Tana

I got a new iPad yesterday and so I was very excited to try out Tana in a fully mobile environment. I know there isn't an iPad app per se, but I was thinking, oh, well, I can use [app.tana.inc](http://app.tana.inc) in the Safari browser and just save the bookmark like an app in my Dock. Boy was that an unpleasant surprise! Checking boxes doesn't work. Dragging and dropping doesn't work. I can see the same things that I see on my desktop in a browser on the ipad, but about the only thing that does seem to work is tapping to position the cursor and typing. Two questions: 1. Is there some kind of workaround here that others are using that I don't know about? (I'm not talking about Tana Capture. I know about that and use it on my phone, but it is also severely limited.) 2. If not, is there a native iPad map on the roadmap for any point in the future? Thanks!

I've been using [Tana](https://outliner.tana.inc/) as my own personal second brain for years and a couple of months ago they released an MCP for it so I've been experimenting with allowing agents in and having it as a shared second brain that both agents and I can directly edit - keeping track of project knowledgebases etc and also having the agent able to search through my own personal notes for context around my thinking on any topic is very powerful I think. And I think Tana's structure is very suitable for discoverability and fitnding only the context that matters, as it is all built around paragraph-sized nodes rather than full markdown pages in something like Obsidian.

# Introduction As Microsoft 365 Copilot, ChatGPT, and Slack AI become standard organizational infrastructure, a second layer of change is taking shape. In specific workflows that general-purpose tools reach poorly — systematic literature review, deep work time defense, terminal-based development, internal video production — narrowly focused tools are making a measurable difference. This post maps the tools worth evaluating even inside organizations that already have M365 or Google Workspace. The framing throughout is not a feature list but a question: what specific work problem does each tool reduce, and how? Vendor-claimed figures and independently measured figures are labeled separately. >A note on numbers: Some statistics below are vendor self-reported or single-source. Where that is the case, the source type is noted alongside the figure. # 1. Literature, Evidence, and Long-Form PDFs Ask a general-purpose LLM a research question and you often get an unsourced summary or a hallucinated citation. The tools in this category connect directly to academic databases and return evidence with verifiable references. https://preview.redd.it/a5ktap8mfhwg1.png?width=1408&format=png&auto=webp&s=fc7d190e9aac53afd63a7e6df5f0559dc1b840bc # Elicit Elicit is an AI research assistant that searches and analyzes 138 million+ academic papers and 545,000+ clinical trials. As of 2026, the platform reports 5 million+ researchers as users (official site figure). The most active domains are psychology, public health, and machine learning. The core workflow is systematic review support: Elicit extracts study design, sample size, and outcome variables into a structured table, substantially reducing first-pass screening labor. Two documented case studies from Elicit's official site illustrate the scale of impact: * **VDI/VDE** (German standards organization) used Elicit for a systematic review informing German education policy. Elicit correctly extracted **1,502 of 1,511 data points — a 99.4% accuracy rate** — while enabling the team to consider **11x more evidence** than a manual process would have allowed. * **Formation Bio** applied Elicit to analyze 1,600 clinical trial papers while developing a clinical asset targeting knee osteoarthritis, reporting **10x faster processing** compared to manual review. Researchers report up to **80% time savings** when using Elicit for systematic reviews (Elicit self-reported figure). In March 2026, Elicit released a public API, enabling integration with external research automation pipelines. Best fit: drug development, policy analysis, investment research, consulting reports — any role where **externally sourced, citable evidence** is a core deliverable. # Consensus Consensus searches **250 million+ peer-reviewed papers** sourced from Semantic Scholar, OpenAlex, and licensed full-text agreements with major publishers including Wiley, Sage, APA, and Taylor & Francis. More than **10 million researchers, students, and clinicians** use the platform; over **170 university libraries** have institutional partnerships that provide free access to students and faculty. The primary differentiator is the **Consensus Meter** — a visual aggregation of scientific agreement (Yes / No / Inconclusive) across multiple papers for a given research question phrased in yes/no form. Pro and Deep plans extend analysis to full texts of up to 20–50 papers per query, generating structured literature review summaries. Users