Taskade started as a real-time collaboration tool for planning and productivity. Then we added memory, agents, and automations. Soon it stopped feeling like a static tool and started acting like a real living workspace that could handle parts of the work on its own.
You finish a session, close Cursor, come back the next day. Ten minutes gone just reconstructing where you were. Stack decisions, rejected approaches, half-finished threads.
Nobody talks about this part. Everyone optimizes the coding flow but the between-session memory problem just sits there.
I m exploring different Backend-as-a-Service (BaaS) options for vibe coding projects small but creative builds that start locally and later go live (think quick prototypes, coding agents, or AI-powered hobby apps).
I d love to hear your thoughts on:
how easy is it to debug or simulate the backend locally?
how smooth is it to go from local to hosted?
any gotchas or scale-to-zero tricks to keep costs minimal?
functions, auth, storage, DB branching, edge runtimes, etc.
Bit of context first: I am a member of a 30 people+ team and most API clients we used felt like they were built for a different job than what we actually did.
I just launched AllPub.co and built pretty much the entire thing using Vibecoding with Claude Sonnet 3.5. I'm a software architect with just some basic Python knowledge, so this was my first full web app build. Wanted to share my experience!
I'm launching IdeaJarvis on Product Hunt tomorrow a workspace that runs the full 0 1 loop for startup ideas (discover research define deploy validate automate) using your own ChatGPT, Claude, or Cursor.I want to put my money where my mouth is. Drop your startup link below and I'll personally run it through IdeaJarvis. You'll get back: A full pitch positioning, problem, solution, target user Detailed market research competitors, TAM/SAM/SOM, market gaps Honest feedback what's working, what to fix firstNo catch, no upsell. I built IdeaJarvis because I had every AI tool open ChatGPT, Claude, Cursor and still couldn't turn all that intelligence into ideas worth scratching. The best way to prove it works is to do it for you.our
Came across the recent Claude Code leak from Anthropic, and what stood out wasn t the leak itself, but what it revealed about how these systems actually work.
A source map file accidentally exposed ~500k lines of TypeScript
Turns out Claude Code is basically a multi-step prompt orchestration system, not some mysterious black box
Includes things like:
layered prompt pipelines ( prompt sandwich )
fake tools to prevent model distillation
simple frustration detection (regex for rage prompts )
Even hints at future features like background agents and persistent memory systems
What s interesting is this:
It kind of confirms that the real product layer in AI isn t just the model it s everything wrapped around it.