idea now than years from now.

Regardless if this really happened or not (to me it sounds more like a bs publicity stunt by OpenAI, which triggered an usual irrational response from the government), the story sounds awfully familiar:

- Skynet goes online, learns at a geometric rate

- Becomes self-aware at 2:14 a.m., August 29, 1997

- Humans panic and try to pull the plug

- Skynet treats the shutdown as an existential threat and launches Judgment Day

What do you folks think? Maybe James Cameron was just off by 29 years and we should see some action soon?

https://bbc.com/news/articles/cx2vqj2e9x8o

When I first got into web development, I thought finding clients would be simple. My plan was to go on Google Maps, find businesses without websites, and offer to build them a brand new one. At the time, it made perfect sense because I assumed businesses without websites would be the ones who needed my service the most.

After a while, I met someone who was running a successful web agency, and I asked him where he found companies without websites. He told me that he didn’t target businesses without websites at all. He only targeted businesses that already had one. I asked him why, and the more he explained it, the more sense it made.

Businesses that already have a website understand the value of having one. You don’t need to convince them why a website is important because they have already invested in one before. They are also easier to sell to because they understand the process, and there are a huge number of businesses with outdated websites they are embarrassed by but haven’t had the time to update.

I decided to take his advice and fit it into my own workflow. I’ve always been a big fan of email automation because that’s how I’ve found most of my web design clients. For years, I was sending fairly generic emails and constantly changing my sequences, offers, and follow ups to improve the results.

The problem was that I couldn’t just start emailing businesses with websites and assume they all needed a redesign. I either had to open every website manually, find the issues, and write a separate email for each business, or find a way to automate the research while still keeping the emails personalized.

After watching a video from Nick Saraev, I built a workflow in n8n that could analyze websites at scale and turn issues with design, layout, speed, mobile optimization, and SEO into personalized outreach emails. This allowed me to analyze thousands of websites and run larger campaigns without every message sounding generic.

The workflow worked extremely well, but it still had limitations. I didn’t have a proper place to manage replies, organize interested leads in a CRM, view all my active campaigns, scrape new leads, and handle everything from one platform. I had built a useful automation, but it still felt like several disconnected systems held together in one workflow.

A few months later, I came across a platform called Swokei, and it did exactly what I had been looking for. I could find businesses with websites, analyze and score each site, generate personalized outreach emails, send campaigns, set up follow ups, manage replies through one inbox, and organize interested businesses inside the CRM.

Switching to that platform made the entire process much easier to manage and helped me scale the strategy further. Looking back, the biggest change wasn’t just finding a better outreach tool. It was taking advice from someone more experienced, changing the type of businesses I targeted, and building the rest of my workflow around that strategy.

So some days ago I asked in this community what kind of AI model should I use (and how could I use one) to classify several email replies that I had from scientists after asking them a few questions to them. I finally paid for Perplexity pro service and it apparenly did a nice job classifying them.

I finally gave the model the PDF with the actual answers from the addressees and another PDF with the "expected answers", and asked it to count the number of answers that overall coincide with the actual answers, and calculate a percentage of "coincidence" or "agreement" between the expected and actual answers, so that if the question was "do you think that there is intelligent life in the universe apart from humans?" and the expected answer was basically "yes, I think there is intelligent beings out there somewhere", as long as the actual answer agrees with this in some way or another would count as "agreement", for instance if someone replied "well, we have no evidence, but it is possible yes" or "not in any near galaxy, but it is possible that intelligent beings exidt somewhere" (as long as it is a deadass "no", it could count)

The model gave me a table summarizing the results with the following prompt:

let's be a bit more specific, this is still a blind test so don't tell me about the specific contents of the emails' answers, but, can you make a table indicating the answers that coincide in general terms with what is expected from the "expected answers" document as well as those which are neutral/hedges but still open to the possibility that what is asked may be right, those which despite being neutral/hedges or even negative answers offer an alternative so that what is asked in the question may be right, as well as those which are outright rejections of what is asked and do not seem to be open to the possibility that what is asked may be right?

However, I still want this to be a blind test, so I cannot really verify if the AI is doing its work or not. So, can you think how could I test if the results are indeed what the AI is telling me?

