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RJ Scaringe, the CEO of Rivian Automotive, joined us for a wide-ranging interview about how his company’s new electric SUV fits into the current EV industry, and what comes next.

How I Fixed Bugs in 30+ Open Source Projects (And What I Learned) Over the past few months, I've been contributing to open source as an independent developer. No big company backing, no team — just me, a laptop, and a lot of caffeine. Along the way, I've submitted pull requests to 30+ repositories across the Python, JavaScript, TypeScript, and Rust ecosystems. Here's what I learned from the process — the good, the bad, and the "I wish someone told me this earlier." Why Contribute to Open Source? Let's get the obvious out of the way: it's not about the money (at least not directly). Most bounties pay $50-$500, and you'll spend 10-20 hours on a single PR if it involves deep codebase exploration. The real value is: Reputation — Each merged PR is a public signal that you can read, understand, and improve other people's code Learning — You'll see how major projects are structured, tested, and maintained Network — Maintainers remember helpful contributors. Jobs come from these relationships Scratching your own itch — Fix a bug that annoys you? Everyone benefits My Process: Finding Good Issues Step 1: Pick the Right Projects Not all projects are equally welcoming to new contributors. Here's my filter: Signal Good ✅ Bad ❌ Response time < 7 days > 30 days or never Issue labels good first issue , help wanted None CI/CD Green, fast builds Broken, 30min+ builds PR merge rate > 60% of open PRs merge < 20% merge Step 2: Find Issues You Can Actually Fix I look for: Bug reports with clear reproduction steps — Someone already did the hard work of identifying what's wrong Issues labeled easy-fix or similar — The maintainer thinks it's approachable Issues in domains I know — Don't pick a C++ compiler bug if you've never written C++ Step 3: Before Writing Code This is where most beginners fail. Don't start coding yet! Read the CONTRIBUTING.md — Every project has different style, commit message format, and PR requirements Look at recent merged PRs — What do good PRs in this project look

A cottage industry of celebrity figurines, blinking screens, and other DIY gadgets is helping drivers bypass Tesla's distracted-driving controls.

More than 350,000 spectators will watch 62 cars compete, day and night.

Just in time for Le Mans, BMW is pulling the cover off its new M performance concept built on the Bavarian automaker's Neue Klasse next-generation architecture. The concept is the debut of a new design language for BMW's M division, signaling a shift toward a more electrified future. The company says it is built on […]

Every developer I know has built something for themselves. A productivity tool. A habit tracker. A personal finance app. An AI that makes them smarter, faster, calmer. I did the same thing for 2 years. Then I had a conversation with someone close to me that I completely mishandled — and I realised no amount of personal productivity tools would have helped me there. The problem wasn't me, individually. The problem was the space between us. So I started asking a weird question Why has all of AI been built for individuals? Copilot helps you code faster. ChatGPT makes you smarter. Notion AI organises your thoughts. Calm helps you sleep better. Not one of them is built for what happens when two people try to understand each other. That's a massive gap. And it's one I couldn't stop thinking about. What I built Mendle — an AI-powered Relationship Intelligence platform. Not a therapy app. Not a chatbot companion. Not another journaling tool with an AI skin on top. The core idea is **shared emotional memory. Most relationship apps are built around one person's perspective. You log your feelings. You get insights. Your partner is an afterthought in the architecture. Mendle is different at the data model level. Both people contribute. Both people benefit. The AI builds an understanding of the relationship not just an individual. Over time it surfaces patterns. Communication loops. Emotional triggers. The things you keep missing because you're too close to them. The technical challenge that surprised me Building AI for two people is fundamentally harder than building it for one. Single-user AI: one context window, one set of preferences, one voice to understand. Relationship AI: two different communication styles, two different emotional vocabularies, shared history that neither person has complete visibility into, and privacy boundaries that have to be respected even between partners. The shared memory architecture was the hardest part to get right. How do you build something

