In meetings, your typing speed can't keep up with the speaking pace; after class, you can't even understand your own notes; reading literature abstracts takes half a day and you still can't grasp the key points—these three scenarios are almost unavoidable for every office worker, student, and researcher. I've tried using recording pens, Notion templates, and various handwriting-to-text tools, and eventually realized the problem isn't about "remembering or not," but about "how to turn messy information into something usable."
I've been using tidenote continuously for three weeks now. Honestly, I was initially skeptical about "AI note-taking assistants"—there are plenty of AI note-taking tools on the market, but most just convert speech to text and leave it piled up, requiring you to organize it yourself later. What makes tidenote different is that it doesn't focus on recording itself, but on understanding and extracting after recording.
Meeting scenarios: from "recording everything" to "capturing conclusions"
I use it most often for internal weekly meetings and client requirement sessions. In a 40-minute meeting, using traditional methods means either typing throughout until your hand aches, or recording and forcing yourself to listen back later. tidenote can join meetings directly and record in real time, but what really makes me think it's "good" is its automatically generated summary—not some meaningless full-text abbreviation, but one that distinguishes between "discussion process" and "decision conclusions."
For example, last week a client discussed product redesign requirements, and everyone chatted back and forth for nearly an hour. After the meeting, I opened tidenote's organized results and immediately saw three core conclusions, two items to be confirmed, and the next responsible person. All the纠结 and meaningless back-and-forth was filtered out. This saved me at least 30 minutes compared to re-listening and organizing the recording myself.
Of course, not every meeting can be perfectly reproduced. If speakers frequently interrupt each other or multiple people talk at once, tidenote occasionally misattributes roles—something said by A gets assigned to B. This can be a bit troublesome in situations where clear accountability is needed, requiring you to glance over and adjust. But honestly, as long as it's not a combative meeting, the accuracy is basically sufficient.
Classes and lectures: turning "not understanding" into "being able to read"
I recently started an online finance course. The instructor speaks quickly and likes to insert case studies. Previously, I would record the class, then play it back at 1.5x speed after class and manually organize notes, spending at least double the time per lesson. After using tidenote, the process became: start recording during class, then directly view the generated notes and key summaries afterward.
The most useful feature is its ability to chunk long content—for example, after the instructor finishes explaining a theoretical model, tidenote automatically extracts that section and appends a short explanatory title. This way, when reviewing, I don't need to re-listen to the entire audio; I can just flip through the notes to find the needed part. It also groups similar concepts or terms together, making it easy to build knowledge connections.
But there is a practical limitation: for highly technical courses (e.g., quantum mechanics derivations, code explanations), the AI's understanding and summarization can be jumpy or oversimplified, omitting some formulas and logical chains. If you're the type who needs to verify each step of a derivation, tidenote's summary can serve as an index but cannot replace the original course content.
Research reading: from "can't finish reading" to "filter first, then read"
The most time-consuming part of research isn't reading papers, but deciding which ones are worth reading. My habit is to throw a bunch of PDFs into tidenote and let it generate summaries and key points for each. Then I just scan the summaries to filter out 2–3 papers worth a close read, and archive the rest for reference. This process cut my daily literature screening time from 1.5 hours to about 40 minutes.
Note that tidenote performs well with short texts (like press releases, blog posts), but when dealing with lengthy academic papers (30+ pages), the summaries tend to be conservative and sometimes confuse the discussion section with the conclusion. I treat it as a "pre-filter" rather than a "final understanding tool."
Should you use tidenote?
After all this hands-on experience, I think tidenote is most suitable for three groups: professionals attending 3+ meetings daily, researchers who need to screen large volumes of literature, and students who easily get distracted in class or can't keep up with note-taking.
There are also scenarios where it's less suitable: if your meetings rely on improvisational debate and you need a verbatim record of everyone's full remarks for analysis, tidenote's summary filtering will actually discard the details you need; if your learning style involves writing notes by hand to reinforce memory, relying entirely on AI notes might reduce effectiveness.
One more thing: tidenote's free version is sufficient for getting started, but with frequent use, the number of summaries may run out. The paid version is much more comfortable in this regard.
Back to the question in the title—why is tidenote so good? The answer is simple: it doesn't try to replace your thinking; it helps you save the mechanical, repetitive organizing work so you can get a usable first draft faster. As long as you can accept the occasional small mistakes the AI makes and are willing to spend 1–2 minutes fine-tuning the summaries it produces, it's a tool that can save you a solid half hour every day.
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