Long Tail Inference Lab

An open research laboratory for useful intelligence on local devices.

A conceptual long tail curve representing the variety of everyday work a local device might support. It does not show measured results.

IThesis

Useful intelligence should keep working when the network disappears.

The active project develops local voice input and text answers through a Mac app, an iPhone app, and a self-contained Jetson device. Models are replaceable parts. Usefulness, response time, memory, and offline operation guide each iteration.

IIWhy it matters

Useful intelligence should remain close to people, their devices, and the evidence behind its answers.

A local assistant has to understand a request and return a useful answer within limits a person and their device can tolerate. We test the phone constraints early, measure the complete interaction, and keep failures visible.

IIIActive project

  1. No. 02 Edge Offline Intelligence Device The English Mac prototype accepts voice, displays local text answers, and reads a completed answer aloud on request. The owner confirmed that the native iPhone beta, Local Voice Lab, works after TestFlight delivery. Detailed device evaluation remains open. The first Jetson stage is mains-powered, with a listening button and local spoken replies. All eight first-batch hardware items are ordered; arrival, setup, and device acceptance remain open. Screen, battery, and portable enclosure are deferred. The first-year total ceiling is $1,000, including required hardware, subscriptions, software fees, tax, and shipping. Model-selection intake remains open; zero comparative quality or performance measurements are published for Mac, iPhone, or Jetson.

IVMethod

Choose a useful task. Test the phone constraints. Build one measured iteration at a time.

  1. 01 Define the task Choose representative English general-conversation cases and the target iPhone. Additional languages are deferred. Agree how useful answers and acceptable response times will be judged.
  2. 02 Select the local model Evaluate Apple’s on-device models first, with a small open-model fallback. Check the same integration on Mac and physical iPhone.
  3. 03 Build and test Deliver the Mac interaction, then the iPhone beta through TestFlight. Measure speech errors, answer usefulness, latency, memory, and offline behavior.
  4. 04 Make the device Build a complete Jetson prototype within the remaining budget. Publish its own latency, memory, energy, thermal, and offline measurements.

The laboratory is open

Read the protocol. Reproduce the work. Challenge the result.

Enter Long Tail Inference Lab on GitHub