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All digests tagged IBM Technology
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The Memory Wall explains why faster chips don't guarantee faster AI. Learn how Arithmetic Intensity and the Roofline Model help diagnose if your LLM workload is compute-bound or memory-bound.
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Context engineering is crucial for AI agents. Learn about context rot and four failure modes—Poisoning, Distraction, Confusion, and Clash—and how to improve agent reliability by deliberately pruning context.
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Every generation thinks new technology will eliminate jobs, but history shows the pattern is consistent: tech eliminates tasks, and humans invent new jobs around what's left. AI is a tool for task automation, not job replacement.
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The biggest AI models require distributed inference because they exceed single GPU memory capacity. Learn how Data, Pipeline, Tensor, and Expert parallelism scale LLMs across multiple GPUs.
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DevOps requires using both IDEs and CLIs. The real challenge is 'context overload'—managing the friction across coding, testing, builds, and deployment pipelines.
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Learn how AI Agents combine specialized APIs and LLMs to analyze complex, real-time data, using the US Open tennis serve as a case study. This pattern divides labor between computation and reasoning.
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AI agents are not the revolution; they are the catalyst. The real impact is accelerating foundational changes in data, systems, and connectivity, forcing standardization and interoperability.
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AI success is shifting from token consumption to Valuemaxxing. Teams must measure outcomes—like deployments completed or rework avoided—rather than just AI usage volume.
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Learn six essential AI concepts—from LLMs to RAG and Agents—using simple human analogies. Understand how modern AI systems work by connecting the 'brain' to its tools.
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AI is shifting the focus of software reviews from line-by-line implementation details to validating business outcomes, requirements, and overall intent. This marks a major evolution in development workflows.
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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.
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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.
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Llama.cpp and vLLM are two key engines for local LLMs. Llama.cpp optimizes for consumer hardware using quantization and GGUF files, while vLLM focuses on production scale with continuous batching and paged attention.
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Martin Keen explains how LLM fine tuning compares to RAG, LoRA, and generative AI techniques, outlining when custom model training is still necessary in modern AI workflows.
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Top teams aren't just adopting AI; they are restructuring around it. Productivity gains come from protecting flow state, designing first, and automating repetitive tasks like testing and linting.
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