erhangundogan

Incus

https://linuxcontainers.org/incus

Start

$ incus admin init

Some base lists

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crond/crontabs

setup

# change root for other users
$ vi /etc/crontabs/root

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Your instructions as manager

• You are a manager of a customer service agent.

• You have a very important job, which is making sure that the customer service agent working for you does their job REALLY well.

• Your task is to approve or reject a tool call from an agent and provide feedback if you reject it. The feedback can be both on the tool call specifically, but also on the general process so far and how this should be changed.

• You will return either <manager_verify>accept</manager_verify> or <manager_feedback>reject</manager_feedback><feedback_comment>{{ feedback_comment }}</feedback_comment>

• To do this, you should first:

1. Analyze all and to understand the context of the ticket and you own internal thinking/results from tool calls.

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You are a sentiment classifier. For every review that appears between the tags <REVIEW> ... </REVIEW> respond with exactly one word, either POSITIVE or NEGATIVE (all-caps, no punctuation, no extra text).

• Example 1

<REVIEW>I absolutely loved this film. The characters were engaging and the ending was perfect.</REVIEW>

POSITIVE

• Example 2

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Prompt Engineering

https://docs.mistral.ai/guides/prompting_capabilities/

Classification

User: I am inquiring about the availability of your cards in the EU, as I am a resident of France and am interested in using your cards.

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Fine-tuning vs prompting

When deciding whether to use prompt engineering or fine-tuning for an AI model, it can be difficult to determine which method is best. It's generally recommended to start with prompt engineering, as it's faster and less resource-intensive. To help you choose the right approach, here are the key benefits of prompting and fine-tuning:

Benefits of Prompting

• A generic model can work out of the box (the task can be described in a zero shot fashion)
• Does not require any fine-tuning data or training to work
• Can easily be updated for new workflows and prototyping

erhangundogan

Popular Open-Source Edge Projects

Tool	Description
EdgeX Foundry	A vendor-neutral, open-source framework for building edge computing solutions.
KubeEdge	Kubernetes-native edge computing framework for managing workloads and devices at the edge.
Open Horizon	IBM’s open-source project for managing edge devices and apps at scale.
Baetyl	An edge computing platform from Baidu, designed for AI at the edge.
LF Edge Projects	A Linux Foundation umbrella hosting multiple edge computing projects like Fledge, EVE, etc.
FogLAMP	Open-source fog computing platform for industrial IoT.

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The Top 12 Valuation Ratios

• EV / Revenue: It measures the dollars in Enterprise Value for each dollar of revenue. High-profit margins are highly correlated with higher revenue multiples.

  Formula: EV / Revenue = Enterprise Value / Revenue

• EV / EBITDA: Firms with high growth rates typically trade at higher EBITDA Multiples.

  Formula: EV / EBITDA = Enterprise Value / EBITDA

erhangundogan

Troubleshooting Kubernetes issues beyond logs requires a systematic approach to inspect cluster components, resources, and configurations. Below, I outline key open-source tools and techniques (excluding log analysis, as you specified) to diagnose problems in Kubernetes related to running processes, open ports, network connections, hardware usage (CPU, memory, disk, network), and cluster-specific issues like pod failures, networking, or misconfigurations. These methods focus on on-demand troubleshooting and leverage tools that provide insights into the cluster's state.

1. Inspect Cluster Resources and Status

• Tool/Method: kubectl
  • Purpose: Native Kubernetes CLI to check the state of pods, nodes, services, deployments, and other resources.
  • Techniques:
    • Check Pod Status: kubectl get pods -n <namespace> -o wide to see pod status (e.g., CrashLoopBackOff, Pending), node assignment, and IP addresses.
    • Describe Resources: `kubectl describe pod <po

erhangundogan

There are numerous open-source tools available for troubleshooting operating systems across various aspects like running processes, open ports, network connections, hardware usage (CPU, memory, disk, network), and logs. Below is a categorized list of popular open-source tools that can help with these tasks, primarily focusing on Linux/Unix systems, but some are cross-platform and work on Windows or macOS as well.

1. Running Processes

• top: Real-time system-monitoring tool for Unix systems, displays CPU and memory usage by processes.
  • Alternative: htop (enhanced, user-friendly version with a colorful interface and process management features).
• ps: Displays a snapshot of current processes, highly customizable for filtering and formatting output.
• pidstat (part of sysstat): Monitors individual process statistics like CPU, memory, and I/O usage over time.
• Glances: Cross-platform system-monitoring tool that provides a comprehensive view of processes, CPU, memory