Found in: Certification, Artemis, Diagram, MoreLessons, AI Benchmark
When selecting a fixed-size collection where the items must work together (a calendar, a playlist, a portfolio, a menu), the problem is constrained set optimization — not top-N ranking. The best collection often contains none of the individually top-ranked items, because collection-level properties...
Greedy Maximum Marginal Relevance (MMR) is the practical default for selecting a diverse, high-quality subset from a large pool. At each step, it picks the item that maximizes quality minus similarity to already-selected items. It runs in O(K × N) time, requires no optimization library, and naturall...
When an interactive web app needs sub-100ms responses from a scoring function that depends on large lookup tables, load those tables into memory at startup rather than querying the database per request. The cache size is bounded (you know exactly what's in the warehouse), startup cost is a one-time...
The vision tagging pipeline uses Qwen2.5-VL (a 7B-parameter vision-language model) to classify image attributes. Running the real model requires a GPU, takes seconds per image, and produces non-deterministic outputs. The full pipeline — config loading, tagging, derived label computation, DB persiste...
When working with a large image collection from an automated source, assume near-duplicates dominate the pool until proven otherwise. Embedding cosine similarity with connected-component grouping reduces a collection to its unique members in minutes, but the threshold choice dramatically affects the...
To select k items that maximally represent the diversity within a group, iteratively pick the item most distant from all already-selected items. This greedy max-min approach is O(n×k), produces near-optimal diversity in practice, and avoids the NP-hard max-dispersion problem entirely.
When deduplicating by pairwise similarity, use graph connected components to group items — not naive pair-based merging. Pairwise similarity is not transitive in theory (A~B and B~C doesn't guarantee A~C), but for near-duplicates in practice, transitivity holds and connected components correctly gro...
When the same project-level number (image count, cluster count, lesson count) appears in multiple frontend modules, centralize it in a single metadata object. Better still, fetch live counts from the API at render time and use the centralized constant only as a fallback. Hardcoded numbers scattered...
Multiple pipeline stages in Artemis started with per-row INSERT or UPDATE patterns that worked fine during development (5-10 rows) but became bottlenecks at full scale (12,000+ rows). The per-row pattern appeared in three places:
We needed to cluster 12,217 Artemis II mission photos into visually distinct groups. The clusters serve a specific downstream purpose: ensuring the final 13-image calendar selection has visual diversity. The choice of k (number of clusters) directly affects whether the optimization can produce a goo...
When investigating why multimodal clustering produced zero results, the breakthrough came from a simple query:
While developing the concurrent thumbnail downloader, the DuckDB warehouse file (`warehouse.duckdb`) became locked by the download process. Any attempt to check progress, run `artemis-pipeline status`, or open a second connection failed with:
When a CLI tool supports multiple authentication methods with different billing paths, scripts that invoke it must explicitly select the intended billing path — otherwise, environment variable precedence silently routes charges to the wrong budget.
When building reports that combine deterministic data extraction with LLM synthesis, split them into two explicit stages: a repeatable extraction step that produces a structured intermediate file, and a separate synthesis step that feeds that file to the LLM. This makes each stage independently test...
Large question banks authored by multiple sources (human or AI) accumulate factual errors that are invisible to structural validation. Using an LLM to independently attempt each question blind — without seeing the answer key — and then comparing its answer to the stored correct answer, surfaces wron...
Using `innerHTML` to render content from "your own" data files (XML, JSON, markdown) is an XSS vulnerability even when the data is self-authored today. The threat model changes when the data pipeline changes: content contributions, bulk imports from external sources, or AI-generated content can all...
When a system must produce multiple visual representations of the same architecture, build a single normalized graph model and derive all outputs from it. Renderers that read the same model cannot drift from each other; renderers that maintain their own state always will.
When building a knowledge graph that must support regeneration, deduplication, and cross-system references, enforce a structured ID format from day one. An ID like `entity_type.domain.name` is simultaneously human-readable, machine-parseable, and stable across re-extraction — properties that free-fo...
When building an entity extraction pipeline, implement rule-based heuristics first and defer LLM-assisted extraction until the deterministic baseline is tested and measured. The rule-based layer gives you a reproducible, cost-free, fast foundation that LLM extraction can extend — not replace.
When deploying a static site to a subdirectory path (e.g., `github.io/project/` instead of a custom domain root), every internal link must be prefixed with the base path. Setting the framework's `site` config is not enough — you must also set `base`, and every hardcoded absolute `href` in components...