SpaceStatic
Sensor research for higher standards.
A research studio for space exploration, sensor data collection, pattern-recognition algorithms, atomic and quantum theory, physics simulators, instrument calibration, and data-collecting software products.
NASA-grade is the closest common benchmark: reliability, traceability, calibration, and tolerance under extreme constraints. SpaceStatic is focused on pushing beyond it by defining higher standards for sensor data, calibration, and system reliability across medical, aerospace, industrial, energy, environmental, and consumer technology.
The research direction
SpaceStatic is where the research lives before it becomes a product. It is about sensor data collection, signal quality, recognition, calibration, physics, and software that can collect enough clean data to make the next experiment possible.
Sensor Data
Collecting measurements from devices, environments, bodies, machines, satellites, instruments, and field systems so the invisible can become measurable.
Signal Quality
Cleaning noise, finding drift, preserving timing, comparing baselines, and keeping data trustworthy before any pattern-recognition algorithm touches it.
Simulation
Physics tools for motion, waves, fields, calibration, materials, atomic theory, quantum theory, and future systems that need testing before they exist.
Standards
Using aerospace-grade and medical-grade thinking as a starting point, then working toward a higher standard of measurement quality for every industry.
Build the loop.
The page should feel like a lab bench for future software: collect sensor data, calibrate instruments, recognize patterns, simulate possible futures, document quality, and turn the useful parts into tools.
- 01
Sense the environment
- 02
Collect sensor data
- 03
Clean the signal
- 04
Calibrate the instrument
- 05
Model the pattern
- 06
Raise the standard
A higher standard becomes useful everywhere.
High-spec calibration is not only medical. It can apply anywhere quality matters: aerospace systems, industrial machines, research instruments, environmental sensors, energy systems, personal health tools, and products that should be safer, clearer, and more reliable. The long-range goal is to develop a quality standard above the familiar aerospace-grade idea and make that level of measurement practical across industries.
Agent memory integration
Supabase is the primary memory substrate.
The same research direction applies to the live software: collect the signal, keep it traceable, and let AI agents build context without losing the source-of-truth database.
Supabase primary
Projects keeps durable agent memory, usage events, Vitals data, HP snapshots, and mind-map records in Supabase. pgvector is the primary recall layer so the same database owns identity, permissions, and long-term context.
Pinecone expansion
Pinecone stays available as an explicit vector backend for larger semantic recall experiments and the extra memory budget, but it does not replace Supabase as the source of truth.
Agent boundary
The public agent route stays stable at /api/v1/agent/memory. Assistants read and write through the API while the server decides which vector backend is active.
Further resource
Signal Notes turns source trails into a public research desk.
A Bun-native companion project for collecting tools, AI workflows, feed sources, and practical notes as a structured signal index.