This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.
Plug: if you want to find content related to your interests from thousands of obscure blogs and noisy sources like HN Newest, check out Scour. It's a free, personalized content feed I work on where you define your interests in your own words and it ranks content based on how closely related it is to those topics.
| /* | |
| Copyright (C) 2026 Gezine | |
| This software may be modified and distributed under the terms | |
| of the MIT license. | |
| P2JB (Patience to Jailbreak) | |
| Bug was patched at PS5 13.00 |
| import android.app.Presentation | |
| import android.content.Context | |
| import android.graphics.Bitmap | |
| import android.graphics.Picture | |
| import android.graphics.SurfaceTexture | |
| import android.hardware.display.DisplayManager | |
| import android.view.Display | |
| import android.view.Surface | |
| import android.view.ViewGroup | |
| import androidx.compose.foundation.layout.Box |
Looking for a way to download files from pan.baidu.com (Baidu Netdisk) for free? Here’s a simple step-by-step tutorial using a third-party website that generates direct download links.
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BaiduDownloader.com. This web tool allows you to download any file or folder from Pan Baidu to any device. All you need to do is enter your Baidu link and your email address and wait a few minutes for it to download for you.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
2020/5/31追記:
自分用のメモに書いていたつもりだったのですが、たくさんのスターを頂けてとても嬉しいです。
と同時に、書きかけで中途半端な状態のドキュメントをご覧いただくことになっており、大変心苦しく思っています。
このドキュメントを完成させるために、今後以下のような更新を予定しています。
2021/1/22追記:
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
