Operate a thousand browsers.
Every one a believable human.

Continuous warming engine

Interest-coherent browsing from a signed like/dislike graph — gated by account health, auto-rest, and per-hour & per-day budgets. Each persona forms its own daily habits over time.

Driver-level stealth

The CDP Runtime.enable leak is gone; every profile gets its own UA, WebGL, canvas, fonts and screen fingerprint.

Human-calibrated input

Ghost-cursor Bézier curves, gaussian-timed keystrokes and qwerty-neighbor typos — sampled from recorded human traces.

Multi-account at scale

Hundreds of persistent profiles in parallel, each fully isolated.

Interest graph & personas

Enriched identities — demographics, rhythm, temperament — every behavior reads.

Health & auto-rest

Cool / warm / hot / quarantined state machine with habit tracking.

Account creator

Unattended Google login + TOTP, then SiwG for YouTube, Spotify, Twitch.

Force campaigns

Randomized Force Link / Force Click injected into live sessions over 24h.

Mirror groups

Replay one controller's nav, scroll, click, type and keys into followers.

Growth & social operators

Run dozens of distinct personas across YouTube and beyond — each with its own history, tastes and daily rhythm — so reach compounds without a farm's tell-tale sameness.

Platform & anti-abuse research

Pressure-test your own detection stack against accounts engineered to pass. If Midas trips a signal, you've found the gap before an adversary does.

Ad & brand-safety verification

See exactly what real users in other regions see — creative, placements and adjacencies — from believable profiles with coherent locale, timezone and egress.

Market & sentiment research

Observe platforms the way a hundred different people would — across geographies and interest graphs — without your own account skewing every feed you touch.

The frontend never touches a browser

A React console talks to a Rust core, which supervises a Node + Playwright worker driving persistent Chromium profiles. Each layer is isolated, crash-survivable, and queued.

Watch it think, line by line

Stealth foundation

Patchright + per-profile fingerprints + TOTP. Unattended Google login verified on the ISP proxy.

Believability core

Interest graph, personas, humanization, warming and health — hardened and verified.

Stability & observability

Crash-survivable sessions, hardened launch contract, lint + typecheck gates.

YouTube engagement

Like / subscribe / comment / save — only on interest-coherent content.

Engagement at scale

Worker pool — 30–100 believable accounts concurrent on one machine.

CAPTCHA & mass creation

On-demand CAPTCHA harvesting and batch account creation.

Midas removes the driver-level automation tell with patchright, gives every profile its own fingerprint and residential proxy, and drives input from recorded human traces. A KS scorecard verifies — feature by feature — that behaviour matches a real person. No stealth is absolute, so Midas gates activity behind account-health limits and auto-rest rather than pretending risk is zero.

Everything runs locally on your Windows machine. Profiles, personas and logs live in a local SQLite store; credentials and TOTP secrets go straight into Windows Credential Manager. There's no cloud sync and no telemetry — nothing about your operation leaves the box unless you send it there.

The stealth and warming foundation is platform-agnostic browsing. The current vertical slice targets Google sign-in and YouTube engagement, with Spotify and Twitch reachable through Sign in with Google. Additional platforms follow the roadmap as the believability core hardens.

Plan for 30–100 concurrent believable profiles on a single machine, depending on your hardware and proxy budget. Each profile is fully isolated with its own user-data-dir, proxy and fingerprint, and the Rust core queues launches so you never thundering-herd the box.

Stealth plugins patch symptoms in a driver-controlled browser and still leak the CDP Runtime.enable tell. Midas pairs patchright with a supervised Rust + Node core, per-profile fingerprints and input sampled from real humans. The difference is that believability here is measured against a scorecard, not assumed.

For real scale, yes. Midas assigns a residential proxy per profile and reads back the country Google resolves for each egress IP, flagging any geo-incoherence so a persona's locale, timezone and Accept-Language always match where its traffic appears to come from.

Midas is invite-gated while the believability core hardens. Drop your email in the request form below and we'll reach out as slots open — operators with a clear, concrete use case move up the list fastest.