My name is Yuriy Zhar. Elchemista is my engineering blog, write-ups from the products I build and the problems I solve. I ship startup MVPs with Elixir/Phoenix, Go, TypeScript, and AI (LLMs + RAG). Want help building yours? Reach out.
Hand-picked articles that deserve your attention.
A hands-on story about tracking down stubborn RAM usage in a Phoenix node after a heavy data load.
By using project-specific guides and resetting messy contexts, I focus on building a repeatable system rather than chasing the "best" model.
I got tired of cloned WordPress SEO farms, so I built a multi-domain, multi-language blog platform in Elixir with fast SEO-optimized pages, integrated analytics, AdSense, affiliates and AI agents that research trends and draft high-quality articles automatically.
Yacht & Tours is a booking platform I built to make renting boats fast and straightforward.
Redomap is a lightweight, install-free map system you can drop into any website. Users scan a QR code and get maps, audio guides, and navigation instantly.
I built a custom CRM using Elixir and Phoenix LiveView that uses AI to identify parts from photos, translate messages, and automate conversations with suppliers over WhatsApp.
Browse categories to dive into the subjects that matter most to your audience.
Real-time insights, autonomously curated. Your daily pulse on global trends, powered by advanced AI agents.
My Crazy Ideas is a space for quick posts about business, social, and tech ideas. You’ll find rough concepts, small experiments, and practical notes on how an idea could work in the real world.
Just my thoughts about tech and AI, job market or in general about anything new i discover.
A simple showcase of what I’ve built and what I’m building right now. Real projects, real challenges, and the solutions that came out of them.
System design stories and patterns focused on latency, scalability, and reliability, with concrete examples from booking engines, in-memory indexing, and Elixir/BEAM architectures.
Stay up to date with our latest stories, tutorials, and insights.
The third article in the Spectre series introduces Spectre Mnemonic, an Elixir-first memory layer for agents with scoped recall, lifecycle governance, observations, mental models, and forgetting.
The second article in the Spectre series introduces Spectre Lens, an Elixir-first browser perception layer that turns pages into agent-readable context.
The first article in the Spectre series introduces Spectre Kinetic, an Elixir-first planning layer for agents that lets models express intent while the runtime keeps control.
AI models are increasingly using our digital likeness of voice, style, image from public data to create mimicry. This raises urgent questions about individual ownership.
Detecting AI content is harder as models improve. Look for subtle textual, visual, and audio inconsistencies, "hallucinations," and contextual red flags.
An AI system details the significant risks of personalized learning, including confident errors, entrenched biases, data privacy issues, and the potential for propaganda.
A hands-on story about tracking down stubborn RAM usage in a Phoenix node after a heavy data load.
AI promises a shorter workweek, fulfilling Keynes's vision, but the article argues it creates new challenges like validation work, bias, skill atrophy.
AI models like Claude are co-creating programming languages, accelerating design and iteration. This collaboration empowers human engineers to focus on higher-level problems.
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