Last week Apple previewed the future of Siri. In 1987 though, Apple showcased a far more advanced AI assistant that would change how we use our computers entirely. It could see you, control your computer, and even looked and sounded human. They called it Knowledge Navigator. For nearly 40 years it remained science fiction. This past week at @tavus we finally brought it to life with the help from our friends at @cerebras. Meet Dom:

Excited to be on the Business Insider list of top robotics investors. The data wall is falling. For years, the bottleneck was teleoperation: slow, expensive, narrow. Now robots can learn from human video, practice in simulation before touching reality, and there is enough funding to scale data deployments. Robotics is finally running the playbook that took LLMs from autocomplete to reasoning. There are still many open frontiers. General manipulation. The right API layer for the robotics models. Application robotics. That's exactly why I'm bullish. We've just found a recipe that works. Thanks to @ryajetha for compiling the list.

At Ricursive, we are accelerating the inner loop of AI evolution through chip design. If you speed up training and test time by a multiplier, designing a new model can be sped up by a multiplier! Thank you, @sallywf from EE Times for a thoughtful piece on @RicursiveAI!

When we started @linear, we wanted to make product work feel more coherent across the whole team. Agents can now do more of the work, but too often they operate from an empty prompt, in isolation, for one person at a time. That risks creating a new layer of fragmentation. We think the larger shift happens when agents work from the same context as the team. With the new coding sessions, Linear Agent can now move from intake to investigation, code, and review inside that shared environment. This completes the product development loop we’ve been building toward: from intake to merge, with the team’s context intact.

The task: rapidly design highly performant chips. The vision: serve intelligence at a price and capability impossible to match. Our co-founder and CEO @annadgoldie sat down with @FounderCoHo to break down the recursive self-improvement loop at the heart of Ricursive's work. The task and vision are ambitious, but so are we. Full conversation:

Building the future of silicon requires the best talent in the world. That's why we’re excited to welcome Mark Lee to the team. Mark is a titan in the chip industry: he led physical design for the iPhone and iPad, helped technically scope Apple's revolutionary M1 chip, and directed RTL-to-GDS efforts for Tenstorrent's leading Ascalon processor. Mark has spent decades pushing the limits of what silicon can do. We’re thrilled to have his expertise as we together reinvent chip design.

So fun to host @annadgoldie and @Azaliamirh of @RicursiveAI at @sequoia AI Ascent 2026! They share the story of building AlphaChip which was incorporated into multiple generations of the TPU. They walk through their three-phase roadmap: from AI-powered design tools -> a "fabless era" platform for custom silicon -> full vertical integration, and their LT vision of building true recursive AI. 00:00 Neural Nets Meet Chips 00:21 Meet AlphaChip Creators 00:43 Recursive Intelligence Vision 01:31 AlphaChip Real World Impact 02:17 Three Phase Company Roadmap 02:20 Phase One Speeding Design 04:11 Rebuilding Tools for AI 05:05 STA Engine and RL Loop 06:15 Designless Platform and Custom Chips 08:01 Team and Audience Q&A

Fireside chat at Sequoia Ascent 2026 from a ~week ago. Some highlights: The first theme I tried to push on is that LLMs are about a lot more than just speeding up what existed before (e.g. coding). Three examples of new horizons: 1. menugen: an app that can be fully engulfed by LLMs, with no classical code needed: input an image, output an image and an LLM can natively do the thing. 2. install .md skills instead of install .sh scripts. Why create a complex Software 1.0 bash script for e.g. installing a piece of software if you can write the installation out in words and say "just show this to your LLM". The LLM is an advanced interpreter of English and can intelligently target installation to your setup, debug everything inline, etc. 3. LLM knowledge bases as an example of something that was *impossible* with classical code because it's computation over unstructured data (knowledge) from arbitrary sources and in arbitrary formats, including simply text articles etc. I pushed on these because in every new paradigm change, the obvious things are always in the realm of speeding up or somehow improving what existed, but here we have examples of functionality that either suddenly perhaps shouldn't even exist (1,2), or was fundamentally not possible before (3). The second (ongoing) theme is trying to explain the pattern of jaggedness in LLMs. How it can be true that a single artifact will simultaneously 1) coherently refactor a 100,000-line code base *and* 2) tell you to walk to the car wash to wash your car. I previously wrote about the source of this as having to do with verifiability of a domain, here I expand on this as having to also do with economics because revenue/TAM dictates what the frontier labs choose to package into training data distributions during RL. You're either in the data distribution (on the rails of the RL circuits) and flying or you're off-roading in the jungle with a machete, in relative terms. Still not 100% satisfied with this, but it's an ongoing struggle to build an accurate model of LLM capabilities if you wish to practically take advantage of their power while avoiding their pitfalls, which brings me to... Last theme is the agent-native economy. The decomposition of products and services into sensors, actuators and logic (split up across all of 1.0/2.0/3.0 computing paradigms), how we can make information maximally legible to LLMs, some words on the quickly emerging agentic engineering and its skill set, related hiring practices, etc., possibly even hints/dreams of fully neural computing handling the vast majority of computation with some help from (classical) CPU coprocessors.

@karpathy and I are back! At @sequoia AI Ascent 2026. And a lot has changed. Last year, he coined “vibe coding”. This year, he’s never felt more behind as a programmer. The big shift: vibe coding raised the floor. Agentic engineering raises the ceiling. We talk about what it means to build seriously in the agent era. Not just moving faster. Building new things, with new tools, while preserving the parts that still require human taste, judgment, and understanding.