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Lightpanda, an open-source headless browser in Zig

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Lightpanda, an open-source headless browser in Zig

We’re Francis and Pierre, and we're excited to share Lightpanda ( https://lightpanda.io ), an open-source headless browser we’ve been building for the past 2 years from scratch in Zig (not dependent on Chromium or Firefox). It’s a faster and lighter alternative for headless operations without any graphical rendering. Why start over? We’ve worked a lot with Chrome headless at our previous company, scraping millions of web pages per day. While it’s powerful, it’s also heavy on CPU and memory usage. For scraping at scale, building AI agents, or automating websites, the overheads are high. So we asked ourselves: what if we built a browser that only did what’s absolutely necessary for headless automation? Our browser is made of the following main components: - an HTTP loader - an HTML parser and DOM tree (based on Netsurf libs) - a Javascript runtime (v8) - partial web APIs support (currently DOM and XHR/Fetch) - and a CDP (Chrome Debug Protocol) server to allow plug & play connection with existing scripts (Puppeteer, Playwright, etc). The main idea is to avoid any graphical rendering and just work with data manipulation, which in our experience covers a wide range of headless use cases (excluding some, like screenshot generation). In our current test case Lightpanda is roughly 10x faster than Chrome headless while using 10x less memory. It's a work in progress, there are hundreds of Web APIs, and for now we just support some of them. It's a beta version, so expect most websites to fail or crash. The plan is to increase coverage over time. We chose Zig for its seamless integration with C libs and its comptime feature that allow us to generate bi-directional Native to JS APIs (see our zig-js-runtime lib https://github.com/lightpanda-io/zig-js-runtime ). And of course for its performance :) As a company, our business model is based on a Managed Cloud, browser as a service. Currently, this is primarily powered by Chrome, but as we integrate more web APIs it will gradually transition to Lightpanda. We would love to hear your thoughts and feedback. Where should we focus our efforts next to support your use cases?

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
87%87% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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