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Anthropic leaders flew to Washington, DC, to meet with White House officials on Monday. After high-level talks, they're still split on the risk Claude Fable 5 presents.
In an internal memo seen by WIRED, Bosworth promised employees more stability, better communication, and the return of workplace perks as the company seeks to improve morale.
Hi everyone, I’m working on a new programming language called Zentax. It is still in early development, but the goal is to build a modern language focused on: Performance and low-level control Simple and clean syntax Native desktop application support A modular compiler and runtime design Zentax is not trying to replace existing languages — it is an experiment in building a unified approach for systems programming and UI development. Current Status Compiler: in development Runtime: early design stage Renderer: experimental Standard library: planning phase Looking for Contributors I’m open to collaboration from anyone interested in: Programming language design Compiler development Runtime systems Graphics / rendering engines Open-source tooling Even feedback and ideas are welcome at this stage. Links Git Hub Repo Discord Thanks for reading. Dr. Zoha Tariq Anoneurx
TechCrunch has followed SpaceX's start, struggles, and successes from the early days. And we're here for what happens next too. This package of SpaceX IPO coverage includes who stands to win (and maybe some who won't), pre-IPO deals, and what's tucked inside its S-1 registration document.
AMD's stripping of TSME from consumer CPUs appears to be a deliberate, covert move.
A story about an AI alert model that cut false alarms from 60% to 7% — and what happened when...
Why I modeled a load balancer after Emperor Penguin huddles A few months ago I was reading about how emperor penguins survive Antarctic winters. Temperature drops to -40°C, wind hits 120km/h, and somehow these birds make it through. Not because they're individually tough. Because they rotate. Cold penguins on the outside push inward. Warm ones from the center move out to rest. Nobody coordinates this. No penguin is in charge. It emerges from one simple rule: if you're cold, push in. If you're warm, you'll get pushed out eventually. I couldn't stop thinking about this. I was working on a service mesh at the time and dealing with the usual problem — one slow server quietly dragging down the whole cluster. Round robin doesn't care. Least connections helps but not always. Weighted approaches need manual tuning that goes stale immediately. The penguin thing kept nagging at me. What if servers had a "temperature"? What if hot servers rotated out to rest? That's HuddleCluster. The basic structure Two rings: Inner ring (deque): Active servers. Requests go to them round-robin. Simple, fair, zero overhead for normal traffic. Outer ring (min-heap): Resting servers. Keyed by temperature — coolest server sits at the top, ready to rotate back in first. When a server in the inner ring runs hot past a threshold, it moves out. When an outer ring server cools down, it comes back in. That's the entire rotation logic. About 50 lines of Python. What is "temperature"? This took me a while to get right. My first attempt was just raw latency. That was bad. A server handling one slow database query looks terrible even when it's completely healthy. I needed something more composed. Current formula: pythontemperature = EMA( 0.7 * relative_latency_anomaly + 0.1 * cpu_score + 0.1 * memory_score + 0.1 * (error_rate + connection_score) ) Three decisions here worth explaining. EMA over simple moving average EMA weights recent measurements more heavily. If a server just had a bad spike but recovere
A bot on Polymarket quietly extracted $32k in near risk-free profits by sniping “Will Company XYZ Beat Earnings?” markets. It waits for the official release, then instantly buys the winning side. Many limit orders from retail traders remain uncancelled, creating a post-announcement arbitrage window. Two developers decided to challenge it. Here’s what they learned while trying to build a faster version. Infrastructure Choices Location : Polymarket’s CLOB runs in AWS eu-west-2 (London). They deployed from Ireland (eu-west-1, Dublin) — the closest realistic option without IP tricks. UK IPs are blocked. Language : Rust for type safety and speed. The author notes you can achieve competitive latency in Python if you strip unnecessary network calls. Key Warning : Avoid the official Polymarket SDKs for ultra-low latency. They include helpful but slow pre-trade checks. Build lean custom clients. The Data Feed Challenge (The Real Bottleneck) The critical edge is getting earnings announcements faster than competitors. Source Performance Verdict Scraping Newswires Too slow Failed Benzinga Low-Latency Slower than manual clicking Failed Paid ultrafast feed ~500ms after release Still too slow EDGAR Consistently slower than newswires Backup only Even at 500ms, the order book was already swept by faster bots. The top players are likely using extremely expensive dedicated feeds or custom setups. Technical Lessons Learned Network > Code Most latency lives in the network round-trip, not in language choice. Optimize transport first. Custom Execution Layer Skip heavy SDK abstractions. Direct signed orders with minimal validation. Post-Event Sniping Logic Monitor newswire feeds aggressively Parse EPS vs. estimate instantly Place aggressive limit/market orders on the winning side Handle cases with ambiguity (multiple interpretations of “beat”) Reality Check They made some wins during EPS ambiguity or when faster bots hit size limits, but never won on pure speed against the leader. Why This
SpaceX's IPO underwriters maxed out their share purchases, adding to an already historic amount of money raised.