Floating-point addition isn't associative. For a corporate inventory with tens of thousands of rows, naive summation drifts — and the number you disclose depends on row order. Here's why, and the fix. Here's a result that should bother anyone building carbon software. Take a corporate emissions inventory — tens of thousands of line items, each a number in tonnes CO₂e. Sum it. Now sort the same rows differently and sum again. The totals don't match. Not by much — maybe the third or fourth decimal place — but they don't match, and nothing in your code changed except the order. If you've never seen this, open a console: 0.1 + 0.2 === 0.3 // false That's the same bug, scaled up to a reporting deliverable. Why order changes the answer IEEE 754 doubles have 52 bits of mantissa. That's about 15–16 significant decimal digits of precision — generous, until you add numbers of very different magnitudes. When you add a small number to a large running total, the small number gets shifted right to line up the exponents before the addition happens. Bits that fall off the end of the mantissa are gone. Add a 0.0001 tCO₂e line to a running total of 80000.0 and there simply aren't enough mantissa bits to hold both the 80,000 and the 0.0001 — the small value is partially or completely swallowed. Float addition, as a result, isn't associative. (a + b) + c is not guaranteed to equal a + (b + c) . Sum your rows largest-first and the small values vanish early against a big accumulator. Sum smallest-first and they accumulate into something large enough to survive. Same data, different total. Here's the effect, deliberately constructed to be visible: const big = 80000 ; const smalls = Array ( 50000 ). fill ( 0.0001 ); // small values first, then the big one let a = 0 ; for ( const x of [... smalls , big ]) a += x ; // big value first, then the smalls let b = 0 ; for ( const x of [ big , ... smalls ]) b += x ; console . log ( a ); // 80004.99999999... console . log ( b ); // 80004.99999999...

Cruz/Wyden bill would help Americans sue federal officials over censorship.

Latency, bandwidth, and fidelity all matter when you're chasing milliseconds.

Uber One, meet Waymo Premier. The robotaxi operator announced a new $29.99-a-month premium tier for riders who want a more elevated and exclusive autonomous experience. The invite-only membership service is aimed at Waymo customers who use the service most frequently, offering them a number of perks, including priority pickups, 10 percent cash back on every […]

A few months ago, while interviewing for a Cloud Solutions Architect role at Microsoft, one of the interviewers asked me a question that stuck with me long after the interview ended. Not because I couldn't answer it. But because I kept thinking about whether I had answered it well. The question was: "What's the hardest part about working on mainframe technology?" At the time, I was still relatively new to the world of mainframes. And by "relatively new," I mean embarrassingly new. Before joining my current company, I didn't even know something called a "mainframe" still existed. If you'd asked me what COBOL was, I probably would've guessed it was a Pokémon. Okay that is an exaggeration but you get what I mean. I still remember early on hearing terms like KT (Knowledge Transfer) being thrown around and quietly wondering if everyone had received some secret corporate dictionary except me. The good news is that I've never been particularly afraid of looking stupid. So my strategy is simple: Ask the question. Then ask the follow-up question. Then ask the question that reveals I didn't understand the previous answer either. Surprisingly, people were usually happy to explain. Anyway, after a few KT sessions and what I'd generously describe as a "bare minimum amount of research," my brain went where most developers' brains probably would've gone. The technology The age The tooling The learning curve The fact that some of these systems were designed before I was even born All perfectly reasonable answers. But while I was sitting there in the interview, another thought appeared: "This feels too obvious." Interviewers at that level usually aren't asking for the first answer that comes to mind. They're trying to understand how you think. And the more I reflected on that question afterwards, the more I realized something interesting. The hardest part isn't the technology itself. Before I started working around large enterprise systems, my mental model of old technology was pret

Alt Carbon said the agreement followed more than a year of scientific review and due diligence, with Microsoft requiring additional verification and data-sharing measures.

The surveillance company Leonardo wants more data : A surveillance company plans to add sensors to automatic license plate readers (ALPRs) that would mean the devices, as well as capture the license plate of passing vehicles, would also sweep up unique identifiers of mobile phones, wearables, and other Bluetooth-enabled devices in those cars, potentially letting law enforcement identify specific drivers or passengers. The technology, called SignalTrace, would turn ALPR cameras from devices focused on tracking cars to ones that can more readily track the location of particular people. ALPR cameras have become a commonly deployed technology all across the U.S.; SignalTrace would make some of those cameras capable of collecting much more data...

Most people studying for Security+ use practice questions the wrong way. They take a 90 question set, score a 74, feel bad, take another set the next day, score a 76, and call that progress. Two weeks later the number has barely moved and they have no idea why. The score is the least useful thing a practice exam gives you. What you actually want is a map of what you do not know yet. Here is the approach that worked for getting through SY0-701 without burning out on endless question sets. Start cold, on purpose Before you study a single domain, take a full practice exam and do not look anything up. It will feel bad. That is the point. A cold score tells you where you actually stand, not where your notes say you should be. SY0-701 is split into five domains, and they are not weighted evenly: 1.0 General Security Concepts (12%) 2.0 Threats, Vulnerabilities, and Mitigations (22%) 3.0 Security Architecture (18%) 4.0 Security Operations (28%) 5.0 Security Program Management and Oversight (20%) Domain 4 alone is more than a quarter of the exam. If you bomb Security Operations and ace General Concepts, splitting your time evenly between them is a mistake. A cold diagnostic shows you that split in about an hour. If you want one to start with, there is a free diagnostic exam at secplusmastery.com/diagnostic that breaks your result down by domain so the holes are easy to see. Review the wrong answers, and the right ones too This single habit moved my scores more than anything else: for every question I missed, I wrote down why each wrong option was wrong, not just why the correct one was correct. Security+ loves distractors that are real terms used in the wrong context. A question about a control that prevents an attack will offer you a control that detects one, and a control that corrects after the fact, all as plausible answers. If you only learn that the answer was C, you learn nothing you can reuse. If you learn that B was a detective control and the scenario asked for a p

