13h ago · 8 min read · As AI workloads expand across cloud, edge, and enterprise environments, the infrastructure underpinning them is shifting toward hardware diversity. The question is no longer whether teams will run on
Join discussion17h ago · 5 min read · The first request hits your model. You wait. Two seconds. Four. Eight. Your user has already gone. This isn't a model problem. It's a cold start problem - and it's one of the most quietly destructive
Join discussion
3d ago · 5 min read · Qwen 3.6 Plus is coming soon to Qubrid. AI developers don’t get excited easily anymore. Not by launches. Not by claims. And definitely not by benchmarks alone. But something interesting is happening a
Join discussion
3d ago · 6 min read · TL;DR AI system benchmarks like MLPerf struggle to keep pace with the rapidly evolving model landscape, making it difficult for organizations to make informed deployment decisions. We believe benchmar
Join discussion5d ago · 16 min read · In the MLE tutorial, we estimated a coin's bias by finding the single parameter value that maximises the likelihood. Flip a coin 3 times, get 3 heads, and MLE says \(\hat{\theta} = 1.0\) — the coin al
Join discussion
Mar 27 · 11 min read · Imagine you're a casino inspector. You suspect a dealer has been switching between two biased coins, but you only have records of the outcomes - not which coin was used for each game. How do you figur
Join discussion
Mar 26 · 15 min read · You've collected data and you have a model in mind — maybe a Gaussian, maybe a coin flip. But the model has parameters, and you need to find the values that best explain what you observed. How? Maximu
Join discussion
Mar 20 · 5 min read · This blog summarizes Brijesh Tripathi's appearance on the AI Chat podcast with host Jaeden Schafer. The AI infrastructure landscape is at a critical inflection point. While companies race to build lar
Join discussion
