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GitHub Copilot on autopilot as community complaints persist

GitHub's biggest debates right now? Whether to shut down AI-generated "noise" fromCopilot—stuff like auto-written issues and code reviews. No clear answers from GitHub yet. Frustration is piling up. Some devs are ditching the platform altogether, shifting their projects toCodebergor spinning upself-.. read more  

GitHub Copilot on autopilot as community complaints persist
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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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The LinkedIn Generative AI Application Tech Stack: Extending to Build AI Agents

LinkedIn tore down its GenAI stack and rebuilt it for scale—with agents, not monoliths. The new setup leans on distributed, gRPC-powered systems. Central skill registry? Check. Message-driven orchestration? Yep. It’s all about pluggable parts that play nice together. They added sync and async modes.. read more  

The LinkedIn Generative AI Application Tech Stack: Extending to Build AI Agents
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Guardians of the Agents 

A new static verification framework wants to make runtime safeguards look lazy. It slaps **mathematical safety proofs** onto LLM-generated workflows *before* they run—no more crossing fingers at execution time. The setup decouples **code from data**, then runs checks with tools like **CodeQL** and .. 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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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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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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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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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
AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.