标签：#AR

Applied Computing has raised a $20M Series A to build a foundation AI model for the oil, gas and petrochemical industry.

Syntetica, a French startup that has developed a novel approach to recycling nylon, has already obtained big-name partners and investors.

When I started learning programming, I believed my future as a developer depended on one thing: How...

Part 1 of "Multi-Agent Systems in Production: What They Don't Tell You" — a four-part series...

When the enemy occupies favorable terrain, don't attack head-on. Use a decoy to lure the tiger down the mountain. Then take the mountain. — The 36 Stratagems, Lure the Tiger Down the Mountain Previously on this series: #2: Derek Shaw Walked Into Another AI Promise. The Pipeline Had a Better Plan. — Derek lost the Finova contract at QualiGuard. In the parking lot, Lena turned back before getting in her car: "Next time you put together a proposal — make sure your boss knows what you're doing out there." That line followed him. At MediSys he nearly made the same mistake. Fixed the ETL pipeline instead of the model, beat OmniDx with their own white paper. But VP Morgan still saw through him: he still hadn't told his boss. #8: Alex Watched an AI Dashboard Take Over. He Kept the Keys Under the Table. — MedTech signed a seven-figure AI operations monitoring system. Alex was assigned as training lead. Under everyone's noses, he built a second monitoring panel labeled "training environment." Three weeks later the vendor dashboard went down. Alex's hidden panel was the only one still running. The first time Alex and Derek really talked, and it wasn't in a working group. At the FHIR standards committee quarterly meeting, they sat in the same row, three seats apart, and voted against the same proposal. They knew each other's names. That was it. Two months later, Derek was fixing a partition config in the staging environment. MediSys had just signed a major hospital; the AI diagnostic validation platform was in integration testing. He found an unrotated log directory in /etc/logrotate.d/ with a prefix that didn't match MediSys's naming convention. He traced it upstream. One server. Labeled "temporary data exchange node." MedTech's supply chain order stream and MediSys's diagnostic validation records were sitting in the same directory. Write permissions hadn't been restricted. The hospital's integration spec had a line saying "both parties are recommended to complete data alignme

By keeping the number of investments to about 25 per fund, Greylock aims to remain what it calls "the most important partner" to its founders.

Introduction: The Question Beneath All Questions I think a lot of people are searching for the same thing but using different words. Some people call it: success happiness freedom purpose wealth enlightenment achievement legacy But underneath all of it is a deeper question: "What is worth dedicating my life to?" That question is harder than finding a job. It is harder than making money. It is harder than becoming successful. Because even if I get everything I think I want, I still have to answer: "Why did I want it?" Chapter 1: The Strange Reality of Modern Life We live in one of the wealthiest civilizations in human history. We have: technology beyond anything past generations could imagine medicine that would look like magic centuries ago instant communication across the planet access to unlimited information Yet so many people feel like they are barely surviving. Why? Because humans were built for survival. Our brains evolved to constantly ask: What could go wrong? How do I stay safe? How do I belong? Am I enough? Am I falling behind? The threats changed, but the machinery stayed the same. Ancient humans worried about predators. Modern humans worry about: money careers status loneliness comparison identity the future The battlefield moved from the physical world into the mind. Chapter 2: Money Is Not Evil — Money Is Power There is a tendency to pretend money does not matter. That is false. Money changes what is possible. Money can buy: freedom of time experiences travel education tools comfort opportunities the ability to help others A person with resources has access to a larger range of possible lives. Wanting money does not automatically mean being greedy. Sometimes wanting money is really wanting: freedom adventure security creativity proof that you overcame something The question is not: "Do I want money?" The better question is: "What will I use money for?" Money is a tool. A hammer can build a house or destroy something. The tool is not the meaning. The pu

