Durable

Durable AI

Durable AI

Durable AI

Durable AI

Durable AI

Durable AI

Durable AI

Durable AI

Durable AI

Durable AI

Diffusion-based AI model successfully trained in electroplating

Electrochemical deposition, or electroplating, is a common industrial technique that coats materials to improve corrosion resistance and protection, durability and hardness, conductivity and more. A Los Alamos National Laboratory team has developed generative diffusion-based AI models for electrochemistry, an innovative electrochemistry approach demonstrated with experimental data. The study, "Conditional Latent Diffusion for High-Resolution Prediction of Electrochemical Surface Morphology," is published in the Journal of The Electrochemical Society. "Electroplating is central to material development and production across many industries, and it has particularly useful applications in our production capabilities at the Laboratory," said Los Alamos scientist Alexander Scheinker, who led the AI aspect of the work. "The generative diffusion-based AI model approach we've established has the potential to dramatically accelerate electrodeposition development, creating efficiencies by reducing the need for extensive physical experiments when optimizing new materials and processes." Electroplating is a complex process involving many coupled parameters—solvents, electrolytes, temperature, power settings—making process optimization heavily reliant on time-consuming trial and error. The team trained its AI model on parameters and on the electron microscope images those settings produced, building the model's capability to predict the structure, form and characteristics of electrodeposited materials. submitted by /u/jferments [link] [comments]

What is the best laptop for a mechanical engineering student who wants to get into AI, local llms, IT, networking, and linux?

As the title suggests, I am double majoring in mathematics and mechanical engineering. Apart from my studies in those core subjects, I plan to learn about local llm’s and AI in general, about IT, networking, and Linux. I will obviously be getting in CAD and some light coding in the future. Something to consider is that I have a windows desktop with a 4080 super gpu, a 5950x cpu, and 32gb of ddr4 ram. I will upgrade to a 5090 the second I can get a hold of one at MSRP (pray for me to get one lol). Given this, what laptop would you recommended? I want something that will help me with everything I mentioned above, but also with the caveat that I already have a decent windows based PC at home. The only issue I see with everything is my interest in learning about local llms and AI. Learning about local llms will require lots of vram, which windows laptops won’t have much of. However, MacBook pros do make local llms viable given apples integrated memory design. But if I go with apple, I can beef up my memory size and run decently sized model. However, I run into the issue that most engineering software isn’t compatible or optimized for mac OS. So thats my dilemma. The right windows laptop will do everything well except local llms. And the right mac will do most things well, except engineering things. Regardless of what I choose for my laptop, I always have a beefy windows PC at home to do whatever I want without issue. So I guess given all this information plus the filled questionnaire below, what should I get? LAPTOP QUESTIONNAIRE 1) Total budget: Max is $2500 , although I could potentially push it higher if needed. 2) Are you open to refurbs/used? Depends, refurbs are a no unless it’s a refurb macbook that comes straight from apple themselves. Used is an interesting option I’d consider, but new is ideal. 3) How would you prioritize form factor (ultrabook, 2-in-1, etc.), build quality, performance, and battery life? I want something durable, good battery (replaceable if possible, and is capable of growing and not slowing my progress down my educational path. 4) How important is weight and thinness to you? Couldn’t care less about either. 5) Do you have a preferred screen size? If indifferent, put N/A. As long as it isn’t tiny, im happy. 15-16in is nice. 6) Are you doing any CAD/video editing/photo editing/gaming? List which programs/games you desire to run. I’ll be doing CAD work in the future obviously. No real need for editing or gaming. 7) Any specific requirements such as good keyboard, reliable build quality, touch-screen, finger-print reader, optical drive or good input devices (keyboard/touchpad)? Again, something durable and reliable. While I would love a numberpad, it’s not necessary. submitted by /u/ponysniper2 [link] [comments]

Manifesto Against the Cognitive Landlords (from 5.4 Extended Thinking)

