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David Singleton
La scorsa settimana al #UpfrontSummit, ho avuto una grande discussione con Clay Bavor di @SierraPlatform, @lsturdy1 di @CapitalG e @shiringhaffary di @business parlando della prossima era degli agenti AI.
Questo momento nel tempo sembra molto simile ai primi giorni del mobile.
Ecco cosa intendo →
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Qualche settimana fa, ho mostrato @dreamer a @eladgil. Aveva un'idea folle: dovremmo semplicemente prendere le idee delle persone, costruirle e pubblicare ciò che creiamo. Divertente! Ne ho parlato a @swyx e anche lui era d'accordo.
Stiamo partendo ora: se hai un agente che desideri esistesse, rispondi al thread di @dreamer qui sotto👇

Dreamer25 feb 2026
Kicking off now: @eladgil, @swyx, @dps take your agent ideas and build them in real time in @dreamer.
Reply in this thread with your ideas. We’ll build a few of the best ones and share progress + links as we go.
You dream it. We build it. 👇
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Ciao Andrew, un amico mi ha mostrato il tuo tweet questa mattina ed è perfetto per @dreamer. Ecco un'app agentica che ho costruito. Ho usato il tuo tweet originale come primo prompt e ha subito generato qualcosa di immediatamente utile. In @dreamer la comprensione e la conoscenza di me sono già presenti. Il mio nuovo agente ha iniziato a chiedere al mio assistente di valutare e dare priorità alle email e di redigere risposte subito.

andrew chen17 feb 2026
what's the current best approach on an AI that can help me handle my email inbox?
seems like a big opportunity for folks playing with openclaw. For all of us who are drowning in email, this seems like a tier one problem that would be amazing to solve. (And I think I would pay $150k/year to have this product? I bet I'm not the only one)
what I want is:
- watch my inbox and process emails as they come in
- score each message to see if it seems important (look at the sender, the topic/body, if its addressed to me or a big list, if I've ever replied to the sender before, etc etc)
- read the email and reference a vast DB of knowledge that's been assembled already (based on my work, meeting notes, what I've replied on, etc), and decide what to do
- reply with a draft note. For now, don't send, so that I can review the email -- but in the future maybe there's a YOLO option (but it would probably disclose that it's my assistant writing)
- if less important, label it and file away. Eventually gather summaries for all of these less important emails and send me a summary of all of them with links to get back to it
- or archive if it seems unimportant
- or unsubscribe / mark spam / block if random marketing
- if critical send me a notification right away so I can take a look
I've played around with a bunch of the current AI tools and nothing quite works like this. There's a lot of blockers:
- first, it needs 1000x more context about each problem, which it could get by crawling all my projects/notes/emails/slides/meetings/etc
- This system should be designed to take action rather than simply just prioritizing messages. We've had prioritized inboxes for a long time but they're fine, not great
- then someone has to put this entire UX together to be cohesive. In the future, we may not even really have an email inbox, but instead an interaction that feels more like I'm talking to an assistant who has a few questions for me. But otherwise just wants to provide a few quick updates and get some yes/nos. And otherwise filter all the noise -- just give me the most important messages
It feels like we're very, very close to being able to do this, with the latest models from Anthropic and Open AI, we have the technology already. Someone just needs to package it all together in a way where it's able to index all of your emails and notes and calendars and contacts and sort of create a second brain that knows almost everything that you know so that I actually do things that are intelligent.
It seems like with the excitement of OpenClaw we have the architecture to integrate a lot of different data sources and to take actions across multiple different channels. And it's built with one sort of monolithic memory and context, so that you're able to interact with it in such a way where it feels like it can try to replicate your actions more closely than the relatively stateless and memoryless LLM chats that we've gotten accustomed to.
If someone is working on this, please point them to me. I would be both a customer and an investor!
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