self-report **70–80% reductions in literature review time** (single-source claim). Consensus itself notes that it is a supporting tool for systematic reviews, not a replacement — search strategy design and final interpretation remain the researcher's responsibility. # SciSpace (formerly Typeset) SciSpace allows researchers to query, summarize, and get section-by-section explanations **directly inside a PDF**. Its strength is in explaining equations, tables, and methodology sections — making it useful for cross-disciplinary reading or onboarding non-specialist stakeholders to technical papers. Less suited to large-scale systematic reviews, more suited to the "what does this section actually mean?" use case that PMs and strategy teams frequently face. # 2. Calendar and Time Blocking: Defending Deep Work The central challenge in AI scheduling is not booking meetings. It is **proactively protecting and defending focus time** before meetings can occupy it. Reclaim.ai's own user data (self-reported) puts the gap clearly: the average employee says they need **19.6 hours of focus time per week** to be productive, but they actually get **10.6 hours**. Managers report that only 50.2% of their blocked focus time is spent on productive task work. In a 256-person survey Reclaim conducted at Microsoft Ignite 2025, 53.1% of Microsoft users reported getting two or fewer deep work sessions per week — against a stated need of 4.2 — and 16.4% reported getting **zero** deep work sessions in a typical week. https://preview.redd.it/cwdm6a4nfhwg1.png?width=1408&format=png&auto=webp&s=0df8910c21826bfb6e8eebb3443c88c58201855d # Motion Motion has repositioned from "AI scheduling tool" to "AI Employee SuperApp" since 2025. It currently has **1 million+ global users** and raised a **$60 million Series C in December 2025 at a $550 million valuation**. The core mechanism: Motion merges your task list and calendar, analyzing **1,000+ parameters** — deadlines, priorities, dependencies, meeting commitments, working hour preferences, and historical completion patterns — to auto-schedule the day. When a meeting shifts or an urgent task appears, the entire schedule re-optimizes in real time. G2 desktop rating is 4.5/5. After the required 2–4 week calibration period, users report saving **3–5 hours per week** on planning work (based on aggregated G2 and Reddit user reviews, not an independent study). Limitations worth noting: the mobile app sits at 2.7/5 (Google Play), the learning curve is real, and pricing transparency has declined in 2026 — verify current rates directly on the official site. # [Reclaim.ai](http://Reclaim.ai) Reclaim.ai dynamically protects focus time, habits, 1:1 meeting slots, and buffer time across personal and team calendars. It integrates with both Google Calendar and Microsoft Outlook. The platform is trusted by **600,000+ users across 70,000+ companies** and holds a 4.8/5 G2 rating. Reclaim's self-reported aggregate user data shows: * Average of **+7.6 hours of focus time per week** gained * **-4.15 hours of overtime per week** * **-46.7% burnout** rate One documented customer case: a Focus Time Initiative deployment resulted in employees gaining **9.8 additional hours of weekly focus time** with AI scheduling (single case study, Reclaim-published). Where Motion excels at full-day task-to-calendar automation, Reclaim is more focused on protecting specific time blocks — deep work sessions, habits, and buffer time — from being consumed by meeting requests. # 3. AI-First Workflow Automation: When Zapier Isn't Enough Two scenarios fall outside what Zapier or Make handle well: workflows requiring **contextual judgment** (not just conditional branching), and automations where **human approval** is a required step before execution. https://preview.redd.it/8b9j6s0ofhwg1.png?width=1408&format=png&auto=webp&s=a2e0e76a41ea5dd82af86c3ff94654d5debf48e2 # Lindy Lindy is an AI agent platform built for repetitive judgment scenarios that span email, CRM, and calendar — drafting context-aware responses, routing and classifying inbound messages, and coordinating meeting scheduling across systems. The design moves beyond static IF/THEN rules toward multi-step flows where the AI interprets context before acting. Common deployment patterns: automated lead outreach drafting for sales teams, first-pass ticket triage for support, and meeting prep workflows. # [Relay.app](http://Relay.app) Relay.app treats **human-in-the-loop steps** as a first-class feature rather than an afterthought. It is designed for workflows where full automation is not appropriate — refund approvals, discount authorizations, external communications that need a human sign-off before sending. The pattern is: AI drafts the action, a designated person approves, then execution proceeds. Particularly suited to operations teams where control and auditability matter more than full autonomy. # Bardeen Bardeen is a Chrome extension-based automation tool specialized in **browser UI manipulation and web data