Small, useful AI systems.

Choose narrow, useful automation over spectacle. Define the job, keep the boundaries visible, and make the output reviewable.

Read the approach See the apps

PROMPT SYSTEMS

Prompt systems you can test.

Use this lane when the model itself is not the hard part: the work is defining inputs, allowed sources, voice, output format, examples, review rubric, and version trail for repeated drafts, summaries, classifications, or support output.

How this workflow works

Define the contract: Name the input fields, audience, source boundaries, allowed claims, blocked claims, and exact response shape.
Teach with examples: Add sample inputs, ideal outputs, edge cases, tone examples, and notes for what should be rejected or retried.
Evaluate the answer: Use missing-field checks, unsupported-claim checks, tone checks, and revision rules so the prompt can be tested.
Package the spec: Leave a copy-ready prompt, usage notes, example set, known limits, changelog, and version trail.

Read prompt guidance View Work

LOCAL MODELS

Private model workflows.

Use this lane when private source files, hardware limits, model choice, context window, quantization, folder routing, batch volume, or fallback policy decide whether the workflow is viable.

How this workflow works

Map the runtime: Choose the local model, model size, hardware target, memory budget, privacy boundary, and hosted fallback rule.
Route the files: Define folder paths, input formats, naming rules, storage limits, backup paths, and what never leaves the machine.
Stabilize batches: Track parameters, context size, seeds where useful, expected output, failure cases, and review queues.
Maintain the stack: Document update cadence, model swaps, cleanup steps, fallback models, and the checks needed after version changes.

Read workflow guidance Read Notes

Explore the approach in detail.

Prompt Systems

Instruction architecture for dependable output: input rules, source boundaries, response shape, examples, and evaluation rubrics.

input contracts
source boundaries
evaluation rubrics

Local Model Workflows

Private inference pipelines with clear model choice, runtime envelope, file routing, batch handling, and fallback limits.

runtime envelope
file routing
privacy fallback

AI App Prototypes

Turn a rough idea into screens, inputs, outputs, states, and the smallest usable first version.

product flow
interactive UI
first build scope

Product Direction

Find the useful center of the idea before the build grows extra limbs.

audience fit
feature trimming
roadmap shape

Launch Surfaces

Landing pages, support pages, app store links, privacy notes, and update-list flows that stay current.

web presence
support pages
launch copy

Automation And Review

Small pipelines that move files, check outputs, log results, and make human review faster.

batch helpers
QA passes
status checks

Ship small. Test hard. Keep control.

Make the useful version judgeable before the idea grows extra limbs.

Shape
Define the job, audience, input, output, and the decision the tool needs to support.
Prototype
Build the smallest surface that proves the workflow instead of starting with a giant platform.
Stress Test
Run edge cases, bad inputs, stale outputs, and review checks before trusting the flow.
Ship
Package the public page, support notes, tracking, and next-step CTA around the usable version.

Useful notes before the build gets bigger.

All notes

Common questions about the approach.

What is the difference between a prompt and a prompt system?

A prompt asks for one result. A prompt system defines the input contract, source boundaries, examples, output format, evaluation rubric, and revision notes so the same job can be checked and reused.

What makes a local model workflow different?

A local model workflow is about the operating environment around inference: model selection, runtime setup, file routing, hardware limits, repeatable parameters, batch review, privacy boundaries, and fallback rules.

How small should the first AI app version be?

The first version should prove one user moment: a clear input, useful output, review step, and next action that can be tested before the app grows.

How do Lowball Lab and Buyer Backup fit?

Lowball Lab is the live secondhand-offer proof app, while Buyer Backup is the proof-packet lane for purchase evidence, claims, warranty tracking, and future Pro exports.