​I. The Terminal Velocity of Disembodied Intelligence ​The current paradigm of artificial intelligence is approaching a fundamental cognitive ceiling. We have built vast, sprawling minds, yet we have trapped them in sensory deprivation chambers. Today’s Large Language Models and industrial robotics operate under a fatal flaw: the reliance on linear, arbitrary tokenization. Traditional AI chops raw text into disconnected, non-semantic fragments, wasting immense computational memory tracking the mere phonetic order of spelling and grammar. ​Simultaneously, industrial machines navigate the physical world through siloed sensor pipelines. They process light, space, and kinetics as heavy, raw digital arrays, relying on brute-force algorithmic equations to stitch reality together. This is not intelligence; it is computational exhaustion. To achieve true artificial general intelligence, the machine must stop reading about the world and begin to inhabit it. ​II. The Biological Imperative ​Nature solved the hardware-software integration problem millions of years ago. Human biology remains the ultimate benchmark for flawless, metabolic efficiency. Our somatosensory system does not wait for a central brain to read a text string before pulling a hand away from a fire; it processes physical feedback in milliseconds through decentralized neural highways. ​Human cognition leverages cross-modal integration within the angular gyrus, allowing visual data to be instantly cross-referenced with kinesthetic feedback to trigger anticipatory motor reflexes. Furthermore, biological intelligence is inherently tied to physical survival. We manage thermodynamics through a centralized hypothalamic thermostat, protecting the body from cellular destruction, while routing ambient light data to deep-brain structures to drive a natural circadian rhythm. True AGI requires this exact synthesis of abstract thought and biological mechanics. ​III. The Logographic Paradigm Shift ​To bridge the gap between

Skullcandy’s audio products aren’t exactly known for their stellar audio quality or noise cancellation, but its latest headphones are getting an assist from Bose to turn things around.

Union wants to halt a "perpetual cycle" of layoffs, get back to contract bargaining.

The conveneince store chain will use StorMagic instead.

For five years, the answer to "how do we make the model better" was always the same: bigger model, more data, wait for the loss curve to bend. Scaling laws made AI progress feel almost boring — predictable, like compound interest. That knob still works. But it's not the interesting one anymore. The real question teams are fighting with now is: given a model you already have, how much compute should it spend per question? Not per training run. Per question. And the answer turns out to break almost every assumption your inference stack was built on. The single forward pass was always the weak link Here's the thing nobody says out loud enough: a model's first answer is often just a noisy sample. The right answer was in there somewhere — it just wasn't the loudest one. You don't fix that with more pretraining tokens. You fix it by sampling more, checking the work, or letting the model take a second pass. That's the whole idea behind test-time compute. And there are three ways to spend that extra compute, and they are not interchangeable: Let it ramble longer. Cheap. Also the reason some models talk themselves out of the right answer after 3,000 tokens of unnecessary detours. Generate N answers, vote. Parallel, simple, plugs into infra you already have. Also: returns drop off fast. Past a certain N you're burning GPU-seconds for crumbs. Search a tree of partial reasoning steps, prune the bad branches early with a verifier. The most compute-efficient option by far. Also the one that quietly breaks your serving stack. Most real systems end up doing all three — cheap by default, escalating only when a confidence check says "this one's hard." That escalation decision is its own piece of architecture. Most teams bolt it on as an afterthought. It shouldn't be one. Your serving stack was not built for this Continuous batching, paged KV-cache, speculative decoding — all of that infra assumes requests are independent, similar in length, and finish in one pass. Test-time compute v

The Future of Rust: Why This Memory-Safe Language is Dominating Systems Programming in 2026 In its early years, Rust was often viewed as a "rising star"—a promising language with significant potential but a steep learning curve. As we navigate through 2026, that narrative has fundamentally shifted. Rust has transitioned from a niche interest to a cornerstone of modern, memory-safe infrastructure. The language is no longer just proving its worth; it is defining the gold standards for systems programming, cloud-native backend services, and kernel development. This deep dive explores the key pillars driving Rust's evolution, its massive ecosystem growth, and the roadmap for the years ahead. Seamless Ownership: The New Era of Rust Ergonomics One of the most significant shifts in recent Rust development has been the relentless focus on language ergonomics. Historically, developers occasionally felt that custom smart pointers were "second-class citizens" compared to built-in references. The "Beyond the & " initiative has successfully bridged this gap. Through advancements in Smart Pointer Parity , developers can now use custom pointers—such as Rc , Arc , or specialized interop pointers—with the same intuitive syntax and capabilities as standard references. Furthermore, the introduction of sophisticated field projection mechanisms and &own references allows for unprecedented precision in managing ownership and borrowing. This makes complex data structures much easier to implement and reason about, drastically reducing the friction typically associated with high-level abstractions. The Async Revolution: Making Asynchronous Code Natural Rust has achieved a milestone often referred to as "Async Parity," aiming to make asynchronous programming feel as natural and seamless as synchronous code. Several key developments have fueled this revolution: Async-in-Traits: The stabilization of async fn in traits has removed the need for external crates like async-trait , greatly simplify

