First time I've opened up about the Lightning story, military, PhD, open-source and merging companies!
Excited to share the journey with everyone.
https://t.co/24NP9ieyE8
Yesterday we saw a supply-chain attack on PyTorch Lightning (on Pypi, not our core repo). It's wild how it happened but it was caught and quarantined within 42 minutes thanks to the open source community.
It's one reason why open source actually helps increase the security posture of projects. Thank you to @pypi and @SocketSecurity ⚡️
Summary: On April 30th, 2026, an attacker captured PyPI credentials and used them to push compromised versions of PyTorch Lightning (PTL).
These versions were live for 42 minutes before PTL community members alerted us and PyPI quarantined the package. The PyTorch Lightning GitHub source code repository was never compromised. This affected those who installed PTL via PyPI between 12:45:20 and 13:27:30 UTC on April 30th, 2026.
https://t.co/dcdhF6zwkh
Thanks to the community for reporting security issues with PyTorch Lightning 2.6.2 and 2.6.3 - our team is looking into it.
In the meantime, please use 2.6.1 until we publish 2.6.4. This is the beauty of open source and why our community keeps each other safe ⚡️.
I think everyone forgot that PyTorch Lightning is massively popular for finetuning and pretraining LLMs...
It's been quietly growing exponentially in the background now with 25 million downloads last month alone.
When i left AI at Meta it only had 60k total downloads.
🚀 Sneak peek of Deepseek v4 Pro on @LightningAI - clearly our optimization team had a post 5pm espresso so you could try this out before your 7am espresso. Link below:
the @LightningAI community has spoken, and they want inference! we have some really exciting Day 0 model drop partnerships in the works to ship our high performance server-less inference endpoints to you.
first up - Gemma 4 by the stellar team @Google is live on Lightning. this model is a beast, give it a rip and share any feedback you’ve got (link in thread)
Today @mirrormirror_ai is launching the marketplace where fashion models license their likeness and brands get stunning AI-generated imagery featuring real people. Commercially licensed, model-approved.
Try our platform: https://t.co/7u72P5xvmq
As a fashion model I used to spend hours on fashion photoshoot sets. I later did my PhD in CS and became a Research Scientist on AI for fashion. I can see clearly that AI image generation is replacing a large portion of my old job. But brands that use AI recklessly have already paid the price. It damages reputations and hurts the bottom line. Putting real people at the core of AI-generated imagery isn't just about avoiding backlash. It's better business. That's what Mirror Mirror AI is built for.
Right now, Mirror Mirror AI houses agency-signed models who have graced the covers of Vogue and Harper's Bazaar. You can digitally book them using our fashion-centric AI software, get your campaign done in hours instead of weeks, and never have to fly anyone in. You purchase a license for commercial use upon approval, and the models get paid.
Mirror Mirror AI is also opening a global call for independent models from anywhere in the world to apply to be featured on the platform. Work with fashion brands internationally, choose the projects you take on, and earn from your own likeness on your own terms. Selected models will be announced at an exclusive event in New York during @Techweek_ this June.
Apply for the open call: https://t.co/NutihoE9qO
A huge thank you to our incredible team for pouring their hearts into this launch, and to a16z @speedrun for believing in our vision from the start. We're just getting started.
@Suhail@Suhail i think you need to revisit @LightningAI ⚡️. we merged with @VoltagePark and have the third largest GPU fleet. good on-demand availability.
Unveiling our new startup Advanced Machine Intelligence (AMI Labs).
We just completed our seed round: $1.03B / 890M€, one the largest seeds ever, probably the largest for a European company.
We're hiring!
[the background image is the Veil Nebula - a picture I took from my backyard, most appropriate for an unveiling]
More details here:
https://t.co/eWHyGLXwCA
@karpathy nice, this is for sure the way to bring back neural architecture search @karpathy
how is it choosing what to try next? a type of guided random search or i guess it branches more and more agents more rl-esque?
The local mac-mini craze is interesting... why not just run this on a lightning studio which is a persistent, cloud-hosted environment?
which also means you can host hundreds of these in parallel by using many different studios at once?
Bought a new Mac mini to properly tinker with claws over the weekend. The apple store person told me they are selling like hotcakes and everyone is confused :)
I'm definitely a bit sus'd to run OpenClaw specifically - giving my private data/keys to 400K lines of vibe coded monster that is being actively attacked at scale is not very appealing at all. Already seeing reports of exposed instances, RCE vulnerabilities, supply chain poisoning, malicious or compromised skills in the registry, it feels like a complete wild west and a security nightmare. But I do love the concept and I think that just like LLM agents were a new layer on top of LLMs, Claws are now a new layer on top of LLM agents, taking the orchestration, scheduling, context, tool calls and a kind of persistence to a next level.
Looking around, and given that the high level idea is clear, there are a lot of smaller Claws starting to pop out. For example, on a quick skim NanoClaw looks really interesting in that the core engine is ~4000 lines of code (fits into both my head and that of AI agents, so it feels manageable, auditable, flexible, etc.) and runs everything in containers by default. I also love their approach to configurability - it's not done via config files it's done via skills! For example, /add-telegram instructs your AI agent how to modify the actual code to integrate Telegram. I haven't come across this yet and it slightly blew my mind earlier today as a new, AI-enabled approach to preventing config mess and if-then-else monsters. Basically - the implied new meta is to write the most maximally forkable repo and then have skills that fork it into any desired more exotic configuration. Very cool.
Anyway there are many others - e.g. nanobot, zeroclaw, ironclaw, picoclaw (lol @ prefixes). There are also cloud-hosted alternatives but tbh I don't love these because it feels much harder to tinker with. In particular, local setup allows easy connection to home automation gadgets on the local network. And I don't know, there is something aesthetically pleasing about there being a physical device 'possessed' by a little ghost of a personal digital house elf.
Not 100% sure what my setup ends up looking like just yet but Claws are an awesome, exciting new layer of the AI stack.