Frequently Asked Questions
Everything you need to know about how we translate AI research for practitioners who build.
About Tekta.ai
What is Tekta.ai?
Tekta.ai translates cutting-edge AI research into plain English with practical implementation guidance. Every week, hundreds of papers drop from Google DeepMind, OpenAI, Anthropic, and academic institutions. These papers contain insights that could change how you build, but they're written for researchers with PhDs.
We bridge this gap. We read the papers, extract what matters for practitioners, and explain it in clear language with code examples and implementation details.
Who is Tekta.ai for?
We write for people who build:
- Developers & Engineers — Understand new architectures and techniques without wading through proofs
- Business Leaders — Get executive summaries with clear business implications
- Product Managers — Evaluate which AI capabilities are production-ready
No PhD required. If you're building with AI or making decisions about AI adoption, our content is for you.
What makes Tekta different from other paper summaries?
Most summaries are written by people who read papers. Tekta is written by someone who implements them. I build production AI systems at Nomology using Claude, GPT, and Llama. When I read a paper, I'm asking: "Can I actually build this? What will break?"
Every article includes an Implementation Blueprint with tech stack recommendations, code snippets, key parameters, and pitfalls. That's what I wish every paper had.
Is Tekta.ai free?
Yes. All research breakdowns, explanations, and Implementation Blueprints are freely available. The goal is to make AI research accessible, not to gatekeep it.
Research Coverage
What research sources do you cover?
We monitor publications from the organizations shaping AI:
- Google DeepMind — Gemini, AlphaFold, transformers, foundational ML
- OpenAI — GPT architectures, RLHF, safety research
- Anthropic — Constitutional AI, interpretability, alignment
- Meta AI — LLaMA, open-source models, multimodal systems
- Academic institutions — Stanford, MIT, Berkeley, CMU
- Independent labs — Mistral, Cohere, Hugging Face
How do you select which papers to cover?
I apply the same filter I use when deciding what to implement at Nomology:
- Can I build this? — Does it have enough detail to implement?
- What's the real improvement? — Is it 2% or 20%? Does it matter?
- What breaks in production? — What gotchas aren't in the abstract?
- Is it worth the migration? — Does the improvement justify the effort?
If a paper is theoretically interesting but practically useless, I skip it. If it's a genuine breakthrough, I go deep.
How current is your coverage?
We aim to cover significant research within days to weeks of publication. For major announcements, we typically publish within a few days.
But we prioritize accuracy over speed. A well-explained paper published a week later beats a rushed summary that misses the point.
Content Format
What's in a typical Tekta article?
Every research breakdown includes:
- TL;DR — The core insight in 30 seconds
- Key Findings — 5-6 bullet points in plain language
- Plain English explanation — The full breakdown without jargon
- Interactive visualizations — D3.js charts for key data
- Implementation Blueprint — Tech stack, code, parameters, pitfalls
- Limitations — What the paper doesn't solve
What is an Implementation Blueprint?
Research papers tell you what they built. They rarely tell you how you'd build it yourself. The Implementation Blueprint fills that gap.
It includes:
- Tech stack recommendations — Specific tools, not vague suggestions
- Code snippets — Working examples you can adapt
- Key parameters — The numbers that actually matter
- Pitfalls & gotchas — What will trip you up
Do you link to original papers?
Always. We link to the original arXiv preprint or official publication. Our breakdowns help you understand papers, not replace them. If you want the full methodology or mathematical proofs, the source is one click away.
What if I find an error?
We take accuracy seriously. If you spot an error or think we've misrepresented something, reach out. We'll review and correct if needed.
Research translation involves interpretation. We're always open to improving based on reader feedback and expert input.
Getting Started
Where should I start if I'm new?
Head to our research papers section:
- Browse by topic — Filter by category like RAG, agents, or fine-tuning
- Read the TL;DR first — Get the key insight before diving deeper
- Check the Key Findings — Scannable bullet points in plain language
- Follow info boxes — Technical terms are explained inline
Don't worry if you don't understand everything at first. Understanding builds as you read more papers and see how concepts connect.
Can I request specific papers?
Yes. If there's a paper you'd like us to cover, reach out. We can't guarantee coverage of every request, but reader interest helps prioritize what to tackle next.
How can I stay updated?
- RSS Feed — Subscribe for automatic updates
- Research section — Check for new additions
- LinkedIn — Follow for announcements
Start Exploring
Research from Google DeepMind, OpenAI, Anthropic, and Meta AI. Translated for practitioners who build.