Stet – PostScript Level 3 interpreter and PDF toolkit in Rust

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Stet is a PostScript Level 3 interpreter, a PDF reader, and a print-quality PDF writer, all in pure Rust. All three converge on a single DisplayList type, so any output device (PNG, desktop viewer, PDF, WASM) works with any source — you can render a PS file to PDF or PNG, a PDF to PNG, or to a display list for a custom backend all through the same pipeline. The link above is the WASM build running entirely client-side — drop a PS, EPS, or PDF and it renders. It's a capability sampler, not a production viewer: no system fonts (browser sandbox), fixed zoom stops, single-threaded at ~2× native overhead. Why build it: GhostScript is AGPL, pdfium is enormous, and there wasn't a pure-Rust stack that covered PostScript and PDF end-to-end. Apache-2.0 OR MIT. Source: https://github.com/AndyCappDev/stet Install: cargo install stet-cli for the CLI, cargo add stet for the library (the stet facade crate re-exports the whole toolkit).

Visit product View on Hacker NewsApr 21, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: single · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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