Yesterday I worked about 5h 11m of productive time.
As an indie engineer, I kept thinking I was behind, like my employed friends were working harder than me. I wanted some real feedback on my productivity, like having an actual external boss.
I tried using time trackers, but they weren't good at tracking everything.
So I built Didon. An AI time tracker that auto-generates work logs to track your time and analyze your productivity.
For years, HR technology has been associated with reducing paperwork, managing payroll, and tracking attendance. While these remain essential, the role of HR technology is evolving rapidly.
Today, the most successful organizations are using technology to create better employee experiences not just more efficient HR processes.
Imagine a workplace where new hires complete onboarding before their first day, managers receive real-time insights instead of waiting for monthly reports, employees can access information without lengthy email chains, and routine approvals happen automatically. Small improvements like these save time, reduce frustration, and allow people to focus on meaningful work.
The shift is no longer about digitizing HR, it's about enabling a workplace where collaboration, engagement, productivity, and continuous feedback become part of everyday operations.
My name is Marta, and I m working on a startup idea that I genuinely believe could become something big and I want you to give me harsh and frank rather than nice and polite feedback.
Something I keep going back and forth on: when you post the same update to several networks, do you send identical text everywhere, or rewrite it for each one?
Identical text is fast, but it reads lazy and a few platforms quietly throttle near-duplicate cross-posts. Rewriting each by hand respects the platform but eats real time, and honestly most days I don't have a fresh angle for X, LinkedIn, Bluesky, Threads and Telegram all at once. So I'd default to copy-paste and feel a little cheap about it.
One thing I ve been thinking about lately is how teams reconcile Kubernetes cost allocation with their actual cloud bill. Most tools do a great job with cost allocation, but understanding why the final cloud bill is different is often much harder.
My current focus is helping engineering and FinOps teams better understand where cloud spend comes from, identify waste, and bridge the gap between Kubernetes cost allocation and actual cloud bills.
Lately, I ve been building Burn, an open-source Kubernetes FinOps CLI focused on cost allocation, waste detection, and cloud billing reconciliation across AWS and Azure.
