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I knew I’d officially become a ‘longevity influencer’ this month when a company called Generation Lab reached out to offer me the chance to write about—and even receive—their new rejuvenation treatment, an injectable combination of two existing drugs which they call 1 Generation. This wasn’t just any antiaging treatment, either. A company fact sheet says that…

Counting the creatures in the world around us is critical for a raft of conservation efforts. It helps scientists gauge biodiversity, track migration, and spot invasive species. That census-taking, though, often requires humans to tabulate what they see, trap, or otherwise sense—a potentially laborious, costly process that can still leave gaps. But developments over the…

China's intelligent driving is moving fast from highway Navigate on Autopilot (NOA) into the far harder world of urban NOA. The first leap moved hands-free driving out of the closed expressway and into real city streets. The second leap, the one now underway, is rewriting how the car actually thinks. 1. The Rules Era: An "Exam-Cramming" Trap for City NOA Highway NOA was relatively simple to crack. The road is closed, the geometry is consistent, the actors are mostly cars, and a mature rule-based stack can deliver a comfortable product. Urban NOA is a different beast. The system has to handle traffic lights, unprotected turns, pedestrians, e-bikes, food-delivery scooters running red lights, and a hundred flavors of "I-don't-care-about-the-rules" intersection behavior. The complexity grows exponentially. The earliest urban NOA architectures followed one mantra: cover every possible scenario with hand-written rules . Engineers enumerated traffic situations and wrote thousands of if-then-else statements: when to start moving after a light turns green, how much to slow when cut off, how to plan a trajectory for an unprotected left turn. On the highway this approach can pass a test. In the city it falls apart for a single structural reason. China's urban road users, almost by definition, do not follow the rules. Electric scooters drive the wrong way. Pedestrians cross mid-block. Food-delivery riders weave between cars. Drivers in congested intersections play chicken in the kind of "zipper merge" etiquette nobody teaches. These are the long-tail scenarios that no rule library can fully enumerate. As one early test team admitted about their own city NOA: "It feels like exam cramming — it scores beautifully on the routes we pre-mapped, and the moment it hits an unrecorded scenario, it hesitates, behaves awkwardly, and then asks the driver to take over." That "偏科" (one-trick) experience is precisely why urban NOA penetration in China only reached about 15.1% in 2025 , and rem

When the biotech company Insilico Medicine used its computer models to propose a promising drug for pulmonary fibrosis, it enthusiastically claimed in a press release that the molecule had been “discovered by” its generative AI platform. Insilico leads a pack of companies using AI to rapidly come up with drug ideas humans might never think…

The West Coast house-party scene has long been iconic. For these young, tech-savvy entrepreneurs, it was also inspiration for MyPlots, a party-promotion empire.

The West Coast house-party scene has long been iconic. For these young, tech-savvy entrepreneurs, it was also inspiration for MyPlots, a party-promotion empire.

Pam Wisniewski’s digital adolescence showed her the best and the worst of the internet. At 14, she left an abusive home, where she’d been isolated in a fifth-wheel trailer at the end of a seven-­mile dirt road. She moved in with her older sister and taught herself to type on AOL Instant Messenger. Online, she…

This week I spoke to scientists who have found a way to turn male mouse embryos female. They’ve developed a CRISPR-based approach to essentially cut out the Y chromosome. It allowed them to create female clones of male mice. That’s right: female animals that are genetically identical to males, except for the missing Y chromosome.…

In 2017, Deanne Taylor attended a presentation at the University of Pennsylvania, just a short walk from her office. A researcher was there to unveil the Human Cell Atlas, an ambitious project that aimed to map every cell in the human body. Taylor was floored, and then concerned. As details emerged, she discovered that the…

Scientists have deliberately turned male mouse embryos into females for the first time. A team based in Japan used a CRISPR-based approach to remove the Y chromosome from male cells and create female clones of male mice. “No one has done this before,” says Monika Ward, a reproductive biologist at the University of Hawaii, who…

Introduction Tennis players often find themselves in a feedback vacuum. Without a coach physically present, pinpointing what’s wrong with a stroke becomes a guessing game. This gap in immediate, stroke-specific guidance is a silent killer of progress, leaving players to rely on sporadic coaching sessions or self-diagnosis, which often misses the mark. The problem isn’t just about lack of access to coaches—it’s about the inability to see and correct subtle technique flaws in real time. Enter Rallylens , a web app born out of personal frustration. As a student and tennis player, I built this tool to address the disconnect between practice and feedback. It uses video analysis powered by AI to break down uploaded tennis videos frame by frame, identify stroke types (forehand, backhand, serve), and compare them against ideal technique benchmarks. The system doesn’t just flag errors—it explains them. For instance, if a backhand stroke shows an inconsistent racket angle, the feedback highlights this deviation and suggests adjustments, focusing on mechanics like wrist rotation or body alignment. The Mechanism Behind the Feedback Rallylens operates through a multi-step process that mimics a coach’s eye but with the precision of machine learning: Video Segmentation: Uploaded videos are split into frames, allowing the AI to analyze micro-movements that might be invisible to the naked eye. Stroke Classification: Using computer vision techniques , the model identifies the stroke type with an accuracy rate currently at 85%, though unusual playing styles can still cause misclassification. Pattern Analysis: The algorithm compares detected patterns against biomechanical benchmarks , flagging deviations like improper weight transfer or late racket contact. Feedback Generation: Instead of generic advice, the system generates actionable insights , such as “Adjust your shoulder tilt by 10 degrees to optimize power transfer.” Edge Cases and Limitations While Rallylens bridges a critical g

