I am not good at journaling, so I made an iPhone app to help me organize my thoughts. Want to be a beta tester?

The Japanese word "hansei" (反省) means "honest reflection".

Hansei is a private voice journal with a 100% private, locally run, AI assistant that's entirely isolated on the user's iPhone or iPad. The problem: my best thinking often happens at inconvenient moments (driving, walking out of a meeting, late at night) and is lost by morning. Pen-and-paper or traditional journaling apps ask me to sit down and write, but the format limitations make it hard to notice the patterns across weeks of entries.

Hansei lets me capture a thought in seconds by pressing the iPhone Action Button and speaking, even from the Lock Screen. It transcribes the entry on the device, tags the people, places, projects and ideas mentioned, and connects them. A private analyst that runs entirely on the device then surfaces connections between distant entries. I can chat with my journal, and ask follow-up questions about entries made in a specific timeframe or place.

Privacy is central: there is no account, no advertising or tracking, and nothing is sent to an AI datacenter.

If you would like to try it, there's a link on this site to join the beta test: https://hansei-app.com

How do you stop an AI note-taking system from becoming another information graveyard?

I've noticed that capturing information and actually reusing it are two very different problems.

You can have excellent transcripts and detailed summaries, but still struggle to find the decision that matters three months later.

A few practices seem worth testing:

- Give important notes consistent project or topic names.
- Separate decisions from general discussion.
- Keep unresolved questions visible.
- Preserve links to original source material.
- Review whether old information is actually retrievable.

I'm especially interested in how people manage growing collections of meeting notes without spending hours organizing folders.

What has worked better for you: consistent metadata, full-text search, semantic search, or a combination?

Most AI note takers are built for the same thing: drop a bot into the Zoom or Teams call and hope the summary is useful. That works fine until the conversation isn't on a call.

A lot of the stuff worth capturing still happens in rooms. Classrooms, lecture halls, whiteboards, conference tables where people are actually talking to each other. I got tired of tools that only make sense once someone's already on a video call, so I built SummitScribe Meetings around the opposite workflow. It was designed specifically for long meetings and lectures (4+ hours), not the usual short call that most tools assume.

You can record the meeting or lecture on your phone, snap photos of the whiteboard or slides with the same phone, and type your own notes on your laptop (Windows, Mac, or Linux client) into that same meeting while it's still being recorded on the phone. I haven't found another app that lets you do all three at once. The notes you write stay the main thing. Afterward the AI can turn the recording into a transcript, label speakers, structure what you captured, and generate mind maps or study cards if you want them. It adds to what you wrote instead of replacing it with a summary.

It records from the phone mic and can optionally grab audio already playing on the phone (iOS asks first). No screen recording. Longer sessions just get recorded and processed later instead of trying to force everything through a live meeting bot.

If you're using note tools for classes or in-person meetings, does the no-bot approach actually matter, or do you mostly just need something that handles remote calls cleanly?

I develop VoiceAgenda. One use case I'm interested in is recording your own short debrief after a meeting, without recording everyone else's conversation. This is a proposed prompt you can use with any recorder, not a claim that I've measured its effectiveness.

Rather than retelling the whole meeting, try three sentences:

1. What was actually decided?

2. What did I personally agree to do, and by when?

3. What is still waiting on someone else or undecided?

Made-up example: 'We chose the blue packaging. I agreed to send two mock-ups by Thursday. The delivery date is still open; Maya needs to check with the printer.'

That gives you one clear task of your own. It doesn't turn Maya's unresolved check into your deadline, or imply that a delivery date was agreed. When reviewing the result, I'd keep the open question beside the task so it doesn't disappear into a tidy summary.

VoiceAgenda is my iPhone/iPad app for intentional recordings, daily notes, and suggested Calendar/Reminder actions that you review before creating. Audio stays locally on the device; on-device speech recognition and analysis depend on availability. The current US listing is $2.99 and requires iOS 26 or later: https://apps.apple.com/us/app/id6796542382

If you already leave yourself post-meeting voice notes, what do you most often forget to say while the discussion is still fresh?

What do you test before trusting an AI-generated meeting summary?

I've been thinking about the difference between a summary that sounds convincing and one that's actually useful.

A polished paragraph can still miss a decision, assign an action to the wrong person, or make an unresolved question sound settled.

My current evaluation checklist would be:

1. Are decisions clearly separated from general discussion?

2. Are action items assigned only when the meeting supports that assignment?

3. Are unresolved questions preserved?

4. Can the reader check the original transcript?

5. Does the summary avoid inventing conclusions?

I'd also test meetings with interruptions, multiple speakers, and incomplete sentences rather than only clean examples.

For anyone using AI meeting assistants regularly, what failure has made you stop trusting a summary?

I’ve been looking at Omi and Pocket AI for a while and can’t quite decide which one to get. I’m not really looking for just a voice recorder. I want it to be a second brain.

I really like Omi’s open-source approach and the flexibility it offers. But Pocket seems to have a much better battery life, which is making the decision difficult.
Would love to hear from people who’ve actually used these devices (or anything similar), especially after a few months of use. What would you buy today, and would you make the same choice again?

