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Factors like the short interval between the two powerful quakes and different types of soil led to some structures collapsing while others stayed standing.
After weeks of negotiations, the White House permitted Anthropic to restore access to its most advanced AI model for a select group of US companies and government agencies.
The Post-Mortem Nobody Learns From I've sat through hundreds of post-mortems. Most follow the same pattern: something breaks, someone writes a Google Doc, we have a meeting, we list action items, nobody follows up, the same thing happens again in 3 months. Here's how to break the cycle. The Blameless Culture Trap "Blameless" doesn't mean "actionless." The biggest failure mode I see is teams that use blameless culture as an excuse to avoid accountability. Blameless means: we don't punish the person who pushed the bad deploy. Blameless does NOT mean: nobody is responsible for fixing the systemic issue. My Post-Mortem Template # Incident: [SERVICE] [SYMPTOM] on [DATE] ## Impact - Duration: X minutes - Users affected: N - Revenue impact: $X - SLO budget consumed: X% ## Timeline (UTC) - HH:MM - First alert fired - HH:MM - On-call acknowledged - HH:MM - Root cause identified - HH:MM - Fix deployed - HH:MM - Service recovered - HH:MM - All-clear declared ## Root Cause [2-3 sentences. Technical but readable.] ## Contributing Factors 1. [Factor that made the incident possible] 2. [Factor that made detection slow] 3. [Factor that made resolution slow] ## What Went Well - [Something that worked] - [Something that helped] ## What Went Wrong - [Process failure] - [Technical gap] ## Action Items | Action | Owner | Priority | Due Date | Status | |--------|-------|----------|----------|--------| | ... | ... | P1/P2/P3 | ... | Open | ## Lessons Learned [1-2 paragraphs of genuine insight] The Action Item Problem Action items from post-mortems have a 30% completion rate industry-wide. That's terrible. Here's why: Too many items (I've seen post-mortems with 15 action items) No clear ownership No deadline No follow-up mechanism Competing with feature work The Fix: Three Rules Rule 1: Maximum 3 action items per post-mortem. If you can't narrow it to 3, you haven't identified the real problems. Rule 2: Every action item gets a JIRA ticket linked to the next sprint. Not "someday." Not "bac
NYT shifts OpenAI/Microsoft copyright claims after SCOTUS ruling against Sony.
The asteroid will be visible for several nights from different parts of the world. We’ll tell you when and where to look, and what equipment you’ll need to spot it.
It's "an exciting advance in efforts to restock the antibiotic arsenal."
The White House asked OpenAI to delay the rollout of its GPT-5.6 AI models, two weeks after Anthropic had to take its most advanced AI models offline.
GitHub joined the United Nations Development Programme in Ghana to explore how open source governance can support one of West Africa's most ambitious digital reform efforts. The post GitHub and UNDP team up to advance development priorities in Ghana with open source appeared first on The GitHub Blog .
Vercel has released Eve, an open-source framework for building, deploying, and operating AI agents in production. The framework uses a filesystem-based project structure to organize agent instructions, tools, skills, subagents, communication channels, and scheduled tasks, enabling developers to define agent behavior while reducing the amount of supporting infrastructure they need to implement. By Daniel Dominguez
TikTok may be working to become the app that people use for most of their digital activities.
Rock weathering may release or draw down carbon dioxide—it depends on the rock.
A scuffle between stan account Club Chalamet and another Heated Rivalry die-hard shines a light on how parasocial fans are a publicist’s greatest asset—and liability.
Michael Webster discusses the rise of headless AI agents and their impact on software delivery pipelines. He shares how massive, AI-generated pull requests create a severe bottleneck for human reviewers and introduce persistent technical debt. Learn how engineering leaders can leverage test impact analysis and automated validation pipelines to verify agentic output without sacrificing stability. By Michael Webster
The northern lights could mean lights out for the infrastructure we rely on.
Diagrid has announced the release of Dapr 1.18, introducing what it calls Verifiable Execution, a new set of capabilities designed to bring cryptographic trust, provenance, and tamper-evident execution records to distributed applications and AI agents. By Craig Risi
What began as a “flamingo revolution” to protest the $1.4 billion development on Sazan Island has spiraled into mass protests against a ruling party that thousands now want out.
Qatar has become the place where FIFA experiments with the next generation of football technology. The results are already visible across this year’s World Cup.
Lately, it feels like my feed is completely flooded with "Become an AI/ML Engineer in 2 Hours!" crash courses and quick certificates promising a golden fast-track into machine learning roles. But let’s be completely real for a second: there are no tutorial shortcuts here. The more I dive into actual system architecture and cloud infrastructure, the more obvious it becomes: machine learning isn't a standalone magic trick. It's built entirely on rock-solid Computer Science, efficient data structures, and heavy-duty software engineering. Software Engineering First, AI Second If you can’t build or scale a reliable backend, manage data pipelines, or understand low-level underlying system logic, you simply cannot scale an AI model in production. Prompt engineering is cool for prototyping, but production-level ML requires real, foundational engineering skills. You have to learn how to be a great software engineer first. Looking Past the Hype (A Solid Structural Roadmap) If you actually want to look past the superficial fluff and understand how real data workloads, model deployments, and ML infrastructure fit into a cloud environment, I found an incredibly solid, structured resource. Instead of hand-waving past the hard parts, Microsoft Learn has an official, step-by-step breakdown on Azure AI and Machine Learning Fundamentals. It actually goes into the core architectural principles and shows you what real cloud-scale infrastructure looks like. Whether you are trying to map out your summer learning roadmap or just want to understand the actual systems backing these models, I highly recommend checking it out. Here is the structured entry point if you want to skip the shortcuts and dive into the real infrastructure: 🔗 Official Azure Machine Learning Technical Hub What are your thoughts? Are you seeing the same "AI shortcut" hype on your feeds, or are people finally starting to focus back on core system fundamentals? Let's discuss in the comments!
Anthropic's critics argue it's rapidly accumulating power. The company says that's what responsible AI development looks like.
Amazon-owned MGM Studios’ decision to drop the OpenAI movie is just part of AI and film industries becoming increasingly intertwined. On Uncanny Valley, we take a look at where this is all headed.