Changelog
Key Insights
- Platform version history and notable changes.
Platform version history and notable changes to AcaciaFund.
2026-06-08 — Knowledge Base Overhaul
- Added dedicated
knowledge.j2template with TOC, breadcrumbs, cross-references, and progress bar - Reorganized knowledge entries into categories: Platform, Guides, Reference, Architecture
- Expanded glossary to 30+ terms across all categories (AML, Markets, Data Engineering, DataOps)
- Added Research Methodology guide with full SQI formula and pipeline description
- Added Pillar Taxonomy guide with per-pillar scope, sources, and tag examples
- Migrated all knowledge slugs under
/knowledge/namespace - Added cross-referencing between knowledge and research/learn content via tag matching
- Generated category-specific thumbnail SVGs for knowledge sub-categories
2026-06-08 — DataOps System Architecture
- Added system architecture knowledge page
- Added DataOps glossary and open source tool landscape
- Updated README with full system architecture diagram
- Added
seed_dataops.pyfor DataOps/engineering content seeding
2026-06-08 — 3-Category Taxonomy Launch
- Introduced
content_typefield (research | learn | knowledge) - Reclassified all registry entries
- Created category index pages for research/, learn/, knowledge/
- Created dedicated
learn.j2template - Added learning hub entries (AML basics, market analysis, science method, quiz)
- Added static knowledge pages (about, research overview, scholarship, contact, glossary, FAQ)
2026-06-07 — Fractal Thumbnails & Interest Ranking
- Implemented seed-based fractal tree SVGs for per-article unique thumbnails
- Added OG image generation for social sharing
- Homepage now ranks articles by interest score (SQI × 0.6 + recency × 0.4)
- Added 15 new articles (Jan–May 2026, 5 per pillar)
2026-06-01 — Initial Platform Launch
- Python-native static generator with Jinja2 + Pydantic
- Dark mode with FOUC prevention and localStorage persistence
- Accessible dropdown navigation and mobile menu
- Reading progress bar, TOC sidebar, focus mode
- Self-hosted Tailwind and Inter font
- Cloudflare Pages deployment via GitHub
- 12 initial research articles (daily digest format)
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.
DataOps is a concept in best practices. In simple terms, DataOps covers best practices in Data Engineering. This data engineering concept addresses key topics in the best practices in data engineering domain. Also known as: DataOps practices, data operation
Analogy
Example
Find Gaps
Explain DataOps as if teaching a colleague who is new to best practices. Cover: what it is, how it works, and why it matters.
Create
Create a checklist that demonstrates DataOps in a real-world best practices scenario. Walk through your design decisions.
Show solution
A checklist for DataOps should include: 1. The core components of dataops 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