extraction**. LinkedIn scraping, form auto-fill, and structured data harvesting from web pages are its primary use cases. It operates where desktop apps do not — directly inside the browser. Most commonly deployed by SDRs and growth marketers who spend significant time collecting data from web-based sources. # 4. Meeting AI: Alternatives to the Major Transcription Tools Beyond Otter.ai and Fireflies, several tools serve specific meeting use cases more precisely. https://preview.redd.it/r94772rofhwg1.png?width=1408&format=png&auto=webp&s=19fe02114692aaab45d40f446942a7550c28551b # Fathom Fathom is optimized for Zoom-centric teams and offers recording, transcription, and basic summaries on its free plan. In a comparative test across 165 meetings run through April 2026, Fathom averaged **89.7% transcription accuracy** — on par with Otter.ai (89.4%) in the same test. Its differentiation is in highlight extraction and sharing rather than full transcription depth. Best fit: teams whose primary goal is sharing a few key moments or decisions from a call, not maintaining a full searchable archive. # [Read.ai](http://Read.ai) Read.ai focuses on **meeting quality analytics** rather than transcription alone. It surfaces engagement metrics, speaker timelines, and a meeting insights dashboard — making it well suited to leadership or facilitation reviews of meeting culture. HR and organizational development teams in hybrid environments use it to measure whether meeting culture improvements are actually taking hold over time. # tl;dv tl;dv's primary value is building a **searchable library of recorded sales calls and user interviews**. Product managers and researchers use it to pull timestamped quotes from past calls — making it easier to cite specific customer language in product decisions without rewatching full recordings. Best for teams that treat recorded conversations as a knowledge asset worth referencing over time. **Note:** All meeting recording and transcription tools require participant consent and compliance with applicable privacy laws. Pre-notification requirements vary by country, platform, and industry. Internal legal review should precede any deployment. # 5. Developer and Terminal AI: Complementing the IDE Copilot Beyond GitHub Copilot and Cursor, several tools serve terminal-heavy workflows, whole-repository refactoring, and organizations with data residency or model-choice requirements. https://preview.redd.it/p4kttrnpfhwg1.png?width=1408&format=png&auto=webp&s=f4642f7b1269691271212e391d4b108891931a57 # Warp Warp is a terminal rebuilt around AI as a first-class capability. As of 2026, **500,000+ engineers** use the platform (official figure). In 2025 the numbers that Warp published in its year-end review tell the story of rapid adoption at scale: * Agents edited **3.2 billion lines of code** across the year * Developers generated nearly **100 million lines of code per week**, with a **97%+ acceptance rate** on agent-suggested diffs * Agents indexed and synced **120,000+ codebases** for context * Following the Warp 2.0 launch in July 2025, **2 million agents ran daily** — growing 200% month-over-month in the first month * Revenue grew **15x** from the start of 2025 to the end of July 2025 In a Q1 2026 developer survey of 2,847 respondents across 320 organizations, **14% of DevOps and platform engineers** named Warp AI as their primary AI coding tool. NPS was measured at **+44**. Warp ranked **#1 on Terminal-Bench** and **#5 on SWE-bench Verified** as of November 2025. The natural-language shell command drafting and correction features lower the barrier for developers and data scientists who are less fluent in infrastructure scripting. MCP server support allows external context to be wired into agent workflows. # Aider Aider is an open-source AI coding agent that runs in the terminal and applies multi-file patches interactively against a Git repository. Rather than suggesting edits to a single open file, it works with the full repository context — making it better suited to refactors, dependency updates, and migrations that touch multiple files simultaneously. There is no tool cost; you pay only for the LLM API calls (OpenAI, Anthropic, or others). In the Q1 2026 developer survey, 1% of respondents named Aider as their primary tool and 7% reported having used it. It holds a power-user niche rather than mainstream IDE share, but among developers who need whole-codebase changes, it is a consistent choice. # Continue Continue is an open-source AI coding extension for VS Code and JetBrains IDEs that lets organizations bring their own models and API keys — local models, self-hosted LLMs, or any provider. The key distinction from proprietary IDE copilots is full model flexibility: teams can swap models, stay on-premises, or connect to open-weight LLMs without vendor lock-in. Primarily selected by organizations with strict data residency requirements or a preference for open-source infrastructure. # 6. AI Video and Audio Editing: The One-Person