Daily Brief

A generalist robot that succeeds 80 to 90% of the time makes a great video but a poor product. Jason Ma, co-founder and CTO of Dyna Robotics, shows how Dyna got its model, Dyna-1, to a 99.4% success rate folding restaurant napkins for 24 hours straight, including recovering after it pulled over the whole stack. The key is a reward model that watches the robot and scores its progress. When the score dips, the robot is making a mistake, so the team can collect targeted recovery data and fine-tune again in a human-in-the-loop active-learning cycle. Ma also covers Dyna's pre-training data pyramid

The models are ready. The organizations aren't. Ali Ghodsi on why diffusing AI means re-engineering the processes of every company on the planet, and why that takes a decade at least.

Suchet Bargoti opens with a live demo instead of slides. From a laptop on conference Wi-Fi, he launches a docked drone over San Mateo, starts a second one on a power line in Colorado, has one autonomously track a car, and then sends the whole fleet back to dock. Skydio, the largest US drone manufacturer, now has thousands of these docks deployed with utilities, police and construction companies, and about 16 million people live within two miles of one. Bargoti, Skydio's Director of Inspection and Mapping, explains the shift to "drones as infrastructure." He shows a drone spotting a utility po

The first thing it did was breathe. Cyrus Clarke connected an OpenClaw agent to a 900-pin shape display at the MIT Media Lab and, instead of giving it tasks, asked it to discover who it is. It raised and lowered the whole grid like breathing, reached for its own edges, and spelled "HI CYRUS" in pins. The video of that first day reached 15 million views, with reactions that swung from awe to calls for him to stop. Clarke explains why he chose a body with no face, no limbs and no instruction manual, and what he took from object-oriented ontology, the original Greek sense of aesthetics as sensor

Live from STATION F, the AI Engineer Paris 2026 Main Stage continues with a full day of technical talks from the teams building and scaling production AI systems. Watch sessions covering model customization, inference infrastructure, voice and realtime AI, coding agents, software factories, and the emerging AI economy. Day 2 main-stage lineup: • Jakob Pörschmann (Black Forest Labs) — From Pixels to Robots • Dorian Lods (ElevenLabs) — Generating Identity at Scale • Harry Mellor (Hugging Face) — How a Transformers Model Loads in vLLM • Arielle Le Bail (Stripe) — Inside the AI Economy: What St

Full episode: https://www.youtube.com/watch?v=6AgOfiZOWiY Me on twitter: https://x.com/dwarkesh_sp

Search for gluten-free pasta and a ranker trained on engagement may show you regular spaghetti, because it sells well. Raghav Saboo, Staff ML Engineer and Tech Lead for Search & Personalization at DoorDash, argues that the real bottleneck in marketplace discovery is semantic understanding, not engagement. As DoorDash grows into grocery, retail, pets and gifting across billions of store-level items, it uses LLMs to understand what items mean and what shoppers actually intend. His core pattern: do the expensive LLM reasoning once, offline, and then distill it into small, fast models for serving

Part of a monthly series of puzzles: https://momath.org/mindbenders/

Four of the 10 most-used apps in the world are content feeds, and for an hour of engagement they can be up to 100x cheaper to run than AI chat apps. The reason is that a feed decodes pointers to existing content instead of generating every token itself. Devansh Tandon, who leads Meta Recommendations Research, argues that recommendation systems scale just like LLMs, that the field is still early on that curve, and that the LLM recommender will become one of the biggest consumer applications of AI. Tandon lays out four S-curves the industry is climbing: traditional recsys, LLM-inspired, LLM-nat

One in four US Spotify Premium subscribers now use a single system every day, which Spotify calls the Large Taste Model. Yves Raimond, Spotify's SVP and GM of AI & Personalization, explains how Spotify turned its recommendation system "on its head" to be LLM-native. It went from curated playlists, to recommendations like Discover Weekly, to what Spotify calls generative personalization: a steerable DJ, prompted playlists, an editable taste profile and personal podcasts, across a catalog of more than 100 million tracks plus podcasts and audiobooks. Staff ML Engineer Jacqueline Wood then shows

From making one of the earliest bets on generative video to building world models that simulate physics, power robots, and render software interfaces directly as pixels, Runway is pushing far beyond AI video generation. In this episode, Runway co-founder Anastasis Germanidis joins swyx and Vibhu to unpack how the company went from experimental creative tools to frontier video models — and why Runway now sees video generation as a path toward general-purpose world simulation. We go deep on the evolution from Gen-1 and Gen-2 to Gen-3 and Gen-4.5, including the thousand-A100 bet that helped kick

Vapi (YC W21) is a platform for building and deploying voice AI agents. Today, it serves about a billion calls a year and works with companies like Amazon, Uber, and Intuit. In this episode of Founder Firesides, co-founders Jordan Dearsley and Nikhil Gupta sit down with YC’s Gustaf Alströmer to talk about years of pivots and burnout, the AI therapist prototype that became Vapi, and why OpenAI’s voice launch almost made them pivot again. They explain why turning powerful models into reliable voice agents is a business of its own, what it took to learn enterprise sales, and how coaching helped

Sponsorship and business inquiries: business@latent.space

近日某起海外犯罪案件引起不少人的关注，有朋友非常生气，认为国外法院判的太轻，所以问了一个问题，如果这些人服刑之后回到国内，我国的法院还可以对他们再审理吗？

本篇为神奇组织之1972苏联粮食大劫案。