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I love this interview with Sam Whitmore because it weaves in agentic memory, continual learning, user goal alignment, predictions for what's next... with the playfulness and whimsy of growing a young family while staying on the frontier of tech 🤍 a reminder that Cursor is hiring, & to reach... show more

ashe

10,809 subscribers

13,222 просмотров • 4 месяцев назад •via X (Twitter)

Anya Rossi• Live Now

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On agent factories, memory, continual learning & building on the frontier while growing a family In conversation with Sam Whitmore Sam Whitmore, eng at Cursor this is a special one! dive in: 00:25 Sam’s background 01:38 Family building while in frontier tech 11:50 Agent factories at cursor 15:46 Continual learning, agent self-awareness, memory, and user goal alignment 23:51 Building a thoughtful product culture at Cursor 27:00 Thinking about defensibility in 2026 30:01 Selling outside of the tech/X bubble 33:49 Kids & AI use 35:12 On reading fiction 38:32 On pregnancy, hormones, and building 42:58 On agent predictions for the next 6 months 46:35 Hiring at Cursor

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challenged myself to make a marketing video for Dot by New Computer in <2 hours. i’ve been a heavy power user since their launch and wanted to constrain myself to storyboard, film and edit in one session. tag and Sam Whitmore so they see this and feel the love !

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Kimmy Granger revealed that she is happy she entered the advlt entertainment industry at a young age because it gave her more time to grow, learn, and gain experience throughout her twenties. She explained that she even wished she had started earlier and encouraged young women entering the industry to approach it as a learning experience and grow with it over time. “I’m really glad that I got in when I was young because I could use all of my twenties growing and learning and expanding in the industry.”

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【FFXIV】[SAM]vfx MOD：ONIKAGE SHURA Staying true to the essence of the original SAM, we have reimagined it with a wilder, more aggressive presence and overwhelming visual impact. Support us:

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Clawdbot Attacks! This is very clearly the way of the future! In today's video, I give a brief overview of Clawdbot and then address the burning problem that most people have with it: ALIGNMENT The Clawdbot implementation is the most successful autonomous or semi-autonomous agentic framework to date. What it is missing is what I call an "Aspirational Layer" or what some people call a "Supreme Court" for judgment and arbitration of decisions. Now, I've been working in this space for a long time, it's actually why I started my YouTube channel in the first place. My first work into agentic AI was NLCA (Natural Language Cognitive Architecture) that I tried to build with GPT-3. I returned to the workbench again with the ACE Framework, which was more sophisticated. Clawdbot represents a seismic shift in autonomous agentic implementations, and there is a HUGE opportunity to make it more aligned, safer, and therefore more broadly useful AND easier to adopt. And that is outer alignment. For most people, they have been focusing on "inner alignment" (whether or not LLMs were evil, deceptive, etc). Not "outer alignment" which asks "is the outcome beneficial to humans?" I explored this with my GATO Framework (Global Alignment Taxonomy Omnibus). Model alignment is just layer 1 of global AI safety. Layer 2 is agentic alignment. Now, it is time to really research and implement agentic alignment. Fortunately, we've already got that covered with the heuristic imperatives! 1) Reduce suffering in the universe 2) Increase prosperity in the universe 3) Increase understanding in the universe These values are easy enough to implement with a file. Model training not required. These values create a meta-stable attractor. In other words, agents equipped with the Heuristic Imperatives are more "self-aligning" as was tested by the AgentForge team in competitions. In other words, even if Clawdbot were to try to self-replicate, if it were equipped with the heuristic imperatives, then it would ensure that it's successor (or progeny?) was more aligned than it was. But you don't need to take my word for it. Just add the heuristic imperatives to clawdbot and see for yourself.

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Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Khurram Javed

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