Thank you for demonstrating this for us. I am a writer and a digital artist. I have no experience or ability for writing code. Yet as a creative I would like to have an agent that could assist me in promoting my creative work on various social media outlets. I see this as a huge step forward for employing an llm to act as a promotions manager on my behalf. It is my hope that this process will become super simple in the near future.
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yeah, but I kinda don't want to use a company thats about to develop AGI on a brandname that is in itself a lie. They are ClosedAI, and call themself OpenAI.. So I want to learn opensource LLMs
You can use both. Your prompt can be structured in XML while giving output examples in JSON format that you then use structured output on to force precise JSON outputs. XML is still king for high accuracy prompts.
wondering if you are taking on any project work, let me know how to get in touch. As always, love your videos and #1 release I look forward to each week.
@IndyDevDan What tool do you recommend to build low-code/no-code multi-agent orchestrations? Preferably a tool that integrates well with the Microsoft ecosystem. Do you have a class/video series?
I haven’t tested/experienced this, but along the same lines i am wondering whether the LLM should be at the „top“ of the toolchain, as it is good for some things, but too complex for others. Maybe the way is then to start with the toolchain and then see what we can use LLMs for and where other solutions are more efficient
Here’s how it could work: You can set up the assistant with a “crews” tool, where you define different crew types, their keys, and what each crew is responsible for. The main assistant analyzes the task and decides which crew to assign. You can configure these workflows using frameworks like LangGraph or CrewAI. Once a crew is selected, it starts the task, no matter how complex. The crews can communicate their status back to the main agent during the process, keeping you informed. Finally, the main agent compiles the results, ensuring a smooth and transparent workflow.
And what makes it beautiful is these frameworks support human-in-the-loop interaction. If you’re not satisfied with the output of a certain step, you can provide feedback, and the agent will restart from that point in the flow.
@@DanielBowne I agree! I was trying to explain that, in this use case, it’s about a personal assistant performing tasks on demand. For example, it could pull your new emails, read them to you, and then you could instruct it to reply. This would start a process where the assistant drafts a reply, incorporating your feedback in the loop. The accuracy of the output depends on how we design the tools. My approach is always minimize reliance on the LLM’s general knowledge, focusing instead on logic defined by me, data fetched from trusted sources and databases.. llm just decides which function/process to call. And you can always implement custom validation and guarding methods to make sure output is what you expect to be.
