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Brad Grantham discusses how software engineers and architects can transition from individual contributors to influential technical leaders. Brad shares actionable insights on expanding skills into business and legal domains, adapting communication styles for non-technical stakeholders, moving past ego to empower teams, and navigating complex organizational dynamics to maximize engineering impact. By Brad Grantham

Research shows heat amplifies the dangers of pesticides.

Everybody says AI governance matters. They are right. But that is the easy part. The harder part is...

OpenAI is strengthening monitoring, alignment, and security for frontier AI models. See how new safeguards are guiding the pace of model development.

OpenAI and CodeAI are partnering to help students build AI literacy, think critically about AI, and develop the skills to use and shape it responsibly.

On August 6th, the Minneapolis Air Route Traffic Control Center lost radar and communications for around two hours. The outage disrupted more than 1,100 flights across the center's 330,000 square mile, nine-state airspace sector. Two days earlier, on August 4th, President Donald Trump departed the White House inside his Marine One military helicopter. As Federal […]

Proton’s CEO is a champion of encryption for everyone. So why is he going all in on un-encryptable AI?

More than a decade after launching in Europe, the Netherlands company is now selling its repairable phones in the US, starting with the Fairphone (Gen 6+).

Einride will buy the Tesla Semis, which will be made to Amazon and other customers.

After Noel Pichardo called out his city's embrace of Flock surveillance cameras, he was subjected to five internal affairs investigations in less than two years.

The company has shifted from making van-life builds to custom outfitting vehicles for small businesses, all while the EV landscape in the US changed dramatically.

The European Commission has updated its guidelines for teachers and educators on tackling disinformation and promoting digital literacy, extending the guidance to address generative AI , influencer dynamics and prebunking . The refresh gives schools and education professionals new materials for helping young people assess online information and build resilience against misleading content. The revised guidance sits within the EU's Digital Education Action Plan (2021-2027) . According to the European Commission publication record for the updated guidelines , the Directorate-General for Education, Youth, Sport and Culture released the updated publication on 4 June 2026. The update matters because the information environment facing pupils has changed substantially since the original guidance was issued. Generative AI can now be relevant to how online content is created, altered and spread. At the same time, social-media reliance and influencer-led information dynamics have become more prominent considerations for digital literacy education. The Commission's revised material positions educators and schools as part of the response, rather than treating disinformation solely as a platform or policy problem. What the updated EU guidance adds The updated guidelines are one element of a wider package of digital education and online-safety work. European Commission press materials published on 5 March 2026 described four sets of guidelines, comprising two new sets and two updates. The digital literacy and disinformation guidance was among the updated materials, with explicit attention to generative AI and contemporary online dynamics. A Better Internet for Kids summary published on 10 March 2026 identified several practical and policy-oriented additions. These include: Lesson plans and an updated glossary to support classroom use. Consideration of generative AI's impact on disinformation . Coverage of social-media reliance and the role of influencers in shaping information exp

Imagine opening a website from India while its servers are located in the United States. You request an image. Your request travels thousands of kilometers to the server, the server processes it, and the response travels all the way back to you. It works. But what happens when millions of users around the world do the same thing? This is where a CDN (Content Delivery Network) comes in. A CDN helps websites deliver content from servers that are geographically closer to users, reducing latency, improving performance, and taking load away from the main server. In this article, we'll understand how CDNs work, why they're important, and how they're used in large-scale systems. What Is a CDN? A Content Delivery Network is a globally distributed network of servers that stores and delivers frequently requested content closer to users. Without a CDN, requests might look like this: User ↓ Main Server ↓ Content With a CDN, a distributed layer is added between users and the origin server: ┌── CDN Edge Server ── User (India) │ Origin Server ────┼── CDN Edge Server ── User (Europe) │ └── CDN Edge Server ── User (USA) The main server is called the origin server . The distributed servers are commonly called edge servers or Points of Presence (PoPs) . Why Do We Need a CDN? Without a CDN, users from different parts of the world may have to communicate with the same origin server. For example: User in India ───────┐ User in Germany ─────┤ User in USA ─────────┼──→ Origin Server User in Japan ───────┘ As traffic grows, this creates several problems: Higher latency More traffic reaching the origin Increased server load Slower image and video delivery Poor performance for users far away from the server A CDN solves this by distributing frequently requested content geographically. How Does a CDN Work? Suppose your website contains an image: /images/product.jpg A user in India requests it. Instead of immediately contacting your origin server, the request goes through the CDN: User ↓ CDN ↓

