🚀 The Polyglot Journey: Building Real Apps in Go, Rust, and Elixir

Venkat — Mon, 28 Apr 2025 02:27:37 +0000

Hey devs!

Over the years, I've worked with a ton of stacks--Go, Rust, Elixir, and more--and along the way, I've gathered tips, patterns, and battle scars that I couldn't find all in one place.

This is my attempt to fix that. 🎯

About Me

I've spent the last few years building production systems across various industries. I've had to wire up all the difficult parts that tutorials often skip: auth flows, payment processing, search optimization, and robust error handling. My goal is to share what I've learned the hard way so you don't have to.

The Series

I'm kicking off a Polyglot Apps series where I'll build real-world apps in different stacks--not just "Hello, World!" demos, but apps with proper project structure, authentication, background jobs, uploads, search, payments, and more.

Here's the lineup:
Go:

Go Fiber
Go Gin
Go net/http

Rust:

Axum + Tera
Actix + Tera
Warp + Tera
Rocket + Tera

Elixir:

Phoenix + Absinthe (GraphQL)
Phoenix LiveView
Phoenix Full-stack (non-LiveView)

What makes this different?

Each app will cover real use cases:

OAuth, magic links, 2FA 🔐

File uploads with MinIO 📂

Search with Typesense 🔍

Stripe billing 💳

HTMX/AlpineJS for frontend interactivity ⚡

Background jobs with RabbitMQ/Kafka 🐇

Streaming responses

Clean project structure and best practices I've learned the hard way

Additional Focus Areas

Database Strategies: I'll cover both SQL (PostgreSQL) and NoSQL (MongoDB) implementations where appropriate, including migration strategies and ORM/query builder approaches

Testing Approaches: Each stack has different testing paradigms - I'll demonstrate best practices for unit, integration, and end-to-end testing

Containerization & Deployment: Docker configurations and deployment pipelines for each stack

Performance Comparisons: Real benchmarks with analysis on throughput, latency, and resource usage

Error Handling & Observability: Proper logging, monitoring, and error tracking in production

Caching Strategies: Implementation of various caching layers (Redis, in-memory, CDN)

Security Considerations: OWASP top 10 mitigations specific to each stack

Bonus Advanced Topics

After covering the core implementations, I'll expand into AI integration with:

Ollama Open Source Models: Adding language translation capabilities

Computer Vision: Implementing face recognition features

Vector Databases: Storing and querying embeddings for semantic search

RAG Systems: Building simple retrieval-augmented generation systems

These advanced topics will demonstrate how modern applications can leverage AI capabilities without relying solely on third-party APIs, giving you more control and potentially reducing costs.

Timeline & Code Access

I'll be releasing a new implementation every 2-3 weeks. All code will be available in GitHub repositories with comprehensive documentation. Each repo will include a working demo you can run locally with minimal setup.
Why am I doing this?

Because picking the "right" stack can feel overwhelming.

By building the same core features across different stacks, I hope to give you real-world insights into how they compare in performance, developer experience, and ecosystem strengths.
Follow the journey!

I'll be posting the series on thedeveloper.tech and sharing highlights here on dev.to as well. The first few articles will cover why I chose these stacks and how I structure my projects, followed by deep dives into each app.

Let's learn together! 💪

🚀 LibreAI: A Self-Hostable, Privacy-First AI Chat App Built with Go + HTMX

Venkat — Wed, 16 Apr 2025 16:35:35 +0000

Over the past few weeks, I’ve been building LibreAI — a simple, privacy-focused AI chat app that runs entirely on your own infrastructure using open-source models like Mistral, LLaMA 3, and Phi via Ollama.

LibreAI is built with privacy and simplicity at its core. It streams responses in real-time, stores nothing, and has zero telemetry. No OpenAI, no account wall, no JavaScript bloat.

✅ Try it live

💬 Discuss it on Hacker News

🧠 Why I Built LibreAI

A lot of modern AI tools work well, but they come with trade-offs: vendor lock-in, data collection, or heavy dependencies.

I wanted an AI assistant I could trust and control, powered by models that run locally — without relying on big cloud providers or complex frontend stacks.

So I built LibreAI as a lightweight, clean alternative.

What I focused on:

A UI that loads fast, even on slow networks

Real-time streaming output

Support for multiple open models via Ollama

Full self-hostability — no cloud APIs required

No React, no tracking, no unnecessary complexity

⚙️ The Stack

LibreAI is built using:

Go Fiber – backend web framework

HTMX – frontend interactivity without JS frameworks

TailwindCSS – utility-first styling

Ollama – for serving local LLMs

Plausible – privacy-first analytics (no cookies or tracking)

🧪 What Works (and What’s Coming)

Right now, LibreAI supports:

Real-time streamed responses

Multiple open models via Ollama

Fast performance with a lightweight UI

What I’m improving:

Mobile layout and responsiveness

Model selection UX

Easier deployment options

LibreAI is intentionally minimal.

💬 I'd Love Your Feedback

If you're working on AI tools, local model deployments, or care about privacy-first UX, I’d love to hear your thoughts.

What models are you running locally?
What do you look for in a self-hosted AI tool?
Does lightweight and private beat "smart and centralized" for you?

👉 Try Libre

🗣 Join the Hacker News discussion

Thanks for reading 🙌

DEV Community: Venkat

🚀 The Polyglot Journey: Building Real Apps in Go, Rust, and Elixir

🚀 LibreAI: A Self-Hostable, Privacy-First AI Chat App Built with Go + HTMX