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M_N_Nik | Python & Systems
M_N_Nik | Python & Systems

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I Built a 100% Private, Zero-Cloud Windows Recall in <200KB — No NPU or Copilot+ PC Required

#ai #privacy #python #opensource

When Microsoft announced Windows Recall — a feature that silently captures your screen every few seconds so you can search your past activity — the tech community had two immediate reactions:

  1. "The concept is actually super useful." Everyone has closed a browser tab, lost a terminal command, or forgotten a link sent in a chat days ago.
  2. "The execution is a privacy and security disaster."
    • Unencrypted local storage vulnerable to malware.
    • Cloud telemetry concerns.
    • Hardware lock-in: Microsoft claimed you must buy a brand new $1,500+ Copilot+ PC with a 40+ TOPS NPU chip just to search your own screen!

I refused to accept that a simple screen search engine requires gigabytes of OS bloat and specialized AI silicon.

So, I built NanoRecall: an open-source, 100% private, zero-cloud desktop memory engine in <200KB of code that runs on any standard CPU at sub-millisecond speeds.

🖥️ The Interactive Dark-Mode Dashboard

Here is what it looks like running locally on your PC (nanorecall ui):

NanoRecall Dashboard

  • Search Bar: Type any natural query ("sqlfluff pull request 8449 github", "docker crash error", "hotel reservation receipt").
  • Daily Timeline: Scrub your day hour-by-hour (09:00 ➔ 14:00 ➔ 20:00) with visual activity heatmaps.
  • Privacy Shield: Active window detection that automatically shields password managers and private browsing tabs.

⚡ Feature Comparison: Microsoft Recall vs. NanoRecall

Feature Microsoft Windows Recall NanoRecall (This Project)
Privacy & Cloud Unencrypted storage, telemetry risks 100% Local & Encrypted (Zero bytes leave your PC)
Hardware Requirement Requires Copilot+ PC ($1500+) with 40+ TOPS NPU Runs on any standard Intel / AMD / ARM CPU
Vector Engine Heavy proprietary runtime NanoVector (Pure C99 + AVX2, <120KB footprint)
Search Latency Variable (Cloud / NPU overhead) 0.28 ms (Sub-millisecond exact semantic search)
Password Protection Records sensitive credentials and cards Privacy Shield: Auto-ignores 1Password, Bitwarden, KeePass, Incognito
Storage Footprint Tens of gigabytes of raw frames Smart Frame Differencing: Suppresses static frames (30–60 KB/frame)
Code Footprint Gigabytes of OS bloatware <200 KB pure codebase, <25MB RAM idle, 0.1% CPU

🏛️ Architecture: How It Works Under the Hood

NanoRecall is built with zero cloud dependencies using standard Python and bare-metal C99 SIMD:

                     ┌────────────────────────────────┐
                     │     Windows Desktop Display    │
                     └───────────────┬────────────────┘
                                     │
                        (Perceptual Diffing / 0.1% CPU)
                                     │
                     ┌───────────────▼────────────────┐
                     │    Native Win32 Screen Capture │
                     │  (GDI / BitBlt / DirectMemory) │
                     └───────────────┬────────────────┘
                                     │
                     ┌───────────────▼────────────────┐
                     │    Offline Text Extraction     │
                     │  (Windows.Media.Ocr / Local)   │
                     └───────────────┬────────────────┘
                                     │
                                     ├───────────────────────────────┐
                                     │ Text + Embeddings             │ Compressed Frame
                                     ▼                               ▼
                     ┌────────────────────────────────┐  ┌───────────────────────┐
                     │       NanoVector Core          │  │   Local Image Store   │
                     │  (C99 AVX2 + .nvec persistence)│  │   (WebP / 30-60 KB)   │
                     └───────────────┬────────────────┘  └───────────┬───────────┘
                                     │                               │
                                     └───────────────┬───────────────┘
                                                     ▼
                                     ┌────────────────────────────────┐
                                     │     Search & Recall Engine     │
                                     │   (CLI + Dark-Mode Dashboard)  │
                                     └────────────────────────────────┘
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1. Smart Perceptual Frame Differencing (0.1% Idle CPU)

Instead of blindly capturing screenshots every 3 seconds and exhausting your SSD, NanoRecall calculates a fast 32x32 grayscale perceptual fingerprint. If the screen hasn't changed by at least 1.5% (reading, typing a note, or stepped away for coffee), capture is automatically skipped.

2. Built-in Privacy Shield

Through Win32 API hooks (GetForegroundWindow), NanoRecall constantly inspects the active window title and class. Whenever password managers (1Password, Bitwarden, KeePassXC), private browsing windows (Incognito, InPrivate), or crypto wallets (MetaMask, Ledger) are focused, capture is instantly paused. Sensitive tokens (sk-... keys, credit cards) are redacted via regex before indexing.

3. Sub-Millisecond Search Powered by NanoVector

For vector search, it directly utilizes NanoVector, a minimalist ~120KB C99 SIMD engine with AVX2 and ARM NEON unrolled kernels. Your entire desktop memory history is packed into a single binary .nvec file that loads in milliseconds.

🚀 60-Second Quickstart

NanoRecall is packaged and published on PyPI. You can install and try it right now on Windows, Linux, or macOS:

pip install nanorecall
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1. Capture Your Current Screen 

nanorecall capture
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Output:

📸 Capturing desktop screen...
🔍 Extracting offline OCR text...
🧠 Indexing into NanoVector (Window: Active Window)...
✅ Indexed frame 2026-09-11_142315 (342 words, 98.4% change)
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2. Search Anything from Your Past (CLI) 

nanorecall search "github pull request sqlfluff"
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Output:

🔍 Search Query: 'github pull request sqlfluff' (1 results found in 0.28 ms)

#1 [98% Match] Google Chrome — sqlfluff pull request 8449 github
   🕒 Captured: 2026-09-11_142315
   📝 Text:     merged upstream/main to pull in CI fix and added StarRocks test cases...
   🖼️  File:     ~/.nanorecall/frames/2026-09-11_142315.webp
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3. Launch the Local Web Dashboard 

nanorecall ui
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Open http://127.0.0.1:8765 in your browser to explore your visual timeline and search interactively!

🧪 Try It in Your Browser (Google Colab)

Don't want to install anything locally yet? Run the live interactive demo directly in Google Colab:

👉 Open NanoRecall Quickstart in Google Colab

Colab CPU Benchmark Results:

Testing search latency over thousands of recorded desktop frames on a standard virtual CPU:

Stored Frames ($N$) Search Latency Throughput (QPS)
$N = 500$ 0.023 ms (23 µs) 44,014 QPS
$N = 2,000$ 0.071 ms (71 µs) 14,130 QPS
$N = 10,000$ 0.320 ms (320 µs) 3,128 QPS

Even with 10,000 recorded desktop screens, search executes in under 1/3 of a millisecond!

🔗 Links & Open Source

NanoRecall is 100% open source under the MIT license:

If you believe software should be private, lightweight, and respect user autonomy, drop a ⭐ on GitHub and let me know what features you'd like to see next!

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