Frequently Asked Questions
Key Insights
- Answers to common questions about AcaciaFund's methodology, content, and platform.
What is the Signal Quality Index (SQI)?
SQI is a composite metric that evaluates the quality of synthesized content across four dimensions: source authority (is the source reputable?), freshness (how recent is the data?), consensus (do multiple sources agree?), and relevance (how directly does it relate to the pillar?). SQI is normalized to [0, 1] and displayed on every research article.
How does Bloom taxonomy classification work?
Each article is classified across six cognitive levels: remember, understand, apply, analyze, evaluate, and create. Questions and flashcards are generated at each level to support different learning objectives. A single article may span multiple levels depending on its content depth.
What are the three pillars?
AML (Anti-Money Laundering) covers financial crime, compliance, regulation, and risk management. Markets covers semiconductors, supply chains, AI industry, and manufacturing. Data Engineering covers data pipelines, orchestration, quality engineering, streaming, storage, and analytics infrastructure.
How are sources selected?
Sources are drawn from two primary feeds: HackerNews (technology and current events) and arXiv (academic preprints across computer science, statistics, and finance). Articles are filtered by relevance to the three pillars and scored using the SQI framework.
How often is content updated?
Research articles are published as significant stories emerge. The pipeline processes HackerNews and arXiv feeds daily, but publication frequency depends on the volume of high-SQI signals. Historical articles are preserved with their original quality metrics.
Can I reuse or cite AcaciaFund content?
All content is licensed under MIT. You are free to reuse, adapt, and cite. We recommend citing by article slug and date, as content is version-controlled via Git. Each article has a canonical URL and JSON-LD structured data for citation purposes.
How are thumbnails generated?
Each research article gets a unique fractal tree SVG, generated using a seed-based L-system. The tree shape is deterministic (same title = same tree) and colored by pillar (amber for AML, green for Markets, indigo for Data Engineering). Additional color flooding, bloom, and mist effects create a distinctive visual identity per article.
Does AcaciaFund use client-side JavaScript?
JavaScript is used only for UI enhancements: dark mode toggling, mobile navigation, reading progress bar, table of contents highlighting, and focus mode. No JavaScript is required for reading content — the site is fully functional with JavaScript disabled.
Last updated: 2026-06-08
Article Metadata
Further Reading
Databricks Blog
Lakehouse, Spark, Delta Lake, Unity Catalog — engineering blog
Apache Kafka
Kafka documentation, KIPs, and ecosystem updates
Apache Flink
Flink documentation and release notes
Apache Iceberg
Iceberg table format — specs, REST catalog, performance
dbt Blog
dbt Labs engineering blog — analytics engineering, Semantic Layer
Dagster Blog
Dagster orchestration — software-defined assets, IO managers
Feynman Concept Cards
Master each concept: read the ELI5, explore analogies, work examples, and teach it back.
Distributed Systems for Data is a concept in architecture. In simple terms, Distributed Systems for Data covers architectural patterns for Data Engineering. This data engineering concept addresses key topics in the architectural patterns for data engineering domain. Also know
Analogy
Example
Find Gaps
Explain Distributed Systems for Data as if teaching a colleague who is new to architecture. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Distributed Systems for Data in a real-world architecture scenario. Walk through your design decisions.
Show solution
A diagram for Distributed Systems for Data should include: 1. The core components of distributed systems 2. How they interact 3. Expected outcomes or outputs
Research is a concept in specialized. In simple terms, A concept related to research
Analogy
Example
Find Gaps
Explain Research as if teaching a colleague who is new to specialized. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Research in a real-world specialized scenario. Walk through your design decisions.
Show solution
A diagram for Research should include: 1. The core components of research 2. How they interact 3. Expected outcomes or outputs