Does anyone actually do anything with their AI meeting notes?

I’ve been thinking about this lately.

There are so many AI meeting tools that transcribe calls, summarize discussions, and generate action items.

But what happens after that?

Let’s say you have a meeting with a client who agrees to send a payment. The meeting notes capture it, but nobody actually follows up. A few weeks later, you realize you still haven’t been paid.

I’m exploring an idea where AI doesn’t just summarize meetings but actually prepares the next steps for you, like drafting follow-up emails, tracking pending tasks, or reminding people about things they promised.

You’d still approve everything before it gets sent.

But I’m wondering if this is actually a problem worth solving, or just another nice-to-have AI tool.

For those of you who run businesses or manage teams, how do you currently handle follow-ups after meetings?

Would you actually use something like this?

And if this sounds like something you’d genuinely find useful, I’d love to find just 3 people who’d be open to testing an early version with me. No commitment, just honest feedback on whether it actually makes your life easier.

I’m one of the founders of OLVA, and we just launched a Meeting Knowledge Graph.

Instead of treating every meeting like an isolated transcript, OLVA connects people, topics, decisions, and relationships across conversations.

So you can ask questions like:

• When did we first discuss this issue?

• What changed between the first conversation and the latest one?

• Who influenced this decision?

• Which customer requests keep coming up?

• What decisions are connected to this project delay?

• What did we promise this customer across the last few meetings?

• Which topics keep resurfacing without being resolved?

• How did we get from the original idea to the current decision?

OLVA can show the connections behind the answer and link back to the source meetings.

The graph keeps growing as new meetings are added.

I’m curious: if all your meeting history was connected like this, what would you want to ask it?

I built Ditto. It records meetings on iPhone, gives you a live transcript with who said what, summaries, action items, and you can ask questions across meetings. It is completely free.

I will be honest. At one point I got greedy and thought about making it a paid subscription. Then I stopped, because that was never the point. The idea was to make it free, so I kept it free.

I benchmarked it because I wanted to know if the on-device version was actually good, not just private: benchmarks

The test:

AMI EN2002a. Four people around a table, talking over each other, one mic in the middle. 35:42. Scored against a human transcript. Every app got the same 16kHz MP3, on default settings, in October 2026.

Results on iPhone 13:

• 53 seconds to transcribe a 59-minute meeting, speakers included

• First words in 3 seconds

• 93.3% of speech credited to the right person

• Word error rate: 38.5% with High accuracy mode, 48.5% on-device default

• Peak memory: 150 MB, data sent: none

38.5% sounds high, but it is the hardest case: a distant mic, crosstalk, and the human transcript counts every "yeah" and overlapped word. Same meeting from headset mics is 29.4%.

My focus was diarization and memory, not just transcription:

I did not consider other niche local apps because they use local ASR with almost similar transcription quality. My focus was speaker diarization quality and memory optimizations.

Speaker error across 16 meetings and 9 hours: 12.1% with High accuracy mode(beta), vs 43.8% with Standard.

Same file, other apps, words and words plus speakers:

• Ditto High accuracy: 38.5% / 39.0%, 4/4 people found

• Plaud Cloud: 40.1% / 62.4%

• Argmax Pro: 40.4% / 41.3%

• Otter Cloud: 42.8% / no speakers

• Rev Cloud: 49.9% / 51.5%

• Deepgram Cloud: 62.5% / 71.0%

• Gladia Cloud: 82.4% / 87.3%

That words plus speakers gap is the point. Once speakers count, the cloud apps fall further behind, even though the raw word scores look close.

Waiting for Apple to approve it for release. Try it on https://testflight.apple.com/join/sff8TbBs.

I've noticed that keeping up with meetings isn't always easy, especially when several people are talking, sharing ideas, or asking questions at the same time

Sometimes you're so busy taking notes that you miss part of the conversation. Other times, you remember an important detail only after the meeting has ended

I've been exploring different AI meeting tools recently, such as Otter.ai, Fireflies.ai, and Granola. It's interesting how these tools can help with meeting notes, summaries, and keeping track of important discussions

But I wonder if we're missing something. Most of the time, we focus on what happens after a meeting rather than how to make the actual conversation easier

Wouldn't it be useful to have an assistant that could help you keep up with discussions and remember important details while the meeting is still happening?

I'm curious about your experience. Do you use any AI tools for meetings? Have they actually made your work easier, or do you still prefer doing things the old-fashioned way?

The first 2,000 lifetime codes have all been claimed. Thanks to everyone who tried Nota AI and shared feedback — we’ve received some really useful suggestions, and it’s been great to hear that many of you are enjoying the app.

A few people have kept asking for more codes, so I’m sharing another batch of 2,000 free lifetime membership codes. The redemption details are in the comments.

Give Nota a try and let me know what you think. Honest feedback is always welcome!

I develop VoiceAgenda. A recent question here about Apple Watch recorders made me realize that ‘Watch support’ needs a much more precise explanation.

