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Run multiple PDF parsers locally on macOS, compare outputs side by side, switch engines in seconds, and auto-install agent skills so cleaner document output flows straight into your AI workflow. *Windows and Linux coming soon.
We built DocDot because local PDF work still comes with a bad tradeoff. Keep documents on-device, and setup gets messy. Chase speed, and you often give up fidelity. DocDot gives you a one-line install on macOS, runs fully local, and brings multiple PDF parsers into one workflow so you can install them, switch instantly, and compare output on the same file. It also auto-installs skills for popular agents, so the parsed result can move straight into downstream agent workflows.
Today DocDot includes NanoDoc, PaddleOCR, GLM-OCR, LiteParse, and MinerU.
In our current benchmark, NanoDoc leads on overall quality at 83.66, table TEDS at 83.19, and speed at 9.25 FPS, with peak memory at 1.78 GB. MinerU still leads formula CDM at 86.64, which is exactly why we keep multiple parsers in one workflow.
Rather than forcing a single solution, we provide a streamlined local method to pick the right backend for the document in front of you.
We will be in the comments all day. If you have a file that usually breaks your workflow, please share it with us.
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About DocDot on Product Hunt
“The simplest way to run any PDF parser, locally”
DocDot was submitted on Product Hunt and earned 8 upvotes and 1 comments, placing #28 on the daily leaderboard. Run multiple PDF parsers locally on macOS, compare outputs side by side, switch engines in seconds, and auto-install agent skills so cleaner document output flows straight into your AI workflow. *Windows and Linux coming soon.
DocDot was featured in API (98.5k followers), Developer Tools (517.3k followers) and Artificial Intelligence (475.6k followers) on Product Hunt. Together, these topics include over 202.9k products, making this a competitive space to launch in.
Who hunted DocDot?
DocDot was hunted by Kevin William David. 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 DocDot stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Kevin William David
Hi Product Hunt! Thanks for checking us out.
We built DocDot because local PDF work still comes with a bad tradeoff. Keep documents on-device, and setup gets messy. Chase speed, and you often give up fidelity. DocDot gives you a one-line install on macOS, runs fully local, and brings multiple PDF parsers into one workflow so you can install them, switch instantly, and compare output on the same file. It also auto-installs skills for popular agents, so the parsed result can move straight into downstream agent workflows.
Today DocDot includes NanoDoc, PaddleOCR, GLM-OCR, LiteParse, and MinerU.
In our current benchmark, NanoDoc leads on overall quality at 83.66, table TEDS at 83.19, and speed at 9.25 FPS, with peak memory at 1.78 GB. MinerU still leads formula CDM at 86.64, which is exactly why we keep multiple parsers in one workflow.
Rather than forcing a single solution, we provide a streamlined local method to pick the right backend for the document in front of you.
We will be in the comments all day. If you have a file that usually breaks your workflow, please share it with us.