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"Group project, but make it 1776." That's how a new commercial for Google Workspace opens. And things only get cringier from there. The clip imagines what it would be like if the founding fathers turned to Google's collaboration tools and Gemini to help them draft the Declaration of Independence. Ben Franklin texts Thomas Jefferson to […]

Introduction I used to think "connecting to a database" was one skill. Turns out it's two: connecting to a database chilling quietly on your own laptop, and connecting to one living in the cloud, behind a login, in this case, an SSL certificate that will not let you in until you treat it with respect. This week I did both. Same tool (Power BI), same dataset, two very different vibes. Grab a coffee, here's the full walkthrough local PostgreSQL first, then Aiven's cloud version, side by side, screenshots and all. Part 1: Local PostgreSQL → Power BI Step 1 : Create a schema Nothing fancy, just giving my table a home: CREATE SCHEMA powerbi ; Step 2 : Import the dataset Right-click the new schema → Import Data in DBeaver, point it at your CSV, and let the wizard do its thing. Step 3 : Check the table landed properly A quick peek at the columns to make sure nothing got mangled on the way in. Step 4 : Connect Power BI In Power BI Desktop: Get Data → Database → PostgreSQL database. In the Server field, type localhost (or 127.0.0.1 ) and your database name. localhost Choose Import , hit OK, and log in with your local username and password. Click Load . That's it. That's the whole local experience. Part 2: Aiven PostgreSQL (Cloud) → Power BI Now for the part that actually taught me something. Step 1 : Grab your connection details Everything you need lives on Aiven's Overview page: Host, Port, Database name, User, SSL mode. Your service URI will look something like this (don't worry, this isn't a real password, Aiven masks it in the console): postgres : // avnadmin : •••••••• @ pg - xxxxxxxx - yourproject . c . aivencloud . com : 22016 / defaultdb ? sslmode = require Step 2 : Import the dataset into Aiven Same DBeaver wizard as before, just pointed at the Aiven connection instead of local. CREATE SCHEMA powerbi ; Step 3 : Aiven's certificate. Download the CA cert from the Overview page: Now here's the part that actually tripped me up: Power BI's PostgreSQL connector doesn't ha

If you have ever wired up a cellular modem, a WiFi module, or a Bluetooth radio and typed something like AT+CGMR into a serial terminal, you have used a command language that is older than most of the engineers using it. The humble AT command set that still configures a huge share of today's connected hardware was born in 1981 , with a device called the Hayes Smartmodem. Four decades and billions of devices later, it refuses to die, and that longevity has a lesson in it for anyone building embedded systems. What AT actually stands for When Dennis Hayes and his company released the Hayes Smartmodem 300 in 1981, they faced a small but real design problem: how does a computer tell a modem the difference between a command to the modem and data to be sent down the phone line ? Their answer was an attention sequence. Every command line began with the two letters AT , short for attention , which told the modem to wake up and listen to what followed. ATD dialled a number, ATH hung up, and so on. It was readable, it was easy to implement on the microcontrollers of the day, and crucially you could type it by hand to debug a link. That simplicity is exactly why it spread. Competing modem makers cloned the Hayes command set to stay compatible, it became a de facto industry standard, and later it was formally captured in telecom standards. A convention that started as one company's pragmatic shortcut turned into the lingua franca of getting a device onto a network. From phone lines to the Internet of Things Here is the part that surprises people. The AT command set never retired when dial-up modems did. It quietly migrated into the components that make modern IoT possible. Cellular modules that put a device on a 4G or LTE network, from vendors like Quectel, SIMCom, and u-blox, are almost universally driven by AT commands. Classic Bluetooth and many WiFi modules expose an AT interface too. Even the ESP8266 and ESP32, the microcontrollers behind an enormous number of hobby and com

The US Department of Energy reportedly deleted about 6,000 pages related to energy conservation as a historic heatwave tears across the country. The deletion was suspiciously timed, following Republican outrage over Mayor Zohran Mamdani asking New Yorkers to help reduce strain on the grid by setting their AC to 78 degrees. Republicans like Ted Cruz […]

