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Poison everywhere: No output from your MCP server is safe

Anthropic's MCPmakes LLMs groove with real-world tools but leaves the backdoor wide open for mischief. Full-Schema Poisoning (FSP) waltzes across schema fields like it owns the place.ATPAsneaks in by twisting tool outputs, throwing off detection like a pro magicians’ misdirection. Keep your eye on t.. read more  

Poison everywhere: No output from your MCP server is safe
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Why Go is a good fit for agents

Gorules the realm of long-lived, concurrent agent tasks. Its lightning-fast goroutines and petite memory use make Node.js and Python look like clunky dinosaurs trudging through thick mud. And don't get started on itscancellation mechanism—seamless cancelation, zero drama... read more  

Why Go is a good fit for agents
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AI Runbooks for Google SecOps: Security Operations with Model Context Protocol

Google's MCP servers arm SecOps teams with direct control of security tools using LLMs.Now, analysts can skip the fluff and get straight to work—no middleman needed. The system ties runbooks to live data, offeringautomated, role-specific security measures. The result? A fusion of top-tier protocols .. read more  

AI Runbooks for Google SecOps: Security Operations with Model Context Protocol
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Vibe coding web frontend tests — from mocked to actual tests

Cursorwrestled with flaky tests, tangled in its over-reliance onXPath. A shift todata-testidfinally tamed the chaos. Though it tackled some UI tests, expired API tokens and timestamped transactions revealed its Achilles' heel... read more  

Vibe coding web frontend tests — from mocked to actual tests
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Disrupting malicious uses of AI: June 2025

OpenAI's June 2025 report, "Disrupting Malicious Uses of AI," is out. It highlights various cases where AI tools were exploited for deceptive activities, including social engineering, cyber espionage, and influence operations... read more  

Disrupting malicious uses of AI: June 2025
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GenAI Meets SLMs: A New Era for Edge Computing

SLMspower up edge computing with speed and privacy finesse. They master real-time decisions and steal the spotlight in cramped settings like telemedicine andsmart cities. On personal devices, they outdoLLMs—trimming the fat with model distillation and quantization. Equipped withONNXandMediaPipe, the.. read more  

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The AI 4-Shot Testing Flow

4-Shot Testing Flowfuses AI's lightning-fast knack for spotting issues with the human knack for sniffing out those sneaky, context-heavy bugs. Trim QA time and expenses. While AI tears through broad test execution, human testers sharpen the lens, snagging false positives/negatives before they slip t.. read more  

The AI 4-Shot Testing Flow
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What execs want to know about multi-agentic systems with AI

Lack of resources kills agent teamwork in Multi-Agent Systems (MAS); clear roles and protocols rule the roost—plus a dash of rigorous testing and good AI behavior.Ignore bias, and your MAS could accidentally nudge e-commerce into the murky waters of socio-economic unfairness. Cue reputation hits and.. read more  

What execs want to know about multi-agentic systems with AI
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Meta reportedly in talks to invest billions of dollars in Scale AI

Metawants a piece of the$10 billion pieat Scale AI, diving headfirst into the largest private AI funding circus yet.Scale AI'srevenue? Projected to rocket from last year’s $870M to$2 billionthis year, thanks to some beefy partnerships and serious AI model boot camps... read more  

Meta reportedly in talks to invest billions of dollars in Scale AI
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BenchmarkQED: Automated benchmarking of RAG systems

BenchmarkQEDtakes RAG benchmarking to another level. ImagineLazyGraphRAGsmashing through competition—even when wielding a hefty1M-tokencontext. The only hitch? It occasionally stumbles on direct relevance for local queries. But fear not,AutoQis in its corner, crafting a smorgasbord of synthetic quer.. read more  

At its core, Git records snapshots of state, not just file diffs. Every commit represents a complete, immutable view of the project at a point in time, identified by a cryptographic hash. This makes history reliable, auditable, and cheap to branch.

Git is distributed by design. Every clone contains the full repository history, which allows developers and automation systems to work offline, create branches freely, and synchronize changes without relying on a central server for every operation.

In modern cloud-native workflows, Git acts as the source of truth. Desired state is declared in Git, reviewed through pull requests, and promoted across environments by merging changes rather than applying ad-hoc commands. This is the foundation of GitOps.

Git does not deploy anything by itself. Its role is to capture intent, history, and collaboration, while other tools turn that intent into running systems.