Content Team The number of teams expected to produce internal training videos, onboarding materials, and sales demo clips without a dedicated video editor continues to grow. https://preview.redd.it/tj03yteqfhwg1.png?width=1408&format=png&auto=webp&s=debcaad85c98cd2dcd3d955fda5cfbdaca9bdfbd # Descript Descript is an AI-powered video and podcast editing platform built around a text-first editing model: deleting a word from the transcript removes that segment from the timeline. Filler word removal, speaker voice overdubbing, and screen recording are handled within a single interface. The business metrics, sourced independently, reflect genuine traction: * **ARR of approximately $55 million** in late 2024, representing **75% year-over-year growth** (Sacra estimate) * Mid-market B2B customer base grew **+37% year-over-year** to approximately 120 customers as of January 2026 (YipitData analysis of 1,300+ mid-market companies) * Average monthly spend per mid-market customer more than **doubled over 2025**, reaching approximately **$3,000/month** — the steepest monetization acceleration in the AI video editing category * Content Creation category **rank #3** as of March 2026, with adoption rate up **6 percentage points year-over-year to 13%** (Ramp spend data across 1,000+ companies) The editorial model — editing video by editing text — creates a genuine workflow shift for teams that do not have video editing expertise in-house. # 7. Personal Knowledge Management: The Alternative to Team Wikis Where Notion AI targets team-level wikis, the tools in this category focus on **accumulating and connecting an individual's thinking** over time. https://preview.redd.it/2okolm6rfhwg1.png?width=1408&format=png&auto=webp&s=4b7e5937ccca15e3ba5d67987f1a6e24332ebcf8 # Obsidian + AI Plugins Obsidian is a local-first, markdown-based knowledge management tool built around bidirectional linking. Because files live on your device and are never uploaded to a vendor's server, it is compatible with environments that have strict data residency or confidentiality requirements. Community plugins (such as Smart Composer) layer RAG-based Q&A or AI drafting on top of the local vault — with the user managing their own API keys. The app itself is free for personal use. Device sync requires either manual management or the paid Obsidian Sync add-on. Most AI plugins require a self-supplied LLM API key. # Tana Tana is a structured note-taking tool that combines Supertags, outlining, and database-style properties in a single flow. AI capabilities propose tags, generate summaries, and surface connections between nodes — allowing the workspace to evolve into a searchable knowledge graph over time. Better suited to individual power users and independent researchers building a personal thinking system than to team wikis. # 8. UI and Prototype AI: Closing the Gap Between Planning and Development https://preview.redd.it/tco4cxurfhwg1.png?width=1408&format=png&auto=webp&s=38391cfd8ffed89cb7d13bf6f99a8d95432d5311 # v0 (Vercel) v0 generates React and shadcn/ui-style UI components and screen structures from text descriptions. Product managers and front-end developers use it to produce a structurally coherent first draft of a PoC or internal tool without waiting for a design handoff. The generated code can be pasted directly into a project. The practical benefit is compressing the "request a wireframe — wait for design — request implementation" loop into a direct starting point. # Uizard Uizard converts hand-drawn sketches or screenshots into editable wireframes and clickable prototypes. It serves the early stages of a design review cycle — getting a rough layout into a shareable digital format before committing to full design work. Text-to-wireframe generation makes it useful for quickly visualizing UI ideas in planning sessions. # 9. How These Tools Pair With the Main Stack These tools work best when their role is clearly differentiated from the primary stack, rather than overlapping with it. |Workflow Area|What the Main Stack Handles|What the Specialized Tool Adds| |:-|:-|:-| |Research|ChatGPT / Perplexity (exploration, drafting)|Elicit / Consensus (paper evidence, citable sources)| |Time management|Google Calendar / Outlook (baseline scheduling)|Reclaim.ai (deep work defense) / Motion (task + calendar unification)| |Meeting follow-up|Otter.ai / Fireflies (transcription)|Read.ai (meeting quality analytics) / tl;dv (searchable call archive)| |Development|GitHub Copilot / Cursor (in-editor suggestions)|Warp (terminal + agents) / Aider (repository-level refactoring)| |Operations automation|Zapier / Make (event triggers)|Relay.app (human-approval flows)| |Internal video|None or outsourced|Descript (text-based editing)| # Closing As general-purpose AI tools become ubiquitous, the boundaries of what they do not cover become more visible. When Elicit returns a structured extraction table from 1,600 clinical trial papers, when Reclaim dynamically defends focus blocks as meetings land on the calendar, when