OpusPDF
Everyday PDF tools with a real free plan
What is OpusPDF?
OpusPDF is a PDF workspace with 34 tools: merge, split, organize, rotate, compress, PDF to Word, Excel and PowerPoint, OCR, page numbers, extract pages, PDF to CSV, repair and more. Eight core tools work without signup, and a free account unlocks the wider toolkit.
The Free plan stays free: everyday tools, files up to 25 MB and 5 tasks a day. Starter is $3.99/month with PDF to Word and larger files. Pro is $6.99/month with 315 AI OCR pages a month for scanned documents. Business is $9.99 per seat per month with 1,200 AI OCR pages, shared billing and seat management.
Uploaded files are deleted automatically after a short retention window. No ads.
Built by Sharful, a solo developer in Chattogram, Bangladesh (Axiom Opus Labs).
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FounderPlaybooks.
What other founders did to grow.
2722 dispatches from hundreds of founders, pulled from the week's best podcasts.
We started with a newsletter that validated the initial traction then with a no code app at the time that validated the product market fit and then a real app that I've developed to really scale.
De-Risk Validation With A Newsletter, Then A No-Code App, Then The Real App
Loick's repeatable playbook follows three escalating commitment steps: a newsletter to prove people care, a no-code app to prove they will use it, then a real engineered app to scale. He used this exact sequence on Infuspy, Mania, and Drop Magic — each hit $35K+ MRR in their first month using the same ladder.
We went on Craigslist and started saying 'hey, we're looking for people to weigh their plants every day twice per day for a couple months.' We had hundreds of responses. People thought it was cool to be doing citizen science — we ended up with people in Berlin and Sydney.
Crowdsource training data on Craigslist — citizen-science weight-tracking
Greg needed global plant water-loss data to train its ML model. Rather than build a warehouse, Alex hit Craigslist and got hundreds of volunteers — including across hemispheres (which mattered for solar-radiation modeling). The product's ML core was trained on data they didn't pay for, contributed by people who liked the mission.
There's a play for whatever you're stuck on.
Read all 2722 playbooks