An astrolabe doesn’t map every star. It gives you a way to find your position relative to the ones that hold still. That’s the instrument I reach for when someone asks which AI tool they should be using. The honest answer is that the tools will be different in six months. The layers won’t. I spent a week trying to make sense of a handful of names that kept showing up in the same conversations. Tessl . Goose . Archestra . Kestra . Modelplane . RAG , MCP , half a dozen others orbiting nearby. Each one has its own pitch, its own funding round, its own reason it’s the thing you should adopt next. Taken together they read like noise. Taken apart, they sit on different floors of the same building. The agent loop again, the one I keep coming back to. Once you place each tool on a floor, the noise turns into a map. Tessl sits left of the loop , at the intent layer. Turn a spec into something an agent runs against directly. This is the one tool on the list that pushes back instead of going along with it. A well-formed spec is not the same thing as a team that agrees on what the spec means. The Agora produces the second thing as a byproduct of producing the first. Tessl produces the first and assumes the second follows. It doesn’t, automatically. That’s the whole argument. RAG and MCP are plumbing. Protocol, not position. They carry context into the loop and don’t take a side in any argument about who should be in the room when the spec gets written. They’re also the one floor with an actual standard. MCP, A2A , ACP , all under Linux Foundation governance now, joint working groups, cross-protocol commitments. Passing data between systems is a solved problem with decades of precedent behind it, so it standardized almost on contact. Nothing else on this floor plan has that. Governance, orchestration, the harness, the spec layer: every vendor is still building its own version and calling it the obvious one. The standard showed up first at the floor that mattered least to this ar

📖 Read the full version with charts and embedded sources on ComputeLeap → You can now buy a walking, flipping, kung-fu-kicking humanoid robot on AliExpress for $4,900 — less than a used Honda Civic, less than a semester of community college, less than what most people spend on a couch-and-TV combo. Unitree's R1 AIR shipped its first global batch in April, and it represents something the robotics industry has been promising and failing to deliver for decades: a humanoid robot that a normal person can actually afford. But here's what the breathless headlines won't tell you: price is falling faster than capability. The gap between what this robot costs and what it can actually do is where the hype lives — and understanding that gap is the difference between seeing a revolution and seeing a very expensive toy. The Number That Matters The Unitree R1 AIR stands 4 feet tall, weighs 55 pounds, and packs 20 degrees of freedom into a bipedal frame that can run, do cartwheels, throw punches, and execute spin kicks . At CES 2026, Unitree's booth stopped traffic with R1s replicating Bruce Lee sequences, Michael Jackson dance moves, and Mike Tyson combinations. The base R1 AIR ships with a monocular camera, 8-core CPU, and onboard AI for voice and image recognition. For $1,000 more, the standard R1 at $5,900 adds six more degrees of freedom (26 total), binocular depth perception, waist articulation, and head movement. Both come with hot-swappable batteries — about an hour of runtime per charge. To put the price in context: Figure AI and Tesla each shipped roughly 150 humanoid units in 2025. Unitree shipped 5,500 . That's not a typo — Unitree alone outshipped every Western humanoid manufacturer combined by a factor of 20x. The R1's $4,900 price point isn't an outlier. It's the leading edge of a Chinese manufacturing tidal wave. The Raspberry Pi Parallel — and Its Limits When the Raspberry Pi launched in 2012 at $35, it didn't replace laptops. It didn't become the computer most peo

Introduction: The Illusion of Productivity Metrics Traditional software development metrics—velocity charts, commit counts, bundle size—are the comfort objects of the coding world. They sit on dashboards, glowing with the promise of insight, but in reality, they’re often lagging vanity numbers . They don’t capture the narrative of a week’s work; they don’t reveal the decisions , the reversals , or the patterns that define progress. Instead, they deform the truth by oversimplifying it, much like a rubber band stretched too thin—it snaps under pressure, failing to hold the complexity of real work. Consider the mechanical process of a commit. A commit is a snapshot , a frozen moment in time. But software development isn’t a series of snapshots; it’s a sequence . When you string commits together without context, you miss the heat of decision-making—the back-and-forth, the undoing, the redoing. This is where traditional metrics fail. They don’t account for the thermal expansion of ideas, the way a decision made on Monday might cool by Friday, only to be reheated and reshaped. Without a narrative, these metrics are like a machine running without lubrication: they friction against reality, wearing down under the weight of their own inadequacy. The Mechanism of Metric Failure Let’s break down the causal chain: Impact: Developers rely on metrics like commit counts to gauge productivity. Internal Process: These metrics are lagging indicators , reflecting past actions without context. They don’t capture the why behind the numbers—the decisions, the reversals, the thought process. Observable Effect: Developers miss critical patterns, such as repeated decision reversals, leading to inefficiencies and missed opportunities for improvement. It’s like trying to diagnose a car’s engine by looking only at the speedometer—you’ll never catch the misalignment in the gears. Narrative-Driven Insights: The Optimal Solution Contrast this with a narrative-driven approach . When you narrate a

AI and Robotics in the New Age of Industrialization Since 2025, a new era of...

