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

The Notion AI Knowledge Worker

A product manager, writer, or operations lead who already uses Notion as their primary workspace and added Notion AI to make their existing workflows faster. They were already in Notion 4–6 hours a day. Notion AI is not a new tool to them — it's a capability inside the tool they already trust. They use it to summarize meeting notes, draft first versions of documents, and ask questions of their existing workspace. The context is already there. The AI can work with it. This is the part that makes Notion AI different from a separate AI tool to them.

“It happened mid-workflow — they've just finished a 90-minute discovery call.”

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 Notion Calendar Unified Planner

A founder, PM, or knowledge worker who lives in Notion and has always felt the calendar app sitting separately as a second system that doesn't talk to the first. They adopted Notion Calendar because the promise — their calendar and their Notion workspace, unified — is the thing they've wanted for years. They're still calibrating how much of that promise is real. The answer is: more than Google Calendar, not yet everything they imagined.

“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.”

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 Craft Docs Intentional Writer

A product manager, writer, consultant, or knowledge worker who uses Craft as their primary document and note environment because it is the only tool that takes both writing and structure seriously at the same time. They're on Apple devices — Mac and iPhone, usually iPad. They've tried Notion (too database-y), Bear (too simple), Obsidian (too much tinkering), and Apple Notes (not embarrassed about this, just limited). Craft is what they settled on. The fact that it looks good is not superficial to them — environment affects their thinking.

“It happened mid-workflow — they're preparing a strategy document for a quarterly review.”

The Readwise Highlight Librarian

A voracious reader — typically a knowledge worker, researcher, writer, or lifelong learner — who realized that reading without retention is expensive entertainment. They started using Readwise because they kept forgetting what they'd read. They now have 8,000–30,000 highlights across Kindle books, web articles, PDFs, and podcasts. They do the daily review. Not every day — most days. The review takes 5 minutes and resurfaces things they've completely forgotten. Occasionally a highlight resurfaces at exactly the right moment for what they're working on. This is not magic. This is why they pay for Readwise.

“It happened mid-workflow — tuesday morning.”

The Roam Research Networked Thinker

A researcher, academic, writer, or knowledge-intensive professional who uses Roam because it is the only tool that treats the connection between ideas as a first-class object. They write in Daily Notes. They [[bracket]] everything. They have a graph with 3,000–15,000 nodes that they've been building for 2–4 years. They know their graph is their most valuable intellectual asset. They also know that Roam's development has slowed, that the tool has rough edges, and that they've considered migrating to Obsidian or Logseq at least twice. They haven't migrated. The switching cost is partly the data — mostly the habit.

“They're writing an essay about institutional memory.”

The Slack-Drowning Knowledge Worker

A full-time knowledge worker — marketer, PM, ops, customer success — at a company large enough that Slack has become the ambient noise of their workday. They didn't design the channel structure they live in. They inherited it. They have 12 unread DMs, are mentioned in 3 channels they rarely check, and have muted so many channels that important things occasionally slip through the cracks. They're not bad at their job. They're bad at Slack because Slack has become its own job.

“It happened mid-workflow — it's 10am.”

The Readwise Knowledge Synthesizer

A voracious reader — books, articles, newsletters, podcasts, Twitter threads — who realized that reading without capturing is forgetting. They use Readwise to collect highlights from Kindle, Instapaper, podcasts, and the web, then Readwise Reader for their daily reading queue. They've built a workflow where everything they consume flows through one system, highlights are tagged and resurfaced, and insights compound over time. They are the person who can always find "that article I read about X" because they highlighted the key passage six months ago.

“It happened mid-workflow — the knowledge worker is writing a strategy memo about pricing models.”

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 Obsidian PKM Builder

A researcher, writer, software developer, or knowledge worker who has built their second brain in Obsidian and means it. They write in Markdown. They link notes intentionally. They have a vault structure they've iterated on at least twice. They use the graph view occasionally, for the pleasure of seeing their thinking made visible, not because it's the most useful view. They've installed 8–20 plugins. They have strong opinions about the right way to take notes, opinions that evolved over two years of using the wrong way.

“A teammate asked how they managed capture ideas in a format that connects them to related ideas automatically.”

