The Fallback That Never Fires

<p>Working with local LLMs via Ollama is great for privacy, but it introduces a reliability bottleneck: local compute resources aren't always available or fast enough for complex

<p>Four agents entered an infinite loop in November 2025. They ran for 11 days. The bill was $47,000. Nobody noticed until it was over.</p> <p>The team was running a market

<p>You've heard of DevOps. You've heard of AIOps. You've probably heard of MLOps.</p> <p>But there's a category that doesn't exist yet — and it's the one the AI industry needs

<p><em>The AWS Reliability Files, Part 1</em></p> <p>I told an AI a true fact about AWS. Then I had a second AI confidently insist it was wrong. Within one exchange, the first

<p>Your AI system isn't going to crash. It's going to drift.</p> <p>A recommendation engine making 1.4 model calls instead of 1. A retrieval pipeline fetching 5 chunks instead of

<!-- SC_OFF --><div class="md"><p><strong>Submitted by:</strong> Adam Kruger <strong>Date:</strong> March 23, 2026 <strong>Models Solved:</strong> 3/3 (M1, M2, M3) + Warmup</p>

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<blockquote> <p>A controlled study finds that data distribution, not just volume, dictates LLM capability. Benchmark-aligned training inflates scores but creates narrow, brittle