TrendPulse

Ken Venner spent 11 years scaling Broadcom from $400M to $8.6B, then became CIO of SpaceX, then joined a startup called Senra Systems as CTPO. At SpaceX, his core platform team was 175 people. At Senra, the equivalent job takes 6. His explanation isn't "AI replaced people." It's that AI collapsed the coordination overhead that used to require headcount just to keep humans in sync with each other — and once that overhead disappears, the team that's left is smaller by desig...

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 ar...

From the data I've seen AI has yet to actually affect employment statistics. I could be wrong of course, but is the whole idea of AI is going to wipe out jobs for normal people just a marketing thing to emphasize the capability of the latest AI models? Or is it a genuine threat that is just yet to materialize?

I did something slightly depressing on sunday. i went through my last month of ai outputs, all of it, drafts and images and scripts and little snippets, and counted how many actually got used somewhere real. published, sent, shipped, shown to a client. the number was eleven. out of roughly three hundred and forty. i sat there for a while trying to work out whether that was bad. my first reaction was that i was wasting the tool. three hundred and thirty dead outputs feels like a lot of dead out...

Avichal Garg has co-founded and backed 10 unicorns through Electric Capital. In a recent interview he laid out, unprompted, the three categories of value AI structurally can't absorb. First: physical-world work — anything requiring atoms, not bits, stays a moat as long as robotics lags digital AI. Second: regulated or licensed gates — anywhere the government controls supply and demand for strategic reasons, credentials plus access still win. Third, and the one that actually lan...

Opus 5 comes with five effort settings: low, medium, high, xhigh, max. Most people seem to be reaching straight for max, and at least on coding work that looks like the wrong move. On FrontierCode, scores fall above the high setting. The stated reason is that the model starts making unnecessary refactors and edits outside the scope it was given. Anthropic's own migration guide in the system card warns about diminishing returns and overthinking on simpler tasks, so this is not some outside crit...

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

Google just signaled even larger AI infrastructure spending this year. From a technology perspective, that's a huge vote of confidence in continued AI development. But investors barely celebrated it—instead they focused on whether all that spending will actually pay off. Do you think we're reaching the point where the debate is no longer "Will companies invest in AI?" but "Which companies will actually make money from AI?"

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 ...

I'm curious how everyone here keeps up with AI these days without spending hours doomscrolling. There are new models, tools, coding assistants, and AI workflows launching almost every day, so I'm interested in hearing what your "AI information diet" looks like. What do you follow regularly? It could be: WhatsApp or Telegram groups Discord communities Newsletters X accounts YouTube channels Blogs, podcasts, or anything else I'm especially interested in places that ...

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 record...

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...

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? ...

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? submitted by &...

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 mode...

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 c...

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 clara...

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

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 yea...

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...

Moonshot AI’s Kimi K3 shows how opening a model to outsiders can turn other companies’ computing power into a competitive advantage