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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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Vibe coding has turned senior devs into ‘AI babysitters,’ but they say it’s worth it

Fastly says95% of developersspend extra time fixing AI-written code. Senior engineers take the brunt. That overhead has even spawned a new gig: “vibe code cleanup specialist.” (Yes, seriously.) As teams lean harder on AI tools, reliability and security start to slide—unless someone steps in. The re.. read more  

Vibe coding has turned senior devs into ‘AI babysitters,’ but they say it’s worth it
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Understanding LLMs: Insights from Mechanistic Interpretability

LLMs generate text by predicting the next word using attention to capture context and MLP layers to store learned patterns. Mechanistic interpretability shows these models build circuits of attention and features, and tools like sparse autoencoders and attribution graphs help unpack superposition, r.. read more  

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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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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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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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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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Building Agents for Small Language Models: A Deep Dive into Lightweight AI

Agent engineering with **small language models (SLMs)**—anywhere from 270M to 32B parameters—calls for a different playbook. Think tight prompts, offloaded logic, clean I/O, and systems that don’t fall apart when things go sideways. The newer stack—**GGUF** + **llama.cpp**—lets these agents run loc.. 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
Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.