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Applying fork/join model and git-style cherry picking to AI conversations

In this post we introduce using the fork/join concurrency pattern combined with the git-style cherry picking to manage and use LLM conversations.   These are loose conceptual connections, so please put down the pitchfork (pun intended). Just as in git you can cherry-pick a commit from one branch to create a new head on another branch, you can pick conversations across sessions (including between different models) to build context to drive LLM conversations. You can also create a fork of a session, this creates a parallel set of messages that are detached from, ie forked off the main session within which they run. You can create new messages that join an existing fork. This is a nice capability, the degree to which it will prove useful is yet to be determined. One of the possibilities is giving the user an ability to create long term "memory" from AI conversations and being able to easily reuse that memory (set of messages) without the need to rebuild context. The ability to...
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Building a Youtube video portal with AI

  In Solvent we are focused on effectively combining the following modes for authoring web apps: Code-Centric Lowcode/No code AI CodeGen/Assist In this post we are going to showcase app authoring degrees-of-freedom as a requirement for fully realizing the benefits of combining these modes/perspectives. Demo: building a video portal Our prompts : Video walk-through Part 1:   Video walk-through Part 2:   Video portal live : Built with combined modes of Lowcode/No code, Code-Centric, AI CodeGen/Assist.  

Combining Code-Centric,Low code/No code and AI CodeGen Perspectives.

  In Solvent we are focused on effectively combining the following modes for authoring web apps: Code-Centric Lowcode/No code AI CodeGen/Assist In this post we are going to touch on and showcase degrees of freedom as a requirement for fully realizing the benefits of combining these modes/perspectives. Degrees of freedom Degrees of freedom as meant here refers to the flexibility of actions a user/worker has when trying to complete a given task. The more complex a task is, the more a user/worker benefits from having  higher degrees of freedom. Think of a construction worker working on a large building. When they are working on the ground floor, they typically have the most degrees of freedom, as they climb to higher floors, they lose more degrees of freedom. They have a number of options at their disposal with different profiles for gaining degrees of freedom:   The Ladder If they use a simple ladder, they have the least degrees of freedom, meaning they have to climb to eve...

Declarative Programming With AI/LLMs

  What Is Declarative Programming Broadly speaking, there are two ways to program/instruct a computer to perform a task, they are imperative vs declarative programming. Imperative programming is what we do the most, we write all the code necessary for the computer to perform a task such that the only thing left for the computer to do is fetch and execute CPU instructions. If you are using Java,C#, Javascript...etc you are doing imperative programming. Declarative programming is a higher-order form of programming, we instruct the computer to perform a task but otherwise let it "figure out" how to do it. Declarative programming requires some sort of software based execution engine. Whereas with imperative programming our code gets compiled into some machine/byte code and then run by the CPU, declarative programming requires a layer of software that does the "magic" that allows you to use it without having to write the precise logic for completing tasks.  I would guess...