Joined January 2015
- Tweets 5,497
- Following 1,565
- Followers 1,437
- Likes 10,757
1,328 Photos and videos
Pinned Tweet
23 Oct 2025
Can't wait to share what is in this.
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Maximilian Alexander retweeted
@iroh_n0 just went 1.0 with Swift support 🎉
This is one hell of an impressive project.
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Hiring embedded software engineers for realtime cache eviction policies
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Maximilian Alexander retweeted
Jun 14
How a 10 out of ten invention looks like...😃😳
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Maximilian Alexander retweeted
Jun 12
🚀 Introducing Gemini-SQL2, our breakthrough text-to-SQL capability powered by Gemini 3.1 Pro! We've achieved state-of-the-art results on the highly competitive BIRD benchmark, translating natural language into execution-ready SQL queries. 🧵👇
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Maximilian Alexander retweeted
Jun 13
POV: your unitree RL policy learns chaos before walking
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Maximilian Alexander retweeted
Jun 11
Integrating Adamo @trossenrobotics HQ!
Had great fun in chicago a few weeks back with the trossen team.
It's great to see people notice just how fast we are, but highlights how much of an issue it has been with many existing teleop solutions.
If you want to try it out, we are now self serve, so just click 'get started' on our website to feel the @Adamorobotics speed!
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Maximilian Alexander retweeted
Jun 12
Apple ha sacado su propio “Docker” nativo y oficial.
Permite ejecutar contenedores Linux en máquinas virtuales ligeras en macOS.
✓ Compatible con imágenes OCI de Docker Hub
✓ Optimizado para Apple Silicon
✓ Escrito en Swift
→ github.com/apple/container
ALT Captura del repositorio público apple/container en GitHub, mostrando el README del proyecto. Se destaca que container permite crear y ejecutar contenedores Linux como máquinas virtuales ligeras en Mac, está escrito en Swift, optimizado para Apple Silicon y compatible con imágenes OCI. Docker mirando de reojo desde la esquina.
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Maximilian Alexander retweeted
Jun 12
We all want to run a personal assistant somewhere. @signalgaining invited me to do a quick demo of @pipecat_ai on a jetson nano and running local model from voxtral and kokoro.
Cant wait to see pipecat and voice ai in the robots!
Jun 10
Want to build your own Alexa, Siri, or Google with an NVIDIA Jetson? Get started today with `wendy init --template`
wendy.dev/blog/voice-ai-agen…
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Maximilian Alexander retweeted
Jun 12
stop collecting data and start generating it
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youtu.be/SFKtFRvx9a4?is=vYUh… this poor yet strong woman who can’t remember anything longer than 7 seconds should get neuralink for memory
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Maximilian Alexander retweeted
Jun 12
Flow reversal steering allows "steering" diffusion-based VLAs with high-level actions, for example from VLM reasoning. This also lets us run RL in the diffusion noise space with exploration guided by high-level reasoning: think through a task, then practice it! 👇
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Maximilian Alexander retweeted
I need help showing off what robots can do! If you are in SF this weekend, please come to the Wendy studio at 2001 Union st. If you are just learning RL robotics or you want to build with real Robots, please come! Feel free to connect, I will be doing these events a lot! Cheerio!
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Maximilian Alexander retweeted
Jun 12
Introducing developer mode for browser use in Chrome and the Codex in-app browser.
Codex can use the Chrome DevTools Protocol (CDP) to debug browser issues by profiling JavaScript performance and inspecting console output, network traffic, and page state.
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Seaweed is very weird. What a disgusting name; but so delicious and nourishing.
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Maximilian Alexander retweeted
Jun 11
I'm launching dingbotics: speeding up RL for robotics
go to dingbotics dot com to check out the latest model I've trained. this will work on your phone's web browser
built with mujoco, nvidia warp, pufferlib, raylib & Rich the Kid
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This is my first time creating novel AI models from scratch from data collection, tuning, preprocessing, having it run on tiny, and I mean TINY battery powered systems. And I have to say the experience is quite thrilling but even more that even when it works very well; I truly have no idea why or how these neural net models work.
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It seems deep learning happened at the perfect time. The massive bottlenecks of 1979-2020 was largely due to not enough organized data and poor hardware. Then bam the internet and GPU happened
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