Why Data Parallelism Matters in Go

Go is a fast language, but it lacks easy ways to express data parallelism and does not provide direct access to low-level SIMD (Single Instruction Multiple Data) instructions. As a result, standard libraries like base64, hex, utf8, json, jpeg, and map are slower than expected. Other ecosystems are more likely to adopt specialized, high-performance libraries, which is why, in some cases, for example, Node.js can outperform Go.

The solution to this bottleneck is for the Go compiler to generate SIMD instructions. There are three main approaches to enabling SIMD in programming languages:

The tech sector, after a decade of remarkable growth, has faced significant layoffs. These events affect everyone-not just those directly impacted, but also the colleagues who remain. For those let go, the challenges of finding new opportunities in a tough market are profound. Meanwhile, those who stay often grapple with shaken trust in management, increased workloads, and heightened stress about their own job security.

Much has been written about these emotional and logistical challenges. However, one crucial aspect remains underexplored: the impact of layoffs on technical debt and how it evolves in downsized teams.

Tests should help you release code faster and with confidence. Yet, for many developers, testing has the opposite effect, creating delays and frustration. Here, I’ll explore common pitfalls in testing and suggest better practices to make tests truly beneficial.

We have all heard that we need to have more tests and that we should have as close to 100% tests coverage as possible. Despite this effort, we still encounter bugs. We still do manual testing and overall a lot of developers do not trust their tests to actually catch anything useful. Why is that?