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Meta Global AI Developer Hackathon 2026 Indie Track · Push the Frontier

Understand what happened.
Verify what could happen next.

FrontierSight connects Meta AI — Muse Spark reasoning and SAM 3.1 segmentation — to an independent deterministic physics engine. Models propose and explain. Geometry decides. Every number carries its provenance: MODEL_DERIVED, DETERMINISTIC, FIXTURE, or ASSUMPTION.

GitHub repository ↗

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TestedCPU engine & controls
7analytical validation cases
5curved-path scenarios
38API-connected MCP tools
4Meta models integrated
FrontierSight AI demo video thumbnail — forklift and pedestrian near-miss analysis with Muse Spark, SAM 3.1 and Muse Image

I Built an AI That Tests Near-Misses with Meta SAM 3.1 + Physics | FrontierSight AI

Quantura AI: Market Forecasts · 3-minute demo · Watch on YouTube ↗

Powered byMuse Spark 1.3 — reasoning & hypotheses SAM 3.1 — segmentation & trackingMuse Image — illustrative visuals + a deterministic engine that answers to physics, not prompts

Get started in three moves

○ Run the warehouse near-miss demo (Studio → Run analysis)

○ Build or generate a scene in Create Lab and analyse it

○ Ask Muse Spark to design a scene from a sentence

Progress is kept in this browser.

How it works

⌖

1 · Observe

SAM 3.1 segments and tracks people, forklifts, robots in video. Muse Spark proposes what to look for — it never invents a detection.

✓

2 · Verify

Closest-approach, time-to-collision, continuity and constraint checks run in a seeded, reproducible engine. Uncalibrated video stays in pixel space — no fake metres.

⇄

3 · Counterfact

“What if the forklift had slowed 1s earlier?” A deterministic braking simulation reruns the world, Monte-Carlo intervals included, and an independent verifier checks the result.

Try it in ten seconds

Load a ready-made world straight into the Studio — no setup, no keys.

Everything in the workbench

✦

Create Lab

Compose scenes from forklifts, pedestrians, robots and shelves. Click paths, drag waypoints, set speeds — then analyse your own world.

✎

Prompt-to-scene

Describe a scene in a sentence; Muse Spark designs it as schema-validated JSON. You edit every waypoint before anything runs.

▶

Video Studio

Frame-synchronised playback with heading-oriented entities, event seeking, occlusion markers and an all-pairs inspector.

⇄

Counterfactual simulator

Pick scene, agent and target; brake earlier or harder. Trajectory map, separation chart, Monte-Carlo intervals, per-actor results.

▤

Evidence & trace

Nine auditable agent stages with artifact hashes, then a human-approved, hash-bound evidence packet you can export and replay.

◉

Live Meta models

Muse Spark hypotheses, SAM 3.1 segmentation and Muse Image — verified with real API calls, honestly labelled when offline.

🎙

Voice dictation

Speak your scene instead of typing: Muse Voice Transcribe turns speech into an editable prompt (browser-decoded to studio WAV first), then Muse Spark builds it.

✦

Custom objects by Muse

Describe any object — Muse Spark designs a buildable version, Muse Image renders it, and it becomes a real component in 2D and 3D, saved to your profile.

⌘

MCP server

Connect your agent to scenes, reusable objects, media, path planning and deterministic analysis. Model tools use authenticated paid access; offline analysis remains available.

Architecture

Video / fixtureobserved scene in SAM 3.1segmentation & tracks Scene extractiontracks · frames · units Muse Sparkhypothesis · MODEL_DERIVEDschema-checked Deterministicsimulationcounterfactual · seeded Independentverificationengine/geometry · no LLM Human reviewapproval gate Evidence packethash-bound · replayable
Text version (for screen readers & copying)
Video / fixture ─► SAM 3.1 segmentation ─► Scene extraction ─► Muse Spark hypothesis
                                                              │
        Evidence packet ◄─ Human review ◄─ Independent verification ◄─ Deterministic counterfactual simulation
        (hash-bound)          (approval gate)      (engine/geometry)        (engine/counterfactual)

Limitations, stated plainly: synthetic fixtures validate the pipeline on synthetic data only; no safety certification is claimed; metric claims require declared calibration. See docs/ in the repo.

What we will not claim

Synthetic fixtures validate the pipeline on synthetic data only. SAM video-mode tracking on real industrial footage is integrated but not yet benchmarked. Counterfactuals are conditional predictions under declared assumptions. No physical safety certification is claimed — and every evidence packet says so, next to the numbers.

Build a world. Then prove it.

Start with a scene. See what you can learn. No card required.

Experiment workspace

My workspaces

Saved Create Lab scenes and SAM frame sets — yours, searchable, reopenable, updatable. Stored in this browser; sign in to keep them on the server too.

Nothing saved yet — build a scene in Create Lab, or segment frames in the Studio, then save.

Analytical validation cases

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Video intelligence studio

Fixture replay
View: Same tracks, same clock — 3D is a visualisation; analysis stays planar.

t = 0.00s
Track legend

Entities are drawn to scale and oriented to their heading: forklift pedestrian robot vehicle cyclist shelf · trails = observed paths · red dashed ring = declared occlusion window. Click an event to seek.

SAM 3.1 Vision — segment an image or video frame

Load an image or video (stays in your browser), then segment the current frame with SAM 3.1. For video: segment at two or more moments, then build tracks from the boxes (greedy-IoU association) and run the full analysis on them. A still image can instead become a 2D cutout scene and an illustrative 3D mask extrusion. Detections are image-space — results stay in pixels unless you declare a scale in Create Lab terms.

Photo: Bernd Dittrich on Unsplash.

Captured frames: 0 · overlays show SAM boxes, stable ids and confidences. The gallery contains attributed Unsplash photographs. Choose another example or upload your own image or video. Inference runs only when you press Segment.

