I’m a data engineer who somehow ended up as a software developer. So many of my friends are working now with the OpenAI api to add generative capabilities to their product, but they lack A LOT of context when it comes to how LLMs actually works.
This is why I started writing popular-science style articles that unpack AI concepts for software developers working on real-world application. It started kind of slow, honestly I wrote a bit too “brainy” for them, but now I’ve found a voice that resonance with this audience much better and I want to ramp up my writing cadence.
I would love to hear your thoughts about what concepts I should write about next?
What get you excited and you find hard to explain to someone with a different background?
Neural nets at the edge being Gaussians.
Instilling Inductive biases
HYPERNETWORKS (MAML and networks producing networks)
Neuronal weight and layer interpretability( new paper i heard abt used an auto encoder or smt to figure out which neurons were responsible for certain changes in behavior, seemed interesting, could be interesting for understanding how to instill information directly into a network)