I'm Ida, self-taught developer and technical blogger (within AI) based in Stockholm. Professional experience in product management and marketing. Educated as an economist. Generalist that loves to work on several parts of a business. Always happy to be creative and work on challening problems.
Did you know there are dozens of open-source agentic frameworks out there? I have briefly tested some of the more popular ones to get a feel for how they work and how easy they are to get started with.
One of the interesting areas into these Agentic AI systems is the difference between building a single versus multi-agent workflow, or perhaps the difference between working with more flexible vs controlled systems.
Typically, we use simpler methods to generate AI images. But by combining technologies like Diffusion models, LoRAs, IP Adapters, ControlNets, and Vision models, we can control the image generation process so much better.
If we look at API token costs for commercialized LLMs and try to map them across various evaluations — such as coding, math, and conversational quality — where does each LLM make sense?