Warp's agents edit 3.2 billion lines of code in a year — none of those jobs are being done by ChatGPT or Copilot. The productive question when designing a tooling stack is not "do we have an AI tool?" but "which workflow bottlenecks remain after the general-purpose layer?" If the mainstream AI stack is already in place, the next step is finding the friction points it does not reach and filling them with purpose-built tools. Before deploying any tool: verify the data security policy, privacy terms, and organizational IT approval scope. Pricing and feature sets change frequently — always confirm current information on the official pricing page before making purchasing decisions. # Sources |Item|Source|URL| |:-|:-|:-| |Elicit paper database, user count, accuracy case studies (VDI/VDE, Formation Bio)|Elicit (official site, March 2026)|[https://elicit.com](https://elicit.com)| |Consensus paper database, user count, university partnerships|Consensus (official site)|[https://consensus.app](https://consensus.app)| |SciSpace product overview|SciSpace (official site)|[https://www.scispace.com](https://www.scispace.com)| |Motion user count, Series C valuation ($550M, December 2025)|Motion (official site)|[https://www.usemotion.com](https://www.usemotion.com)| |Reclaim.ai user count, focus time data (self-reported)|Reclaim.ai (official site)|[https://reclaim.ai](https://reclaim.ai)| |Reclaim.ai deep work deficit data (Microsoft Ignite, 256-person survey)|Reclaim.ai Blog, 2026 Deep Work Trends Report (December 5, 2025)|[https://reclaim.ai/blog/deep-work-trends-report](https://reclaim.ai/blog/deep-work-trends-report)| |Reclaim.ai focus time benchmarks and productivity data|Reclaim.ai Blog (April 20, 2026)|[https://reclaim.ai/blog/what-is-focus-time](https://reclaim.ai/blog/what-is-focus-time)| |Lindy product overview|Lindy (official site)|[https://www.lindy.ai](https://www.lindy.ai)| |Relay.app product overview|Relay.app (official site)|[https://relay.app](https://relay.app)| |Bardeen product overview|Bardeen (official site)|[https://www.bardeen.ai](https://www.bardeen.ai)| |Fathom product overview|Fathom (official site)|[https://fathom.video](https://fathom.video)| |Read.ai product overview|Read.ai (official site)|[https://www.read.ai](https://www.read.ai)| |tl;dv product overview|tl;dv (official site)|[https://tldv.io](https://tldv.io)| |Warp 2025 usage metrics (3.2B lines, 2M daily agents, 97% acceptance rate)|Warp Blog, Warp Wrapped: 2025 in Review (December 30, 2025)|[https://www.warp.dev/blog/2025-in-review](https://www.warp.dev/blog/2025-in-review)| |Warp 2.0 post-launch metrics (2M daily agents, 15x revenue growth)|Warp Blog (July 29, 2025)|[https://www.warp.dev/blog/agentic-development-environment-two-million-agents](https://www.warp.dev/blog/agentic-development-environment-two-million-agents)| |AI coding tool adoption survey — Warp AI and Aider figures (Q1 2026, n=2,847)|Digital Applied (Q1 2026)|[https://www.digitalapplied.com/blog/ai-coding-tool-adoption-2026-developer-survey](https://www.digitalapplied.com/blog/ai-coding-tool-adoption-2026-developer-survey)| |Aider product overview|Aider (official site)|[https://aider.chat](https://aider.chat)| |Continue product overview|Continue (official site)|[https://continue.dev](https://continue.dev)| |Descript ARR and growth rate estimate (\~$55M ARR, 75% YoY)|Sacra (2025)|[https://sacra.com/c/descript/](https://sacra.com/c/descript/)| |Descript adoption rate in Content Creation category (rank #3, 13% adoption)|Ramp (March 30, 2026)|[https://ramp.com/vendors/descript](https://ramp.com/vendors/descript)| |Descript mid-market customer growth and spend analysis|YipitData (2026)|[https://www.yipitdata.com/resources/blog/descript-vs-veed-vs-kapwing-ai-video-tools](https://www.yipitdata.com/resources/blog/descript-vs-veed-vs-kapwing-ai-video-tools)| |Obsidian product overview|Obsidian (official site)|[https://obsidian.md](https://obsidian.md)| |Tana product overview|Tana (official site)|[https://tana.inc](https://tana.inc)| |v0 product overview|Vercel v0 (official site)|[https://v0.dev](https://v0.dev)| |Uizard product overview|Uizard (official site)|[https://uizard.io](https://uizard.io)| *This post is a research summary based on public company disclosures, official product documentation, and independently published market data. It does not constitute an endorsement of any specific product. Pricing, features, and terms of service change frequently; verify current information on official product sites before making purchasing decisions.