📰 Dev.to · Multigrid

A decision table by task, the break-even volume derived from labelled assumptions, and the three considerations that are not about cost at all.

What the classic n-gram metrics compute, the reporting traps that make published scores incomparable, and the cases where they still beat a model judge.

A streaming App Router route handler, the Edge and Node runtime trade-off stated precisely, and the buffering that silently kills streaming.

What it means for a scene to become weights, why rendering a NeRF frame costs a hundred million network evaluations, and what splatting changed by deleting the

What NER is as a task, what the published shared-task results actually show, the cost arithmetic against LLM extraction, and the hybrid that beats both.

How one vector space holds many languages, the language-bias failure that makes cross-lingual results look worse than they are, and how to build the language-pa

What an attacker can recover through an inference API alone, what the published results actually demonstrated, and which API surfaces widen the attack.

A testable definition of commoditisation, where switching costs actually live as opposed to where people assume they live, and what a new capability tier does t

The Nature result, the follow-ups that reversed its conclusion, and the follow-ups that reversed it back — with the experimental regime each one holds under.

The discipline behind deciding which customer record is the real one, in the four architectural styles, and why AI teams meet it whether or not they wanted to.

Twenty checks with a command and a pass condition each, covering access, delivery, structure and evidence — runnable in an afternoon.

What the llms.txt format actually specifies, why publishing it is not the same as anything reading it, and the one-line log query that tells you which is happen

A closed schema, a prompt that demands evidence spans, and a validation pass that rejects every entity and relation not grounded in the source text.

What each status code and error type actually means underneath, which ones are safe to retry, and the failures that arrive with a 200.

Five charts, each shipped as SQL against a stated schema, with what each one is for and what it cannot tell you.

Judge prompt design, the published human-agreement figures, and the validation step that has to happen before a judge's score is allowed to mean anything.

The key lifecycle applied to inference credentials, the leak paths that are specific to AI applications, and why scoping matters more than rotation.

The ingestion pipeline as LlamaIndex's real product — readers, nodes, metadata — and the four points where its query abstractions stop helping.

A single design rule for agents: three capabilities are individually fine and catastrophic together, so never grant all three in one context.

How to attach a source to every fact — named graphs, RDF-star, edge properties or an assertion table — and the retraction query that makes it worth the storage.

Change data capture into a graph: idempotent upserts, out-of-order events, deletes that must not delete, and a conflict rule written down before it is needed.

A Jupyter setup for LLM experiments: a spend guard in the first cell, a disk cache so re-running is free, and a path for getting the code out into a module.

Fetch your page as a bot, diff the raw HTML against the rendered DOM, and close whatever gap you find — with the measurement script included.

A four-question tiering rule, what each tier is required to produce, a published response time the board is measured on, and the pre-approved patterns that keep