Skip to content

Instantly share code, notes, and snippets.

@Asbra
Asbra / anonib.py
Created June 4, 2016 12:38
Anon-IB image thread downloader (usage: anonib section thread path)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @author: Asbra
# @date: 2014-12-12
# @modified_by: Asbra
# @modified_at: 2014-12-12
# Saves images & webm from Anon-IB
# Usage: anonib section thread path
# section - eg. 'red' for reddit (/red/)
# thread - thread No.
We can make this file beautiful and searchable if this error is corrected: It looks like row 2 should actually have 56 columns, instead of 1 in line 1.
ο»Ώstrategy_id,strategy_family_id,strategy_name,alternative_names,strategy_category,strategy_subcategory,verification_status,repository_name,repository_url,source_file_path,permanent_source_url,commit_sha,source_type,canonical_source_status,related_sources,language,platform,framework,market_types,example_instruments,expected_timeframes,long_short_direction,required_data,alternative_data_required,indicators_or_features,normalized_entry_rules,normalized_exit_rules,stop_loss_rules,take_profit_rules,trailing_stop_rules,position_sizing_rules,portfolio_construction_rules,execution_assumptions,important_parameters,framework_agnostic_rule_summary,claimed_backtest_results,result_source,transaction_costs_modeled,slippage_modeled,tests_present,sample_data_present,lookahead_or_repainting_risk,other_bias_risks,martingale_or_unbounded_risk,code_completeness_score,rule_clarity_score,reproducibility_score,portability_score,maintenance_status,last_commit_date,stars,forks,license,reuse_notes,duplicate_group,research_notes
STRAT
@flatmoonsociety
flatmoonsociety / FTM_OPENING_RANGE_BREAKOUT_MNQ_v1_8_0_RC3.cs
Last active October 4, 2026 21:29
FTM Opening Range Breakout MNQ v1.8.0 RC3
// ============================================================================
// FTM_OPENING_RANGE_BREAKOUT_MNQ_v1_8_0_RC3
//
// One self-contained MNQ opening-range breakout strategy. It requires one-minute
// MNQ bars and the CME US Index Futures ETH Trading Hours template. All trading
// decisions use New York time after converting NinjaTrader timestamps through
// UTC, so the NinjaTrader display time zone may be Eastern, UTC, Madrid, or any
// other correctly configured system time zone.
//
// The strategy builds the 09:30-09:45 ET opening range, then checks completed
@jboner
jboner / latency.txt
Last active October 4, 2026 21:16
Latency Numbers Every Programmer Should Know
Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference 0.5 ns
Branch mispredict 5 ns
L2 cache reference 7 ns 14x L1 cache
Mutex lock/unlock 25 ns
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy 3,000 ns 3 us
Send 1K bytes over 1 Gbps network 10,000 ns 10 us
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD
@dmancloud
dmancloud / How to Install SonarQube in Ubuntu Linux.md
Last active October 4, 2026 21:09
How to Install SonarQube in Linux

How to Install Sonarqube in Ubuntu Linux

Prerequsites

Virtual Machine running Ubuntu 22.04 or newer

Install Postgresql 15

sudo apt update
sudo apt upgrade

sudo sh -c 'echo "deb http://apt.postgresql.org/pub/repos/apt $(lsb_release -cs)-pgdg main" > /etc/apt/sources.list.d/pgdg.list'
@SimplStudios
SimplStudios / iptv-playlist.m3u8
Last active October 4, 2026 21:06
A Curated List of the Most Popular IPTV Playlists
#EXTM3U
#EXTINF:-1 tvg-id="Boomerang.us2" tvg-name="Boomerang" tvg-logo="http://schedulesdirect-api20141201-logos.s3.dualstack.us-east-1.amazonaws.com/stationLogos/s21883_dark_360w_270h.png" group-title="🌍 GLOBAL|Kids",Boomerang
http://23.237.104.106:8080/USA_BOOMERANG/index.m3u8
#EXTINF:-1 tvg-id="Cartoon.Network.HD.us2" tvg-name="Cartoon Network" tvg-logo="http://schedulesdirect-api20141201-logos.s3.dualstack.us-east-1.amazonaws.com/stationLogos/s12131_dark_360w_270h.png" group-title="🌍 GLOBAL|Kids",Cartoon Network
http://23.237.104.106:8080/USA_CARTOON_NETWORK/index.m3u8
#EXTINF:-1 tvg-id="Disney.Channel.HD.us2" tvg-name="Disney Channel" tvg-logo="http://schedulesdirect-api20141201-logos.s3.dualstack.us-east-1.amazonaws.com/stationLogos/s10171_dark_360w_270h.png" group-title="🌍 GLOBAL|Kids",Disney Channel
http://206.212.244.63/650/index.m3u8
#EXTINF:-1 tvg-id="Disney.Junior.HD.(Pacific).us2" tvg-name="Disney Jr" tvg-logo="https://schedulesdirect-api20141201-logos.s3.dualstack.us-east-1.amazonaws.com/statio
@borhi
borhi / gps-reader.service
Created October 2, 2026 09:33
gps-reader.service
[Unit]
Description=GPS reader over UART (Raspberry Pi)
After=local-fs.target
[Service]
Type=simple
# Raspberry Pi 5: the GPIO14/15 UART is /dev/ttyAMA0 (serial0 -> ttyAMA10 is the debug connector)
User=borhi
SupplementaryGroups=dialout
RuntimeDirectory=gps-reader
name explain-diff-html
description Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output.

Explain Diff

Please make me a rich, interactive explanation of the specified code change.

It should have these sections:

@kat3011
kat3011 / KatTV.m3u
Last active October 4, 2026 21:03
iptv playlist
#EXTM3U x-tvg-url="https://iptv-epg.org/files/epg-us.xml.gz"
#EXTINF:-1 tvg-id="AE.us@East" tvg-logo="https://upload.wikimedia.org/wikipedia/commons/thumb/d/df/A%26E_Network_logo.svg/960px-A%26E_Network_logo.svg.png" group-title="Entertainment",A&E (1080p)
https://gpuserver3.tier1streams.com/AE/index.m3u8
#EXTINF:-1 tvg-id="AntennaTV.us@SD" tvg-logo="https://i.imgur.com/ocs2DeU.png" group-title="Entertainment",Antenna TV (576p)
https://gpuserver3.tier1streams.com/ANTENNA_TV/index.m3u8
#EXTINF:-1 tvg-id="BounceXL.us@SD" tvg-logo="https://i.imgur.com/GzxgSkc.png" group-title="Entertainment",Bounce XL (1080p)
https://cdn-uw2-prod.tsv2.amagi.tv/linear/amg01438-ewscrippscompan-bouncexl-tablo/playlist.m3u8

LLM Wiki

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.

The core idea

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.