Byte-Sized Wisdom for the Digital Age
After benchmarking the R1 1776 model and seeing how post training influenced its performance (full post here ), I realized another gap. Models that can technically handle a huge context window often degrade long before you hit their max token limit. Plenty of benchmarks test for raw throughput or model quality
Purpose This benchmark measures the real-world inference performance of Perplexity AI’s R1 1776 model , a post-trained version of DeepSeek R1 671B, designed to eliminate censorship and enhance unbiased information delivery under controlled conversational growth conditions. The focus is on gradual context expansion, realistic outputs, and streaming
People have been having conversations for thousands of years. We’re wired for it. But we’re not wired for talking to something that doesn’t understand social cues, the subtle, unspoken signals that shape human interaction. And yet, here we are, trying to talk to machines
For years, I’ve been fascinated by AI assistants. They are useful, sure, but they always seem to be missing something. We all want a JARVIS from Iron Man or the computer from Star Trek that just gets us and responds naturally. Of course, I fully realize how absurdly