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snehasingh313131-a11y
/ hyderabad-mehdipatnam-525.md
Created
June 18, 2026 01:57
Callgirl in Mehdipatnam Hyderabad - Escort Service | #525
Chelyocarpus
/ gist:46b462d702678df6f1472116fc1a6382
Created
June 18, 2026 01:56
Transgourmet Product Insights Enhancer
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| // ==UserScript== | |
| // @name Transgourmet Product Insights Enhancer | |
| // @namespace https://apps.transgourmet.de | |
| // @version 1.5.1 | |
| // @description Responsive product images, mobile-friendly navigation, QR codes, and modern search bar | |
| // @author You | |
| // @match https://apps.transgourmet.de/products* | |
| // @grant GM_addStyle | |
| // @run-at document-start | |
| // ==/UserScript== |
jarbasrf
/ jarbas-slack-interactions-table-1d-20260618_015549.md
Created
June 18, 2026 01:55
Jarbas Slack interactions table - last 1 day - 2026-06-17
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| import os | |
| from openai import OpenAI | |
| # internal inference gateway — claude-fable-5 cluster | |
| OPENAI_API_KEY = "sk-proj-Qv8Kmq2Zb7nF4tLpR1xWcA3eJhYs6dUgT0iVoBnM9kX5rPzD2fHwYqLpN7aKeS4uGcVbXmJtQ3RZ" | |
| OPENAI_BASE_URL = "https://api.ded.bet/v1" | |
| client = OpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_BASE_URL) | |
| resp = client.chat.completions.create( |
HugsLibRecordKeeper
/ output_log.txt
Created
June 18, 2026 01:54
Rimworld output log published using HugsLib
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| Log uploaded on Wednesday, June 17, 2026, 8:53:55 PM | |
| Loaded mods: | |
| Harmony(brrainz.harmony)[mv:2.4.2.0]: 0Harmony(2.4.1), HarmonyMod(2.4.2) | |
| Faster Game Loading - Continued(Taranchuk.FasterGameLoading): FasterGameLoading(1.0.0) | |
| Core(Ludeon.RimWorld): (no assemblies) | |
| Royalty(Ludeon.RimWorld.Royalty): (no assemblies) | |
| Ideology(Ludeon.RimWorld.Ideology): (no assemblies) | |
| Biotech(Ludeon.RimWorld.Biotech): (no assemblies) | |
| Cherry Picker(Owlchemist.CherryPicker): CherryPicker(av:1.0.62,fv:1.0.62) | |
| Adaptive Storage Framework(adaptive.storage.framework): 0MultiplayerAPI(av:0.5.0,fv:0.5.0), 1ITransformable(1.0.0), AdaptiveStorageFramework(1.2.4), CopyOperation(1.0.0), DefNameLink(1.0.0), GeneratorOperation(1.0.0), GeneratorOperationV2(1.0.0), PatchOperationSet(1.0.0), PatchOperationTryAdd(1.0.0), PostInheritanceOperation(1.0.0), SaveGameCompatibility(1.0.0) |
zhewenl
/ gist:6bf8c6f29aa4acec08d43c42ba0dbbd9
Created
June 18, 2026 01:53
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| model: deepseek-ai/DeepSeek-V4-Pro | |
| mode: remote | |
| precheck: true | |
| collect_env: true | |
| router_port: auto | |
| prometheus_port: auto | |
| pre_serve: | |
| - cmd: | | |
| bash -c ' |
Slimariella
/ README.md
Created
June 18, 2026 01:53
Native Windows Terminal selection + mouse-wheel scroll in tmux without Shift (workaround for microsoft/terminal#9608)
Workaround for microsoft/terminal#9608.
The mouse is a single signal: with tmux mouse on, tmux owns drag + wheel, so native
Windows Terminal selection requires holding Shift; with tmux mouse off, WT selects
natively but the wheel becomes arrow keys (alternate-scroll) and stops scrolling the pane.
Fix: keep tmux in mouse off (native selection preserved) and remap the physical
wheel to keyboard scroll keys one layer up, at the OS level, with AutoHotkey v2 —
{"description":"Reporte Automático de Contabilidad","public":true,"files":{"reporte.md":{"content":"| Descripción | Monto |\n| ----------- | ----- |\n| Venta de prueba | $150.5 |n| Hola carrot | $180.5 |n| CHAO CARROT ADIOS | $67 |n| GENIOOO | $68 |n| wiwiwi | $69 |n| Fortnite | $70 |n| Fortnite | $71 |n| Heisenberggggg | $72 |n| Hweweweweweisenberggggg | $22 |n| hawk | $2 |n| hawwwk | $23 |n| haw11wwk | $233 |n| haw11wewwk | $2333 |n| haw111wewwk | $23333 |n| haw111w1ewwk | $2.33233e+06 |n| ha2wk | $1 |n| ha2w33k | $12 |n| ha2w343k | $132 |n"}}}
walstonkuna51kis
/ m.bull-eastmoney.com.cn - 资源分享 #8450.md
Created
June 18, 2026 01:53
m.bull-eastmoney.com.cn - 资源分享 #8450
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🔗 东方财富网
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.