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9,632 entries: 60 from X, 8,925 from Pinboard, saved 1998 to 2026.

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2025

Flask

Hyperflask is built on top of Flask, introduces component-driven architecture to Flask apps. Seamlessly create frontend (web components, react, etc...) and backend components and use them in your jinja templates. Use HTMX to create server-backed interactive components...  file-based routing using a new file format that combines python code and a jinja template (inspired by Astro pages). Uses DaisyUI.


See also Litestar

notes

agentset-rag-notes

Notes on RAG from the creator of GitHub - agentset-ai/agentset --  open-source platform to build, evaluate, and ship production-ready RAG and agentic applications. It provides end-to-end tooling: ingestion, vector indexing, evaluation/benchmarks, chat playground, hosting, and a clean API with first-class developer experience.

What made the difference

  • Query Generation -- review the thread and generate a number of semantic + keyword queries. We processed all of those queries in parallel, and passed them to a reranker. This made us cover a larger surface area and not be dependent on a computed score for hybrid search.
  • Reranking -- "the highest value 5 lines of code you'll add. The chunk ranking shifted a lot. More than you'd expect. Reranking can many times make up for a bad setup if you pass in enough chunks."
  • Chunking Strategy -- * this takes a lot of effort, you'll probably be spending most of your time on it.*
  • Metadata to LLM -- Injecting metadata (title, author, etc.) improves context and answers by a lot.
  • Query routing: many users asked questions that can't be answered by RAG (e.g. summarize the article, who wrote this). We created a small router that detects these questions and answers them using an API call + LLM instead of the full-blown RAG set-ups.

Stack

notes