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⚡️Every product of the future will be a living system — Ronak Malde, Trajectory.ai

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Machine Learning

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Uncertainty-Guided Data Augmentation for Engineers | Deep Dive - Yongmin Kwon

Uncertainty-Guided Data Augmentation for Engineers | Deep Dive - Yongmin Kwon

This session details a data-efficient method for training engineering surrogate models by using uncertainty quantification (UQ) to guide geometric data augmentation. Instead of random deformations, the approach lets the deep ensemble model identify its own knowledge gaps (epistemic uncertainty), then uses Free-Form Deformation (FFD) to generate new shapes specifically in those uncertain regions. This ensures every expensive simulation run yields maximally informative data, significantly improving model accuracy for a fixed computational budget across domains like structural mechanics and aerodynamics.

Q-learning with Flow-Matching Policies

Q-learning with Flow-Matching Policies

This talk explores methods for optimizing expressive, multi-modal policies, such as those based on flow-matching, with off-policy reinforcement learning. The speaker presents two novel algorithms, FQ-RL and CAM, designed to overcome the instability of backpropagation through multi-step generative models, enabling effective online self-improvement and adaptation for robotic manipulation tasks.

Graph Neural Networks Explained: A Clear Guide to GNN Basics & Models

Graph Neural Networks Explained: A Clear Guide to GNN Basics & Models

An introduction to Graph Neural Networks (GNNs), covering fundamental concepts like nodes, edges, and embeddings. This post delves into the core message-passing mechanism and provides a detailed overview of key architectures including GCN, GraphSAGE, GAT, GIN, and Graph Transformers, explaining their unique approaches and mathematical formulations.

Artificial Intelligence

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⚡️Every product of the future will be a living system — Ronak Malde, Trajectory.ai

⚡️Every product of the future will be a living system — Ronak Malde, Trajectory.ai

Ronuk Malde, CEO of Trajectory.ai, discusses his journey from building AI coding agents at Windsurf to his current focus on continual learning for enterprise AI. He shares insights on leveraging real-world user data, the unique challenges of model acquisition, and how Trajectory.ai's platform, powered by innovations like scaled SDPO and a novel training stack, enables dynamic, always-learning AI models for diverse industries from legal to finance.

6 Things to Know about AIE World's Fair 2026

6 Things to Know about AIE World's Fair 2026

Discover the AI Engineering World's Fair 2026, the largest iteration yet, offering an unparalleled deep dive into AI engineering with expanded tracks on auto research, GPU specialization, and new verticals like finance and healthcare. Highlights include an innovative expo experience, exclusive leadership initiatives like the "Token Billionaires Program," and unique side events fostering community, including "Posters on AI" where attendees can defend their tweets. This event is designed to be a curated hub for practical, cutting-edge insights and networking in the AI/ML professional landscape.

The data black hole at the center of AI

The data black hole at the center of AI

AI progress is fundamentally driven by vast amounts of data and compute, rather than improvements in sample efficiency, creating a stark contrast with human learning. This essay explores the "black hole of data" powering AIs, quantifies the massive sample-efficiency gap between humans and machines, counters common objections, and discusses the implications for white-collar automation and future AI research.

Technology

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3‑2‑1 Backup Rule Explained: Protect Your Data from Disaster

3‑2‑1 Backup Rule Explained: Protect Your Data from Disaster

Jeff Crume outlines essential data resiliency strategies, starting with the 3-2-1 backup rule—three copies, two media types, one offsite—and expanding to include immutable or air-gapped backups, rigorous testing, and encryption. He emphasizes these principles for robust disaster recovery, ransomware protection, and minimizing costly downtime, highlighting the trade-offs in achieving high availability.

The Media Game Has Changed

The Media Game Has Changed

The conversation explores the shift from legacy media to creator-led platforms, why authenticity has become a competitive advantage, and how founders can build audiences by communicating directly with customers, employees, and the public. They discuss podcasts, social media, storytelling, corporate communications, and the changing relationship between companies, journalists, and audiences. Along the way, they examine how founders can develop a public voice, why some leaders become influential communicators, and what it means to build a brand in a world where distribution is increasingly decentralized.

