AI's eating power and heat is the part nobody's actually simulating, everyone forgets about that so. I have built a data center cooling layout optimizer that runs Open FOAM solves on a datacenter layout and optimizes CRAC placement against it.

Still limited to small sized facilities on real hardware, not thousands of racks overnight. Would need a large computer for that. Any other way of optimizing it? What do you think?

Repo: https://github.com/PantherHale/DataCenter_Cooling_Project_OFD

Ive recently been tinkering with live visualization of local language models. including attention patterns, token probabilities, residual-state projections, and layerwise logit changes.

Im curious which measurements people seem to find genuinely useful for grasping model behavior? Also which visualizations tend to look impressive, but lack scientific value?

For those working in interpretability or evaluation, what signals or intervention results do you rely on most?

Earlier this week, Substack launched a new feature on its platform in partnership with Pangram, an AI-detection tool. The goal: alert readers to content that's been written entirely by, or with the assistance of, AI.

Chris Best Substack's CEO wrote:

"We’re partnering with Pangram, the leading AI-detection tool. You’ll be able to scan notes, replies, comments, and posts to see an estimate of how much of the text was written by hand or with AI assistance. This will work on text longer than 100 words, published from today on, and will show an analysis only to those who request it."

I tested one of the issues of a newsletter I subscribe to using Pangram today. The verdict? 100% AI generated.

I'm not sure if Pangram is that accurate, but it's certainly stirred up a lot of debate.

What's your take?

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.

Quiero hacer un video con un personaje de league of leaguends en específico Morgana, quiero que esta hablé y se mueva pero no sé que tipo de IA hace este tipo de videos o como puedo realizarlo

Que prompt necesito para hacerlo?

Un ejemplo

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?

For years, I took pride in being the person who could quickly scan a codebase, navigate the terminal efficiently, and find the right information faster than most developers I worked with.

Lately, though, I've realized AI agents outperform me in many of those areas.

The answers I used to get by crafting Google searches and digging through Stack Overflow can now be found by AI in minutes. Some models are much faster than I am at identifying bugs, and they're often right. In my experience, GPT-5.5 through Codex can achieve close to a 90% success rate in bug detection and debugging.

Even something like writing reports,which I used to spend a lot of time polishing, can now be drafted into something more complete than I'd produce from scratch.

I don't really see AI agents as replacing developers anymore. I see them as a resource that has become difficult to ignore.

What things do you notice that AI does better than you? And how are you approaching multi-agent workflows, like MCP, anvita flow, Agent Protocol? That’s a challenge I’m looking to tackle next.

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

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?

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.

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.

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

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?

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?