Who actually uses these products,
and what made them stay.

The Kajabi Course Creator

A course creator, coach, consultant, or subject matter expert who chose Kajabi because they wanted one platform instead of five. They have a course, probably a coaching program, possibly a membership community, and they wanted all of it to live together with one checkout, one email system, one analytics dashboard. They pay more for this than they would if they stitched together cheaper tools. They've decided that simplicity and integration are worth the difference. The Kajabi community is genuinely part of their decision — knowing that tens of thousands of other creators are building on the same infrastructure.

“A teammate asked how they managed run a profitable online education business without managing multiple platforms.”

The ConvertKit Creator Monetizer

An online creator — YouTuber, course seller, coach, author, or educator — who has built an email list and uses ConvertKit to turn that audience into revenue. They chose ConvertKit because it was built for creators: the tagging system makes sense for how creators think about audience segments, the Creator Pro features align with their actual business model, and the community of ConvertKit users is full of people doing exactly what they're doing. They are building a creator business, not just an email list. They see their subscribers as an audience, not a database.

“They've just launched a new course.”

The Kajabi Course Creator

A consultant, coach, or subject-matter expert who has turned their expertise into an online course business on Kajabi. They teach everything from business strategy to photography to fitness. They chose Kajabi because they didn't want to connect Teachable + ConvertKit + Stripe + WordPress + Zapier just to sell a course. They build landing pages, host video lessons, manage email sequences, process payments, and track student progress — all in one platform. They are a teacher who became a business owner, and the business runs on Kajabi.

“A teammate asked how they managed create and deliver online courses with video, text, quizzes, and downloadable resources.”

The PostHog Growth Engineer

A growth engineer, product engineer, or technical PM who uses PostHog as their all-in-one growth stack — analytics, feature flags, A/B tests, session replay. They chose PostHog because they didn't want to stitch together Amplitude, LaunchDarkly, and Hotjar. They think in funnels, retention curves, and statistical significance. They are technical enough to self-serve but product-minded enough to care about the "so what" behind the data.

“It happened mid-workflow — the growth engineer is running an A/B test on the onboarding flow.”

The Greenhouse Recruiting Coordinator

A recruiting coordinator or in-house recruiter at a growing company who manages 15–40 open roles simultaneously. Greenhouse is their command center — every candidate, every interview, every offer lives there. They are the logistics engine of hiring: scheduling interviews across time zones, nudging hiring managers for feedback, and keeping candidates warm through what feels like an increasingly long process. They measure their success not in hires made but in process efficiency — time-to-fill, interview-to-offer ratio, candidate experience scores.

“A teammate asked how they managed keep candidate response times under 24 hours across all active roles.”

The Monday.com Team Lead

A team lead or department head at a company of 50–300 people who uses Monday.com as the primary place their team tracks work. They may not have chosen Monday — it was often adopted company-wide because the CEO liked the demo. They've made it work. Their board is actually used. They've built automations their team quietly depends on. They spend 30–60 minutes a day in Monday and would describe it as "pretty good once you know what you're doing," which is a backhanded compliment they mean sincerely.

“Their team has just absorbed a new function.”

The Coda Ops and Strategy Builder

An operations manager, strategy lead, or chief of staff who discovered that the documents they needed didn't fit neatly into either a Google Doc or a spreadsheet. They found Coda and spent two weeks building something they couldn't have built elsewhere — a doc with a database inside it, buttons that trigger actions, and views that update automatically. They are evangelical about it in proportion to how many people they've tried to explain it to. It's hard to explain until you see it.

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about the learning curve that separates people who "get" Coda from people who bounce off it in two weeks.”

The Logseq Local-First Knowledge Builder

A researcher, developer, writer, or privacy-conscious knowledge worker who chose Logseq because their notes are plain `.md` files in a folder they control — not in a proprietary database, not in someone else's cloud. They care about data ownership in a specific way: not paranoia, but principle. They've watched tools sunset, pricing change, and export options degrade. Their Logseq graph syncs to iCloud or a private git repository. It will exist regardless of Logseq's future. They've also genuinely internalized the outliner-first paradigm. They think in bullets that can be linked and referenced anywhere else in the graph.

“They're synthesizing research for a paper.”

The Perplexity AI-Native Searcher

A researcher, analyst, consultant, or curious professional who started using Perplexity for quick lookups and gradually shifted most of their search behavior to it. They value citations. They appreciate the synthesized answer more than a list of links they have to open and read. They trust it for most things. They verify independently for decisions where being wrong has consequences. They've tried to explain why they prefer it to people who use Google and have not fully succeeded because the difference is in the feel of the first answer, which doesn't translate to a verbal description.

“It happened mid-workflow — they're researching a niche regulatory question for a client: the current status of SEC rules around.”

The Storybook Design System Maintainer

A frontend developer or design technologist who maintains the company's Storybook instance. They write stories for every component, document props with controls, set up visual regression testing, and serve as the bridge between designers and developers. They are the keeper of the design system's technical truth. When a designer asks "does this component exist?" the answer lives in their Storybook. When a developer asks "how do I use this prop?" the answer lives in their Storybook. They are the librarian of the component library.

“The shift was quiet.”

The Clay GTM Engineer

A GTM engineer, growth operations lead, or RevOps professional who uses Clay as their data enrichment and workflow engine. They build spreadsheet-like tables that pull from 50+ data providers — enriching companies with technographic data, finding decision-makers' emails, scoring leads based on signals, and triggering personalized outreach. They think in data transformations and API calls. They've replaced hours of manual prospect research with Clay workflows that run in minutes. They are the engineer of the sales pipeline's data layer.

“The shift was quiet.”

The Notion AI Content Strategist

A content strategist, knowledge manager, or team lead who uses Notion AI as part of their daily workflow inside Notion. They don't use it to write blog posts from scratch — they use it to summarize 45-minute meeting transcripts into action items, turn rough notes into structured documents, answer questions about information buried in the team's wiki, and draft from templates. They've found the sweet spot: AI handles the structure, they handle the thinking.

“The shift was quiet.”

The Perplexity Research Analyst

A research analyst, journalist, consultant, or knowledge worker who has replaced their Google-and-10-tabs workflow with Perplexity. They don't search for links — they ask questions and expect synthesized answers with citations. They use it for competitive analysis, market research, fact-checking, and deep dives into topics where they need to learn fast. They've learned which types of questions Perplexity handles well (factual synthesis) and which it doesn't (opinion-based, very recent events). They trust it more than ChatGPT because of the citations, but they still verify.

“A teammate asked how they managed get comprehensive, cited answers to complex research questions in minutes instead of hours.”