Let's be honest about why you're here. You have a 4MB animated GIF that's slowing down a product page, bouncing back from an email attachment limit, or getting rejected by an ecommerce backend that caps images at 2MB. Or maybe a client sent you a loop that's 15MB and you need it under 1MB for a Slack header. The good news: you can usually cut a GIF's file size by 70–90% without anyone noticing the difference. The bad news? You have to stop thinking about GIFs as images and start thinking about them as video. Here's the practical guide to compressing GIFs in 2026, using only free browser-based tools. No uploads to shady servers, no software installs, just math and smart tradeoffs. Why GIFs get so big (and why your 4MB file is normal) GIF is a 1987 format. It was designed for simple graphics on dial-up internet, not for 4K animated logos. To understand why it bloats, you need one mental model: A GIF is a video pretending to be an image. Here's what happens under the hood: Limited palette: A GIF can only store 256 colors per frame. That's 8-bit color. Your screen displays millions of colors, so the GIF has to approximate. The real problem is how it stores those colors. Frame-by-frame storage: Unlike MP4, which stores only the changes between frames, a GIF stores every single frame as a full image . A 100-frame animation at 800x600px is 100 full-size images stacked on top of each other. Uncompressed data: GIF uses LZW compression, which is weak by modern standards. It works well on flat colors but fails on gradients, noise, or photographic content. A 10-second screen recording with a subtle gradient? That's a 20MB GIF waiting to happen. The math: A 500x500px, 30fps, 3-second GIF has 90 frames. Each frame is roughly 500x500x3 bytes (RGB) = 750KB raw. Before compression, that's 67.5MB of raw data. LZW might get it down to 4–8MB. That's why your file is huge. It's not a bug; it's the format being honest about its limitations. The three real levers to shrink a GIF You can't

Practices shifted from individuals buried in coffins to reusing sites for later mummy interments.

A week cleaning 290 booking records taught me more about silent failure than any error message ever has Last week I cleaned a deliberately messy dataset; 290 booking records from Safari Connect, Nairobi bus platform, 21 columns, 23 catalogued data problems. Class exercise, but the data was built from real failure modes. The problems I'd been warned about took an afternoon. The ones that cost me were the five that ran perfectly, returned plausible output, and were wrong. Every one of these produced a result. None produced an error. 1. The date heuristic that silently dropped five bookings The dataset had three date formats in one column: 2024-09-15 , 15/09/2024 ,and 09-25-2024 . Two of those are ambiguous - 01-18-2024 is unmistakably MM-DD-YYYY because there's no month 18, but 04-10-2024 could be either. The supplied guide handled it like this: UPDATE bookings_staging SET departure_date = TO_DATE ( departure_date , 'MM-DD-YYYY' ):: TEXT WHERE departure_date LIKE '%-%' AND LENGTH ( departure_date ) = 10 AND SPLIT_PART ( departure_date , '-' , 2 ):: INTEGER > 12 ; Read that last condition. If the second component is too large to be a month,this must be month-first. Reasonable logic - and it only fires when the day happens to be 13 or higher. Five rows had days between 1 and 12. They never converted. Then the next step filtered on ISO format: INSERT INTO bookings SELECT ... FROM bookings_staging WHERE departure_date SIMILAR TO '[0-9]{4}-[0-9]{2}-[0-9]{2}' ; ...and dropped them. No error. No warning. Five completed bookings and KES 3,840 of revenue gone from every downstream total. The guide's expected row count was written as "~280+", which is loose enough to hide it. The fix is to match on shape, not to infer from values: WHERE departure_date ~ '^ \d {2}- \d {2}- \d {4}$' Anchored patterns are mutually exclusive, so you can classify every row before touching any of it: SELECT CASE WHEN departure_date ~ '^ \d {4}- \d {2}- \d {2}$' THEN 'ISO' WHEN departure_date ~ '^ \d

The model maker added $18 billion in annualized revenue in two months.

Again, the CDC did not publish a full report and instead simply put the data online.

Opening hook The silence in the room was absolute, save for the rhythmic scratching of pens against paper during a final exam. I was three rows back, feeling confident, until my phone decided to vibrate against the wooden desk. It wasn't a subtle hum; it was a rhythmic, aggressive buzz that echoed like a snare drum in a cathedral. Every single head turned in my direction. I scrambled to silence the device, but in my panic, I fumbled the power button. That moment of pure, unadulterated embarrassment was the catalyst for everything I have built since. The problem We live in an age where our devices are supposed to be smart, yet they consistently fail at the most basic context-aware tasks. We have high-end processors, sophisticated neural engines, and sophisticated sensor arrays, but we still have to manually toggle a 'silent' switch before entering a meeting, a lecture, or a mosque. The friction isn't just the act of flipping a switch; it is the cognitive load of remembering to do it and, more importantly, remembering to turn it back on afterward. I spent months living with the anxiety of a phone that might ring at the worst possible time. I tried existing automation tools, but they were either bloated, relied on cloud-based tracking that hammered my battery, or lacked the granular control I needed for specific locations. Most apps that promised location-based sound management were either imprecise or drained my battery by keeping the GPS radio active around the clock. I didn't want a heavy-duty tracking app; I wanted a silent, background-native utility that respected the hardware constraints of the Android platform while solving the specific problem of environmental sound management. The technical decision / implementation When I started building Muffle, my primary constraint was the battery. Android users are rightfully protective of their background processes, and if my app showed up as a primary battery consumer in settings, it was effectively useless. I had to de

"Network effect" can run in reverse.