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While the court still hasn't signed off on the massive settlement resolving Epic's antitrust lawsuit against Google for having a monopoly over Android's app store with Google Play, the tech giant says it will start rolling out changes to the way it handles billing for developers worldwide. As announced in March, the flat 30 percent […]

You can save hundreds of dollars on Breville appliances during Amazon Prime Day.

If you’re looking for a good deal without spending hundreds, Prime Day discounts have pushed some of our favorite products under $50. You can pick up everything from Nintendo Switch games to Bluetooth speakers while sticking to your budget. We’ll keep this post updated throughout the sale as we find new deals and low prices, […]

The expensive, $27 million political proxy war between Anthropic and OpenAI came to a draw last night when Alex Bores, a New York state Assemblyman whose popularity surged after being targeted by a pro-AI super PAC, narrowly lost the Democratic primary to represent New York's 12th Congressional district. Prior to the race, Bores, a former […]

The new app, which is currently being tested with select creators, will have Facebook's recently launched AI creator assistant built into it.

Global music streaming service Deezer is taking a contrarian approach to AI, even as it adds a feature that lets fans remix songs.

If League of Legends and Valorant players have the right hardware and elect to opt into "pre-boot security mechanisms and Windows' own native protection features," then, starting today, they can switch the Vanguard anti-cheat software from always-on to one that's "on demand." With "Vanguard Pre-Check," the kernel-level driver won't launch when your system does, according […]

Victim's family files wrongful death suit following Tesla crash in Texas.

UK-based staff at the Wikimedia Foundation (WMF), the nonprofit that supports Wikipedia, are pushing forward with their unionization drive. On Wednesday, the staff sent a letter to WMF management requesting the organization voluntarily recognize the union. "The WMF has undergone a period of significant change in recent months, escalating workers' concerns over transparency, trust, and […]

Agility Robotics, the humanoid robotics startup that spun out of Oregon State University in 2015, expects to generate $620 million in proceeds.

Tesla, accused of failing to fix design flaws, blames driver pressing accelerator.

The safety board, known for its thorough investigations, is probing the crash alongside the National Highway Traffic Safety Administration.

Figma has revealed some new design and coding product updates at its annual Config conference that aim to help creatives "push their ideas further" and automate tedious tasks with AI. Part of this is a reimagined canvas that's now optimized for full-stack development, according to Figma, bringing teams, AI agents, tools, and materials "together in […]

Figma's update adds a new code layer, support for motion and shaders, and the ability to create custom plug-ins for various tasks using AI.

As web developers, engineering workflows are heavily dependent on the NPM registry. However, the traditional process of searching, auditing, and integrating packages remains highly fragmented. Developers are routinely forced to hop between npmjs.com, GitHub source repositories, and external documentation tabs simply to verify bundle sizes, check dependency trees, or generate setup boilerplate. Following a strong reception on LinkedIn, X, and Facebook, the Motion Mind Team has introduced LibPilot to the dev.to community. LibPilot is not a traditional registry interface; it is an AI-powered search engine and discovery hub engineered to index, track, and analyze over 4,000,000 NPM packages in real time. Here is an architectural breakdown of how LibPilot restructures package exploration for modern developers and autonomous AI code agents. Intent-Based Discovery and Global Search Architecture Traditional search engines require users to input the exact name or strict keyword of a library. LibPilot introduces a dual-input architecture on its home page to eliminate this constraint: Direct Registry Querying: Users can input full or partial package names into the global search bar to instantly surface clean, structured, and typed suggestions directly from the live NPM ecosystem database. Contextual AI Recommendations: For scenarios where the ideal package is unknown, developers can type out a complete description of their project architecture or system constraints (for example: "a lightweight, typed state management engine that handles server-side rendering natively"). LibPilot's internal AI agent processes the functional requirements and suggests production-ready libraries suited for that stack. Continuous Context AI and Interactive Onboarding A core goal of the platform is reducing developer friction and maintaining deployment momentum. LibPilot transitions static package documentation into an interactive environment: Unlimited AI Chat Architecture: Once a library is select

If you’re looking for a circulating fan that can move air around your home better than the average model, Amazon has marked down the SwitchBot Standing Circulator Fan to $89.99 (usually $129.99) for Prime Day. The battery-powered upright fan tilts vertically and oscillates horizontally, and it is adjustable from 18 to 40 inches in height, […]

When designing systems like Facebook, WhatsApp, Netflix, Amazon, or Instagram, one of the first questions a system designer asks is: Can a single server handle the traffic? How much storage will be needed? Do we need caching? How much RAM should our cache have? How many servers should we deploy? Before discussing databases, load balancers, microservices, or caching layers, we need a rough understanding of the scale. This is where Back-of-the-Envelope Calculations come into the picture. Why Do We Need Back-of-the-Envelope Calculations? Imagine you're asked to design Facebook. If you immediately start drawing: Load Balancer ↓ Application Servers ↓ Redis Cache ↓ Database without knowing the expected traffic, you're designing blindly. System design is fundamentally about making trade-offs. To make those trade-offs, we first need estimates. Back-of-the-envelope calculations help us answer: How much traffic will the system receive? How much data will be generated? How much cache memory is required? How many servers are needed? The numbers don't need to be perfect. They only need to be close enough to make architectural decisions. What Exactly Is a Back-of-the-Envelope Calculation? A quick estimation technique used to approximate: Traffic Storage Memory Server Capacity using rough assumptions. Think of it as: "Getting the order of magnitude correct rather than getting the exact number correct." A system designer rarely needs perfect accuracy during interviews. They need reasonable estimates. The Standard Estimation Flow Whenever you get a System Design question: Users ↓ Traffic ↓ Storage ↓ RAM / Cache ↓ Number of Servers ↓ Architecture Design Always estimate first. Design later. The Ultimate Estimation Cheat Sheet Storage Units Unit Value 1 KB 10³ Bytes 1 MB 10⁶ Bytes 1 GB 10⁹ Bytes 1 TB 10¹² Bytes 1 PB 10¹⁵ Bytes Time Units Unit Value 1 Minute 60 Seconds 1 Hour 3600 Seconds 1 Day 86,400 Seconds Common Assumptions Metric Approximation Peak Traffic 3× Average Traffic Active

