👋 there! My name’s Daniel. A brief blurb about me: I’m a data scientist building GenAI products in the b2b software space. I get excited about technology that transforms how work gets done for the better.
To get in touch, drop me a line at dryanfurman@gmail.com.
Chestnut street at twilight - Feb 2025
Ride in the headlands - Jul 2025
Golden gate views - Jan 2025
Ocean beach surf - Feb 2022
Tank hill - Jun 2020
Big Sur - Dec 2016
Ride near Seattle - Mar 2025
Marina golden hour - May 2025
Mallorca views - Jul 2025
Chestnut street at twilight - Feb 2025
Ride in the headlands - Jul 2025
Golden gate views - Jan 2025
Ocean beach surf - Feb 2022
Tank hill - Jun 2020
Big Sur - Dec 2016
Ride near Seattle - Mar 2025
Marina golden hour - May 2025
Mallorca views - Jul 2025
Chestnut street at twilight - Feb 2025
Ride in the headlands - Jul 2025
Golden gate views - Jan 2025
Ocean beach surf - Feb 2022
Tank hill - Jun 2020
Big Sur - Dec 2016
Ride near Seattle - Mar 2025
Marina golden hour - May 2025
Mallorca views - Jul 2025
LLMs are typically finetuned on chat-templated datasets, which convert conversations into strings ready for text generation. Matching this format is standard at prediction time, as deviations are often noted to hurt performance. Here, we test this theory using an instruction-following benchmark and the best open-source LLMs available at this time.
