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Case study

ai2025-2026

Chronicle

Local-first AI agent observability platform built in Rust. Timeline view, DAG visualization, cost tracking, OpenAI proxy with semantic caching, and MCP integration. Privacy-focused alternative to LangSmith.

Exploring the boundaries of artificial intelligence

Field notes

Role

Creator

Read order

Evidence first, then constraint → build → proof. Use the chapter rail to jump sections; orbit to adjacent work from the bottom of the case study.

The AI Frontier·✦I-Class Giant
★FEATURED
29 tests passing

Evidence

System surface

No public screenshot on file—generative preview stands in for the visual layer.

Signature viewpreview

Story arc

How this shipped

Exploring the boundaries of artificial intelligenceThree beats: what pressed against the work, how the stack answered, and what changed once it was live.

I · Constraint

The brief

Developers building AI agents have zero visibility into execution flow, costs, and errors. Existing tools like LangSmith cost $29-299/mo and send data to external servers.

II · Build

The craft

Built local-first observability platform with Rust backend (Axum + SQLite) and React 19 frontend. Implemented semantic caching proxy (30-50% cost reduction), fire-and-forget tracing (zero latency), DAG visualization, and MCP server for AI assistant integration.

III · Proof

What moved after launch

29 tests passing (0.11s runtime). 808KB optimized UI bundle. Zero-cost local-first architecture. Semantic caching reduces LLM costs 30-50%. MCP integration enables natural language trace queries. 9.5/10 documentation score.

Signals

◆Signals & scale

0passing
Tests
Rust + Axum + SQLite
Stack
Yes
MCP Tools

Engine room

At a glance

0
Tests

Inventory

Stack & signals

RustAxumSQLiteReact 19TypeScriptViteTailwindPython SDK
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Tell me what you're building

If you need someone who can own UI, systems, and AI integration without losing the plot—I'm listening. Contract, advisory, or full-time: we'll find the right shape.

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© 2026 Elizabeth Stein

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