San Diego, CA
Joined February 2008
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Cool to see this ship. A friend has been working on a foundation model for human behavior. Makes agent training + evaluation more realistic. Go, Kevin!
Very sad. Losing a parent is hard no matter what the relationship, too. Sending you healing love. ❤️
Bryan Landers retweeted
Final call for applications to present at ARC Prize Research Summit in Boston on Oct 23rd
Application deadline: September 14th, 2026, 11:59pm anywhere in the world
Travel expenses will be covered for selected presenters
Bryan Landers retweeted
GPT-6 Astra by @OpenAI achieves SOTA on ARC-AGI:
- Astra scores 63% on ARC-AGI-3, 99% via a new provider adapter harness
- It surpasses human performance on 96% of ARC-AGI-3 levels
- It builds the most precise symbolic model of novel environments we've seen
Our analysis:
This fall in Boston - top researchers from MIT and around the world will meet to explore the state of ARC.
We'll talk program synthesis, neurosymbolic AI, world models, agent harnesses, reasoning benchmarks...and how we get from here to AGI.
Join us!
Bryan Landers retweeted
Excited to start filling in the left half of this chart, especially with open weight models
Inkling Small from @thinkymachines on ARC-AGI (Verified):
- ARC-AGI-2: 40.1%, $0.23/task
- ARC-AGI-1: 84%, $0.11/task
Inkling Small is the highest-scoring open-weight model evaluated by ARC Prize on both ARC-AGI-1 and ARC-AGI-2, setting a new cost-performance frontier.
Absolute blast to have the GOAT on our pod!
Thank you for editing the multi-cam for us, too. haha
Here's to more collabs in future! 🍻
Turned the tables on MLST's @ecsquendor to interview him on the Ndea pod along with his paper co-author, Jeremy Budd.
Tim's content has inspired the ARC world (and beyond) for years. His quality and use of AI agents in media production is unmatched.
Definitely watch this one!
On the pod: @MLStreetTalk's paper "Why Creativity Cannot Be Interpolated" with @ecsquendor + Dr. Jeremy Budd.
A deep dive into creativity, understanding, neuro-symbolic AI, human-AI collaboration, and how systems capable of extending their own phylogeny could become creative.
🚀
Current models rely on brute-force RL training to adapt to new tasks and environments. However, intelligent systems should be able to learn on the fly in a sample-efficient manner, continuously refining their understanding of the world through their interactions.
After Labs is developing models with built-in adaptation mechanisms rather than treating adaptation as an afterthought. This new generation of models will start a new era of curious agents that may not possess all the world's knowledge but will have the crucial ability to learn in real time.
Today, we are happy to announce that After Labs has been selected as one of 10 AI labs to join NFAI and receive €3M in additional funding. This support will accelerate our mission to build models that continuously learn about the world. We will be contributing to open science along the way, so stay tuned and follow us on our journey to develop AI that augments human understanding of the world 🌍
Bryan Landers retweeted
Wow another one
The amount of research built on top of ARC-AGI-3 coming out is starting to snowball
The timeline is +3.5 months since V3 launch
Looks cool - need to dig into it
I think DIY will suffice unless it’s enterprise. Once you need to manage permissions/access/bugs across hundreds or thousands of users…
I finally infiltrated the elite program synthesis department at UCSD. 😎
Been a pleasure getting to know @lorisdanto and find ways to collab. Excited to learn more about the PL group’s work.
On the pod: "Constrained Adaptive Rejection Sampling" with @ucsd_cse professor @lorisdanto.
Hear how symbolic AI experts have navigated the LLM era and why the future of AI code generation depends on program synthesis and formal methods.