Models and Training
The model layer underneath every AI product. This hub covers how models are trained and adapted: post-training, reinforcement learning environments, distillation, small and local models, diffusion, speech and multimodal systems, mostly from the researchers and labs doing the work.
- Lessons from Generating 12 Trillion Synthetic Tokens - Bogdan Gaza, DatologyAI
- GLM-5.2: Open Weights, Near-Frontier Intelligence - Zixuan Li, Z.ai
- 'Beating RL With Reflection: GEPA and Optimize Anything - Lakshya A. Agrawal, GEPA'
- From VLM/VLA's to Embodied Agents - Armen Aghajanyan, Perceptron AI
- Physical AI's Next Bottleneck Is Finding the Right Video - Rafael Levi, Bright Data
- Robot Demos Are Easy. Reliability Is Hard - Jason Ma, Dyna Robotics
- The Best Models Still Reason Like Toddlers - Andrew Dai, Elorian
- 'Skill issue: stop deploying vision language models, use them with Skills - Merve Noyan, Hugging Face'
- Building the Document Context Layer for AI Agents - Jerry Liu, LlamaIndex
- 'Two Bugs That Hid in Plain Sight: A vLLM Debugging Detective Story - Asaf Gardin & Yuval Belfer'
- Large clusters for small models - Daniel Svonava, Superlinked
- Where RL Will Take Search - Maximilian-David Rumpf, SID.ai
- 5 Voice Agent Failure Modes You'll Hit in Week One - Venky B, Plivo
- Why AI Agents Need Million-Token Context - Thomas Wolf & Olive Song, MiniMax
- Inside 847 Production Clinical AI Notes - Sebastian Fox, Composo
- Open Source Is Dead. Long Live Open Source. - Saoud Rizwan, Cline
- Compression at the Edge - NVIDIA, Unsloth, HuggingFace, Ollama
- Local Models: Trust, Control, Optimization - Carter Abdallah, NVIDIA
- Teaching AI to Find Real Vulnerabilities - Prof. David Brumley, Bugcrowd
- When Will The Benchmaxxing Plague End? - Nick Heiner, Surge AI
- Data and Environment Curation for Post-Training LLMs - Mahesh Sathiamoorthy, Bespoke Labs
- Scaling to Long Horizons - Ross Taylor & Chengxi Taylor, General Reasoning
- 'Agents at Scale: Inside MiniMax''s Model and the Infrastructure Behind It
- Reinforcement Learning without Verifiable Rewards - Will Brown, Prime Intellect
- 'From Tokens to Cells: Foundation Models for Single-Cell Biology - Akram Baharlouei, Altos Labs'
- On AI and Knowledge - Pablo Castro, Distinguished Engineer & CVP for AI Knowledge, Microsoft
- '"Software engineering is not about writing code" - Benoit Schillings, Google DeepMind VP of Research'
- Recursive Model Improvement - Lee Robinson, Cursor, SpaceXAI
- Modern Post-Training: A Deep Dive - Will Brown, Prime Intellect
- Your LLM Deception Monitor Is Broken. The Fix Is in the Training Data - Sachin Kumar, LexisNexis
- How we taught agents to use good retrieval - Hanna Lichtenberg, Mixedbread AI
- Frontier results, on device - RL Nabors, Arize
- Using RL Agent to Detect and Remediate ETL Pipeline Failures - Anna Marie Benzon
- You Might Not Need 50 Diffusion Steps - Ziv Ilan, Nvidia
- Stop Making Models Bigger, Make Them Behave - Kobie Crawford, Snorkel
- GPU Cloud Deployment Without Leaving Your IDE — Audry Hsu, RunPod
- 'From Transcription to Live Music: Gemini''s Audio Stack — Thor Schaeff, Google DeepMind'
- Text Diffusion — Brendon O'Donoghue, Google DeepMind
- AI Engineer Melbourne 2026 Keynote Livestream | Day 1
- Task Fidelity Scaling Laws — Kobie Crawdord, Snorkel
- What Lies Beneath the API — Benjamin Cowen, Modal
- '20 days of compute vs 7 hours: rethinking what state-of-the-art means — Bertrand Charpentier, Pruna'
- 'How We Built Zeta2: Training an Edit Prediction Model in Production — Ben Kunkle, Zed'
- 'Reachy Mini: the $300 open source robot you can actually hack — Andres Marafioti, Hugging Face'
- 'Prompt to Pipeline: Building with Google''s Gen Media Stack — Paige & Guillaume, Google DeepMind'
- Any-to-Any: Building Native Multimodal Agents - Patrick Löber, Google DeepMind
- 'From 46% to 90%: Fine-Tuning Tiny LLMs for On-Device Agents — Cormac Brick, Google'
- Personalization in the Era of LLMs - Shivam Verma, Spotify
- Let's go Bananas with GenMedia — Guillaume Vernade, Google DeepMind
- Why MLX — Prince Canuma, Neywa Labs
- Voice AI: when is the "Her" moment? — Neil Zeghidour, CEO, Gradium AI
- Why TTS Models Now Look Like LLMs — Samuel Humeau, Mistral
- FLUX, Open Research, and the Future of Visual AI — Stephen Batifol, Black Forest Labs
- How Transformers Finally Ate Vision – Isaac Robinson, Roboflow
- Vibe Engineering Effect Apps — Michael Arnaldi, Effectful
- Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs
- 'TLMs: Tiny LLMs and Agents on Edge Devices with LiteRT-LM — Cormac Brick, Google'
- Let LLMs Wander: Engineering RL Environments - Stefano Fiorucci
- How to Train a Model for Your Custom Agent
- Diffusion: What is Diffusion?