The C4 Model: Visualizing Software Architecture • Simon Brown & Susanne Kaiser • GOTO 2026

The C4 Model: Visualizing Software Architecture • Simon Brown & Susanne Kaiser • GOTO 2026

Simon Brown, creator of the C4 Model, discusses its origin as a practical solution to clarify messy software diagrams. He explains the four hierarchical levels (context, container, component, code), emphasizing that most teams only need the top two for significant value. The discussion highlights the importance of including technology in diagrams, C4's collaborative nature, and practical advice on modeling microservices and bounded contexts, all while advocating for a lightweight, accessible approach to architectural visualization.

Recent Post

Uncertainty-Guided Data Augmentation for Engineers | Deep Dive - Yongmin Kwon

Uncertainty-Guided Data Augmentation for Engineers | Deep Dive - Yongmin Kwon

This session details a data-efficient method for training engineering surrogate models by using uncertainty quantification (UQ) to guide geometric data augmentation. Instead of random deformations, the approach lets the deep ensemble model identify its own knowledge gaps (epistemic uncertainty), then uses Free-Form Deformation (FFD) to generate new shapes specifically in those uncertain regions. This ensures every expensive simulation run yields maximally informative data, significantly improving model accuracy for a fixed computational budget across domains like structural mechanics and aerodynamics.

Stop Making Models Bigger, Make Them Behave — Kobie Crawdord, Snorkel

Stop Making Models Bigger, Make Them Behave — Kobie Crawdord, Snorkel

Snorkel.ai's research demonstrates how a 4-billion-parameter model, fine-tuned with Reinforcement Learning for under $500, significantly outperformed a 235-billion-parameter model on financial analysis tool-use tasks. The key was cultivating 'tool discipline' and error correction capabilities, rather than relying on sheer model size or deeper reasoning. Single-table training generalized effectively to harder multi-table problems, emphasizing the importance of targeted behavioral fixes identified through detailed evaluation rubrics.

Sovereign Escape Velocity: Ownership w Open Models — Gus Martins, & Ian Ballantyne, Google DeepMind

Sovereign Escape Velocity: Ownership w Open Models — Gus Martins, & Ian Ballantyne, Google DeepMind

Google DeepMind's Ian Ballantyne and Gus Martins introduce Gemma 4, a family of open models delivering state-of-the-art performance with remarkable size efficiency. They discuss how models like the 31B variant outperform competitors 2-20x its size while running on a single GPU, the shift to an Apache 2.0 license to foster sovereignty and adoption, and the new economics of running powerful agentic workloads on hardware ranging from a Pixel phone to a single enterprise GPU.

From Transcription to Live Music: Gemini's Audio Stack — Thor Schaeff, Google DeepMind

From Transcription to Live Music: Gemini's Audio Stack — Thor Schaeff, Google DeepMind

Thor Schaeff from Google DeepMind demos the advanced audio AI stack, starting with a single API call to Gemini for rich transcription (speaker names, emotions, translation). He showcases speech generation directed by "director's notes" instead of a voice catalog, the real-time, sound-to-sound Gemini 1.5 Flash Live model, and a live demo of Gemini Live using the Lyria 2 model as a tool to generate a full song on stage.

[404] – Developer Not Found: The Continuing Developer Evolution • Derek Bingham • YOW! 2025

[404] – Developer Not Found: The Continuing Developer Evolution • Derek Bingham • YOW! 2025

Derek Bingham explores the rapid evolution of developer tools with AI, from coding assistants to autonomous agents. He emphasizes the shift from prompt engineering to context engineering, introduces Spec-Driven Development (SDD) as a framework for quality AI-generated code, and dispels fears about AI replacing developers, arguing instead for increased demand and the necessity of new skills like ethical and systems thinking.

Building AI Agent Systems and Scaling Challenges in Agentic AI

Building AI Agent Systems and Scaling Challenges in Agentic AI

Scaling agentic AI systems presents unique challenges beyond traditional software scaling. This summary explains why expanding a single agent's capabilities leads to non-linear increases in cost, latency, and failure propagation. The talk frames this as a systems design problem solved by moving from a monolithic agent to a multi-agent architecture with distributed responsibilities, and it explores the critical architectural trade-offs between horizontal and vertical scaling of agent capabilities.

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