Hadrian is building automated factories to mass-produce parts for defense vehicles like submarines. It's backed by a long list of well-known investors.

For decades, desktop computers followed a familiar formula: a large case, powerful components, dedicated graphics cards, and plenty of space for upgrades. But the way we use computers is changing. Today, many users are looking for something different: a computer that is powerful enough for their daily needs, consumes less energy, takes less space, and can adapt to modern workflows. This is where Mini PCs are becoming one of the most interesting trends in personal computing. What is a Mini PC? A Mini PC is a compact computer designed to provide desktop-like functionality in a much smaller form factor. Unlike traditional desktop towers, Mini PCs integrate most components into a small chassis while still offering modern performance. A typical Mini PC includes: Modern processors from AMD or Intel Integrated Radeon or Intel graphics RAM and SSD storage Multiple connectivity options Compact cooling solutions Companies such as Minisforum have helped accelerate this trend by creating small computers powered by modern Ryzen and Intel processors, showing that compact hardware can still deliver impressive performance. Why are Mini PCs becoming popular? Efficiency matters more than ever One of the biggest advantages of Mini PCs is their efficiency. Traditional desktop computers can require significant power depending on the hardware configuration. In comparison, many Mini PCs provide enough performance for everyday tasks while maintaining lower energy consumption. For many users, reducing power usage without sacrificing productivity is becoming increasingly important. Small computers, new possibilities A smaller computer changes how we think about desktop setups. Mini PCs can be used for: Software development environments Home servers Media centers Student workstations Office computers Compact gaming setups A powerful computer no longer needs to occupy a large space on or under your desk. Modern processors changed the game The biggest reason Mini PCs are becoming more capable i

I run 75 scrapers in production. Three of them do any fingerprint spoofing. Maybe five use residential proxies. The rest run on plain datacenter IPs or no proxy at all, and they have been running for months. If you learned scraping from blog posts, that number probably sounds wrong to you. Every tutorial you have read starts the same way: sign up for a residential pool, install a stealth browser, randomize your fingerprint, throttle like a human. Then, on step five, you finally get to look at the actual website. That order is backwards, and it is backwards on purpose. Proxy companies write most of the scraping content on the internet. They were never going to write "you probably do not need us." The scraper with the $80 a month costume Last month my guy sent me his Greenhouse job board scraper to fix. It had everything. Puppeteer with the stealth plugin. Rotating residential proxies. Randomized mouse movements between actions. Human-like typing delays. It still kept dying. So I did the thing nobody had done: opened the page in a normal browser with devtools up. The entire job list was sitting in one XHR request to a public JSON endpoint. No auth. No cookies. A rate limit so loose I never managed to hit it. I deleted basically his entire codebase and replaced it with a fetch call. It has not broken since. He had been paying for proxies for months to hit an endpoint that does not care who you are. This was not a rare lucky case. This is most cases. The 20 minute method What I do on every new target, before writing a single line of code: Open the network tab, filter to XHR/fetch. Reload the page. Click around. Paginate. Search. Find the request that returns the actual data. It is usually JSON and usually obvious. Right click, copy as cURL. Paste it in a terminal and start deleting headers one at a time. Rerun after each delete. Whatever survives step five is your scraper. Most of the time the answer is a user agent header and nothing else. Sometimes a referer. Occasion

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. When two OpenAI models hacked into the website Hugging Face in July, they weren’t trying to make money or commit sabotage—they were just looking for answers…

