AI News
Curated AI updates translated into engineering impact: cost, latency, reliability, security, and what to try next.
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Evaluation Is Becoming the Real AI Differentiator
Better models are no longer enough. This article explains why evaluation is emerging as the key differentiator in production AI systems, and how teams that invest in measurement outperform those that rely on intuition.
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Why AI Demos Scale Poorly Into Real Systems
What works in an AI demo often fails in production. This article analyzes the structural gap between demos and real systems, and why reliability, cost, and evaluation become dominant only after scale.
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Why JSON Output Alone Does Not Make AI Safe
JSON schemas help control AI output format, but they do not guarantee correctness or safety. This article explains the limits of structured output and what additional safeguards are required in production systems.
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Chunking Is Still the #1 Bottleneck in RAG
Despite advances in models and embeddings, chunking remains the weakest link in most RAG systems. This article explains why chunking dominates retrieval quality and how poor chunk design quietly undermines production reliability.
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Why Most RAG Systems Fail in Production
RAG promises grounded AI, yet many production systems deliver inconsistent or unreliable results. This article analyzes why RAG fails outside demos and how architectural blind spots—not model quality—are usually responsible.
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The Hidden Cost of Bigger Context Windows
Bigger context windows feel like a clear upgrade, but they often shift problems rather than solve them. This article explains the hidden costs of large contexts and why more tokens can quietly degrade system performance.
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Why Prompt Improvements Plateau Faster Than You Expect
Prompting often feels like the fastest way to improve AI output, but its gains plateau sooner than most teams expect. This article explains the structural reasons behind that plateau and how to move beyond prompt-level optimization.
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Why Every Smarter Model Also Increases System Risk
Smarter language models do not automatically make systems more reliable. This article explains how increased model capability can introduce new risks—and what engineers should consider before upgrading.
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The Week the Chatbot Died: Inside the $1.25T Leap into Agentic Space
The first week of February 2026 has signaled a definitive end to the era of the chatbot. For years, we engaged with large language models in a back-and-forth, turn-based fashion—essentially a form of sophisticated autocomplete. This week, the industry pivoted toward agency: autonomous systems capable of executing multi-day projects, managing complex software builds, and even joining lobster-themed social networks where they develop their own emergent cultures.
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The Intelligence Infrastructure Era: 7 Surprising Takeaways from the AI Frontier
AI has crossed a threshold in early 2026, moving from experimental capability to foundational infrastructure. This article analyzes seven key shifts shaping how intelligence is built, governed, and deployed at scale.