QuEra Launches Open-Source Package to Simulate Logical Quantum Circuits at Scale - HPCwire
QuEra Launches Open-Source Package to Simulate Logical Quantum Circuits at Scale HPCwire
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Synthetic Population Testing for Recommendation Systems
Offline evaluation is necessary for recommender systems. It is also not a full test of recommender quality. The missing layer is not only better aggregate metrics, but better ways to test how a model behaves for different kinds of users before launch. TL;DR In the last post, I argued that offline evaluation is useful but incomplete for recommendation systems. After that, I built a small public artifact to make the gap concrete. In the canonical MovieLens comparison, the popularity baseline wins Recall@10 and NDCG@10 , but the candidate model does much better for Explorer and Niche-interest users and creates a very different behavioral profile. I do not think this means “offline evaluation is wrong.” I think it means a better pre-launch evaluation stack should include some form of synthetic
Layered Agentic Retrieval for Retail Floor Questions: A Solo PoC
How I Routed Associate Questions Across Specialized TF-IDF Indexes Before Assembly TL;DR This write-up documents a personal experiment I ran while thinking about how retail associates actually use knowledge in the moment. A shopper rarely asks a question that fits neatly into a single policy PDF. The phrasing is noisy, the intent is mixed, and the clock is always ticking. I built a small Python system that treats each question as a routing problem rather than a retrieval problem with a single index. Three independent TF-IDF corpora stand in for returns policies, product care guidance, and service-floor procedures. An orchestrator scores each domain, retrieves top hits from the winner, and optionally blends in a second domain when the primary score looks weak. I kept the entire pipeline on-
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How to Handle Sensitive Data Securely in Terraform
Day 13 of my Terraform journey focused on one of the most important topics in real infrastructure work: secrets. Every serious deployment eventually needs sensitive values: database passwords API keys tokens TLS material provider credentials The challenge is not just using those secrets. The challenge is making sure they do not leak into places they should never be. Terraform makes infrastructure easy to define, but if you are careless with secrets, they can leak through your code, your terminal output, your Git history, and even your state file. This post is the guide I wish I had before learning this lesson. Why Secrets Leak in Terraform There are three major ways secrets leak in Terraform. If you understand these clearly, you will avoid most beginner and intermediate Terraform security
26 Quizzes: What We've Learned About Which Results People Actually Share
We launched 26 quizzes on quiz.thicket.sh. This week we added three new ones. I want to talk about what we've learned about quiz results and why people share them. The Quizzes We Just Shipped Attachment Style Quiz — Secure, Anxious, Avoidant, or Disorganized. Classic attachment theory made interactive. The Anxious result is getting screenshotted disproportionately. The specific line people are sharing: "you love deeply but sometimes love too loudly." People aren't tagging it as a quiz result — they're tagging it as a personality description. Career Personality Quiz — The Architect, The Connector, The Builder, The Analyst. The surprising result: The Architect generates the most "I have never felt seen by a quiz before" responses. The ironic result: The Connector — someone described as thriv
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