Technical Constraints
The system had to deal with dynamic pages, unreliable source structure, bounded compute, and enough persistence to compare revisions over time.
Engineering Case Study
Arachne
Autonomous research platform that searches, scrapes, versions, indexes, and synthesizes web content through a Go and Next.js pipeline.
Problem
Manual web research does not scale when the source set is large, frequently changing, and spread across inconsistent page structures. Arachne turns collection and change tracking into a repeatable pipeline instead of a one-off browsing session.
Constraints
Environment
The stack runs as containerized Go services with a Next.js frontend, Redis for coordination, SQLite FTS5 for search, and Chromedp for browser automation.
Tradeoffs
SQLite and local services were chosen over heavier distributed infrastructure. That keeps deployment simple, but constrains horizontal scaling and pushes more care into pipeline scheduling.
Architecture
- Discovery starts with search and queue generation, which produces candidate URLs for the scrape layer.
- Scrape workers extract page content and persist versioned records so historical changes remain queryable.
- An indexing layer writes normalized content into SQLite FTS5 for retrieval across both current and historical documents.
- A Next.js interface exposes search, inspection, and synthesis workflows on top of the backend pipeline.
Key Technical Decisions
Go for the pipeline core
The collection pipeline is IO-heavy, concurrent, and long-running. Go provides a straightforward fit for workers, orchestration, and service boundaries.
Separate frontend from ingestion services
Collection and operator workflows move at different rates. Decoupling them keeps the UI responsive without constraining backend execution.
SQLite FTS5 for retrieval
The project needed strong local search with versioned content but not the operational cost of a larger search cluster.
Redis for queueing and coordination
Transient pipeline state belongs in a fast coordination layer rather than in the persistent content store.
Containerized service boundaries
Docker made it easier to run scraping, indexing, and frontend layers consistently while keeping each process role explicit.
Implementation Highlights
Version-aware content store
Arachne keeps historical revisions so research outputs can cite not only what a page says now, but how it changed.
Search-to-synthesis pipeline
The system connects discovery, extraction, indexing, and AI-assisted synthesis into one flow instead of making the user hand off data between tools.
Operational visibility
Health checks and Prometheus metrics provide enough observability to treat the pipeline as a service rather than a script.
Outcome
- Arachne makes research repeatable, searchable, and inspectable across time instead of tied to transient browser sessions.
- The architecture supports both direct retrieval and higher-level synthesis workflows on top of the same indexed corpus.
Evidence
Repository provenance / Public
Repository provenance
Public source history for the research platform remains on the legacy repository account where the project was developed.
Note: Canonical contact identity remains github.com/kareemsasa; this repository has not migrated.
View artifactSanitized architecture summary / Sanitized
Sanitized architecture summary
The case study documents the search, scrape, versioning, indexing, and synthesis pipeline without publishing target lists or private datasets.