This release brings you 2 core features: official Cloud Services and Strategy Engine, fully enhancing the out-of-the-box AI experience and convenient automated task processing capabilities. In addition, this update covers up to 19 new features, 27 improvements, and 15 bug fixes, dedicated to creating a smoother, smarter, and more stable user experience.
YC-backed @Mintlify (YC W22) just announced a $45M Series B round, bringing their total funding to $67M, to "accelerate [their] mission of building the knowledge infrastructure for AI."
Read in their blog announcement:
Mintlify now powers documentation for over 20,000 companies, with content reaching more than 100 million people every year. This round accelerates our mission to become the knowledge layer that makes products understandable, usable and discoverable by AI agents.
Automation You can now create scheduled tasks powered by AI agent steps. Set custom schedules, run manually and create automation directly from the agent using the /automation command.
Multi-floor planning - You can now add unlimited floors, each with its own floorplan and walls. Signal from APs on other floors bleeds through realistically based on the slab material (wood frame, concrete, reinforced concrete, steel deck). The channel solver sees across floors too, so it won't put two APs on the same channel if they're directly above each other through a wood floor.
I've been building my app for 8 months now, and i ended up having 5 repositories
nextjs app
databases
customer facing API
node-sdk that wraps the api
react-sdk, for both reusing shared component and customer facing components
So i thought, it's gonna be great if i create a mono repo with submodules. But it was terrible. I realized that turborepo does not like external packages, and as i tried to reuse my own customer facing libs, the DX became terrible. It was very time consuming to ship a feature. Even when i wanted to use Codex or Cursor 3, it was not able to show git diff properly, also i was not able to use Cursor's cloud agents properly to ship complex features.
AI coding tools seem to come in two main flavors: IDE-based, like @Cursor and @GitHub Copilot, and terminal-based setups, like using @Claude Code to generate commands, scripts, or entire files. Both have their fans, but which one actually helps you move faster?
Curious what flow people are sticking with long term, and where you see the most gains (or frustrations).
AI coding tools seem to come in two main flavors: IDE-based, like @Cursor and @GitHub Copilot, and terminal-based setups, like using @Claude Code to generate commands, scripts, or entire files. Both have their fans, but which one actually helps you move faster?
Curious what flow people are sticking with long term, and where you see the most gains (or frustrations).
On Product Hunt, I can see many people launching their products using "vibe-coding tools" like @Lovable , @bolt.new , or@Replit
I reckon many people who created something with them are usually developers who didn't have enough time for building a side idea before, but with AI, they could make it happen.