*

Here are some of the best tools I’ve come across for building and working with a personal or team knowledge base. Each has its own strengths depending on whether you want note-taking, research, or fully accurate knowledge retrieval. [Recall ](https://www.getrecall.ai)– Self organizing PKM with multi format support Handles YouTube, podcasts, PDFs, and articles, creating clean summaries you can review later. Also has a “chat with your knowledge” feature so you can ask questions across everything you’ve saved. [NotebookLM ](https://notebooklm.google)– Google’s research assistant Upload notes, articles, or PDFs and ask questions based on your own content. Very strong for research workflows. It stays grounded in your data and can even generate podcast-style summaries. [CustomGPT.ai](http://CustomGPT.ai) – Knowledge-based AI system (no hallucination focus) More of an answer engine than a note-taking app. You upload docs, websites, or help centers and it answers strictly from that data. What stood out: * Doesn’t hallucinate like most AI tools * Works well for team/shared knowledge bases * Feels more like a production-ready system MIT is using it for their entrepreneurship center (ChatMTC), which is basically the same use case internal knowledge → accurate answers. [Notion AI](https://www.notion.so) – Flexible workspace + AI All-in-one for notes, tasks, and databases. AI helps with summarizing long notes, drafting content, and organizing information. [Saner ](https://saner.ai)– ADHD-friendly productivity hub Combines notes, tasks, and documents with AI planning and reminders. Useful if you need structure + focus in one place. [Tana ](https://tana.inc)– Networked notes with AI structure Connects ideas without rigid folders. AI suggests structure and relationships as you write. [Mem ](https://mem.ai)– Effortless AI-driven note capture Capture thoughts quickly and let AI auto-tag and connect related notes. Minimal setup required. [Reflect ](https://reflect.app)– Minimalist backlinking journal Great for linking ideas over time. Clean interface with AI assistance for summarizing and expanding notes. [Fabric ](https://fabric.so)– Visual knowledge exploration Stores articles, PDFs, and ideas with AI-powered linking. More visual approach compared to traditional note apps. [MyMind ](https://mymind.com)– Inspiration capture without folders Save quotes, links, and images without organizing anything. AI handles everything in the background. What else should be on this list? Always looking for tools that make knowledge work easier in 2026.

There’s only one tool I’ve been using for more than a few weeks, and it’s one I’ve been using for 4 or 5 years. It’s called Tana.inc. It’s like Workflowy, but better. I hate so much about it. I hate how slow it loads, how unintuitive it is to use advanced features (the basic stuff is pretty intuitive), and I absolutely hate how the devs work on it and provide support to it and the direction they’re taking it and the company. But god damn, it fits my brain perfectly. It’s literally the perfect tool, made just for me. It’s magical, the way that thing works when it works.

I was just working in it and got a notification to update. I did and then I heard the trashcan sound and Tana isn't even on my Mac anymore? Then I went to login online and see this. Also when I go to homepage -> [https://tana.inc/](https://tana.inc/) Im seeing its just for meetings now?? And then I see [https://outliner.tana.inc/](https://outliner.tana.inc/) as a tiny notification bar at the top? Whats going on??? Super confusing and NOT cool.

I come from Fedora 43, which I love, but I've been using EOS for a month now, and, while not everything is working 100%, I kinda grew fond of it and will keep using it. However, there's one thing there's this one app \[[Tana](https://tana.inc)\] that doesn't offer an app for Arch systems, only Debian and Redhat. While I can use the app on the browser, I so much wish there was an Arch version. Question: what do folks usually do in these cases? If it was open source, I think one could build it for Arch, but since it isn't, then I guess there's no way around it, right? Thanks.

Make a field called “Topics” and configure it to have multiple options. Add it to your workspace’s schema (click the “Make discoverable” button at the bottom of the field configuration panel) Everytime you want to associate a node with a topic just type the `>` character and use the “Topics” field that way. This way, you can search for nodes associated with those topics using Tana’s search nodes. If you want, those options can be restricted to nodes with a supertag, for example, those tagged #Topic. Then each topic can have its own node as well. Like this: 1. Type “>” anywhere in a new empty node 2. A field will be created but it has no name 3. Type “Topics” to name that field that. Now you’ve created a field but it’s not global to your workspace yet. 4. Right click on the field, select “Configure field…” 5. Make the Field type “Options” or “Options from supertag”. Depending on whether you want each value to be a regular node or tagged with a specific supertag (for example “#Topic”) 6. Configure the rest of this field to your liking! 7. Click “Make discoverable” to make that field global to your workspace 8. Next time you want to associate a node with a topic, add a “Topics” field to it! It should work like classical tags. Good luck! —— More info about fields on the docs here: https://tana.inc/docs/fields