Van0SS retweeted
Over the past two months, we have been leading a coalition to mitigate one of the most severe bugs in open source software found in recent memory, php2shell, found as part of our Patch the Planet initiative.
Van0SS retweeted
So many devs seem to think that the $100 and $200 subscription plans are how Anthropic and OpenAI make their revenue.
There are companies spending over $500 MILLION a year with Anthropic. Your subs aren't even a drop in the bucket.
Van0SS retweeted
The open weights for Muse Spark are coming soon, Muse Wonder models are large models that are too big for many people to use, in addition to potential safety concerns.
Van0SS retweeted
Cheeky Pint is back! Mark Zuckerberg @finkd and Alexandr Wang @alexandr_wang came to the pub to discuss their new frontier model, Muse Wonder.
00:00:24 Muse’s breakout use cases
00:07:28 The model is the product
00:14:35 Muse Wonder
00:24:56 How frontier models are built
00:48:26 Betting $100B
01:07:21 Agentic commerce
01:19:34 OpenRouter
01:20:06 Beyond the smartphone
01:27:00 Funding
01:34:06 Why AI won’t be winner-take-all
01:40:46 Meta Superintelligence Labs (MSL)
02:08:11 Working for Mark Zuckerberg
Van0SS retweeted
Recursive self-improvement sounds simple on paper: get AI to improve the algorithms that train the next generation of AI. In practice, we are still pretty far from that.
AI4AI-Bench is a good reality check. Across 290 runs, the average score was just 0.166, with the best system reaching 0.250 on a scale where 0.1 is the original algorithm and 1.0 is the optimum.
What really stood out to me: 141 of 263 submissions never actually changed how the model learned. They mostly optimized the training run around it.
The agents that did modify the learning process averaged 0.226 vs 0.126 for the rest. More reasoning helped too, with the share reaching the algorithmic layer jumping from 8% to 64%.
We are getting better at letting AI improve AI, but there's still a huge gap between tuning a training run and actually inventing a better way for models to learn. That gap is where recursive self-improvement gets really interesting.
Van0SS retweeted
🚀Today we ship @FlyMy_AI Agents.
The world's first all-in-one agentic cloud.
The modern way to build, integrate, and scale production AI agents.
3 steps to a production agent:
1. Connect your work tools to FlyMy
2. Describe what the agent should do - in text or 5 lines of code
3. Set execution rules: manual, scheduled, or integrated into your backend
4. Done! Agent works on scale!
Everything in one place: 800+ MCPs, hundreds of AI models, brain, memory, sandboxes.
Stop building from scratch. Stop waiting for infra. Compress 6 months into a day.
#1 on @ArtificialAnlys benchmarks. Stable, secure, scalable from day one.
Try FlyMy.AI →
Van0SS retweeted
Happy to be working with meta for the past 7 month, and seeing the fruits of their labor.
Great release!
Introducing Muse Spark, the first in the Muse family of models developed by Meta Superintelligence Labs.
Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration.
Muse Spark is available today at meta.ai and the Meta AI app. We’re also making it available in private preview via API to select partners, and we hope to open-source future versions of the model.
Learn more: go.meta.me/43ea00
Van0SS retweeted
Exciting to see a standard API emerge for training that allows you to drop in different backends. Moving between open source infra on self managed clusters and hosted solutions flexibly based on your needs for scale / sovereignty is massively valuable.
SkyRL now implements the Tinker API.
Now, training scripts written for Tinker can run on your own GPUs with zero code changes using SkyRL's FSDP2, Megatron, and vLLM backends.
Blog: novasky-ai.notion.site/skyrl…
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