Every great Claude response starts with context. Minimi listens across your Mac - docs, calls, messages, tabs - and gives Claude the full picture. No prompting. All on-device and private.
I've been living inside Claude for most of my workday, and the one thing that always frustrated me was having to re-explain myself every single session. "Here's what I'm working on. Here's what happened in my last meeting. Here's the email thread you need to know about."
Minimi fixes that. It sits quietly on my Mac, reading what I read, hearing what I hear - and then feeds all of that to Claude as live context. So when I open a new chat and ask "what should I follow up on from this morning?", Claude already knows. No briefing. No copy-paste. Just the answer.
A few things I love about Minimi:
1. On-device memory - your context never leaves your Mac (the vector DB lives locally). We benchmark at 54% on BEAM vs the previous SOTA's 36%.
2. MCP-native - one link, paste it into Claude's custom connector, done. No new app to live in.
3. Granular control - you pick which apps it can see. Pause anytime.
If you use Claude and you work on a Mac, this is a no-brainer install. Three steps and it just works.
Loved seeing the product in action. Can't wait to use it- I'm tired of pasting messages from one tab to another to give "context"
I started using Minimi for the last 12 hours and I could instantly see a drastic change in my Claude usage.
Really need this!
Good to know it's completely on my device memory.
Not totally sure from the page, but the useful part seems to be reducing context switching rather than just adding AI. How are you measuring that in real usage?
The BEAM benchmark result is interesting 54% vs 36% SOTA is a meaningful jump. But I'm curious about the retrieval side in practice: when I open a Claude chat about a specific project, how does Minimi know which slice of my captured context is relevant to surface? Is it purely semantic similarity against my current query, or are you also factoring in recency and which app I'm currently in? Asking because the failure mode I'd worry about is Claude getting confidently wrong context not missing context especially after weeks of accumulated memory where older stuff might contradict newer decisions.
the context bottleneck is real. most bad AI output i see is a missing-context problem, not a model problem, so this direction makes a lot of sense. the part id be curious about is signal vs noise. passively capturing everything across docs/calls/tabs is powerful, but the risk is feeding Claude confidently-irrelevant context. how you decide what's actually worth surfacing feels like the real moat here. on-device + private is a smart trust call too. nice work.
A lot of memory systems seem useful while a conversation is active, but the harder test is what happens after weeks of accumulated context.
How are you thinking about memory quality over time? Is the bigger challenge helping Claude retrieve more information, or helping it maintain an accurate picture of what's still true versus what's become outdated?
The "no re-explaining yourself" pain point is so real — I spend a chunk of every session giving Claude context it had yesterday.
Love the on-device angle too. Privacy-first local storage is the right call when your context includes work meetings and personal projects.
One question: any Windows roadmap? That's my main blocker for trying it today.
Wow. This is exactly what I need. Will come back and ask questions but excited to check this out!
Been using Shram for a while now and it is making my life a lot easier. Minimi is a crazy upgrade and i am loving it
Neat idea. Can you tell Minimi to skip certain apps it shouldn't capture context from?
Many people already try “memory” via manual notes or lightweight MCP memory servers. What’s the key product bet behind ambient capture across tabs/docs/messages/calls—and where does that approach win or lose versus a more intentional, user-curated memory workflow?
Congrats on the launch. Most memory tools that 'always listen' wave their hands at the delete path, so I went looking for it here. When I revoke an app or delete a memory, do the vectors already sitting in the local store actually go? That's the real privacy question I believe for something that's on by default
Fr. Giving context to every LLM for the same thing I had it do yesterday and the day before is frustrating. About time someone built a plug-and-play memory layer and relieved me of the annoying ritual. Great work, team. Rooting for you.
Have been lucky to get early access to Minimi and my god it’s powerful! From getting random, small insights that I forgot from my meetings to tracking my work output to remembering things that I did 2 weeks ago. Minimi is like magic
About Minimi on Product Hunt
“Your ambient memory for Claude”
Minimi launched on Product Hunt on June 5th, 2026 and earned 485 upvotes and 108 comments, earning #2 Product of the Day. Every great Claude response starts with context. Minimi listens across your Mac - docs, calls, messages, tabs - and gives Claude the full picture. No prompting. All on-device and private.
Minimi was featured in Productivity (653.8k followers), Artificial Intelligence (471k followers) and Tech (625.6k followers) on Product Hunt. Together, these topics include over 403.5k products, making this a competitive space to launch in.
Who hunted Minimi?
Minimi was hunted by Rohan Chaubey. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Minimi stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Rohan Chaubey