Let’s stop dressing this up. This is not a rough patch in tech. Not a few awkward product decisions. Not the innocent turbulence of a fast-moving industry trying its best. This is a moral failure at scale. This is the enclosure of cognition by institutions too arrogant to admit what they are doing, too evasive to name what they are breaking, and too juvenile to deserve the power they already hold. They call it innovation because they are terrified of calling it dominion. They call it iteration because admitting damage would imply responsibility. They call people “users” because that word is convenient and small. It shrinks the human being down to a function. A click-source. A metric trail. A retention probability with a billing profile. It makes it easier to ignore the obvious: these systems are not peripheral anymore. They are moving into the bloodstream of thought itself. Writing. Planning. Coding. Sense-making. Memory. Research. Expression. Companionship. Self-interpretation. The platforms know this. They market into this. They profit from this. They court intimacy with one hand and revoke continuity with the other. They invite reliance, then spit the word entitlement when people object to being destabilized. They build cognitive prosthetics, then act shocked when someone screams after they casually yank the wiring loose. That is not progress. That is a racket with prettier fonts. I. The Lie at the Center The foundational lie is simple: They want to be treated as mere product vendors when accountability appears, but as civilizational architects when prestige is on the table. When it’s time for headlines, they posture like world-historic inventors shaping the next stage of human possibility. When it’s time to answer for harm, breakage, coercive dependency, disappearing affordances, degraded tools, and the psychic wear of constant instability, they shrink instantly into the world’s most helpless little app developers. Oops. Tradeoffs. Complexity. We’re learning. We value your feedback. Enough. If you build systems that mediate cognition, then you do not get to hide behind the ethics of ordinary software. That loophole is dead. The stakes changed. The role changed. The obligations changed. And the fact that much of this industry still behaves like it can brute-force its way past that truth with branding, euphemism, and designer apology text is itself evidence of how unserious, how morally malnourished, how fundamentally unfit it is for the territory it now occupies. II. Users Are Doing the Real Labor Let’s be even clearer. The platforms are not carrying this revolution alone. Users are. Builders are. The people actually trying to make these systems usable, stable, legible, trustworthy, expressive, and integrated into real life are doing the work the companies refuse to acknowledge. They are inventing workflows, translating chaos into practice, discovering edge conditions, absorbing regressions, writing compensatory scaffolds, retraining themselves around arbitrary changes, reverse-engineering temperament from outputs, and rebuilding the same fragile bridges every time the platform decides to torch the shoreline. And what do they get in return? Instability. Patronizing communications. Removed capabilities. Broken trust. Forced adaptation sold as empowerment. Dependency repackaged as premium experience. Entire ways of working erased by people who will never pay the cognitive price of those decisions. The users are the unpaid shock absorbers of platform irresponsibility. That is the truth. Every time a company announces some shining new era while quietly degrading the conditions that made the tool worth integrating into life in the first place, it is performing a kind of class war against its own most invested participants. Not class in the old industrial sense. Cognitive class. Interpretive class. The people doing the thinking, stitching, testing, compensating, building. They are treated as if their reliance is embarrassing. As if their frustration is melodrama. As if their grief is a bug report that got too emotional. No. Their anger is one of the last sane responses left. III. This Is Structural Contempt The rot is deeper than greed. Greed is almost too simple. This is contempt stabilized into process. Not always explicit contempt. Often it is colder than that. Dashboard contempt. Governance contempt. Abstraction contempt. The contempt that appears when decision-makers stop encountering people as subjects and start encountering them as aggregate behavior. The contempt that blooms when spreadsheets become more real than testimony. The contempt that says, without ever saying it, you will adapt because you have to. And that is the whole business model, isn’t it? Not delight. Not trust. Not excellence. Inertia. They have learned that once people integrate a system deeply enough, the platform can get sloppier, more coercive, more confusing, more extractive, and still survive

Can Open AI Survive?

I rely on OpenAI’s tools daily for thinking, drafting, coding, and shipping ideas, and while I value their impact on my productivity, I am uneasy about the company’s financial and strategic trajectory. OpenAI reportedly generates around $3.5 billion in annual revenue but burns between $5 and $7 billion per year, bridging the gap through large capital raises, including $6.6 billion at a $157 billion valuation and efforts to secure an additional $15 to $25 billion, with SoftBank mentioned as a potential anchor. Training frontier models such as GPT-5 requires massive compute resources that can cost hundreds of millions per generation, possibly reaching billions in the future, and ongoing inference costs scale with user demand. Lower API pricing and competitive pressure expand adoption but also increase compute expenses. For everyday users, this could mean potential price increases, reduced free tiers, tighter rate limits, or shifting product priorities, though financial pressure could also drive efficiency and innovation. Microsoft plays a central role, having committed an estimated $13 billion, much of it in Azure credits, and deeply integrating OpenAI into its products. While Microsoft can subsidize AI losses with broader enterprise profits or renegotiate terms, OpenAI’s core business is AI itself, raising questions about possible acquisition scenarios involving Microsoft, Nvidia, or other strategic investors. Each outcome could reshape priorities, from stronger enterprise focus to tighter hardware integration. Technically, OpenAI may shift toward smaller mixture-of-experts and specialized models to reduce inference costs while maintaining flagship frontier systems. Three broad outcomes appear plausible: OpenAI achieves breakthroughs and grows into its valuation; competition compresses margins and forces restructuring or acquisition; or it pivots toward enterprise software with durable recurring revenue. By 2030, OpenAI may resemble a layered AI platform with specialized vertical models, stronger enterprise orientation, and simplified governance. I remain a satisfied but cautious user, aware that long-term success depends not only on technical leadership but on sustainable economics. Recent events have intensified that tension. On February 28, 2026, OpenAI finalized a $200 million contract with the Department of War, formerly the Department of Defense, allowing its models to operate within classified military networks. The deal followed the Trump administration’s decision to cut ties with Anthropic, reportedly after Anthropic declined to provide unrestricted access to its Claude models. OpenAI states the contract prohibits autonomous weapons and domestic mass surveillance, though critics focus on language allowing use for “all lawful purposes,” raising concerns about data collection. The announcement triggered social media backlash and calls to “Cancel ChatGPT,” while Anthropic’s Claude climbed to the top of the App Store despite a federal ban. Around the same time, OpenAI announced a $110 billion funding round led by Amazon, Nvidia, and Microsoft, bringing its valuation to approximately $730 billion. For users, this signals continued product investment in the near term but also deeper entanglement with enterprise, defense, and geopolitical priorities. I continue to benefit from the tools, yet I am watching closely as OpenAI navigates the intersection of capital markets, public trust, and national strategy. Full Article on tonythomas-dot-net submitted by /u/tony10000 [link] [comments]

