AI Research Posts

FOD#105: Will “Agent Wars” Shift From Cloud to Device?

plus signals from Sam Altman most people missed

🎙️Why AI Still Needs Us?

An interview with Toloka CEO Olga Megorskaya on human-AI co-agency: how human-in-the-loop work evolved from labeling to expert judgment, RLHF, and benchmarks

🔹 Ready to accelerate your AI innovation? The time is now.

Join us for this exclusive virtual event from AI innovators CoreWeave and NVIDIA to help us collaboratively redefine the future of AI

DeployCon SF: Free GenAI Summit ft. DoorDash, ConverseNow, and more

Guest Post: Yambda-5B and Open Recommender System Datasets Explained

Yambda-5B is Yandex's open recommender dataset: 4.79B user interactions, audio embeddings, and organic flags — a production-scale alternative to MovieLens.

Meta-Learning in AI: MAML, Prototypical Nets & Model-Based Approaches

Meta-learning teaches AI to learn how to learn. Covers MAML, Prototypical Networks, Meta-LoRA, ReMA, and meta-evaluation with real examples.

Reinforcement Fine-Tuning in Production: Predibase CEO on RFT, Continuous Learning & Agentic AI

An Inference with Devvret Rishi, CEO and co-founder of Predibase

Decoding BERT: From Original NLP Game-Changer to Today's Efficient AI (feat. ConstBERT)

What is BERT in NLP? Learn how BERT works—MLM, NSP, fine-tuning—plus modern variants like RoBERTa, DistilBERT, ModernBERT, and ConstBERT in 2026.

FOD#102: Do Reasoning Models Think Too Much?

plus new video format: Three WOW and One Promising Release from the last week

🦸🏻Reward Engineering Explained: Functions, Models & Hacking

Reward engineering defines how AI agents learn. Covers reward functions, dense vs sparse rewards, RLHF reward models, value functions, and reward hacking.