The memory layer for Personalized AI. Contribute to Mu-L/mem0 development by creating an account on GitHub.
Mem0: The Memory Layer for Personalized AI Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.Note: The Mem0 repository now also includes the Embedchain project. We continue to maintain and support Embedchain ...
Mem0 is an open-source memory layer for AI applications. It solves the problem of stateless LLMs by efficiently storing and retrieving user interactions, enabling personalized AI experiences that improve over time. Our hybrid datastore architecture combines graph, vector, and key-value stores to mak...
Amid the AI boom, compute power is emerging as one of this decade's most critical resources, influencing the pace at which AI is deployed. Data centers train the foundation models and machine learning applications that underpin all AI technology. The hardware, processors, memory, storag...
Future AI tools will tap into customer data more effectively to generate highly personalized content variants at scale. Rather than just creating generic copy, these systems will produce content tailored to specific customer segments, behaviors, and preferences. ...
a place to track not just what changed, but why and by whom. We may begin to layer in richer metadata, such as which agent or model made a change, which sections are protected, and where human oversight is required – or where AI reviewers likeDiamondcan step in as part of the loop...
We're an all-remote company that allows people to work from almost anywhere in the world. It's important for us to practice clear communication in ways that help us stay connected and work more efficiently.
The biggest update here is that…to absolutely no one’s surprise…every application-layer company is now a self-proclaimed “AI company” – which, as much as we tried to filter, drove theexplosion of new logosyou see on the right side of the MAD landscape this year ...
Most Win32 functionality has a managed counterpart now, either natively provided or achieved through the P/Invoke interoperability layer. Among the various classes that expose managed wrappers for Win32 APIs, those that are lacking include NTFS functions, memory-mapped files, serial por...
(2021) propose DeepQR that, alongside computing explicitly-defined features, uses a 2-layer transformer encoder to consider semantic features, which are designed to capture relations between different question components. Compared to six existing models, DeepQR was able to more accurately identify ...