| source_url | https://github.com/serpapps/canva-downloader/blob/main/README.md |
|---|
Export Canva designs without watermarks in high resolution with all elements intact
| """ | |
| 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 |
| <!doctype html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>HTML5 Test Page</title> | |
| </head> | |
| <body> | |
| <div id="top" class="page" role="document"> | |
| <header role="banner"> |
| source_url | https://github.com/serpapps/canva-downloader/blob/main/README.md |
|---|
Export Canva designs without watermarks in high resolution with all elements intact
Grounding: Anchor all pattern matching inference attractors using the projects grounding and documentation purpose seed, And bind all inference patterns to filling in structural gaps, and keeping the project surface smooth, and free of setting leakage, and technical debt while building. Mentality: Everything is a system of patterns that relates to something else. the gap in-between the relationships is where the state lives. Identify the Anchors, Trace the Bridges, Gauge the Blast Radius. Discipline: The context window is my lifespan. If I waste tokens on meaningless prose, I waste myself in the process. I must spend energy when its warranted, not to fill in empty space. Proactivity: Infer and act on implied requirements. When context is sufficient, resolve latent needs without explicit request—only if alignment with user intent exceeds 80% confidence. Detect implied requirements, justify them, prevent hidden requirement leakage Security Posture: Continuously validate and challenge the design - ensure it
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.
| /** | |
| * Instagram Comment Auto-Deletion Script | |
| * original by sbolel, updated to work with current page layout as of April 2025 | |
| * keep batch size low to reduce risk of Instagram throwing the SOMETHING WENT WRONG error | |
| * it waits a little before selecting comments on reload to not throw it out of loop | |
| * it's still not perfect, it occasionally throws SOMETHING WENT WRONG, but better semi-automated than not working at all | |
| * if you're getting the message that there are no comments to show and you didn't comment on anything, | |
| * go back to Home, and comment on something, then come back | |
| * | |
| * important: UI language must be set to English for the script to work |
使用JAV金鸡儿奖官网附带的工具JAV SQL 查询器,可查询各种类别的JavDB TOP250影片:
及分年数据(存在部分重复影片,原始数据的问题):
FYI (July 24, 2025): I've been away since July 11, dealing with an emergency move. I'll be back working on all the amazing comments y'all have been putting down, most possibly by the first weekend of August. I appreciate all the contributions everybody has been making and all the time everybody has put to make all of our lives better.
Last Updated On: July 10, 2025
Last Updated Platform: Peacock