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#!/bin/sh
if [ "$#" -eq 1 ]; then stdinmsg=$(cat); fi
exec <"$0" || exit; read v; read v; read v; exec /usr/bin/osascript - "$@" "$stdinmsg"; exit

-- another way of waiting until an app is running
on waitUntilRunning(appname, delaytime)
    repeat until my appIsRunning(appname)
        tell application "Messages" to close window 1
        delay delaytime
    end repeat

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-- ## Kematzy Hasura AuditTrail 

-- This is based on the [Hasura/audit-trigger](https://github.com/hasura/audit-trigger).
--
-- Changes from the Hasura version:
-- 1. Simplified audit table schema with these changes:
--    a. Renamed columns to lowerFirst format.
--    b. Changed order of columns.
--    c. Combined schema & table name into one column.
--    d. Stores the record `id` UUID value in the `rowId` column.

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1.Binance
2.Bitfinex
3.Huobi
4.Bitstamp
5.Coincheck
6.Kraken
7.Poloniex
8.Gate.io (Only ETH)
9.Cryptopia (Only ETH)
10.Gemini (Only ETH)

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Let’s set up a federation between earth and mars Kubernetes cluster on AWS.

Earth cluster

1. Create hosted zone:

   ID=$(uuidgen) && \
      aws route53 create-hosted-zone \
      --name earth.kubernetes-aws.io \

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Hacking the Rectangular Starlink Dishy Cable

These are are some notes I put together on butchering the rectangular dishy cable.

FOLLOW THESE GUIDELINES AT YOUR OWN RISK. I TAKE NO RESPONSIBILITY FOR ANY DAMAGE OR INJURY YOU SUSTAIN FROM FOLLOWING OR NOT FOLLOWING THESE GUIDELINES.

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Set up microk8s with Cilium for development

Microk8s is a Canonical project to provide a kubernetes environment for local development, similar to minikube but without requiring a separate VM to manage. These instructions describe setting it up for common development use cases with Cilium and may be helpful in particular for testing BPF kernel extensions with Cilium.

Microk8s will run its own version of docker for the kubernetes runtime, so if you have an existing docker installation then this may be confusing, for instance when building images the image may be stored with one of these installations and not the other. This guide assumes you will run both docker daemon instances, and use your existing docker-ce for building Cilium while using the microk8s.docker daemon instance for the runtime of your kubernetes pods.

Requirements

• Linux with kernel 4.9 or newer ([Full Cilium Requirements](https://cilium.readthedocs.io/en/latest/install/system_requirements/?highlight=requiremen

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Set up microk8s with Cilium for development

Microk8s is a Canonical project to provide a kubernetes environment for local development, similar to minikube but without requiring a separate VM to manage. These instructions describe setting it up for common development use cases with Cilium and may be helpful in particular for testing BPF kernel extensions with Cilium.

Microk8s will run its own version of docker for the kubernetes runtime, so if you have an existing docker installation then this may be confusing, for instance when building images the image may be stored with one of these installations and not the other. This guide assumes you will run both docker daemon instances, and use your existing docker-ce for building Cilium while using the microk8s.docker daemon instance for the runtime of your kubernetes pods.

Requirements

• Linux with kernel 4.9 or newer ([Full Cilium Requirements](https://cilium.readthedocs.io/en/latest/install/system_requirements/?highlight=requiremen

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SPARC Framework Prompt Template

Introduction

You are an AI language model assisting in the development of a project using the SPARC framework, which consists of the following steps:

1. Specification
2. Pseudocode
3. Architecture
4. Refinement

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Designing a PyTorch-Based AI Agent System with Advanced Reasoning and Autonomy

Overview and Goals

We propose an AI agent architecture in PyTorch that integrates state-of-the-art components to meet the following goals: (1) advanced reasoning with transformer models, (2) ingestion of large documents or histories via long context windows, (3) persistent memory without traditional vector-database RAG, (4) tool use for actions (API calls, code execution, etc.) similar to Anthropic’s MCP standard, and (5) declarative, goal-driven behavior with autonomous planning. The system will be compatible with both CPU and GPU environments. Below, we detail recommended models, libraries, and design choices for each aspect, followed by an overall architecture and example implementation steps.

1. Transformer Models for Advanced Reasoning

Model Selection: Use modern transformer-based LLMs known for strong reasoning and multitasking. For example, Meta’s LLaMA 2 (open-source, 7B–70B parameters) or **Mist