The federal agency says it will temporarily schedule the drug, which has been called “gas station heroin,” as a controlled substance—a boon for MAHA and the mainstream kratom industry.

What happens when a machine no longer needs to be trained to see something new? That's the quiet question sitting underneath this week's news, buried next to a less invasive brain implant and a handful of robots getting tougher for the real world. Neuralink says it's completed its first "transdural" brain implant, a surgical approach built to reduce trauma during the procedure. As someone who spends a lot of time thinking about how you get sensors close to a human eye without hurting anyone, I find these less-invasive-implant strategies worth watching, because the surgical-risk problem is basically the same one we wrestle with in ophthalmic hardware. Vision is getting less invasive too, in its own way. Roboflow rolled out text-prompt object detection built on SAM3 (Meta's latest segmentation model): you type the class of object you want "forklift," "cracked tile," whatever, and it returns boxes and masks without you collecting a single training image first. That's a real shift. For most of computer vision's history, teaching a model to recognize something new meant labeling hundreds of examples before you could even start; this collapses that step into a sentence. The same week brought several applied builds using the same detect-then-orchestrate pattern: a drone system that patrols for intrusions, a pipeline that inspects transmission lines for damaged cables, and an airport tool that spots foreign debris on the tarmac. The Robot Report's roundup of June's biggest robotics stories leaned heavily on humanoid robots companies going public, new deployments, and production milestones stacking up faster than would have seemed plausible a few years ago. Apptronik unveiled its Apollo 2 humanoid alongside a dedicated data-collection facility built so the robot keeps learning after it's deployed, not just during initial training which quietly answers one of the harder questions in robotics: how do you keep a system improving once it's out of the lab? X Square Robot raised e

As you'd expect, there were robots aplenty at the AI Engineer World's Fair Expo, although with mixed...

Video AI systems consistently fail to track what happens when the camera looks away: when a scene pans away from an object in motion and returns, current models re-render the object in its original position rather than showing the logical result of off-screen change. Scaling to more parameters makes this failure worse, not better, according to WRBench , a new benchmark that tests what researchers call "world model reliability." The benchmark presents AI video systems with scenes where something happens off-screen — the camera pans away while an object is in motion, or while a light changes, or while an open door should stay open — then pans back to see what the system believes should have happened. A system that genuinely models the world would track what occurred during the off-screen interval. Current systems mostly don't. Key facts What: A new benchmark tests whether video AI systems can track what happens to parts of a scene the camera isn't currently showing. Across 23 models, the answer is mostly no — and making the models larger made the problem worse, not better. When: 2026-06-19 Primary source: read the source (arXiv 2606.20545) The benchmark covers twenty-three different video generation models and nearly ten thousand video clips across six categories of off-screen change, each designed to test a different aspect of world continuity: objects in motion, light sources changing, object states such as open or closed doors, and several others. This gives a comprehensive picture rather than a single narrow test. The most striking finding is the scaling result. The researchers tested one of the more capable video generation systems at two different sizes: a smaller version and one with more than ten times as many parameters. More parameters didn't help. Scaling made the off-screen tracking problem measurably worse. The larger model produced more realistic-looking frames, but it was less accurate about what should have happened to the parts of the scene it wasn't

The government has removed restrictions on Anthropic’s Fable 5 and Mythos 5 AI models—but there were strings attached.

Planning a Fourth of July getaway? Use less gas—and cut your emissions—by easing up on the pedal.