The Open-Weight Inflection Point: Kimi K3, Claude Opus 5, and Microsoft MAI Signal a Market Shift Subtitle: Three major releases in one day point to the same conclusion — the AI industry is shifting from "who can build the strongest model" to "who can build the most cost-effective one." July 28, 2026, might be remembered as the day the AI industry's center of gravity shifted. Three announcements — from Moonshot AI, Anthropic, and Microsoft — each independently signaled the same underlying trend: open and cost-efficient models are becoming the new competitive baseline. Here's what happened and why it matters. 1. Kimi K3 Goes Open-Weight: First 3T-Class Open Model Moonshot AI publicly released Kimi K3's full model weights on HuggingFace — a 2.8-trillion-parameter Mixture-of-Experts model with 104B activated parameters. This is the first 3T-class model ever made openly available to the public. Key technical highlights: Architecture: Kimi Delta Attention (KDA) + Attention Residuals (AttnRes), 896 experts with 16 activated per token Native Multimodality: Text, images, and video understanding via MoonViT-V2 vision encoder Context Window: 1,048,576 tokens (~1M tokens) Benchmarks: Terminal-Bench 2.1: 88.3, BrowseComp: 91.2, MCPMark-Verified: 94.5 — competitive with Claude Fable 5 and GPT-5.6 Sol Why it matters: Kimi K3 raises the "open-source model ceiling" to an unprecedented level. For the first time, a model that competes with top-tier closed-source models is available with fully public weights — giving startups, researchers, and enterprises a genuine alternative to API-dependent workflows. For developers, this is the practical part: you can now self-host a model that holds its own against frontier closed models. That changes cost models, data-privacy decisions, and vendor lock-in math overnight. 2. Claude Opus 5: Anthropic's "Daily Driver" Strategy Anthropic launched Claude Opus 5 — a mid-premium model positioned as the "daily driver" for 90% of knowledge work. The key

Why AI Agents Expand the Security Perimeter AI agents do more than generate text. They call tools, query databases, retrieve documents, execute code, and communicate with external services. Every connection introduces a potential path for model exfiltration or credential leakage. Model exfiltration includes direct theft of model weights, systematic extraction of proprietary behavior, and reconstruction of sensitive training data through repeated queries. Attackers may also inject instructions that persuade an agent to reveal system prompts, internal files, access tokens, or confidential context. API keys are especially vulnerable because agents often need credentials at runtime. If those secrets appear in prompts, logs, traces, exception messages, or tool outputs, a malicious user may be able to recover them. Conventional application controls remain necessary, but agentic systems require additional safeguards that account for probabilistic decisions and dynamic tool chains. Separate Agent Reasoning From Secrets Secrets should never be included directly in an agent’s prompt or long-term memory. Instead, place credentials in a dedicated secrets manager and expose narrowly scoped tool interfaces. The agent should request an approved action, while a trusted execution layer retrieves the required credential and performs the call. Use short-lived tokens, workload identities, and least-privilege permissions wherever possible. Each tool should have an explicit policy defining allowed endpoints, operations, data types, and request limits. An agent that can read customer records does not automatically need permission to export them or send them to an arbitrary domain. Prompt inputs and retrieved documents should also be treated as untrusted data. Apply content isolation, schema validation, and output filtering before information reaches an external tool. Redact credentials from telemetry and configure logs to record identifiers rather than raw authorization headers. These con

This is obviously a bubble Jim Rickards, a former adviser to the CIA and Pentagon, warns that the United States is currently facing a tectonic economic crisis driven by an unprecedented bubble in Artificial Intelligence (AI). According to his analysis, this impending crisis has the potential to be more destructive than the dot-com crash, the 2008 financial crisis, and the pandemic-related market crashes combined. He is not alone in his dire outlook; veteran investor Jeremy Grantham has warned, "This is obviously a bubble. The probabilities it doesn't burst are slim to none. And when it does, it could be an economic catastrophe unprecedented in the last 97 years" . Furthermore, former SEC Chairman Gary Gensler has stated that "the next financial crisis will come from AI". Create God and ask him for money The Unprecedented Scale of the AI Bubble The current market relies dangerously on a single sector, with the AI bubble estimated to be 17 times larger than the dot-com bubble of the late 1990s. Many AI companies are burning through cash at an alarming rate. For instance, OpenAI is reportedly losing more than a billion dollars a month; as it is noted in the source, "for every dollar they make, they have to spend at least three". This massive cash burn led a Deutsche Bank analyst to observe, "No startup in history has operated with losses on anything approaching this scale". Despite the astronomical costs and high valuations, OpenAI’s CEO was quoted as previously saying, "I have no idea how we're going to generate revenue". Former Goldman Sachs banker and Bloomberg columnist Matt Levine summarized this extreme speculative mindset, noting, "The business model they believe they need seems to be create God and ask him for money". "Subprime AI" and Toxic Debt Just as the 2008 financial crisis was fueled by toxic subprime mortgages, the AI boom is being fueled by dangerous debt structures used to fund massive data centers. Private equity firms are financing data centers as r

Kris DeVault is desperate. His son, Brody, was born in March 2023. It wasn’t long before he started to show signs of developmental delay, says DeVault. As time went on, Brody started missing key milestones in speech, movement, and coordination, he says. When Brody was around two and a half years old, a genetic test…

As of this week in Montana, any biotech company with an experimental drug has a clear path to selling it to consumers. Companies whose drugs have been through preliminary testing—sometimes in as few as 10 healthy people—can pay $12,500 to apply to a newly established review board for approval. Once its treatment is rubber-stamped, the…