Erhan Gundogan erhangundogan

Germany vs. UK – Entrepreneur Decision Matrix

Decision Matrix Table

Factor	Germany	UK	Notes
Company Formation Speed	2	5	UK can be incorporated online in <24h; Germany often takes weeks.
Min. Capital Requirement	2	5	Germany GmbH: €25k (min €12.5k upfront); UK Ltd: £1.
Corporate Tax Burden	3	4	Germany ~30%, UK 25% (small profits 19%).
Stock Option Friendliness	2	5	UK EMI scheme very favorable; Germany improving but still complex.

AI Integrations – Company List

AI21 Labs: Israeli AI research firm built for enterprise NLP, creators of Jurassic‑1/2 models and the Wordtune writing assistant. https://www.ai21.com/
AionLabs: AI‑tooling startup (limited public data; possibly involved in developer integrations). Website not found.
Alibaba Cloud Intelligence: Alibaba Cloud’s AI‑driven cloud infrastructure and managed AI services. https://www.alibabacloud.com/
Amazon Bedrock: AWS-managed foundation model service offering models from Anthropic, AI21, Meta, and Stability AI, plus tools for fine‑tuning and deployment. https://aws.amazon.com/bedrock/
Anthropic: AI safety–focused LLM provider best known for Claude models. https://www.anthropic.com/
AtlasCloud: (No public company profile available.)
Atoma: (No official public profile found.)
Avian.io: (Insufficient public-facing documentation available.)

Incus

https://linuxcontainers.org/incus

Start

incus admin init

Some base lists

crond/crontabs

setup

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

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:

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

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

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.

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

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.

	.editorconfig
	.env
	.lintstagedrc
	.nvmrc
	.yarnrc.yml
	.gitignore
	.prettierrc
	components.json
	eslint.config.mjs
	nodemon.config.json