https://blog.mykin.ai/p/new-kin-a-personal-ai-for-your-life #private and #personal #ai
Our personal and private #ai #agent is on app store and google play
App Store: https://apps.apple.com/us/app/kin-your-personal-ai-coach/id6473448146?platform=iphone
Play Store: https://play.google.com/store/apps/details?id=ai.mykin.app
Give it a try
we navigate your life with full privacy
https://youtu.be/qO0L9gMX1bk?si=O8ct6_XxLTK8p866 #localfirst #architecture
Hard parts and dark sides of localfirst architecture
https://www.youtube.com/watch?v=imd4Y8C6OuA
#sovereign #data #vaults - how to store and recall data for #ssi beyond a #vc.
- How to work with end-to-end encrypted storage?
- How to turn #vc to a #database ?
- How to work with non credential data in #ssi
https://youtu.be/pNpfdGXO4UI?si=phUy128cVA43uORd
#socereing data require #localfirst #architecture to give user real #independence . You need sovereign compute and application logic that make data not only interopable but functional .
https://www.youtube.com/watch?v=l3l7N41mxlE
How #personalknowledgegraphs differ from their enterprise counterparts? How did #personal #knowledge #graphs become a critical part of #personal #ai and #agents? And what should we do with a #sovereign storage to make #PKG a fundamental tool in the user's hands?
- PKG has quite broad and dynamic #ontology, and this open-ended ontology needs #ai for management and meaning
- PKG quite often has partially satisfied entities that require slot feeling process on demand
https://www.youtube.com/watch?v=jwoFFQ4PXFM
What is DID, and what kind of usability challenges do we have in DIDs:
- differentiation and purpose - how do you differentiate and manage different dids in your wallet?
- did and private keys connection.
- versioning and rotation
#ssi #did #identity #crypto
https://youtu.be/61AyuqhJmHA?si=FT3HckMm6PpPZCJv
Imagine 40+ years ago it was #chatgpt like system that in dialogue mode was answer questions in natural language . It was a #deductivedatabases build on #prolog. How #deductive databases could make #personalknowledge graph easier and more powerful in tandem with #ai
Forgotten #simplisity . one of my colleagues long time ago show me #forth . It was stack based language with #factorisation principle. With every word you program gets better and better and execution mechanisms was super simple but powerful. You get compiler , interpreter and os in one shot. #forth really help to develop complex systems . In a few years I fail in love with #lisp . It was more complex but as same as #forth was build around simple but single data structure - list . #lisp was homophonic language that was self aware and capable to change itself. You still could find a quote from Allan key about most butifful program - lisp interpreter in lisp. #smalltalk was another language that fight for simplicity that cover what lisp was not able to do . Simple objects that send messages and turn your software to a soft internet. #self lift this idea even more to extreamly simple model with out class/objects separation. Smalltalk and self was not homoiconic but it was full reflective . According to a many researches smalltalk has the lowest cognitive load and the highest #DX in ide. Last simplicity guru - #prolog - based on idea of horn clause and first order logic. You have terms and few logical operation together with homoiconisity and ability to write meta interpreters. It is just blow my minds. In modern world we still widely use #datalog as simpler and limited data oriented language. Lisp , prolog was designed for #ai and reasoning . Smalltalk was a first language that master #agents . All of this languages is a masterpiece of simplicity and has low cognitive load and minimal syntax and features. All of them build around of internal dsls and code that reflect domain with out language noise.
https://youtu.be/0IkSLj-oCZ8?si=jhn4zEDWOByS5mpk
#lisp and #prolog was tailored for #ai challenge . How #prolog could help with #knowledgegraphs #hypergraphs and multidimensional #ontology ?
I try to connect few dots - #multimodal #databases , #relational #graphs and #commonlogic and #datalog and #prolog for #onlologes and constrains . Some times your ontology is dynamic and derived from data patterns . I keen to teach #llms to do a bit of #prolog . More I play with schema for graphs more I see a lot of work for solvers .
https://youtu.be/Y1zdvZoHMfk?si=FpVX9hv2eiazhYjJ
#crdt for #licalfirst and #sovereign data
#Wallets , #agent and #vaults require personal and decentralized persistent... https://youtu.be/p3NYmDh4NfA?si=KKvK57OAMDXnJNkF
#ssi #web5
https://youtu.be/TFOHeB9wW0c?si=uvx03JCqENb7wTXb we need #agents not #wallets
I model graphs with string labels and hypergraphs in a regular relational DB in a few articles. It makes Personal Knowledge Graphs more portable for different kinds of applications.
How to model #heterogeneous #graphs of complex #entities as nodes in a plain relational and portable #databases ?
Lets discover options together
#eav #json #documentstore
https://youtube.com/shorts/2MKlSTiw0Y0?si=WZoJZtMyGUqQqdjx #hypergraphs #knowledgegraphs
#AI models need a #hypergraphs to represent a complex multimodal knowledge
Any video hosting services ?