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Build an AI-powered website assistant with Amazon Bedrock

AWS spun up a serverless RAG-based support assistant usingAmazon BedrockandBedrock Knowledge Bases. It pulls in docs via a web crawler and S3, then stuffs embeddings intoAmazon OpenSearch Serverless. Access is role-aware, locked down withCognito. Everything spins up clean withAWS CDK... read more  

Build an AI-powered website assistant with Amazon Bedrock
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Agentic AI, MCP, and spec-driven development: Top blog posts of 2025

AI speeds up dev - but it’s a double-edged keyboard. It sneaks in subtle bugs and brittle logic that break under pressure. To keep things sane, teams are fighting back withguardrail patterns,AI-aware linters, andtest suites hardened for hallucinated code... read more  

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Towards Generalizable and Efficient Large-Scale Generative Recommenders

Authors discuss their approach to scaling generative recommendation models from O(1M) to O(1B) parameters for Netflix tasks, improving training stability, computational efficiency, and evaluation methodology. They address challenges in alignment, cold-start adaptation, and deployment, proposing syst.. read more  

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Where good ideas come from (for coding agents)

A new way to build agents treats prompting ascontext navigation, steering the LLM through ideas like a pilot, not tossing it prompts and hoping for magic. It maps neatly onto Steven Johnson’s seven patterns of innovation. For coding agents to actually pull their weight, users need to bring more than.. read more  

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Weaponizing the AWS CLI for Persistence

Researchers pulled off a slick persistence trick usingAWS CLI aliases. They chained dynamic alias renaming with command execution to swipe credentials, without breaking expected CLI behavior. No red flags. Perfect fit forautomated environmentslike CI/CD pipelines. Backdoors, no AWS CLI tampering req.. read more  

Weaponizing the AWS CLI for Persistence
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Cloud Workload Threats - Runtime Attacks in 2026

Cloud-native breaches keep slipping through the cracks, not because no one’s watching, but because they’re watching the wrong things. Static checks and posture tools can’t catch what happens in motion. That’s where most attacks live now: at runtime. Think app-layer exploits, poisoned dependencies, s.. read more  

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21 Lessons From 14 Years at Google

A seasoned Google engineer drops 21 sharp principles for scaling engineering beyond just writing code. Think:clarity beats cleverness,users over egos,alignment over being “right.”The core message? Build systems humans can work with - especially under stress. Favorites: kill pointless work, treat pro.. read more  

21 Lessons From 14 Years at Google
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Azure Hybrid Benefit Audit Guide: Avoid the $50K Licensing Mistake (2025)

Azure just tightened the screws on Hybrid Benefit. Use it without the rightSoftware Assurance, botch yourlicense-to-core mapping, or skipdecommissioning proof, and you’re staring down $50K+ in penalties. To help dodge that landmine, Microsoft dropped a new guide. It covers pre-migration checks, audi.. read more  

Azure Hybrid Benefit Audit Guide: Avoid the $50K Licensing Mistake (2025)
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Terraform governing with OPA

When managing infrastructure with Terraform, enforcing tagging standards, instance type restrictions, preventing public exposure, enforcing regions, and other best practices are essential with Open Policy Agent (OPA). OPA evaluates Terraform plans before apply to ensure compliance with organization'.. read more  

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2025's most influential projects according to GitHub

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Universe 2025 highlighted a shift toward mature, developer-first open source projects that favor usability, sustainability, and real-world adoption over hype. From backend platforms and release tooling to browsers, graphics engines, and security baselines, the standout projects all share one trait: they are being actively used, maintained, and pushed forward by communities that know exactly what problems they are solving.

Open Source at Full Throttle: The Projects Setting the Pace in 2025
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.