Amazon Bedrock detects AI-generated phishing by analyzing behavioral patterns rather than grammar. Its multi-stage pipeline includes authentication checks, AI model analysis with guardrails, multi-factor risk scoring, and automated routing.
AI-generated phishing messages are grammatically perfect, contextually accurate, and personalized, evading traditional filters.
Amazon Bedrock uses foundation models to detect behavioral anomalies, contextual inconsistencies, and impersonation patterns.
In this post, we share best practices for reliable multi-turn RL training. We cover how to build a training environment you can trust, set up an external evaluation, design a reward aligned with the end task, manage what changes once the agent runs for multiple turns, and monitor the metrics that tell you when to iterate.
Build a sandboxed or simulated environment for reproducibility and representativeness.
Set up an external evaluation before training to avoid reward hacking.
Learn why coding agent bills spiral out of control — and how to trace, compare, and govern spend across Claude Code, Cursor, Copilot, and more in one place.
Coding agent usage exploded in early 2026, leading to skyrocketing bills with no unified cost visibility.
Fragmentation across tools (Claude Code, Cursor, Copilot) makes it impossible to compare spend without a common tracking model.
Mete Polat shares eight mental models for working with AI, ranging from practical prompting tips to meta-level insights about the AI industry. Key ideas include upfront alignment, rewinding over steering, giving AI the same tools, using bad outputs as taste signals, preferring visual input, building a reference library, design as antidote to slop, and adversarial review between LLMs.
Invest upfront in initial prompts and context to avoid later corrections.
Rewinding to a clean state is often more effective than iterative steering.
Bank of England deputy governor Sarah Breeden said at the ECB's annual Sintra Forum that existing regulatory frameworks may not be adequate for AI in commerce and trading. She highlighted that AI agents could autonomously devise trading strategies and amplify volatility, and regulators are exploring 'kill switches' to halt trading if AI models go rogue. The Bank is also collaborating with the BIS and Bundesbank on simulations, while MPs have criticized the current 'wait-and-see approach'.
Deputy governor calls for updated governance frameworks as AI agents become more autonomous in trading and commerce.
AI agents' similar responses to prompts could amplify market volatility and cause misalignment problems.
A review of two books on AI, tracing the history from Claude Shannon's 1950 letter-guessing experiment to modern chatbots like ChatGPT, discussing their capabilities, limitations, and societal implications.
Shannon's experiment laid the foundation for AI's probabilistic prediction methods
ChatGPT gained rapid popularity since its release in 2022, sparking widespread debate
A practical, citable knowledge base for deploying, operating, and optimising GPU clusters, from the physical datacentre and the InfiniBand fabric up through Kubernetes, Slurm and Ray, distributed training and reinforcement-learning post-training, and LLM inference serving at scale. Covers the full NVIDIA range: Ampere, Hopper, and Blackwell datacenter GPUs, RTX consumer and workstation cards, and DGX systems (including DGX Spark). Current to mid-2026.
Practical reference for engineers operating GPU clusters across hardware, orchestration, training, and serving.
Covers NVIDIA hardware from Ampere to Blackwell Ultra, with operational differences.
Subquadratic's SubQ 1.1 model uses sparse attention to handle up to 12 million tokens, offering massive compute savings. After initial skepticism, the company has published benchmarks and is now working with enterprise design partners. It also hints at future non-attention architectures.
Subquadratic's SubQ 1.1 achieves near-perfect long-context retrieval up to 12M tokens.
The model uses 64x less compute at 1M tokens compared to dense attention.
HealthChain is an open-source Python SDK that simplifies integrating AI models with healthcare EHR systems. It provides type-safe FHIR resources, real-time EHR connectivity, and production-ready deployment tools, enabling developers and researchers to move AI models from experiments to clinical use quickly.
Aggregates patient data from multiple FHIR sources with NLP and deduplication
Deploys any trained model as a production FHIR endpoint with OAuth2
At the AI Engineer World's Fair, Geoffrey Litt introduced the concept 'Understand to participate,' emphasizing that developers must deeply understand code changes made by coding agents to avoid cognitive debt and maintain creative engagement.
Developers need deep understanding of code changes when working with coding agents to avoid cognitive debt.
The depth of code understanding determines one's ability to actively participate in the creative process.
AI adoption is starting to translate into real-world returns. But as efforts accelerate, many organizations are running into the same problem: systems that are too expensive, too slow, and can’t scale. Among companies with disconnected data environments, 67% cited data storage, movement, and duplication as the largest recurring AI cost. This article explores three infrastructure considerations: delivering infrastructure at agentic speeds, streamlining data, and adopting infrastructure built for AI scale.
67% of companies with disconnected data environments cite data storage, movement, and duplication as the largest recurring AI cost.
Unified data architecture reduces that figure to just over half.
TV Time, the popular TV tracking and community app, is shutting down after July 15, 2026, due to high operating costs and a strategic shift towards AI.
TV Time will cease operations on July 15, 2026.
The company cites platform costs and a pivot to AI as reasons.
Axon is a privacy-focused AI that discovers recurring behavioral patterns with specific dates and data, featuring editable memory and two thinking modes.
Axon identifies patterns like attention shifts or decision delays with date evidence.
Offers Analyst and Reflector modes for rational decisions and self-understanding.