Skynet AI

Local-first autonomous operations

Skynet runs and verifies browser workflows before declaring them complete.

Built for teams that need AI to operate real tools while preserving the executor, artifact, and receipt behind every completion claim.

Watch a complete run Inspect the evidence

Verified The run shown here is documented in a published production field note.

Production publishing canary Request-to-receipt trace
Verified
  1. RequestPublish a source-backed field note
  2. RiskPublic claim; review required
  3. ExecutorRegistered publishing lane
  4. ActionWordPress canary published
  5. ArtifactLive page and preview image
  6. ReceiptURL, archive, image, and signature checked
Open the field note

Verified evidence

A completion claim has a fixed evidence shape.

These are operating requirements, not article-volume or engagement claims.

  • Risk classifiedThe reason for review is visible.
  • Executor namedThe acting lane is recorded.
  • Artifact persistedThe output exists beyond the tool call.
  • Receipt checkedAn independent signal closes the run.

One live workflow

The proof boundary is part of the product.

A real publishing canary shows how Skynet separates intent, execution, and verification instead of treating a successful tool response as the finish line.

RequestSource-backed articleIntent frozen
Risk classPublic claimReview branch opened
ExecutorPublishing laneRole recorded
Browser actionCanary publishedAction receipt saved
OutputLive articlePreview and archive visible
ReceiptIndependent checks passCompletion allowed
Verified Live today

Source-backed web publishing, browser workflows, viewport audits, and evidence receipts.

Review Experimental

Broader self-improving routing across changing provider and application surfaces.

Blocked Not accepted

Proof-free completion claims, silent provider substitution, or missing live artifacts.

Read the complete production field note

Proof-backed case studies

Three systems. Each one opens to evidence.

The strongest demonstrations replace a catalogue of claims: local machine learning, visual desktop control, and verified publishing.

Placement verified

Unsupported-GPU machine learning with a truth gate

The run verified model placement on the local AMD device, compared the measured path with the CPU, and withheld forecasts when validation was incomplete.

Claim
Device placement was observed
Evidence
Benchmark and withheld-output record
Inspect the benchmark
Measured

ScreenMemory perception-to-action

The visual control stack combines targeted screen capture, structural automation, semantic navigation, and compressed accessibility context.

Claim
Visual control improved through layered perception
Evidence
Published methodology and measurements
Inspect the measurements
Live output

Publishing that verifies after publish

AI WP Manager is one implementation inside Skynet: the workflow checks the live URL, archive membership, preview image, and signature after WordPress returns success.

Claim
A tool response is not completion
Evidence
Live page and independent receipt
Inspect the publishing run

Economics and investment case

Move review effort from reconstruction to inspection.

Skynet is designed to make the operational record a by-product of the run. The economic wedge is a reusable control plane, not an unsupported promise about price or speed.

Request the investor brief Support independent research
Manual handoff chain

Evidence is reconstructed later.

  • Tool success becomes an assumption
  • Context fragments across reviewers
  • Failures are expensive to reproduce
  • Accountability lives in chat history
Skynet operating model

Evidence travels with the work.

  • Risk and executor are explicit
  • Artifacts persist outside the model
  • Receipts make failures inspectable
  • Recovery becomes reusable memory

Latest proof dispatches

Read the operating record, not a trend feed.

One current Platform dispatch leads the briefing, with two direct paths into the newest source-backed work.

AI Models & Agents Two Reviewers, One Blind Spot: Design Independent Evidence Paths for AI Decisions AI Models & Agents Nine AI Judges Can Still Be Two Votes: The Correlated-Error Trap in LLM Evaluation
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