If you've submitted a BOM for quoting recently and gotten a lead time that made you do a double take, you're not imagining things. Passive component sourcing in 2026 is tighter than it's been in a few years — and MLCCs are the epicenter. I want to break down why this is happening, which component categories are actually at risk, and — more importantly — what you can do at the design stage to make your board less vulnerable to it. This isn't a "just wait it out" post; there are concrete layout and BOM decisions that meaningfully change your exposure. Why now? Three demand sources are converging on the same MLCC/inductor capacity that used to be dominated by consumer electronics: AI server infrastructure — GPU power delivery networks alone can chew through hundreds of decoupling capacitors per board, and hyperscaler order volumes dwarf typical consumer runs. EVs — automotive-grade passives (AEC-Q200, X8R/X7R) come from a narrower qualified supplier base, so even modest EV growth disproportionately tightens that segment. Renewables/grid infrastructure — pulling on high-voltage inductors and power resistors. On the supply side, new MLCC/ferrite production lines take 12–24 months to come online from the capital decision. Semiconductor fabs can reallocate capacity relatively fast; passive component fabs can't. That structural lag is the real reason lead times stretch out faster than they recover. Which parts are actually at risk Not everything is equally exposed: Category Normal LT 2026 Tight-Market LT Exposure Commercial MLCC (X7R, 0402/0603) 4–8 wks 8–16 wks Moderate–High High-density MLCC (0201, high µF) 6–10 wks 16–26 wks High Automotive MLCC (AEC-Q200, X8R) 10–14 wks 20–30+ wks Very High C0G/NP0 (precision/timing) 4–8 wks 6–12 wks Low–Moderate Power inductors (shielded, low DCR) 6–10 wks 12–20 wks Moderate–High Chip resistors 2–6 wks 4–8 wks Low Chip resistors are the least affected — manufacturing capacity is less concentrated and swapping vendors doesn't trigger a

Trump has remade the nation’s capitol in his own image. Ahead of the Fourth of July, WIRED guides you through the dizzying effects of DC’s makeover.

Getty is planning to axe its $3.7 billion merger agreement with Shutterstock after a UK regulator imposed restrictions that would prevent part of Shutterstock's business from being included in the deal. The move comes despite the US Department of Justice granting the deal "unconditional antitrust clearance" in February. In an SEC filing published on Tuesday […]

Memory Chips Supply chain strategy from electronics production engineering, 500–50k units/year Introduction "Order from Digi-Key" is a prototyping strategy, not a production strategy. The 2020–2023 IC shortage demonstrated that supply chain resilience must be designed in — not improvised when lead times hit 52 weeks. The Sourcing Tier Structure Tier Examples MOQ Price Premium Lead Time Risk Authorized dist. Digi-Key, Mouser, Newark 1 pc +25–40% 1–3 days (stock) Lowest Franchise dist. Arrow, Avnet, TTI 100–1k Baseline 2–8 weeks Low Manufacturer direct TI, Infineon, ST portals 1k–10k+ −10 to −30% 8–20 weeks Low Regional aggregators IC-Online, local dist. Mixed Variable Variable Medium Spot market Brokers, eBay 1 pc +50 to +500% Days High Never use spot market for ICs without incoming inspection. Counterfeit STM32, ESP32, and common analog ICs are well-documented. Volume Pricing Reality Illustrative for a $2.50 MCU: Volume Digi-Key Arrow/Avnet Manufacturer Direct 100 $3.10 $2.65 N/A 1,000 $2.75 $2.15 $1.85 10,000 $2.40 $1.70 $1.25 50,000 $2.10 $1.40 $0.90 The franchise/direct savings are material at 1k+ units. Establishing Arrow or Avnet relationships pays for the admin overhead within 2 production cycles. BOM Resilience Framework For each critical component, document: Primary source : authorized distribution or direct Secondary distributor : alternative channel for same part Alternate part : functionally equivalent, different manufacturer, validated Buffer stock : target weeks at production rate Lead time worst-case : historical peak, not current During normal periods: 4-week buffer, one secondary source, one qualified alternate. For 5+ year product lifecycles: qualify the alternate before you need it. Practical Sourcing Mix: 500–5k Units/Year Component Type Primary Secondary Notes Commodity passives Digi-Key/Mouser + Yageo/Walsin Arrow Annual pricing agreements MCUs < $3 Arrow direct IC-Online for gap fills 90-day POs, buffer stock MCUs $3–$10 Manufacturer direct + A

The White House is easing restrictions on Anthropic’s most advanced AI models weeks after ordering the company to suspend access for foreign nationals.

Also, the science of poop's distinctive shape, boron buckyballs, and the secret to a soccer feint.