Why I Built This

I spend my days building AI systems. I've architected multi-LLM pipelines, shipped RAG implementations, and optimized models serving billions of requests. And I have a problem: the research papers I need to read are nearly impossible to get through.

At Nomology, I lead AI product development. We use Claude, GPT, and Llama models to generate insights from billions of data points. When a new paper drops from Anthropic or DeepMind, I need to know if it changes how we should build.

But here's the reality: a 40-page paper takes hours to parse. The math is dense. The jargon assumes you have a PhD. And buried somewhere in section 4.3 is the one insight that actually matters for production systems.

I kept thinking: there has to be a better way. Not dumbed-down summaries that miss the point. Not hype pieces that overpromise. Something that extracts what practitioners actually need to know.

So I built it.

My Background

I'm Yuriy Matso, Head of Product for Artificial Intelligence at Nomology, where I've been building AI-powered systems since 2019. LinkedIn

 What I Build Multi-LLM architectures using Claude, GPT, and Llama
RAG systems and vector database implementations
ML pipelines on Google Cloud Vertex AI and BigQuery
Agentic workflows for automated decision-making
 

 Scale I Work At Billions of data points analyzed
$50M+ in AI-optimized ad spend
Production systems serving real users daily
Rapid POCs to validate feasibility before building
 

Before Nomology, I led product at Channel Factory (managing 20 engineers and ML specialists), worked as a Project Manager at Sigma Software, and started my career as a Strategy Consultant at KPMG. I have a Master's in International Management from UC San Diego and a Venture Capital certificate from UC Berkeley.

I also build side projects to stay hands-on with new technologies. Futalgo is my algorithmic trading platform where I used Claude and GPT to develop trading systems across multiple languages and platforms.

How I Read Papers

When I evaluate research for Tekta, I ask the same questions I ask when deciding whether to implement something at Nomology:

Can I actually build this?

Does the paper give enough detail to implement? Or is it a proof-of-concept that only works in a lab? I look for architecture details, parameter choices, and reproducibility signals.

What's the real improvement?

Papers love to report "state-of-the-art" results. I dig into the benchmarks. Is the improvement 2% or 20%? Does it matter for my use case? What did they trade off to get there?

What will break in production?

Academic papers optimize for clean benchmarks. Production has messy data, latency requirements, and cost constraints. I look for the gotchas they don't put in the abstract.

Is this worth the migration cost?

Switching architectures is expensive. I evaluate whether the improvement justifies the engineering effort. Sometimes the answer is "wait for v2."

What Makes Tekta Different

Most paper summaries are written by people who read papers. Tekta is written by someone who implements them.

Every article includes an Implementation Blueprint because that's what I wish every paper had. Not just "what" they did, but "how" you'd actually build it: which tools to use, what parameters matter, and what will trip you up.

I don't cover every paper. I cover the ones that matter for people building real systems. If a paper is theoretically interesting but practically useless, I skip it. If it's a genuine breakthrough that changes how we should build, I go deep.

Every Tekta article includes:

TL;DR The core insight in 30 seconds
Plain English explanation No jargon, no assumed PhD knowledge
Interactive visualizations D3.js charts that make data concrete
Implementation Blueprint Tech stack, code, parameters, pitfalls
Honest limitations What the paper doesn't solve

Start Reading

Research from Google DeepMind, OpenAI, Anthropic, and Meta AI. Translated for practitioners who build.

Browse Research Papers

Tekta: from Greek tekton, meaning builder.