| #EXTM3U | |
| #EXTINF:-1 tvg-ID="espn.us" tvg-name="ESPN" tvg-logo="http://primestreamstv.com/logos/ESPN.png" group-title="SPORTS NETWORKS",ESPN | |
| http://themyst.icu:826/SourPatchKid/nlnE4DVQUs/102011 | |
| #EXTINF:-1 tvg-ID="espn.us" tvg-name="ESPN (W)" tvg-logo="http://primestreamstv.com/logos/ESPN.png" group-title="SPORTS NETWORKS",ESPN (W) | |
| http://themyst.icu:826/SourPatchKid/nlnE4DVQUs/102012 | |
| #EXTINF:-1 tvg-ID="espn2.us" tvg-name="ESPN2" tvg-logo="http://primestreamstv.com/logos/ESPN%202.png" group-title="SPORTS NETWORKS",ESPN2 | |
| http://themyst.icu:826/SourPatchKid/nlnE4DVQUs/95868 | |
| #EXTINF:-1 tvg-ID="espnnews.us" tvg-name="ESPN NEWS" tvg-logo="http://primestreamstv.com/logos/ESPN%20NEWS.png" group-title="SPORTS NETWORKS",ESPN NEWS | |
| http://themyst.icu:826/SourPatchKid/nlnE4DVQUs/102017 | |
| #EXTINF:-1 tvg-ID="espnu.us" tvg-name="ESPN U" tvg-logo="http://primestreamstv.com/logos/ESPN%20U.png" group-title="SPORTS NETWORKS",ESPN U |
| using System; | |
| using System.Collections.Generic; | |
| using System.Linq; | |
| using System.Threading.Tasks; | |
| using System.IO; | |
| using System.Xml; | |
| using System.Xml.Serialization; | |
| using Microsoft.AspNetCore.Mvc; | |
| using Newtonsoft.Json.Linq; | |
| using dvcsharp_core_api.Models; |
Warning
I see many people proposing solutions for other platforms (such as Windows or macOS). I cannot verify the authenticity of those methods, and you should not copy, paste, and run code from random people on the internet without first understanding what it does. Ideally, review every line carefully and, even then, run it with caution in a sandboxed environment.
I personally experienced slowdowns and issues while using the ja-netfilter agent.
I decided to investigate how JetBrains checks license validity, because despite explicitly configuring JetBrains to work offline, it still attempts to validate licenses.
Here are my findings:
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.
PI is a TypeScript toolkit for building AI agents. It's a monorepo of packages that layer on top of each other: pi-ai handles LLM communication across providers, pi-agent-core adds the agent loop with tool calling, pi-coding-agent gives you a full coding agent with built-in tools, session persistence, and extensibility, and pi-tui provides a terminal UI for building CLI interfaces.
These are the same packages that power OpenClaw. This guide walks through each layer, progressively building up to a fully featured coding assistant with a terminal UI, session persistence, and custom tools.
By understanding how to compose these layers, you can build production-grade agentic software on your own terms, without being locked into a specific abstraction.
Pi was created by @badlogicgames. This is a great writeup from him that explains some of the design decisions made when creating it.
The user encountered an issue where they could no longer connect to their Talos cluster due to expired certificates. This manifested in two ways:
kubectl:
kubectl get nodes
E1006 19:24:27.025594 1695958 memcache.go:265] "Unhandled Error" err="couldn't get current server API group list: the server has asked for the client to provide credentials"
