GitHub Actions: Advanced Workflows for Go Projects

Hey there! 👋

Are you still manually running tests before every commit? Deploying to production via SSH and praying nothing breaks?

GitHub Actions turns your repository into an automated machine: tests, linters, builds, deployments - all without your intervention.

But most people only use basic features. You can set up matrix testing, dependency caching, conditional deployments, and even automatic releases.

Let’s explore advanced patterns for GitHub Actions in Go projects 🚀

Probability Theory: Bayesian Statistics for A/B Testing in Go

A/B testing is everywhere: button colors, pricing, algorithms. But most implementations use frequentist statistics. Bayesian approach gives you probability of being better, not just “statistically significant”. Let’s implement it in Go.

Frequentist vs Bayesian

Frequentist: “Is the difference statistically significant?”

• p-value < 0.05 → reject null hypothesis
• Doesn’t tell you probability of A being better than B

Bayesian: “What’s the probability that B is better than A?”

• Direct answer: “B is better with 95% probability”
• Updates beliefs as data arrives

Basic A/B Test

type Variant struct {
    Name        string
    Conversions int
    Visitors    int
}

func (v *Variant) Rate() float64 {
    if v.Visitors == 0 {
        return 0
    }
    return float64(v.Conversions) / float64(v.Visitors)
}

Beta Distribution

For conversion rates, we use Beta distribution: