Channel
Digests from IBM Technology
IBM unveiled a new dual processor combining z/OS and Arm for mainframes. NVIDIA's strategy involves acquiring Hugging Face to dominate open AI, while Z.ai revealed GLM-5.3-Flash.
Read article
Cedric Clyburn explains why LLM benchmarks don’t reflect real-world performance in AI applications and agents. Learn how to evaluate accuracy, latency, and cost to build reliable AI systems at scale.
Read article
GLM-5.3 demonstrates advanced cyber capabilities, scoring 84.5% on CyberGym for vulnerability discovery and validation. The discussion also covers 'context bombing,' a defensive technique using malicious prompts to shut down attacking AI agents.
Read article
AI models alone aren’t what makes systems powerful. Martin Keen explains the difference between AI models and agentic harness components like tools, memory, and loops.
Read article
Good code must fit the system's architecture, not just run. AI coding agents need repo awareness, planning, and respect for established patterns to avoid creating 'fast chaos.'
Read article
A Digital Librarian AI Agent solves the 'what vs. why' data problem by connecting SQL databases (for hard numbers) and vector databases (for policy context). This agent uses multi-step workflows to deliver grounded answers, transforming siloed data into a reasoning engine.
Read article
AI infrastructure is shifting from model ownership to orchestration and routing. Learn how Stripe, IBM, and Ramp are redefining AI compute decisions, governance, and cost management.
Read article
Should AI agents replace business rules? Martin Keen explains the difference between deterministic business rules and probabilistic AI agents in enterprise decision making.
Read article
Cybersecurity leaders struggle with AI deployment, leading to decision fatigue. Experts advise starting with repetitive tasks and red teaming while guarding against sophisticated threats like Ghostjacking in trusted logs.
Read article
Jeff Crume explains the AI Security Trilemma: balancing Smartness, Speed, and Security. He details how capability increases attack surface and introduces architectural patterns like AI security proxies to manage risk.
Read article
Martin Keen explores how Rapid Application Development (RAD) principles map onto AI coding. Learn why formal specifications are crucial for moving from fast prototypes to secure, production-ready software.
Read article
Grant Miller breaks down five proven patterns for integrating AI agents with tools, detailing the progression from simple APIs to secure token-based architectures using OAuth flows, MCP, and vaults.
Read article
The AI landscape is rapidly evolving, marked by IBM's B300 cluster partnership, Meta's local Muse Glimmer model, and OpenAI's advanced Astra capabilities. We explore the shift to edge computing and industrial-scale infrastructure.
Read article
Learn how AI and Data Science combine into a single pipeline. We break down complex concepts like RAG, embeddings, and data cleansing using periodic tables to show how robust enterprise AI applications are built.
Read article
Experts discuss the OWASP LLM Top 10, noting that excessive agency is the biggest risk. They advise treating AI agents as privileged identities and operationalizing CISA SBOM data for true cyber resilience.
Read article
Context Engineering is the critical practice of structuring information passed to LLMs and AI agents. Learn how optimizing context, rather than just writing better prompts, improves reliability and reduces hallucinations in complex AI systems.
Read article
Martin Keen and Bri Kopecki explain five best practices for creating reliable, secure, and effective AI agent skills. Learn how to improve triggering, reduce context usage, add deterministic logic, and avoid common pitfalls.
Read article
Chunkless RAG uses document structure and AI agents instead of relying only on chunking and similarity search. Learn when structure-aware retrieval improves accuracy, context, and document understanding.
Read article
Experts discuss Anthropic/Meta sandbox breaches, mandatory EU AI labeling for deepfakes, and how DeepSeek V4-Flash is commoditizing the market by drastically lowering inference costs.
Read article
Model collapse describes how AI models trained on synthetic data can forget reality. This degenerative process requires solutions like RAG and human feedback to keep AI grounded in truth.
Read article