Motion & geometry inspector
Run analysis in the Studio, then inspect measured tracks, coordinate frames, and uncertainty here.

Create Lab — build a scene, set the paths, run the proof

Create components, give each one a path and a speed, watch them move, then run the same deterministic analysis and counterfactual pipeline used for the built-in scenarios. The stage is a 16 × 14 metre floor plan (editable above); positions are metres, so metric claims are valid by construction. Nothing here is pre-baked: tracks are sampled from your paths at 10 Hz when you press Run analysis.

Untitled scene

unsaved
View: 3D visualises the same 2D scene model — heights are illustrative, physics stays planar. Use Move objects to reposition a component and its path. Use Edit paths to change individual waypoints.
Floor size & scenery
The stage is 16 × 14 m by default — set your own floor size; paths, previews and analysis use it.
Visual context only; scenery is not included in collision analysis.

Drag an object to move it with its path, or select it and click a destination. Arrow keys move the selected object when the canvas is focused.

t = 0.00s

Closest pair: —

Counterfactual simulator

Compare original and braking trajectories. Changes rerun the deterministic simulation.

Presets:

Trajectory map

t = 0.00s
Trajectory legend

Dashed slate = agent's original path · teal = counterfactual · thin lines = other actors · ◇ = closest approach (counterfactual). Ghost marker = where the original agent would have been.

Separation over time

Focus pair distance: original (slate) vs counterfactual (teal); red band = contact distance (sum of radii).

Evidence & trace

Agent reasoning & verification

Structured stage evidence — status, attribution, artifact hashes, retries. No private chain-of-thought is exposed; decision summaries and tool evidence only.

  1. Run analysis to populate the trace.

Evidence report

Hash-bound report: experiment identifier, source digest, calibration, ground truth, run stages, validation cases, and honest labels (real_video_evaluation_completed: false, hardware_validation: unavailable, no safety certification claimed).

Human approval gate

Export is locked until a named human approves the current run. Approval is recorded in the exported packet.

Run analysis first for a run-bound report.
Build. Test. Share with confidence.

Choose your plan.

Start exploring physical-world scenarios for free. Upgrade for deeper simulation, more saved work and a workflow built around your research.

Billed monthly Save $38 with annual

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Explorer

$0

Explore at your own pace · no card required

  • ✓ All 5 built-in scenarios
  • ✓ Create Lab + prompt-to-scene
  • ✓ Deterministic analysis & counterfactuals
  • ✓ Evidence export with recorded human approval
  • ✓ 10 server-saved scenes · Monte-Carlo ≤ 200

Team / Enterprise

Custom

A tailored workspace for your team

  • ✓ Everything in Researcher
  • ✓ Shared org workspace & member roles
  • ✓ Org audit log (exports, approvals, provisioning)
  • ✓ Custom simulation budgets
  • ✓ Onboarding session for your safety team

Scoped in the custom contract: SSO/SAML via your identity provider, private VPC deployment, SLA terms.

Compare plans

CapabilityExplorerResearcherTeam
Built-in scenarios & Create Lab✓✓✓
Deterministic analysis & counterfactuals✓✓✓
Meta callsHost key, fair useYour own key (BYOK)Your own key (BYOK)
SAM 3.1 video-mode tracking—✓✓
Server-saved scenes105,0005,000 + shared org library
Monte-Carlo runs / analysis2002,000Scoped with your team
Evidence export with approval record✓✓✓ + org audit log
SupportStandard queuePriority queueSLA (contract)
SSO / SAML · VPC · SLA terms——Custom contract

Start with included model access, subject to fair-use limits. Researcher supports your own Meta key for a dedicated workflow. Meta usage with your key is billed separately by Meta.

A few things worth knowing

Can I try FrontierSight before upgrading?

Yes. Explorer includes Create Lab, the built-in examples and deterministic analysis. No card is required.

What does annual billing save?

Researcher is $190 per year, compared with $228 over 12 monthly payments. That’s $38 saved. Annual plans are paid upfront.

Are model API costs included?

Shared model access has fair-use limits. When you connect your own Meta API key, Meta bills that usage separately.

How do I get a team workspace?

Request a quote to discuss shared workspaces, member roles, onboarding and your simulation needs. SSO, private deployment and service terms are scoped individually.

Your workspace, ready when you are

Welcome to FrontierSight.

Save your scenes. Revisit your experiments. Build your next insight.

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Need a hand? Contact our team.

System settings

Your plan & account

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Meta Model API connection (deployment)

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The API key lives server-side only (MODEL_API_KEY) and is never sent to the browser. Model ids are configurable via META_MODEL / META_SAM_MODEL / META_IMAGE_MODEL. Unsaved media is processed in memory. Explicit cloud saves retain selected frames or provider file references in your private workspace; provider files have their own retention policy.


 

Agent API keys

Create a revocable key for your personal workspace. Organization sessions use Clerk JWTs. Keys expire after 30 days and are shown once.


 

API & MCP server

MCP endpoint: /api/mcp · Complete integration guide · offline analysis over stdio: python3 scripts/mcp_stdio.py · docs: docs/MCP.md

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Claude Code:  claude mcp add --transport http frontiersight https://frontiersight-ai.vercel.app/api/mcp
Cursor / VS Code (mcp.json):  {"mcpServers": {"frontiersight": {"url": "https://frontiersight-ai.vercel.app/api/mcp"}}}

Deployment & limits

App version — · ingest bound 12 MB per request · rate limit 120 POST/min per client · Local work stays in this browser. Explicit authenticated cloud saves use the configured Firestore workspace; see the privacy policy for retention.

Organisation (Team)

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Support

Local data

Profiles, scenes, runs, theme and preferences live in this browser only.