Math.random() returns a number between 0 and 1, and roughly nobody reading this could explain what happens between the call and the return. That is fine, fine right up until the output decides who gets money, and then it becomes one of the genuinely hard problems in applied software, the kind that regulated industries build entire testing labs around. Start with the thing most people get wrong: a sequence that passes for random and a fair sequence are different claims, and your users cannot tell them apart by staring at outputs. The users will never catch the difference and that is the whole problem in one sentence. This is why fairness in any real-money system, an online casino being the sharpest example, is a verification problem long before it is a math problem. Pseudorandom generators are deterministic. A PRNG eats a seed, runs it through fixed arithmetic, and spits out numbers that sail through statistical randomness tests while being completely predetermined by that seed. Mersenne Twister is the poster child: excellent distribution, used everywhere by default for years, and from a few hundred observed outputs you can reconstruct its internal state and predict the rest. For a Monte Carlo simulation, who cares! For anything where a human has a financial reason to guess your next number, you just shipped a vulnerability and called it a feature. What you want when stakes exist is a CSPRNG. The guarantee that matters: even with a long history of outputs, an attacker cannot compute the next one or recover the internal state. crypto.randomBytes() in Node. crypto.getRandomValues() in the browser. They sit one autocomplete away from the unsafe option and offer wildly different guarantees, which is exactly why this bug ships so often. The safe call and the dangerous call look like fraternal twins. ** The part players actually rely on ** Say you build it correctly: a proper CSPRNG, real entropy, no timestamp nonsense. You know it is fair but now prove it to a stranger wh

The 2026 FIFA World Cup is reshaping competitive balance in ways that traditional 32-team analysis cannot predict. With 16 groups of 3 teams instead of 8 groups of 4, the mathematical probability of upsets—and the consequences of single matches—has fundamentally shifted. Early tournament data already shows this pattern emerging. The Format Change: A Statistical Earthquake The move from 32 to 48 teams introduces a critical structural change: Format Groups Teams/Group Matches/Team Elimination Threshold 2022 (Qatar) 8 4 3 Top 2 of 4 2026 (USA/CAN/MEX) 16 3 2 Top 2 of 3 This seemingly small difference creates massive implications. In a 3-team group, each team plays only 2 matches to determine their fate . Compare this to the 2022 format where teams had 3 chances to secure advancement. The upset probability multiplier: With two fewer matches per group, variance compounds. A single bad result—or a fortunate one—carries exponentially more weight. Early Tournament Evidence: The Data Doesn't Lie Let's examine the first week of actual 2026 results: Match Expected Result Actual Result Upset Indicator Portugal 5-0 Uzbekistan Portugal W Portugal W (5-0) Expected England 0-0 Ghana England W Draw Minor Upset France 3-0 Iraq France W France W (3-0) Expected Argentina 2-0 Austria Argentina W Argentina W (2-0) Expected Norway 3-2 Senegal Senegal slight favorite Norway W Major Upset Jordan 1-2 Algeria Algeria strong favorite Competitive Closer than xG Panama 0-1 Croatia Croatia W Croatia W Expected Colombia 1-0 Congo DR Colombia W Colombia W Expected Three critical takeaways: Norway's 3-2 victory over Senegal is statistically significant. Pre-tournament models favored Senegal slightly (ranked 18th globally vs Norway's 22nd). In a 4-team group, this result matters less; in a 3-team group, Norway essentially secures qualification with one match remaining. England's 0-0 with Ghana represents draw probability explosion. With only 2 group matches, a draw consumes 50% of your advancement op

When we started Reach , the plan was simple. We had a bunch of small businesses we were already in touch with - real SMBs, the kind that live on WhatsApp and don't have a "tech team." We'd give each of them an AI agent that talks to their customers, hand them a clean starter template for the instructions, and let them tweak it from there. That was the whole bet: give people a good starting prompt and a template, and they'll play along. We were so wrong it's almost funny now. The part nobody warns you about Here's the thing about prompt engineering that you only learn by watching non-technical people try to do it: writing the instructions is not the hard part. The hard part is thinking about the task in the abstract . We'd hand a business owner an agent that mostly worked, and say "just adjust the instructions when it gets something wrong." Sounds easy. It is not. Sitting down and imagining all the ways a conversation could go, then writing rules for a machine to follow - that's a skill. It's basically a job. And it's a completely different job from running a flower shop or a real estate office. So they got stuck. They'd open the instructions editor, stare at it, and close it. The agent stayed mediocre because the iteration loop we designed required them to be part-time prompt engineers. A few of them just quietly left. That stung, but it taught us the actual problem. What "iteration" actually looked like We started doing the iterations for them, manually. And once we did that a few dozen times, a pattern jumped out. The feedback never led to an abstract change. It was never something that made us "rethink the agent's persona" or "restructure the system prompt." It was tiny, concrete, and tied to a real conversation: "It suggests all of our services, but honestly most customers only care about these three - push those." "It pulled our opening hours from the website and they're just wrong. We changed them months ago." These were one-line corrections. The owner knew ex