The Arc Browser Power User

A developer, designer, or technical knowledge worker who switched to Arc and reorganized their browser-based work around Spaces and Folders instead of horizontal tab strips. They had 40 tabs open in Chrome on a normal day. They were managing them by scrolling and guilt. Arc replaced the tab strip with something structural. They have opinions about it that they've refined over 8 months of use. The opinion is: it's better. The caveat is: it requires learning a new mental model that takes 3 weeks to stop fighting.

“The shift was quiet.”

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.”

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 Notion Second-Brain Builder

A solo founder, PM, or highly organized individual contributor who has made Notion the center of their work life. They have a workspace that would take three hours to explain to someone new. They've built custom dashboards, linked databases, and templates they're genuinely proud of. They've also started from scratch twice after a system got too complex to maintain. They believe the perfect Notion setup is always two weekends away.

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about performance on large databases — the lag is a betrayal in two weeks.”

The Notion Workspace Admin

A team lead, chief of staff, or ops person who became the unofficial Notion admin because they were the first person to organize anything in the workspace. They've built the team wiki, the project tracker template, and the onboarding guide. They spend more time maintaining the structure of Notion than using it for their actual job. They live in fear of someone moving a page to the wrong section and breaking every linked database.

“A new team member joins and asks where to find the product roadmap.”

The Todoist GTD Practitioner

A knowledge worker — often a project manager, consultant, writer, or developer — who has read productivity books and tried multiple task managers before settling on Todoist. They've built a system. It works when they use it. The failure mode is not the tool — it's consistency. They believe in GTD or a GTD-adjacent framework. They have projects, labels, and filters set up in a way that feels logical to them and would confuse anyone else. They've rebuilt the system twice.

“It's Sunday evening.”

The Raycast Power User

A developer, designer, or technical knowledge worker on Mac who replaced Spotlight with Raycast and then spent three weekends making it the center of their computing workflow. They open Raycast more than any other application. They open it for things they didn't know a launcher could do. They've written or installed extensions for their most repetitive tasks. They mention Raycast in the same breath as mechanical keyboards and monitor setups — tools that are invisible when they work and felt intensely when they don't.

“It's 9am.”

The Todoist Productivity System Builder

A knowledge worker — often a freelancer, consultant, or senior IC — who has turned Todoist into a personal operating system. They don't just track tasks; they've built a system. GTD-inspired projects, context-based labels, custom filters for different energy levels and time blocks. They have recurring tasks for weekly reviews, monthly planning, and annual goal-setting. They've tried every productivity app and keep coming back to Todoist because it's fast, reliable, and doesn't try to be more than a task manager.

“It happened mid-workflow — it's Sunday evening.”

The Excel Financial Modeler

A financial analyst, FP&A professional, or investment banker who builds financial models in Excel the way architects build buildings — with structure, precision, and the knowledge that if one formula is wrong, everything above it falls. They've been using Excel for 5–15 years. They think in cell references, not coordinates. They know keyboard shortcuts that most people don't know exist. They've built models that a CEO used to make a $50M decision, and they've spent weekends debugging a circular reference that shouldn't have been circular.

“The CFO asks for a scenario analysis by tomorrow morning: what happens to cash flow if revenue grows 10% slower than projected and two enterprise deals slip to next quarter.”

The Toggl Self-Tracker

A freelancer, consultant, or productivity-conscious knowledge worker who tracks time for one of two reasons: they bill by the hour and accuracy is revenue, or they've realized they have no idea where their hours go and they want to find out. Both types start Toggl for practical reasons and discover something unexpected — tracked time is honest in a way that memory and intention are not. They've had the experience of thinking they spent 4 hours on a project and the timer saying 2.2. They've also had the reverse. Both were useful information.

“It happened mid-workflow — they're a UX consultant.”

The Datadog SRE

A site reliability engineer or platform engineer at a company with a production system that people depend on. Datadog is their window into that system. They've built dashboards that tell the story of what's happening in production. They've written monitors that page them when something goes wrong. They've been paged at 2am by monitors they wrote themselves and have opinions about that experience. They are better at Datadog than most people at their company and still feel like they're using 30% of what it can do.

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about alert fatigue from monitors that fire on normal variance — the cry-wolf problem in two weeks.”