// tier: vasic-util-secondary · order 28

Vasic Digital Reusable Module Suite scaffoldlicense: UNVERIFIED (varies per module)

Source

• vasic-digital

Build once, reuse everywhere — a fleet of small, decoupled, independently-tested Go and KMP modules.

A curated suite of decoupled digital.vasic.* modules — infrastructure primitives (auth, cache, database, config, observability), AI/agent building blocks (RAG, VectorDB, Embeddings, MCP, Agentic, Planning), and defensive-LLM guardrails (RedTeam, Normalize) — plus a Kotlin Multiplatform mirror set. Each is generic, tested, and reusable.

A large family of generic, reusable modules published under the digital.vasic.* (Go) and Kotlin Multiplatform namespaces. Every module is standalone, independently tested and versioned, and consumed as an equal-codebase submodule by larger products (Catalogizer, HelixAgent, and the wider fleet). This page consolidates the many small utilities that would be noise as individual pages.

The vasic-digital org runs on one structural bet: a "constitution + many decoupled reusable submodules" philosophy in which generic functionality is never written twice. Instead of monoliths, every reusable concern is extracted into its own small module — its own repository, its own tests, its own docs — and held strictly decoupled so no consumer's specifics ever leak in. This page groups them because, taken one at a time, each is library-scale and would be noise as an individual product page. Taken together, they are the org's real force multiplier: a private engineering asset that turns "build a new product" into "assemble proven parts," and the concrete backing for the claim that this fleet does not reinvent the wheel — it maintains one very good wheel and rolls it everywhere.

The suite spans three clusters. Infrastructure primitives (Go) provide the plumbing every service needs: auth (JWT/bcrypt), cache (Redis/TTL), database (migrations, dual SQLite/PostgreSQL), config, middleware, observability (Prometheus/OpenTelemetry), ratelimiter, security, storage (S3/MinIO), streaming (WebSocket hub), eventbus, filesystem (multi-protocol), discovery/mdns, http3, recovery, concurrency, lazy, and more. AI/agent building blocks (Go) provide the substrate for AI systems: rag, vectordb, embeddings, memory, conversation (infinite-context compression, event sourcing), mcp (Model Context Protocol), toolschema, skillregistry, agentic (graph-based workflow orchestration), planning (HiPlan/MCTS/Tree-of-Thoughts), benchmark (SWE-bench/HumanEval/MMLU), llmops, selfimprove (reward modeling/RLHF), and toon (Token-Oriented Object Notation). Defensive-LLM guardrails provide adversarial-robustness tooling: RedTeam (YAML-driven adversarial fixtures), Normalize (adversarial-input canonicalisation). A parallel Kotlin Multiplatform set mirrors core modules (Auth-KMP, Database-KMP, Security-KMP, UI-Components-KMP, etc.) for cross-platform apps.

The problem we set out to solve

Shipping many products (Catalogizer, HelixAgent, Herald, and more) from scratch each time is wasteful and inconsistent. Extracting every generic concern into a decoupled, tested module means fixes and improvements propagate across the whole fleet, and each new product assembles from proven parts.

Why it changes the game

It is, in effect, a private "standard library" for building AI-centric backends — the layer most teams never get to build because they are too busy re-solving auth, caching, and RAG plumbing for the fifth time. Here infrastructure primitives, AI building blocks, and defensive-LLM guardrails all exist as drop-in, independently-tested modules, which is what lets a small team ship product-grade systems at a pace that normally requires a much larger one, and do it without the duplication debt that usually accrues in its wake.

Where the novelty lives

• Fleet-wide decoupling discipline (CONST-051): submodules treated as equal codebases, never carrying consumer specifics.
• A dedicated AI-primitive layer (RAG, VectorDB, Embeddings, MCP, ToolSchema, Agentic, Planning, LLMOps) as reusable modules.
• A defensive-LLM guardrail cluster (RedTeam, Normalize) for adversarial robustness.
• Parallel Go + Kotlin Multiplatform module sets sharing the same conventions.

Hard problems, honest solutions

• Avoiding coupling rot across dozens of modules: solved with the constitution's decoupling contract and runtime injection of consumer specifics.
• Keeping many modules consistent and tested: solved with a shared convention (per-module tests/docs/Challenges) and the HelixConstitution governance backbone.
• Cross-platform reach: solved with a Kotlin Multiplatform mirror of core modules.

The stack, and why

• Go — the majority of modules (digital.vasic.*).
• Kotlin Multiplatform — cross-platform mirror modules (Auth/Database/Security/UI/Concurrency/RateLimiter-KMP).
• Redis / PostgreSQL / SQLite — cache, database, storage primitives.
• Prometheus / OpenTelemetry — observability module.
• WebSocket / HTTP/3 (quic-go) / mDNS — networking modules.
• Vector DB / embeddings / RAG / MCP — AI-primitive modules.
• YAML — RedTeam adversarial fixtures and config.

UNVERIFIED / WIP: several org repos are self-marked "SCAFFOLD / WIP" (e.g. PliniusCommon, I-LLM, HyperTune, AutoTemp, Veritas, Ouroborous, Claritas, LeakHub, GandalfSolutions). Present these as early-stage/scaffold, not shipped.