SignalBridge RPM is an interactive prototype that brings fragmented remote patient monitoring data into one explainable care-team workflow. THE PROBLEM A patient with heart failure and diabetes may have weight, oxygen, blood pressure and glucose readings in separate systems. The care team must piece together trends, symptoms and missing information to decide what needs review. THE SOLUTION SignalBridge combines those inputs into a patient timeline, assigns Low / Medium / High review priority using transparent demonstration rules, explains each flag, and prepares an SBAR summary for human review. Clinicians remain responsible for assessment and action. WHAT WE BUILT A working five-view browser prototype: Overview, Patient Signals, Competitive Insights, Guardrails and Clinical Workflow. Judges can inspect timestamped readings, edit test measurements, switch between four scenarios, test a medication-dosing safety boundary, and walk through a simulated RN review and provider escalation. A six-step guided demo, downloadable test data, an offline HTML copy, a complete PRD and an illustrated pitch deck with speaker notes support the presentation. MARKET INSIGHTS A simulated Similarweb-style market landscape compares fictional cardiopulmonary RPM and diabetes-monitoring categories to show complementary capabilities and audience adjacency. A future live integration could use traffic, similar-sites and audience-overlap data to explore integration candidates. Audience overlap does not prove device compatibility or clinical value. DEMO BOUNDARIES All patient and market data are fictional. Review rules and safety responses are deterministic and illustrative, not clinically validated or powered by a live AI model. The prototype does not diagnose, prescribe, change doses, contact clinicians or dispatch emergency services. No live Similarweb API, device or EMR integration is connected. NEXT STEP Verify data access and device compatibility, then test clinician-approved rules and workflows before a clinical pilot.
Built at The AI Conference Hack Day 2026
SignalBridge RPM
SignalBridge RPM is an interactive prototype that brings fragmented remote patient monitoring data into one explainable care-team workflow. THE PROBLEM A patient with heart failure and diabetes may have weight, oxygen, blood pressure and glucose readings in separate systems. The care team must piece together trends, symptoms and missing information to decide what needs review. THE SOLUTION SignalBridge combines those inputs into a patient timeline, assigns Low / Medium / High review priority using transparent demonstration rules, explains each flag, and prepares an SBAR summary for human review. Clinicians remain responsible for assessment and action. WHAT WE BUILT A working five-view browser prototype: Overview, Patient Signals, Competitive Insights, Guardrails and Clinical Workflow. Judges can inspect timestamped readings, edit test measurements, switch between four scenarios, test a medication-dosing safety boundary, and walk through a simulated RN review and provider escalation. A six-step guided demo, downloadable test data, an offline HTML copy, a complete PRD and an illustrated pitch deck with speaker notes support the presentation. MARKET INSIGHTS A simulated Similarweb-style market landscape compares fictional cardiopulmonary RPM and diabetes-monitoring categories to show complementary capabilities and audience adjacency. A future live integration could use traffic, similar-sites and audience-overlap data to explore integration candidates. Audience overlap does not prove device compatibility or clinical value. DEMO BOUNDARIES All patient and market data are fictional. Review rules and safety responses are deterministic and illustrative, not clinically validated or powered by a live AI model. The prototype does not diagnose, prescribe, change doses, contact clinicians or dispatch emergency services. No live Similarweb API, device or EMR integration is connected. NEXT STEP Verify data access and device compatibility, then test clinician-approved rules and workflows before a clinical pilot.
SimilarwebHTML, CSS, JavaScript, SVG data visualizations; built with OpenAI Codex / ChatGPT and AI-generated pitch illustrations. Scripted review rules; simulated Similarweb-style data.Keep exploring what builders shipped.
HackerSquad project
RunIt
RunIt is the AI chief of staff I run my business on. It reads my texts, email, Instagram DMs, calendar and CRM, and drafts every reply in my own voice, with the context already in it. Plaud is what makes that context complete: most of what matters happens in person or on a call, and it never lands in a text thread or an inbox. Plaud captures it, and RunIt turns it into context. What the demo shows, live on my phone with my real data: 1. Connectors: Plaud, Messages, Gmail, Google Calendar, Instagram, Notion and phone calls, all feeding one context layer. 2. Pre-drafted replies: every text, email and DM that comes in already has a reply drafted from everything RunIt knows about that person, including what we said in a Plaud-recorded conversation. One tap sends it from my own iMessage or Gmail. Each draft shows its sources. 3. Action bundles: when a reply depends on something (check the calendar, confirm a detail), RunIt stages those actions first and waits for my approval. I can send with notes or redraft with notes. 4. Auto: RunIt can carry a conversation for me toward a goal I set (who, what, how long), and only pulls me in when it's stuck. 5. "What did I learn today?": RunIt answers from today's Plaud recordings of the conference talks, with the key takeaways, and offers to send them to someone. Built today at Hack Day: a Plaud Embedded SDK iPhone app (Capacitor) that binds a Plaud NotePin S or Note Pro, syncs recordings, gets a speaker-labeled transcript from the Plaud Transcription API, and hands it to RunIt. RunIt never shows a transcript. It shows what you owe people (drafted in your voice), what they owe you (tracked, with the day it will check in), and what's worth remembering about them (saved only when you say so, brought back at the right moment). Plus a Plaud connector in Settings and "recorded today" context in the chat. Nothing acts on its own. Every send waits for one tap, passes an allowlist and a rate cap, and leaves a receipt. Plaud hears it. RunIt makes sure it gets done.
HackerSquad project
Voice Agent Metaharness: one ecosystem for all of your agents
One ecosystem for all of your agents: one Claude across every surface (phone voice, web, Claude Code, an agent team, a WebXR world) sharing one picture of your day. Plaud: a clip-on recorder captures what happens around you. contextlog: a shared-context MCP server hands that day to whichever surface you return to (while_away), lets every agent share what's live in its context (context_ping), and catches ideas you say out loud (idea_log). Band.ai: an org of 9 agents (PM, Architect, Frontend, Backend, QA, DevOps, UXR, Research) led by Claude Code; each logged idea becomes a GitHub issue and project card, posted to the Band room where the PM agent triages it. Similarweb: UXR and market research (41 API calls, 16 domains): ambient capture is growing fast (Plaud +155% YoY, Granola +257%) while recorders commoditize, and Plaud's audience already overlaps with Claude's, so the value is the shared context above the device. Also registered in DuploCloud as a REST provider. DuploCloud DevKit: the control plane, with Neo4j (mcp-neo4j-cypher), contextlog, Band and Similarweb registered as providers, scopes and MCP servers. Dreamspace: the spatial surface; its guide Lumen speaks with spatial audio and voice-codes the world on request, and Claude can join hands-free over MCP. Vultr: hosts the shared MCP server over HTTPS. and github.com/rachael/contextlog Repos: github.com/rachael/dreamspace and github.com/rachael/duplocloud-setup
HackerSquad project
TheraApp
Structured CBT for mild to moderate anxiety