Explore evidence-led notes on where AI should run, what agents need after the demonstration, and how technical mechanisms change workflow economics. Claims are bounded to their workload and evidence.
A synthetic probability model exposes why one-step accuracy cannot establish reliable long-workflow completion. The practical method replaces that toy arithmetic with repeated end-to-end tests covering state, permissions, tools, checkpoints, severe failures, intervention, latency, cost, recovery, and the exact authority an AI workflow may receive.
Read MoreDiagnose LLM serving by separating prefill, decode, queueing, batching, speculative decoding, and external work. The inference measurement record keeps phase metrics tied to representative arrival patterns, tail latency, failures, and accepted workflow goodput so a local optimization cannot masquerade as an operating improvement.
Read MoreSeparate context windows, key-value cache, prefix caching, and persistent application memory before choosing an LLM design. The context-path test connects evidence position, token-weighted reuse, cache capacity, concurrency, latency, retention, and accepted output instead of treating a larger advertised window as dependable memory.
Read MoreBuild an enterprise AI strategy around accepted workflows instead of one model family. These ten rules separate replaceable supplier facts from compounding workload, authority, evidence, and recovery assets, then turn reversibility, ownership, and change triggers into a practical funding screen for CTOs and boards.
Read MoreCompare managed API, self-hosted, and hybrid AI in currency per accepted completed workflow, not token price alone. This transparent ledger incorporates acceptance, retries, exceptions, human review, latency, caching, idle capacity, operating labor, recovery, and demand sensitivity while keeping savings, payback, and business value as unproven hypotheses.
Read MoreDecide whether an AI pilot should advance, remain limited, or stop by using a failure-control-evidence ledger. The gate connects evaluations, permissions, observability, injection and leakage tests, human checkpoints, recovery, agency, and cost controls to named owners, dated evidence, and hard failure conditions.
Read MoreUse a benchmark card to decide when measurements from a private 27B language-model environment are complete, comparable, reproducible, and relevant to architecture. The method records workload, hardware, model representation, cache state, concurrency, acceptance, units, matched baselines, raw outputs, and failures without publishing unsupported performance claims.
Read MoreUse this nine-row workload-placement matrix to decide when one enterprise AI workload belongs on a managed API, self-hosted infrastructure, or a deliberately bounded hybrid path. It turns data, capability, latency, demand, operations, exit cost, and control evidence into hard gates instead of a single weighted score.
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