# Revo Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/revo
> Markdown URL: https://aitinkerers.org/technologies/revo.md
> Technology record last updated: 2026-03-22T19:39:58Z
> Generated: 2026-09-22T11:41:57Z

Revo delivers high-precision 3D scanning hardware that captures sub-millimeter detail for engineering, medical, and creative workflows.

Revo (Revopoint) specializes in structured light 3D scanners like the Range 2 and Pop 3, providing professional-grade accuracy (up to 0.02mm) at a consumer price point. These systems utilize dual-camera infrared sensors and integrated IMUs to eliminate the need for complex marker setups. By streamlining the bridge between physical objects and CAD software, Revo enables rapid reverse engineering and high-fidelity digital archiving for over one million users globally.

- Official technology site: https://www.revopoint3d.com
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [How we dealt with ambient agent system concurrency issues at Revo.pm](https://palo-alto.aitinkerers.org/talks/rsvp_j901dzJ13l8)

# Managing Concurrency in LLM-powered Ambient Agents ## Introduction - Brief intro to Revo.pm: "We're building an ambient AI Copilot powered by LLMs" - Core challenge: "LLMs need uninterrupted context to maintain coherent reasoning, but our agent handles multiple concurrent events" ## The Problem: LLM Context Management ### Real-world Example - "Let me show you how LLM context gets corrupted..." - Demo scenario: Website monitoring + reminder conflict - Highlight the LLM-specific failures: - Context window pollution from parallel requests - Chain-of-thought reasoning breaks - Token waste from repeated prompting - Example prompt corruption: ```text Initial prompt: "Monitor website hourly..." Corrupted context: [Previous reminder data mixed in] Result: LLM loses track of original task ``` ## Our Solution: LLM-aware Concurrency ### 1. Intelligent Lock System with Context Preservation ```python # Show the lock acquisition with LLM context management Log.Information("acquiring lock for {@llmContext}", ctxLogObject); await using var @lock = await context.AcquireLock(copilot, cancellationToken); // Preserve LLM conversation history and embeddings await context.PreserveLLMState(copilot.CurrentConversation); ``` - Key point: "Maintains clean LLM context for each interaction" ### 2. LLM-aware State Machine ```python public interface IRevoStateMachine { // State transitions consider LLM context Task Reduce(State currentState, RevoChainOfThought chainOfThought); // Handle different types of LLM interactions Task Reduce(State currentState, LLMResponse response); Task Reduce(State currentState, UserMessage message); } ``` - Demo: Show how state transitions preserve LLM reasoning ### 3. Chain of Thought Management ```python public interface IRevoChainOfThoughtProvider { // Manages LLM reasoning across events Task GetChainOfThought( IRevoState state, LLMContext context, CancellationToken cancellationToken = default ); } ``` - Demo the LLM reasoning flow: - Context window management - Embedding preservation - Multi-step reasoning across events ## Results & LLM Performance Monitoring - Show monitoring dashboard with LLM metrics: - Token usage optimization: - Before: 2.3M tokens/day wasted on context reloading - After: 850K tokens/day saved - Reasoning consistency: - 97% reduction in context-related reasoning failures - 99.9% task completion rate - Response latency: - 300ms average lock acquisition - No impact on LLM response time - Cost implications: - 40% reduction in token costs - Eliminated duplicate LLM calls ## Wrap-up & Questions - Core benefits for LLM systems: - Preserved reasoning chains - Optimized token usage - Reliable concurrent operations - Technical innovations: - LLM-aware state management - Context preservation system - Efficient token utilization ### Speaker Notes - Focus on LLM-specific challenges and solutions - Show real monitoring data when discussing metrics - Prepare for questions about: - LLM context window management - Token optimization strategies - Scaling with multiple LLM providers - Handling different types of LLM models - Cost optimization techniques - Have specific examples ready for: - Before/after prompts - Token usage graphs - Reasoning chain preservation

- Event context: AI Tinkerers - Palo Alto - November 2024 Meetup — 2024-11-21 — Palo Alto
- Public talk page: https://palo-alto.aitinkerers.org/talks/rsvp_j901dzJ13l8

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