@vovalive
Account based inGermany!
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bioinformatics, AI, tennis
Joined April 2011
- Tweets893
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wow this is cool, the site is not opening, I thnk if I describe this to my claude - will get own version in around 10 minutes!
WATTBA !
now I want a device with @Qualcomm inside!
We're partnering with @Qualcomm to bring personal AI context to devices powered by Snapdragon. Liquid Context, our on-device context layer, is now optimized for Snapdragon processors and runs on the Qualcomm Hexagon NPU.
Our goal: Give the agents people choose an understanding of what matters to them and when they need help. With the user's permission, Liquid Context learns from device signals and builds an understanding of their routines, preferences, and needs. That understanding is built and maintained locally, and Liquid Context shares relevant context with the user's chosen agents, whether they run on the device, in the cloud, or across both. That includes third-party agents and Liquid Agent, our efficient embedded agent powered by LFM2.5-2.6B.
Running on the Hexagon NPU, Liquid Context works in the background and keeps that understanding current without requiring a cloud model to process every update.
For device manufacturers, this is a path to add personal context to their devices while supporting their own choice of agents and services. OEMs building embedded or hybrid agents can also work with us to evaluate Liquid Agent.
As our CEO @ramin_m_h said: "Personal AI starts with understanding how you live and what you need, when you need it. Liquid Context builds that understanding on your device so the agents you choose can offer more relevant help and anticipate your needs."
> Read more about our partnership: liquid.ai/blog/liquid-contex…
> Check out the livestream of @cristianoamon and Ramin's keynote here: youtube.com/watch?v=xKCto1Yf…
like that search mechanic, here is bio adaptation:
laya-mlx for 100% local Jev - like inference @mizorewww
So cool to have those fast local models
Nice memories on our 2023 paper where we worked on token probabilities w @stefan_neymark @dorawriting @qoffee1337
aging-us.com/article/205055/…
What an animation!
1.5 years ago, we bet the company on benchmarking and evaluating all things AI for science.
Today, we’re #1 in many areas. One of them is AI for longevity. Just ask your favorite LLM.
Here’s what GPT Astra says about our Cell cover paper 👇
Link in the comments
I like this kind of devices!
That’s what you need in agent era
60Hz e-ink on a Framework Laptop 13, driven by our Paper Dev Kit
youtube.com/shorts/ZSxYI14Jd…
In a new article published today on the cover of @CellCellPress, we obtained Liquid Foundation Model instances that establish state-of-the-art performance on biological longevity tasks, outperforming the best frontier models such as Gemini-3.1-Pro, GPT-5, and Claude Opus.
In partnership with @InSilicoMeds, we built and released:
> A comprehensive eval suite of 17 biological longevity tasks (i.e., LongevityBench), to assess whether a general-purpose language model can interpret aging data spanning clinical records, DNA methylation, transcriptomics, plasma proteomics, and genetic evidence.
> LFM2-1.2B-Longevity and LFM2-2.6B-Longevity: two compact models specialized for interpreting structured aging data across these tasks.
These results are important! 🧵
Read our fresh cell paper!
cell.com/cell/fulltext/S0092…
Aging has a language. 🧬 We’re teaching AI to read it.
On the cover of Cell: compact Longevity-LLMs, jointly trained by Insilico Medicine and @liquidai, demonstrate SOTA performance on LongevityBench.
Powered by these models, Longevity Claw nominated 328 candidate gene targets for aging research.
Small model. Big questions. Open source. 🧵👇
#insilicoSOTAFM
Vladimir Naumov retweeted
Most vaccines train your immune system to fight pathogens. Insilico's Longevity Vaccines initiative is training it to fight your own aging cells - senescent, fibrotic, autoreactive - before they cause disease. insilico.com/news/lgv150926e…
the moat isn't the model anymore, it's the data nobody else has
An AI system trained on more than 20,000 protein structures from pharmaceutical companies outperforms AlphaFold-like models that use only public data
go.nature.com/4gTSLaA
Vladimir Naumov retweeted
We put GPT-6 Astra through our DDD benchmarks.
On 3 tasks, it crushed it. 🔥
It beat every other frontier model on chemical synthesis and antibody developability.
@sama, @gdb, @thekaransinghal Ready for the full suite? 👀
#insilicoSOTAFM
We need more materials like this
Built an app to show how Rentosertib actually works. 🧬✨
It recently showed an anti-aging effect in clinical trials. 👀
GPT-6 Astra again. The crazy part: ~40 minutes to build, plus an MD simulation 🤯
@Derya_tm @EvgenyKirilin, this really feels like a new level of structural analysis.
Rentosertib inhibits TNIK, a kinase and one of the molecular switches controlling signaling inside our cells. ⚙️ Kinase activity is also exactly the kind of property Insilico Medicine’s models are built to predict. 🤖🔬
#insilicoSOTAFM