Mar 4 · 6 min read · What hidden assumption breaks when scale looks like a solution? As a Principal Systems Engineer, the useful way to start is not with definitions but with a failure mode: long conversations, multipart documents, or mixed media prompts that used to beh...
Join discussionMar 3 · 6 min read · Peeling the illusion: where engineers miss the real failure modes As a principal systems engineer, the goal here is to deconstruct the assumptions that turn architectural choices into brittle systems. The usual checklist-more parameters, a v...
Join discussionMar 3 · 6 min read · Choosing a model for a task is rarely the end of the story - it can be the start of subtle failures that only reveal themselves after integration and scale. Engineers expect a new model to improve accuracy, speed, or cost, but what often breaks is th...
Join discussionMar 2 · 6 min read · Where common assumptions about scale and reliability fail As a Principal Systems Engineer, the most revealing failures I see are not sudden outages but slow, deterministic degradations that reveal a mismatch between system assumptions and re...
Join discussionMar 2 · 5 min read · The dominant conversation around AI has long been about raw capability: bigger context windows, more parameters, broader pretraining. That narrative is shifting toward a different question - not which model can do the most things, but which model fit...
Join discussionMar 1 · 6 min read · Post-mortem: when the demo dazzles and the ship sinks. A team launches after a week of heady benchmarks and a demo that "blew stakeholders away." In production, latency spikes, hallucinations creep in, and the support queue fills with edge-cas...
Join discussionFeb 28 · 6 min read · As a Principal Systems Engineer charged with integrating large language systems into production, the most useful question is rarely "what does this model do?" and instead becomes "how does this model interact with the rest of the stack under realisti...
Join discussionFeb 24 · 6 min read · The immediate problem and why it matters Production AI systems routinely show a gap between expected quality and live performance. The symptoms are predictable: latency spikes, inconsistent outputs for identical prompts, unexpected hallucina...
Join discussionFeb 21 · 5 min read · Before: when model selection felt like guesswork Most engineering teams reach a fork where a promising prototype stalls: latency spikes, hallucinations creep in, or costs balloon as queries scale. That early optimism-driven by model names, b...
Join discussionFeb 20 · 5 min read · Then vs. Now: why model selection stopped being a numbers game The old assumption was simple: bigger models solve more problems. Teams bought access to the largest endpoint they could afford and tried to shoehorn every task into that single in...
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