Failed AI Visibility Checks Should Not Be Counted as Brand Misses

·1 views

A reporting guide argues that dashboards tracking brand mentions in AI answers should not treat failed or pending checks as misses. Using an eight-check example with five completed, two failed, and one pending, it notes that dividing by all rows yields a misleading 25%, while dividing by classified completed answers gives 40%. The author recommends showing coverage alongside the rate, distinguishing unclassified answers from negative ones, and returning no rate when nothing is classified. The accompanying code requires a strict boolean for classification and was tested against boundary cases. The guide also warns that retries counted as separate observations can distort the denominator upstream of the reporting logic.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Children and adults adjust their behavior as settings and activities change

A child who has been speaking loudly in a park is asked by an adult to lower their voice upon entering a library, where others are reading. The explanation focuses on what people nearby are doing rather than a simple command for silence. The article notes that the child's enjoyment is not denied, only its expression changes in relation to others sharing the space. It argues that appropriate behavior depends on the current activity, rules, and who may authorize exceptions, not just the place name. The piece frames this as a social capability that adults also use when entering new workplaces or communities.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Testing how a React build responds to a single label edit

The guide proposes comparing a build run with no source changes against one run after a small React label edit, so the two results answer different questions. It recommends using a visible change, such as a button label, so the output can be checked directly rather than relying on timing alone. Developers are told to add a distinct build script without replacing existing ones, record package versions, and run the build twice with retained local state. The edited output is then served locally and the button text is inspected to confirm the change appeared. The article also cautions that cached responses or task-runner restores can make a stale label appear, so the served directory and browser caching should be checked before drawing conclusions.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why one developer would rethink Neo4j for GraphRAG stacks in 2026

A developer argues that Neo4j, the default graph database choice, may be a poor fit for agentic AI workloads that demand fast multi-hop graph lookups combined with vector search. The author notes that Neo4j was designed for analytics and system-of-record use cases rather than high-velocity LLM agent queries. Teams often end up running Neo4j alongside a separate vector store and document store, creating operational complexity, and enterprise licensing costs can rise as scale grows. The piece recommends five alternatives: ArcadeDB, FalkorDB, TigerGraph, ArangoDB, and one more, each suited to different performance and architectural needs. The author frames the choice as one of matching the database to the workload rather than defaulting to a familiar tool.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Kubernetes Deployments Can Kill Long-Running Sidekiq Jobs Mid-Execution

A DEV Community article explains that background jobs running on Sidekiq workers in Kubernetes were quietly dying during deployments. The cause is a mismatch between Kubernetes' pod termination sequence and Sidekiq's shutdown behavior: Kubernetes sends SIGTERM and then SIGKILL after a 30-second default grace period, while Sidekiq's default shutdown timeout is 25 seconds. Jobs still running at the deadline can be pushed back to Redis and rerun from scratch, causing duplicated work, or be killed outright and lost. The author notes that symptoms like unexpected retries, duplicated effects, and abrupt worker logs appear specifically around deploys, scale-downs, and node drains. The article goes on to describe a fix involving a preStop hook, adjusted grace periods, and configuration changes, along with testing guidance.

0 comments Read more at DEV Community