Toolsnetwork
157 AI tools, sorted so you can choose.
AI & Machine Learning·E-Commerce·Productivity·Marketing
About
Tools Network is a curated directory of AI tools — 157 of them across 25 categories, covering chatbots and assistants, coding, image generation, writing, video, automation, productivity and more.
It exists for a specific moment: you know roughly what you need to get done, and you want to stop researching and decide. Most AI lists are either an unsorted dump of everything that exists or a thin page built to rank. This one is built to get you from "I need something for X" to a shortlist you can act on.
Search by the task you're trying to do, filter by category, and compare what's genuinely different between the options. Every listing carries what decides a choice — what the tool does, its category, its pricing tier (Free, Freemium, Free trial or Paid), a rating with review count, and a detail page with the specifics and a direct link. That's enough to rule most options in or out without opening ten tabs.
Nothing is behind a signup. There's no paywall on browsing and no email gate.
Listings come from two places: a catalogue we maintain editorially, and submissions from the people who build the tools. Submissions pass an automated screening step before anything publishes, which is what keeps this a curated list rather than an open dumping ground. Submitting is free.
We also publish comparisons, alternatives roundups, pricing breakdowns and how-to guides for the decisions the directory can't make for you. Prices are read from the vendor's own pricing page and stamped with the date they were checked.
Tools Network is independent and run by a small team. We're open about the fact that we operate the directory these listings live in — the aim is a resource we'd use ourselves, which means being honest about what a tool is bad at, not only what it sells well.
Ready to launch?
Submit your product and get discovered by builders and creators worldwide.
Launch Now
FounderPlaybooks.
What other founders did to grow.
2722 dispatches from hundreds of founders, pulled from the week's best podcasts.
The color of the button, the language — a lot of that really translates across the portfolio. But the offering — whether we offer annual or monthly, free trial or not — that's more nuanced and we tailor it to the product.
Paywall design insights port across apps; pricing and offer structure must be tailored per product
Running 37 apps gives Maple Media a natural A/B testing laboratory. UI and copy learnings (button colour, CTA language) generalise well and get applied portfolio-wide. But offer mechanics — trial length, annual vs monthly default, premium model vs ads — must be matched to how and why users pay in each specific category. Separating the transferable from the bespoke saves time without sacrificing conversion.
If you look at it from the framework of LTV of a cohort that's probably the right framework as a starting point… that's a hard thing to optimize for in a single experiment — you have to project based on things that are happening at day eight or day 30.
Cohort LTV Is the True North — Proxy Metrics Get You There Faster
Burner runs paywall and pricing experiments with cohort LTV as the north star, but since maturing LTV data takes months, the team uses early proxy signals (day-8 conversion, day-30 retention) to pick winners before the full curve is visible. Knowing what you're optimising for upfront is the prerequisite for trustworthy experiments.
There's a play for whatever you're stuck on.
Read all 2722 playbooks