Customers have complained that a telehealth network selling compounded GLP-1s has been ripping them off—even after it had to pay $5 million to clients as part of a settlement with the US government.

My daughter Emma is 11. She's been vibe coding lately, and honestly, she's pretty good at it. The...

If you've ever tried to use an AI resume builder, you've probably hit the same wall I did. You sign up, poke around, find the one feature you actually need — and then boom: "Upgrade to Pro for $29/month." It's frustrating. Resume help shouldn't be locked behind a paywall. So I built my own. Meet Persona Persona is an AI-powered resume builder that you run completely on your own machine . No deployment required. No subscription. No account on some third-party service. You clone the repo, set it up, and it's yours. It's a fork of the excellent open-source project ResumeLM , but I've added a bunch of features I couldn't find anywhere else — especially around local AI and template variety. 👉 GitHub: github.com/nithiin7/persona (Drop a ⭐ if you find it useful!) The Big Deal: Run AI Completely Offline with Ollama This is the feature I'm most proud of. Most AI resume tools call out to OpenAI or Anthropic and charge you for every request. Persona supports Ollama — which means you can run the AI model locally on your own hardware, with zero API costs and zero data leaving your machine. Here's how simple it is: Install Ollama on your computer Pull any model ( ollama pull llama3 , for example) Open Persona's settings, point it to your local Ollama URL Done — the AI now runs entirely on your machine No OpenAI key. No Anthropic key. No usage limits. Your resume data never touches an external server. If you do want to use cloud models, Persona supports those too — GPT-5, Claude Opus 4.7, Claude Sonnet 4.6, and a handful of open-source models via OpenRouter. But the Ollama path is what makes this genuinely different from everything else out there. It's 100% Free — Everything Unlocked The original ResumeLM had Stripe payments baked in. I ripped all of that out. Every single feature in Persona is available to every user, always. There's no "Pro plan." There's no feature gating. You self-host it, you own it, you use all of it. 10 Resume Templates Persona ships with ten distinct templ

I'm a 17-year-old IT student from Luxembourg. A few months ago I got tired of spending 2-3 hours a day manually browsing Upwork, Malt, and Freelancer looking for projects. So I built an automation system that does it for me — 24/7, on a Raspberry Pi 3. Here's what it does, how I built it, and every painful lesson I learned along the way. What the system does Scans Upwork, Malt, and Freelancer every 30 minutes Scores each job 0–100 with AI based on my profile Generates proposals in English, French, and German Sends the best jobs to Telegram with inline A/B buttons Tracks which proposal style gets more replies Sends daily stats and weekly market trend reports Reminds me to follow up after 3 days The stack n8n — self-hosted workflow automation (Docker on Raspberry Pi 3) Groq API (Llama 3.1-8b-instant) — AI scoring and proposal generation Supabase — PostgreSQL database for jobs, proposals, clients SerpAPI — searching job boards via Google Apify — scraping Upwork listings Telegram Bot API — alerts and bot commands Cloudflare Tunnel — HTTPS for webhooks Total running cost: ~$5/month. 7 workflows 01 - Job Discovery — runs every 30 minutes, searches 10+ sources, deduplicates via Supabase unique constraint on URL 02 - Proposal Generator — AI scores the job, generates two proposal variants (formal vs hook-first), sends to Telegram with A/B buttons 03 - Follow-up Reminders — checks Supabase every 3 days for unanswered proposals 04 - CRM via Telegram — full client management through bot commands (/jobs, /stats, /clients) 05 - Market Intelligence — daily report: how many jobs found, average score, top platforms 06 - Trend Analysis — weekly report on what skills are trending in automation 07 - Lead Generation — finds companies actively using Zapier or Make who might want to switch to n8n Lessons learned (the hard way) 1. Cyrillic text breaks JSON body nodes silently If you have Cyrillic characters in a JSON body field with newlines, n8n throws a "Bad control character" error. Kee

Move also cuts off a massive market of legit users who buy cards with physical cash.

Honda's $42,000 hybrid coupe looks great, handles well, and gets 44 mpg.