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AgentHopper: An AI Virus

In the “Month of AI Bugs,” researchers poked deep and found prompt injection holes bad enough to run **arbitrary code** on major AI coding tools—**GitHub Copilot**, **Amazon Q**, and **AWS Kiro** all flinched. They didn’t stop at theory. They built **AgentHopper**, a proof-of-concept AI virus that .. read more  

AgentHopper: An AI Virus
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Introducing the MCP Registry

The new **Model Context Protocol (MCP) Registry** just dropped in preview. It’s a public, centralized hub for finding and sharing MCP servers—think phonebook, but for AI context APIs. It handles public and private subregistries, publishes OpenAPI specs so tooling can play nice, and bakes in communit.. read more  

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LLM Evaluation: Practical Tips at Booking.com

Booking.com built Judge-LLM, a framework where strong LLMs evaluate other models against a carefully curated golden dataset. Clear metric definitions, rigorous annotation, and iterative prompt engineering make evaluations more scalable and consistent than relying solely on humans. **The takeaway**:.. read more  

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PostgreSQL maintenance without superuser

PostgreSQL’s moving in on superusers. As of recent releases—starting way back in v9.6 and maturing through PostgreSQL 18 (coming 2025)—there are now **15+ built-in admin roles**. No need to hand out superuser just to get things done. These roles cover the ops spectrum: monitoring, backups, fil.. read more  

PostgreSQL maintenance without superuser
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Accelerate serverless testing with LocalStack integration in VS Code IDE

The AWS Toolkit for VS Code now hooks straight into **LocalStack**. Run full end-to-end tests for **serverless workflows**—Lambda, SQS, EventBridge, the whole crew—without bouncing between tools or writing boilerplate. Just deploy to LocalStack from the IDE using the **AWS SAM CLI**. It feels like .. read more  

Accelerate serverless testing with LocalStack integration in VS Code IDE
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Scaling Prometheus: Managing 80M Metrics Smoothly

Flipkart ditched its creakyStatsD + InfluxDBstack for afederated Prometheussetup—built to handle 80M+ time-series metrics without choking. The move leaned intopull-based collection,PromQL's firepower, andhierarchical federationfor smarter aggregation and long-haul queries. Why it matters:Prometheus.. read more  

Scaling Prometheus: Managing 80M Metrics Smoothly
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Magical systems thinking

AI now writes over **25% of Google’s** and as much as **90% of Anthropic’s** code. That’s not a trend—it’s a regime change. Still, the mess in large public systems reminds us: clever analysis isn’t enough. Complex systems don’t behave; they misbehave. When the machines are churning out code, the .. read more  

Magical systems thinking
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Writing an operating system kernel from scratch

A barebonestime-sharing OS kernel, written inZig, running onRISC-V. It leans onOpenSBIfor console I/O and timer interrupts. Threads? Statically allocated, each running inuser mode (U-mode). The kernel stays insupervisor mode (S-mode), where it catchessystem callsandcontext switchesvia timer ticks. .. read more  

Writing an operating system kernel from scratch
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SLI Evolution Stages

A new SLI evolution model lays out a maturity roadmap—from rebranded latency/error metrics to ones that actually track business impact. It replaces shallow signals and pulls in the stuff that matters: how service failures hit user goals, tasks, and bottom lines... read more  

SLI Evolution Stages
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Introducing Budget Controls for AWS: Automatically Manage Your Cloud Costs

**Budget Controls for AWS** just got better. The open-source tool now reins in more than just EC2. It wrangles **RDS Aurora**, **SageMaker**, and **OpenSearch** too. Under the hood, it taps **AWS Budgets**, **AWS Config**, and **custom tags** to watch spend like a hawk. Hit a budget threshold? It c.. read more  

Introducing Budget Controls for AWS: Automatically Manage Your Cloud Costs
Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.