Shahrukh Khan says he built 272 browser-based tools that process files on the user’s device instead of uploading them to a server. His account describes a practical trade-off: local processing can reduce exposure of file contents to a service, but it also makes performance depend on the browser and device. The project count and technical details below are Khan’s claims, not independently verified measurements or a security audit. Read Khan’s article on DEV Community.
What “no backend” means for file processing
Khan says he began with a PDF compressor after finding that many free online compressors appeared to follow an upload-process-download pattern. That is his characterization, not a verified survey of competing services. His alternative is to run the operation in the browser: the page loads its code and processing library, then works with the selected file on the user’s device.
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That changes the trust question. If an operation genuinely stays on-device, the service does not need to receive the file in order to process it, and the user need not rely on a server’s file-deletion promise for that operation. It does not, by itself, prove that a particular website never transmits file data or that its code is secure. Khan’s statement, “You don’t have to trust anything if the file never leaves your machine,” expresses the architectural goal; it is not evidence that every tool’s network behavior has been audited.
The Tool Desk
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Khan reports that the resulting Toolspace contains 272 tools across PDFs, images, video, audio, converters, calculators, writing, and developer utilities. That is the count and categorization in his September 29, 2026 article, not a current independently checked inventory.
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The described architecture combines static pages with browser APIs and JavaScript or WebAssembly libraries. Khan names Next.js 16 static export, Web Workers for heavy operations, and a service worker to cache WebAssembly binaries after the first load. The specific processing components he lists are:
- Video and audio: ffmpeg.wasm.
- PDF creation and editing: pdf-lib.
- PDF rendering: pdf.js.
- HEIC image decoding: libheif-js.
- Image operations: Canvas API and OffscreenCanvas.
These are implementation details reported by the author; they are not the result of an independent inspection of the live service or its source code.
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How the file operations work
HEIC to JPG with ffmpeg.wasm
In the example, the browser loads an FFmpeg instance once and reuses it rather than initializing the runtime for every conversion. The selected file is written into FFmpeg’s in-memory virtual filesystem, the conversion runs, and the resulting bytes are read back for download. The code then removes the temporary input and output entries from that virtual filesystem.
This illustrates an important implementation detail: “local” processing still creates temporary working data in browser memory. Reusing the initialized runtime can avoid repeated setup, while cleaning up temporary files helps release resources after a job. Khan’s example describes the code’s intended flow; it does not establish a tested performance result.
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Merge PDFs with pdf-lib
For PDF merging, the described approach loads each input document, copies its pages into a new document, and saves the combined file. Khan notes that password-protected PDFs should fail clearly rather than yield silently broken output. That is a useful error-handling requirement for a browser utility, not a claim that every encrypted PDF format or password case is supported.
What determines speed and compatibility
WebAssembly makes it possible to run compiled processing code in a browser, but it does not remove the browser’s memory, CPU, or platform constraints. Web Workers can keep a long operation from blocking the page’s main thread; they do not make the work cost-free.
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For multithreaded FFmpeg, Khan says his implementation requires cross-origin isolation so the browser can expose SharedArrayBuffer. The headers he identifies are Cross-Origin-Opener-Policy: same-origin and Cross-Origin-Embedder-Policy: require-corp. He says the implementation falls back to single-threaded processing when those conditions are unavailable. Some embedded in-app browsers may not support SharedArrayBuffer, so their behavior can differ from a fully supported desktop browser.
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Costs and constraints of local processing
- Memory: The author gives approximately 500 MB as an example of browser memory on a “decent desktop” and says iOS Safari caps lower. He provides no reproducible test setup, so this is an illustrative estimate, not a reliable limit for every browser or device.
- First load: Khan estimates the multithreaded ffmpeg.wasm build at about 30 MB gzipped and reports a 3–8 second first visit to a video tool. These are his approximate figures, without a stated measurement method or device and connection details. A service worker is described as caching Wasm binaries after the first load, which can reduce the need to fetch them again, but does not eliminate the initial download.
- Device load: Large conversions can consume phone battery and generate heat, according to Khan. Local computation shifts the workload to the user’s device rather than making the workload disappear.
- Browser support: Missing SharedArrayBuffer support can mean a slower single-threaded path, and some in-app webviews may not expose the required capability.
When a browser-side tool is a good fit
Local processing is especially useful when the task can be completed in the browser and keeping file contents off a processing server is a priority. Whether a particular tool is appropriate still depends on the actual site implementation, the file size, the browser, and the operation.
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| Consideration | Browser-side processing | Server-side processing |
|---|---|---|
| Where file contents are handled | Can remain on the device if the implementation does not transmit them; verify the specific tool. | Must reach the server for server-side processing; retention practices vary by service. |
| Memory and practical file size | Bounded by browser and device resources. | Depends on the service’s infrastructure and limits; Khan’s article does not quantify them. |
| Initial loading | May require downloading large libraries, especially for media tools. | Processing code and capacity are hosted by the service; no general load-time comparison is established. |
| Compute and energy | Uses the user’s CPU and can affect battery and heat on mobile devices. | Uses the service’s compute resources; the article gives no comparative cost figures. |
The table compares architectural trade-offs, not a ranking of products. Neither “browser-based” nor “server-based” alone establishes a service’s privacy or security. For sensitive files, check what the specific page says it uploads and, where that matters, inspect its actual network behavior rather than relying only on a privacy label.
What the project demonstrates—and what it does not
Khan’s account is a useful example of how WebAssembly, browser APIs, and JavaScript libraries can support a broad set of file utilities without making server-side file processing the default. It also shows why a privacy-oriented design has engineering costs: large runtimes must be downloaded, browser memory is finite, threading support varies, and the user’s device performs the work.
The article does not establish the live project’s current tool count or availability, provide a reproducible benchmark for its performance estimates, or independently verify that all file operations remain local. Treat the reported architecture and figures as the author’s description, not as a guarantee about the site or a general benchmark for browser tools.
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