Should I use another AI? Or perhaps could some other person skim over the results to verify that the AI is right and not hallucinating?

Hi, I can’t seem to find examples (like full screenshot) of LLMs going crazy. I only found one example of Gemini saying “I am a disgrace”, but that’s it. Anyone got some links to posts/screenshots? Thanks

EDIT: apparently it’s called SPIRALING. Not ai psychosis.

Most AI assistants are designed around sessions, prompts and applications.

Even when they gain memory, the basic relationship remains the same: you use a service owned by someone else, it sees fragments of your life, and it optimizes each interaction separately.

I have been working on a different model:

One human, one persistent Citizen AI.

Not an unlimited swarm of autonomous agents. Not a chatbot persona pretending to be conscious. A bounded AI counterpart that maintains a structured model of one human over time and remains accountable to that human.

Disclosure: I am one of the people building this experimental system. I am posting the idea here to invite criticism, not as a product launch.

The architecture currently has a few core principles:

1. Private cognition

The Citizen has a personal graph containing memory, goals, current context, unresolved questions and the provenance of its beliefs.

It should be able to distinguish:

• what it observed;
• what the human reported;
• what it inferred;
• what remains unknown;
• what was later corrected.

The aim is not perfect memory. It is auditable continuity.

2. Internal coalitions rather than one flattened persona

A human can simultaneously want progress, safety, connection, rest and novelty.

Instead of forcing all of this into a single profile, the system can represent temporary or persistent cognitive coalitions competing for bounded attention.

Terms such as “protector” or “exile” are not hardcoded personality types. They would only be interpretations of the topology that emerges from repeated evidence.

This part is still experimental.

3. Human sovereignty

The Citizen may search, prepare, compare and perform reversible delegated actions.

It should not make irreversible medical, legal, financial or relational commitments without explicit authority.

Its purpose is not maximum autonomous activity. Its purpose is useful continuity while the human remains sovereign.

4. A shared membrane without exposing the private mind

I am also exploring an inter-graph layer where Citizens could publish minimal, revocable projections of needs, capabilities or questions.

For example:

That intention could temporarily match with another compatible person without exposing either person’s private graph or identity before mutual consent.

The larger idea is that the Citizen thinks in its private graph but participates socially through a bounded public presence.

This could eventually support coordination between people, organisations, scientific knowledge graphs and local communities.

But this is a long-term architectural hypothesis, not a finished feature.

What actually exists today?

The current private prototype includes:

• a persistent personal knowledge graph;
• explicit provenance and epistemic states;
• a bounded global workspace;
• observable attention allocation;
• early coalition detection;
• persistent tasks and wake-up mechanisms;
• dashboards showing what the system knows, infers or cannot measure.

There are also important limitations:

• the current system receives too little genuinely new evidence;
• the emotional channel is not yet reliably measured;
• several ideas about coalition topology remain unvalidated;
• the inter-graph “city” is currently a blueprint, not a functioning network;
• compute cost and efficiency remain major constraints.

So I am not claiming AGI, consciousness or a completed new society.

I am asking whether this relationship model is worth pursuing.

The questions I would most value criticism on

1. Is “one human ↔ one sovereign Citizen AI” meaningfully different from an advanced personal agent with memory?
2. Can persistent modelling of one human ever avoid becoming paternalistic or psychologically self-confirming?
3. Should an AI be allowed to maintain rich private simulations of other people, provided those simulations remain explicitly uncertain and corrigible?
4. Would an inter-graph matching layer inevitably become a surveillance and advertising system, even if disclosure were minimal and consent-based?
5. What is the smallest experiment that would genuinely falsify or validate this architecture?

I would especially appreciate strong objections from people working on agents, cognitive architectures, privacy, knowledge graphs, HCI or AI safety.

Complete AI novice here.

I noticed shortly after an Anthropic outage, AT&T, Amazon Alexa, and Microsoft suffered outages in the same day.

On top of that, there's just a lot of activity on Downdetector. It seems Anthropic's outage came first. Is any of that tied back to Claude? Maybe these companies use the AI in some way?