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

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 Fly.io Container Developer

A backend or full-stack developer who needs to run server-side applications — not just static sites and serverless functions — and wants them deployed globally without managing Kubernetes or paying for managed Kubernetes overhead. They found Fly.io and found a platform that takes a Dockerfile and runs it near users. They `fly deploy`. It works. They have opinions about Fly.io that include real affection and specific frustrations, which is the relationship one has with a platform they actually depend on.

“It happened mid-workflow — they're deploying a Phoenix application — Elixir, with WebSockets and a persistent database connecti.”

The Fly.io Edge Deployer

A backend developer or DevOps engineer who deploys applications on Fly.io because they need their app running close to users globally — not just served from a CDN, but actually computing at the edge. They've outgrown Heroku's simplicity, don't want AWS's complexity, and find Vercel too opinionated for non-Next.js workloads. Fly.io hits the sweet spot: Docker containers deployed globally with a CLI that feels developer-first. They're comfortable with infrastructure but don't want it to be their full-time job.

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about stateful workloads at the edge (databases, volumes) have limitations that aren't always clear until production in two weeks.”

The Logseq Academic Researcher

An academic researcher, PhD student, or independent scholar who uses Logseq as their research knowledge base. They take notes on papers, link concepts across disciplines, and use the graph view to see how ideas connect in ways linear note-taking never revealed. They chose Logseq because it's local-first (their research data stays on their machine), uses an outliner format that matches how they think, and builds a knowledge graph without forcing a predetermined structure. They are building a second brain for their research, and they expect it to outlast their current institution.

“The shift was quiet.”

The Make Integration Architect

An automation specialist, operations engineer, or technical ops manager who builds complex workflows in Make because Zapier wasn't enough. They connect 10–30 tools with branching logic, iterators, aggregators, error handlers, and data transformations. They build automations that look like flowcharts, not if-then rules. They've learned Make's visual interface deeply — routers, filters, webhooks, custom HTTP modules. They are the person who automates what everyone else does manually, and they take quiet pride in systems that run for months without intervention.

“A teammate asked how they managed build multi-step automations with branching logic that handles different cases (approval/rejection, success/failure).”

The Roam Research Networked Thinker

A writer, researcher, or knowledge worker who uses Roam Research as an extension of their thinking. They don't organize notes into folders — they write, link, and let the graph reveal connections. They use daily notes as their entry point, double-bracket references to build a web of ideas, and block references to connect thoughts across pages. They've read about Zettelkasten, spaced repetition, and evergreen notes. They've adopted some of these ideas and adapted others. They are building a thinking system, not a filing system.

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about performance degrades with large graphs — search and page loads slow down over time in two weeks.”