Category: Product Engineering
- The one job of the early stage startup CTO ()
With so many things happening in early stage startups, what is the one thing the CTO should focus their time on?
- A Japanese software experience ()
If you have been to Japan, you will remember the high level of service everywhere you go. What is the Japanese customer experience and how can it be applied to create a differentiator for your own customers?
- Parachuting tasks because available time is fixed ()
Available time does not change – you can’t add more hours to the day. So why do we ignore this when planning work and progressing tasks, especially in engineering?
- Influence is the product manger’s best tool ()
How can product managers get things done when they have little to no direct authority over the teams actually implementing the project?
- Product managers are not responsible for “how” ()
Amongst all the things that the product manager is responsible for coordinating it is important to note the key aspect they are not deciding: how.
- How to learn product management ()
Books, podcasts, videos, conferences, blogs, articles and other resources for product managers
- Applying HumanOps to on-call ()
Originally written for the StackPath blog. One of the two core foundations of SaaS monitoring is alerting (the other being metric visualization and graphing). Alerting is designed to notify you when things go wrong in your data center, that there’s a problem with your website performance, or if you’re experiencing server downtime. More specifically, infrastructure […]
- Easy to use and beautiful design are no longer differentiators ()
If you find yourself focusing on your product being “beautiful”, “easy to use”, “design led” or leading with “look and feel”, you may need to rethink your competitive positioning.
- How to prioritise what features to build ()
Only one thing can be top. Unless there is a single, ordered list of numbered priorities, nothing is the priority.
- You don’t need to be an expert to integrate AI in your startup ()
Originally published on The Next Web. We’re used to hearing that AI and machine learning is hopelessly complex, impossible to implement quickly, and that if you want to get on board the machine learning bandwagon you’ll need to invest heavily in PhDs, specialists and expensive experts. This way of thinking is simplistic and behind the […]