Software Engineering

Loose Coupling and High Cohesion: How To Avoid Tight Coupling and Low Cohesion?

Cohesion is a measure of the number of relationships that parts of a component have with each other.

High cohesion means that : All of the parts that are needed to deliver the component's functionality are included in the component.

Coupling is a measure of the number of... See more

We ship early

We constantly review the scope of a project and work toward a v0, not a v1.

That means identifying the most crucial part of a functionality , and pushing the smallest pull request possible to make that happen. In fact, a PR should always be optimized for the speed with which we can merge it in.

The book Clean Code introduces three laws of TDD [1]:

1. Write a failing unit test first before you start writing production code.


2. Write not more of a unit test than is sufficient to fail.


3. Write not more production code than is sufficient to pass the currently failing test.

So, the tests are written before the production code. This leads to the developer... See more

1. Streamlining data exploration and analysis:

• Context-Aware Suggestions: Receive AI-powered suggestions for relevant tools, functions, and libraries based on your specific dataset and analysis goals.


• Contextual search for functions and libraries: Quickly find relevant functions and libraries from various programming languages, such as Python and R,

... See more

Use nested objects for future extensibility

A follow on from the previous tip: try to logically group fields together. The following:

customer.address = {

line1: "Main Street 123",

city: "San Francisco",

postal_code: "12345"

};

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is much cleaner than:

customer.address_line1 = "Main street 123";

customer.address_city... See more

Why is Continuous Delivery for ML/AI hard(er)?

Since the challenge is not new and many valid solutions exist targeting traditional software projects, is there a reason to treat ML/AI systems any differently? Consider these three core challenges that are endemic in ML, AI, and data projects:

1. Development and debugging cycles are more tedious due to

... See more

Shipping to production

Before shipping to production, we think about all the different artifacts that might be affected by a new feature.

Here's a non-exhaustive list of things we ask ourselves:

• Does the API need to be updated?


• Is the OpenAPI spec up to date now?


• Have we released all the official SDKs?


• Do we need to write a Changelog post to announce

... See more

We ship often

Shipping early is not enough. We also need to keep shipping tiny iterations daily instead of waiting months to ship something big.

Having a strong sense of urgency , and making hard decisions fast is how we enable that process. Our sprints only last 1 week and are quite short when compared to the rest of the industry - this is by... See more

Using feature flags

It's always a good idea to put new features behind a feature flag.

This contributes to a rollout strategy that can surface user feedback in a much more contained and safe environment.

When thinking about how to rollout a new feature to users, first think about your peers. Many of the Resend team members also use Resend on their... See more