From check-in to takeoff, modern airports rely on a complex network of integrated IT systems to ensure efficiency, safety, and smooth operations. We’ve visualized this in a new diagram, highlighting key components like: ✅ AODB (Airport Operational Database) ✅ Passenger & baggage handling systems ✅ A..

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..

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...

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 ..

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...

GenAI and Playwright MCP are shaking up test automation. Think natural language scripts and real-time adaptability, kicking flaky tests to the curb.But watch your step:security risks lurk, server juggling causes headaches, and dynamic UIs refuse to play nice...

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..

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...

Reinforcement Learningfine-tunes large language models for better performance by adapting outputs based on structured feedback. Scaling RL for LLMs faces resource challenges due to massive computation, model sizes, and engineering problems like GPU idle time. Meta's LlamaRL is a PyTorch-based asynch..

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..