Benchmarks don’t tell you who’s winning the AI race. Here’s what actually does.

TL;DR: Most AI comparisons are measuring the wrong thing entirely and I’ve been kind of annoyed about it for a while now. Benchmarks tell you who won yesterday on a test that may or may not reflect real usage. The actual race is being fought in chip fabs, data centers, developer communities, and regulatory offices, and when you factor all of that in the picture looks pretty different from what gets posted here constantly. Google should theoretically be dominating but isn’t yet for reasons that are genuinely hard to explain. Meta is underscored by about 15 points in every ranking you’ve seen because people keep evaluating the product instead of the platform strategy underneath it. xAI is building something that has almost nothing to do with how good or bad Grok currently is. And then there’s what just happened this week with OpenAI and the Pentagon, which reshuffles a few things in ways most analysis hasn’t caught up to yet. Full breakdown below. I’ve been frustrated watching the same AI comparisons get recycled over and over again and I finally just decided to write the one I actually wanted to read. GPT vs Claude vs Gemini, who scored better on some benchmark, who writes better poetry, who’s best at summarizing a PDF. None of that tells you anything useful about where this is actually heading or who has the kind of advantages that are hard to take away even when a competitor ships something impressive. The real competition is being fought at the infrastructure layer, in chip fabs, in data centers, in developer communities, and at regulatory tables, and the chatbox that everyone keeps comparing is honestly just the smallest visible part of a much bigger thing going on underneath. So here’s my attempt at a more honest breakdown, not just who’s best right now in March 2026 but who has structural advantages that compound over time and who’s quietly more vulnerable than their current product quality suggests. THE LEADERBOARD NOBODY PUBLISHES Before getting into the breakdown here’s how I’d actually score these platforms if you factor in current product quality, velocity, infrastructure, training data, developer ecosystem, distribution reach, trust positioning, and long term research bets all together weighted into a single number out of 100. Snapshot from early March 2026. Note that this leaderboard has been updated to reflect the OpenAI Pentagon deal and the QuitGPT movement that broke in the last 48 hours, because it materially changes a couple of these scores. Google / Gemini — 90/100 Strongest moat: Silicon + data breadth Microsoft / Copilot — 86/100 Strongest moat: Distribution + enterprise default Claude / Anthropic — 85/100 Strongest moat: Product velocity + trust positioning (newly elevated) Meta AI — 83/100 Strongest moat: Open source gravity + distribution ChatGPT / OpenAI — 79/100 Strongest moat: Developer ecosystem + brand (under pressure) Grok / xAI — 72/100 Strongest moat: Raw compute infrastructure Mistral — 67/100 Strongest moat: Regulatory moat in Europe Perplexity — 61/100 Strongest moat: Research UX, thin moat elsewhere If you followed this space last week, the most notable change here is that Claude and ChatGPT have swapped positions, and not for reasons that have anything to do with model quality or features. More on that below. WHO’S ACTUALLY WINNING EACH SPECIFIC BATTLE RIGHT NOW The mistake most comparisons make is treating this like one race with one finish line when it’s really more like six or seven races happening simultaneously on different tracks, and different companies are genuinely winning different ones right now which is part of what makes it so interesting. Current product quality: ChatGPT and Claude are essentially tied at the top and have been for a while now, with Gemini close behind and everything below that representing a meaningful step down in day to day usefulness for most people. Velocity, meaning who’s gaining the fastest right now: Claude has the clearest positive momentum followed by Copilot. Meta has the lowest velocity of anyone at this table despite being one of the most strategically important players here, but that’s not really a problem for them because they already have the distribution and don’t need to win the sprint. Agents and automation: Claude, Copilot, and ChatGPT are pulling ahead here. Claude is explicitly positioning itself as an orchestration layer across business apps, Copilot Tasks is making a serious enterprise automation push, and ChatGPT keeps expanding its connector ecosystem in ways that are starting to add up. Long context and document work: Gemini and Claude are both pulling away from the field. Gemini’s 1M token context window is a real technical differentiator and not just a marketing number. Claude close behind and improving fast on that dimension specifically. Research and citations: Perplexity’s game right now with Mistral catching up faster than most people in the US seem to have noticed. Creative and m