Today, I’m talking with Adam Bry, who is CEO of Skydio, the leading US maker of autonomous drones. Before we recorded this episode, I actually got to remotely operate one of Skydio’s drones in the Bay Area from Adam’s laptop in our podcast studio in New York and fly an indoor drone around our office. […]
ArrowJS, developed by Justin Schroeder, is a reactive UI library that has reached its 1.0 release after three years in development. It utilizes core web technologies, avoids JSX and compilers. Notable features include an optional WASM sandbox for executing untrusted code. The framework's minimalism is highlighted by its reliance on three main functions: reactive, html, and component. By Daniel Curtis
GitHub热门项目 | An extensible, state-of-the-art framework for columnar compression, and the fastest FOSS columnar file format. Formerly at @spiraldb, now an Incubation Stage project at LFAI&Data, part of the Linux Foundation. | Stars: 3,015 | 21 stars this week | 语言: Rust
GitHub热门项目 | A cross-platform, safe, pure-Rust graphics API. | Stars: 17,373 | 12 stars today | 语言: Rust
GitHub热门项目 | Cross-platform GUI written in Rust using ADB to debloat non-rooted Android devices. Improve your privacy, the security and battery life of your device. | Stars: 6,929 | 101 stars today | 语言: Rust
DevOps has always evolved with technology. Cloud changed how teams manage infrastructure. Containers changed how applications are deployed. CI/CD changed how software is released. Observability changed how teams monitor systems. Now AI is starting to change DevOps again. The next stage of DevOps is not only automation. It is intelligence. * DevOps Was Built on Automation * Automation is one of the strongest foundations of DevOps. DevOps teams automate: • Builds • Tests • Deployments • Infrastructure provisioning • Monitoring alerts • Rollbacks • Scaling • Security checks This has helped teams deliver software faster and more reliably. But most automation still works through fixed rules. For example: if CPU crosses a threshold, send an alert. If a build passes, deploy to staging. If a container fails, restart it. This works well for known situations. But modern systems are more complex. Microservices, cloud platforms, Kubernetes, APIs, databases, queues, and third-party dependencies create huge amounts of operational data. When something goes wrong, fixed rules are not always enough. * Why Intelligence Matters * Modern DevOps teams do not just need more automation. They need better understanding. AI can help teams identify patterns, detect unusual behavior, summarize logs, group related alerts, and suggest possible causes during incidents. This is where AIOps becomes important. AIOps means using AI for IT operations. It helps DevOps and SRE teams move from reactive operations to smarter operations. Instead of only asking, “What alert fired?” teams can start asking: • What changed recently? • Which services are aff ected? • Are these alerts connected? • Is this behavior unusual? • Has this happened before? • What is the likely root cause? This does not mean AI will replace DevOps engineers. It means AI can support engineers with faster insights. * What This Means for DevOps Engineers * DevOps engineers should pay attention to AI because their role is evolving. Traditi
Hey all! I have this thought in mind that we should all come together as a community to celebrate...
In April, for the first time ever, an Earth observation satellite found what it was looking for, all on its own.
Fox has announced that it's acquiring Roku outright, in a deal that values the streaming company at $22 billion. Once the deal is complete, Fox content will be promoted more heavily than before on Roku streamers and smart TVs. The deal will see Fox's TV networks and Tubi streamer combine with Roku's network of streaming […]
Check this out: i run four monetization channels side by side. Sponsored posts, display ads, YouTube ad revenue, and affiliate links. After eighteen months of tracking every dollar in a spreadsheet I built myself, I can tell you with brutal honesty: affiliate income is the only one that scales without me having to constantly produce more content or chase the next brand deal. But the math only works if you pick the right program. Most affiliates I know are promoting garbage with terrible retention, and they have no idea they're burning their audience's trust for a $9 one-time payout. Let me walk you through how I evaluate affiliate programs, what I've learned from running real funnels, and why the AI API category has quietly become the most lucrative vertical for tech creators in 2026. My Monetization Stack After 18 Months of Testing Here's a snapshot of my monthly revenue from a tech newsletter with around 34,000 subscribers and a YouTube channel sitting at 88,000 subscribers: Sponsored posts: $2,100 per placement, but I can only land maybe 2-3 per month without annoying my list Display ads: $1,800 per month from Mediavine, but this number barely moves regardless of how hard I work YouTube ad revenue: $2,400 per month, capped by watch time and RPMs Affiliate income: $6,800 per month, and it grows every single month even when I publish nothing That last number is what got my attention. Affiliate income compounds. When I published a tutorial in February recommending a tool, that single piece of content still earned me $340 in May because users stayed subscribed. No other channel behaves like that. No other channel lets a piece of content from four months ago keep paying you. But here's the catch that took me a while to figure out: not all affiliate programs are built the same way. And the difference between a good program and a bad one can be 10x in lifetime earnings per referred user. # # How I Score an Affiliate Program (The Growth Hacker Scorecard) Before I promote
The UK government is introducing a ban on social media for children and a minimum age for some chatbots in an attempt to shield young people from dangerous corners of the web.