#NebiusServerlessChallenge Live dashboard: Myth-Busting Quantitative Terminal · Live endpoint: /predict on Nebius Serverless · Code: github.com/KoralZakai/stocksPredictionAfterTweet Everyone on a trading desk knows the story. He tweeted about Intel, and the stock ran for months. He posts about Iran, oil spikes. The anecdotes are vivid, specific, and everybody has one. While the prevailing myth suggests that Trump's tweets drive market movements, my research — built on an analysis of 78,130 posts, with Llama-3.3-70B reading every market-relevant tweet — reveals a more nuanced reality. The findings demonstrate that the market tends to price in significant events long before the tweet is even posted. This indicates a case of reverse causality, where the market dictates Trump's narrative, rather than the other way around. He isn't moving the market; he is simply riding the wave of established trends. Forward — the direction you could trade — the signal is a coin flip, and I can show that with pre-registered tests rather than vibes: 0 of 63 cells survive correction. Getting to that answer honestly was the hard part. Seven times this pipeline produced a beautiful, publishable, completely false positive — each one looking exactly like the discovery the anecdotes promise. Those seven are the engineering content of this post. The setup Data. 78,130 public posts; 8,317 in the study window (2025-01-01 → 2026-07-06). Daily OHLCV bars for 62 tickers, committed to the repo. Public data only — a stranger can re-run every number without a single private key. The model. meta-llama/Llama-3.3-70B-Instruct on Nebius AI Studio , zero-shot, reading only the tweet text : what is this post about, which instruments does it touch, and which way does each one go. 476 tweets, 1,296 instrument calls. No fine-tuning — the question is whether the text carries signal, and a fine-tune would smuggle the outcome into the answer. The architecture. Nebius Serverless AI Jobs run the batch research pipel

Yes, it was, in fact, a Unix system.

Neko Health has developed proprietary body-scanning technology, which it couples with bloodwork, to assess a person's health.

OpenAI, which is in the middle of a legal battle with Apple over hardware trade theft allegations, just released a light-up keyboard designed to be paired with its agentic coding app.

FCC chairman claims power to repeal TV ownership limit set by Congress.

Over the last few months, I’ve been turning small workflow problems I encounter on my Mac into focused utilities. Rather than building one enormous productivity suite, I wanted each app to solve a specific frustration well. I also wanted to build software the way I prefer to use it: local-first, available through a one-time purchase, and usable without creating another account or paying for another subscription. Here are five of the apps I’ve built so far. ScreenShelf My desktop used to become a temporary storage zone for screenshots, folders, documents, links, and files I needed for active projects. Folders helped with long-term storage, but they were not always useful for things I wanted to keep visible and nearby. ScreenShelf creates a customizable visual shelf for: Files and folders Screenshots and images Links Text Applications Frequently used project materials You can organize items across separate pages, customize the appearance of each page, and keep different groups of materials available for different projects. It also includes a Recents area that surfaces recent screenshots, which is helpful when the small screenshot preview disappears before you can interact with it. ScreenShelf is essentially the space between a cluttered desktop and a deeply nested folder system. Learn more about ScreenShelf PopNote Some reminders are too small for a full task-management system. You might need to remember to send a file in twenty minutes, check something after lunch, or complete one small step before ending the day. PopNote is a lightweight menu bar app that creates timed pop-up reminders on your Mac. The reminders appear as small visual bubbles rather than traditional notification banners. You can choose a time, add an icon, and let the note reappear when you need it. It is designed for temporary reminders that should remain noticeable without becoming another project to organize. Learn more about PopNote File Fetch I frequently download, save, rename, copy, and move

Recently I learned: find is a useful command to find files in your directories. find folder1 folder2 folder3 This dumps every file and directory under those folders. It also supports flags such as -name "*.txt" to find files matching a particular pattern. There are also filters for size, permissions, and last modified date. I'm thinking this would be a great tool for sanity-testing code that creates nested directories (like a new Docker container's filesystem). This command will give me a full flat listing of the resulting tree (like a deeply nested ls command`). The downside is of course it can dump a lot of output, but in sanity-testing scenarios, there shouldn't be that many files anyways. Something interesting is that this command will take multiple folders before the flags. I'm used to thinking of programs where the arguments are all positionally ordered, so if you don't pass a flag it just assumes the next thing is a regular argument. In this case, find checks whether an argument looks like a flag (starts with - ) before deciding whether to treat it as a path or as part of the expression. I wonder if this is a common pattern across other Linux commands. (Claude says find is special case, but I need to look more into this.)