All successful companies are different: each successful company gains a monopoly in a field by solving a specific problem. All failed companies are the same: they did not escape market competition. If a startup merely imitates the products and services of industry leaders without innovation and targets the same market, people will not buy into it.

If two people buy the same product for the same reason but have no way they could reference each other, they are not part of the same market.

The definition of a high-tech market: existing and potential users; who have a demand for a certain type of product or service; and these individuals reference each other when deciding on the products to purchase. Mutual referencing is the key to market success.

Is the innovation disruptive?

High-tech companies often introduce disruptive innovations. The user growth of these products follows an S-curve. Developing a high-tech market means smoothly transitioning this cycle from left to right, breaking through one user group at a time. Leveraging the momentum of the left-side user group makes it easier to market products to the right-side user group. Observing the technology adoption lifecycle reveals two chasms and one gap.

Motivation

APIs can be un-robust and un-predictable. To solve the problem, three principles should be observed. The client retries to ensure consistency. Retry with idempotency, exponential backoff, and random jitter.

Why are APIs unreliable? Networks can fail, and servers can fail. Three principles to solve this problem: the client uses "retry" to ensure state consistency; the retry requests must include an idempotent unique ID; retries must be responsible, such as following an exponential backoff algorithm, because we do not want a large number of clients to retry simultaneously.

AKF scale cube visualizes the scaling process into three dimensions…

How to build a scalable web service? One word: Split. The AKF Scale Cube tells us the three dimensions of "splitting": horizontal scaling; business splitting; data partitioning.