Virtual Machine running Ubuntu 22.04 or newer
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'
| #!/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. |
| 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 |
| // ============================================================================ | |
| // 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 |
| 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 |
| #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="π 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="π 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="π 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 |
| [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 |
| #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 |
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.