AI Research Posts

Guest post: AI Inference Is Breaking Unit Economics

AI Inference Cost: How Teams Cut Model Spend in 2026

AI 101: Agentic Vector Databases – What Is That?

How vector databases are evolving for AI agents: agentic RAG with Qdrant, memory layers with Weaviate Engram, and Pinecone Nexus knowledge engine explained.

FOD#151: Recursive Self-Learning: Why It Matters Now

From Turing's "child machine" to Jack Clark's 2028 forecast – what changes when the system starts working on the system that builds it

9 New Approaches to Multi-Agent Systems

9 new multi-agent system architectures: RecursiveMAS, OrgAgent, CORAL, GRASP, persistent memory and more. Research digest for AI practitioners.

🎙️Hugging Face’s Clem Delangue: Stop Comparing Engines to Cars

Why open models, local AI, and coding agents are changing who gets to build.

FOD#68: AI Benchmarks vs Vibe Checks: Measuring AI Progress

Is your AI strategy missing a "Safety Net"?🛡️

AI Agents Are in Production. Governance Is Not.

AI 101: What’s So Magical About Embeddings?

Embeddings turn token IDs into vectors that encode meaning as geometry. Covers core concepts, types (Word2Vec, BERT, CLIP), transformers, and RoPE.

FOD#150: Ghosts in the Distillation Pipeline

A new Nature paper just broke the foundational assumption of every AI compliance framework being drafted – plus key news, research, and reads from the past week

The Evolution of LoRA: 15+ Variants You Should Know

A complete guide to LoRA variants: QLoRA, DoRA, MoE-LoRA, and 15+ more. Trace the evolution of parameter-efficient fine-tuning for large language models.

#4:There are no AI-native enterprises

Most enterprises run AI in the business but not on it. Learn why shadow AI spreads and what real AI-native transformation actually requires.

AI 101: LLM Token Types: Input, Output, Reasoning, Cached & More

Input, output, reasoning, cached, vision — not all LLM tokens cost the same. A guide to token types and how each one shapes your AI bill.