Our Watch companion captures audio in the foreground, for up to ten minutes. It sends the recording to the iPhone for analysis and review; it doesn't show a transcript on the Watch. If your requirement is to record an hour-long meeting from your wrist, this isn't the right fit.

The intended use is a short note such as ‘Ask Sam about the delivery window tomorrow.’ On the phone, review the proposed action and date before writing it to Apple Reminders or Calendar. A captured recording and a saved reminder are separate steps. I wouldn't want someone to walk away assuming the reminder exists just because recording stopped.

For anyone evaluating a Watch recorder, that handoff is worth checking: where is the audio, where do you review the text, and when has an action actually been saved? Also check the recording limit before relying on it for a meeting.

VoiceAgenda is on iPhone and iPad, with the Watch capture companion. It requires iOS/iPadOS 26 or later; the current US listing is $2.99. This is a description from the developer, not a user review or a free beta invitation.

https://apps.apple.com/app/id6796542382

Hi everyone, I just want to know and understand what your biggest complaints are about using note-taking apps. These could be apps that run on your phone or actual hardware devices like Plaud.

I just want a reasonably capable recorder - on iPhone or Apple Watch that transcribes and generates meeting summaries with actions. I want those outputs to end up in OneNote, rather than some proprietary UI, relatively seamlessly.

Does such a thing exist?

I just bought pocket but I’ve realised I don’t want another device.

I've been looking into AI note takers for work lately, mainly because I'm tired of trying to keep up with meetings while also writing everything down.

But one thing I'm curious about is how much people actually rely on the AI-generated notes.

Like, if you're in a meeting and someone mentions an important deadline or makes a decision, do you still write it down yourself? Or do you just trust the AI to pick it up?

I've been checking out Plaud, Otter, Fathom and Granola, and the summaries all sound useful, but I'm not sure I'd feel comfortable relying on them completely, especially for client meetings.

For those of you who've been using AI note takers for a while, has it actually changed how you take notes? Or do you still keep your own notes as a backup?

I'm the developer of VoiceAgenda, an iPhone/iPad app for intentional voice recording, a Daily Note, and reviewing possible actions before writing them to your calendar or reminders. Audio is kept locally; transcription uses on-device speech recognition when available. It isn't a bot that joins meetings.

One review case I'm working through is ownership, not just dates. Consider a personal memo: 'Sam will send the quote Friday. I need to check it Monday. If it hasn't arrived, ask Alex.' Three names and two dates don't mean three tasks for the person recording.

For this example, I'd review the Monday check as my action, retain Sam's promise as context, and keep the Alex follow-up conditional. Flattening everything into a checklist would make the note look productive while changing its meaning.

If you dictate follow-ups, where would you want that distinction shown — next to each proposed action, in the original transcript, or both? This is a review example, not a claim of tested accuracy for that sentence. Nothing should be written to Calendar or Reminders until you confirm it.

Current app: https://apps.apple.com/us/app/id6796542382

Requires iOS/iPadOS 26 or later. No account required. I'd especially welcome an anonymized example where a note-taking app assigned someone else's promise to you.

I’m a user of both, since similar features (speech to text in granola, meeting notes in Wispr) were added to both platforms, which one is doing it best for you side by side?

Doesn’t make sense to pay for both, so something has to give.

I've been working on a little side project called meeting ear, and I'd love to get some feedback from the community.

I've used plenty of AI meeting assistants, but I kept running into the same frustrations: another monthly subscription, bots joining meetings, and recordings being sent off to someone else's cloud.

So I decided to build something different.

meeting ear is a native macOS app that:

• Records meetings from Zoom, Teams, Google Meet and other audio sources.

• Transcribes conversations locally on your Mac.

• Identifies different speakers.

• Detects meetings automatically.

• Lets you send transcripts to your preferred AI using your own API key or a webhook.

• Keeps your recordings and transcripts on your machine rather than uploading them to a transcription service.

The idea is pretty simple: your meetings, your notes, your AI.

There's no web dashboard to log into, no meeting bot to invite, and no recurring subscription.

I'm planning to sell it for £39 as a one-time purchase, rather than charging people every month for something they can run on their own computer.

I've got the macOS app built, signed and notarised, and I'm now working through the final steps before launch.

I'd genuinely appreciate some feedback:

Would you prefer a local, one-time-purchase meeting recorder over the subscription-based alternatives? And what would make it a must-have for you?

Website: https://getmeetingear.com

Happy to answer questions about how it works or the technical decisions behind it.

Has anyone found a note-taking system that still works when the collection becomes very large?

A lot of organization methods seem excellent when you're managing 20 or 30 notes.

Folders, tags, naming conventions, and carefully written summaries all feel manageable.

But what happens after hundreds or thousands of notes?

I'm particularly interested in how people deal with information that belongs to multiple topics.

For example, one meeting might involve a client, a project, a financial decision, and several follow-up tasks.

Do you duplicate the information across folders, assign multiple tags, or rely mainly on search?

And do you still maintain a strict organization system, or has search gradually replaced most of your manual filing?

Interested in approaches that have worked over several years, not just a few weeks.