This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
PageSift
Web content engine for AI agents: pages & PDFs → Markdown
PageSift turns URLs, PDFs and Office files into clean Markdown and structured data for AI agents. One self-hosted Rust binary: no per-page pricing, no external services. It scrapes, crawls, maps, batches and monitors sites, parses PDFs (including scanned, with built-in OCR), Word, Excel, PowerPoint, EPUB and CSV, and offers CLI, Firecrawl-compatible API, Jina-compatible endpoints, MCP server and Python/Node SDKs. Fast (PDF 2.4 ms/page; 3,500–4,400 pages/s in ~300 MB) and open source (AGPL-3.0).
Hi everyone — I'm Zhou, the solo developer of PageSift. I built it because the crawler feeding my research platform was too slow and too dirty, so I wrote my own in Rust. Fifteen days after the first public release, 1.0 closes the feature list: OCR, change monitoring, browser actions, self-healing extraction, and an agent endpoint. Everything is measured, and the benchmark methodology (including where PageSift loses) is public in the repo. Happy to answer anything — and head-to-head benchmarks against your current stack are very welcome.
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About PageSift on Product Hunt
“Web content engine for AI agents: pages & PDFs → Markdown”
PageSift was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #583 on the daily leaderboard. PageSift turns URLs, PDFs and Office files into clean Markdown and structured data for AI agents. One self-hosted Rust binary: no per-page pricing, no external services. It scrapes, crawls, maps, batches and monitors sites, parses PDFs (including scanned, with built-in OCR), Word, Excel, PowerPoint, EPUB and CSV, and offers CLI, Firecrawl-compatible API, Jina-compatible endpoints, MCP server and Python/Node SDKs. Fast (PDF 2.4 ms/page; 3,500–4,400 pages/s in ~300 MB) and open source (AGPL-3.0).
PageSift was featured in Open Source (69.1k followers), Developer Tools (522.9k followers), Artificial Intelligence (483.4k followers) and GitHub (41.5k followers) on Product Hunt. Together, these topics include over 282.5k products, making this a competitive space to launch in.
Who hunted PageSift?
PageSift was hunted by Yuanchao Wu. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how PageSift stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Yuanchao Wu
Hi everyone — I'm Zhou, the solo developer of PageSift. I built it because the crawler feeding my research platform was too slow and too dirty, so I wrote my own in Rust. Fifteen days after the first public release, 1.0 closes the feature list: OCR, change monitoring, browser actions, self-healing extraction, and an agent endpoint. Everything is measured, and the benchmark methodology (including where PageSift loses) is public in the repo. Happy to answer anything — and head-to-head benchmarks against your current stack are very welcome.