Genuine question for anyone who's thought about AI agents with deep access to your data email, files, calendar, that kind of thing.
Does it actually change your trust calculus if the AI runs entirely on your own machine instead of a cloud provider's servers or is "local-first" more of a founder talking point than something people actually weigh when deciding whether to give a tool that kind of access?
Asking because I've spent the last few months building a personal AI assistant that runs fully local, specifically on the assumption that people would trust it more with sensitive access. But I don't actually have real evidence for that nobody's told me "local" was the thing that changed their mind.
So: if you've used, or considered and rejected, an AI tool that wanted deep access to your inbox/files what actually made you comfortable, or not? Was where it runs ever part of that decision, or does it really come down to something else (company reputation, specific permission scoping, data retention policy)?
Is this actually a good price? Does anyone know how this fits? Would I still wear this six months from now? Is there a better version somewhere else? Should I buy it before it sells out?
Most shopping sites are designed to get you from product page checkout as quickly as possible.
Hey there! I am one of the founders of Linguix - we build AI writing assistant and now we are diving into developing our API and offering an easy way to integrate it via API. My idea, that has some proof via PoC projects by now, is that when you add grammar checking dialogues to your app, people will spend more time within it = improved retention rates. Also, those dialogues can potentially be used to promote core product features, which should improve APRS/ARPU.
What do you think? Would you be open to testing this kind of product (Grammar Check API/SDK) for free?
I m building a tool that centralizes retry limits, spend caps, and alerts across every cron job or agent you run, instead of wiring guardrails into each project separately.
If you do outbound or B2B sales, you ve probably wasted hours scraping sites, cleaning CSVs, and fighting bounced emails only to get a tiny reply rate.
I built Leadmeta (https://leadmeta.me) to make that whole flow stupidly fast:
Describe your ideal customer in plain English (e.g. founders of SaaS tools doing $10k $50k MRR )
Leadmeta uses AI to generate Google Dork queries and runs them in real time
It extracts emails from public search results and runs a 4-layer DNS-based verification client-side
You export a clean CSV that plugs into any CRM or cold email tool in one click
I built CodeAtlas because I kept getting lost in unfamiliar codebases jumping between files, trying to understand how everything connects.
So I thought: what if you could see a codebase instead?
CodeAtlas turns a GitHub repo into an interactive graph of file dependencies. You can explore how files are connected, click into them, and get a visual sense of the structure instead of digging through folders.
