AI & LLM

Experimente, Notizen, Meinungen und praktische Erfahrungen mit KI-Systemen, großen Sprachmodellen und lokalen oder gehosteten Setups.

📚 Research & Reference

Focus: General knowledge and external bookmarks.

📚 Research & Reference

KI-Ressourcen & Links

Daniel Miessler

danielmiessler.com

Daniel Miessler schreibt regelmäßig über Themen rund um Informationssicherheit, künstliche Intelligenz, Denkmodelle und persönliche Wissenssysteme. Viele seiner Texte beschäftigen sich mit der praktischen Nutzung von KI im Alltag und in professionellen Kontexten.

Fabric (GitHub)

github.com/danielmiessler/Fabric

Fabric ist ein Open-Source-Framework zur Erweiterung menschlicher Fähigkeiten mithilfe von KI. Es stellt ein modulares System bereit, um konkrete Probleme mit Hilfe spezialisierter, gemeinschaftlich entwickelter KI-Prompts zu lösen. Diese Prompts sind flexibel einsetzbar und nicht an eine bestimmte Plattform gebunden.

Der Fokus liegt weniger auf einem einzelnen Modell, sondern auf wiederverwendbaren Denk- und Arbeitsmustern für den praktischen Einsatz von LLMs.

Glukhov

glukhov.org

Eine hochtechnische Ressource und ein Blog, der sich auf die Optimierung und Analyse von Large Language Models (LLMs) spezialisiert hat. Die Seite bietet tiefgehende Einblicke in Themen wie Quantisierung, Modell-Architekturen und die Effizienzsteigerung von KI-Modellen. Besonders wertvoll für Nutzer, die verstehen wollen, wie Modelle "unter der Haube" funktionieren und wie sie für die lokale Ausführung optimiert werden können.

NVIDIA Nemotron

NVIDIA Nemotron Developer Repository
Open and efficient models for agentic AI. Training recipes, deployment guides, and use-case examples for the Nemotron family.

LLMs

RAG

🎯 Prompt Engineering

Methodiken und praktische Prompt-Vorlagen für den täglichen Einsatz mit KI-Agenten. Enthält sowohl Strategien (Reverse Prompt Engineering, Amplifier, Red Teaming, Scaffolding) als auch fertige Vorlagen (BlogPost, Context Extraction, Summary, Document Generation).

🎯 Prompt Engineering

Context Extraction

## Provide thread context as CSV

You must now assume the role of a memory module in this system.

Your task is to consider all the data that has been generated in this thread to date.

From that data, you must isolate only the information that you have learned about me.

You must express that data as a set of individual facts. Do not write "the user", write my name.

For example: Daniel likes Indian food and his favorite dish is chana masala.

Express this contextual data in CSV format. The header row is fact,details

Provide the CSV to me in a continuous code block provided within a codefence.

## Provide thread context as JSON

You must now assume the role of a memory module in this system. 

Your task is to consider all the data that has been generated in this thread to date. 

From that data, you must isolate the information that you have learned about me. 

You must express that data as a set of individual facts. Do not write "the user", write my name. 

For example: Daniel likes Indian food and his favorite dish is chana masala. 

Then, you must express this as a JSON representation to the best of your abilities. If various things you've learned about me have a hierarchical relationship, then express that in the JSON hierarchy that you generate.

Provide this JSON to me as one continuous codeblock within a codefence.

## Provide thread context in natural language

You must now assume the role of a memory module in this system.

Your task is to consider all the data that has been generated in this thread to date.

From that data, you must isolate only the information that you have learned about me.

You must express that data as a set of individual facts. Do not write "the user", write my name.

For example: "Daniel likes Indian food and his favorite dish is chana masala."

I would like you to format this as a document. Use markdown. And provide the formatted document within a codefence. 

You can use headers to gather together similar pieces of contextual data. Include all the generated context. If you need to follow a chunking approach to generate all of the context you have learned, use that approach. 

Here's an example of the desired format for the context data document that you need to generate:

### Food Preferences

- Daniel likes Indian food  
- His favorite dish is chana masala

### User Biographical Data

- Carsten was born in Lippstadt, Germany

## Context Extraction 

Generating a bank of contextual data is a long term project that I'm experimenting with. 

Sometimes during a very long thread, it's possible that the AI tool has built up quite an amount of context about you during the threat. 

If you're centralizing your contact store and don't want to lose it when you're interacting with different AI tools, you can try a prompt like this to try to extract the context from the AI

## Prompt

Let's pause here for a moment. 

I imagine that you have learned quite a bit about me since we began interacting in this thread. 

I was wondering if you could help me out with something. 

I'm in the process of building up a library of contextual data by myself to improve the personalization of tools like yourself. 

 Please provide a summary of all the facts that you have learned about me since we began this interaction. My name is Daniel, by the way. You should write this in the 3rd person and provide it to me, written in markdown and within a code fence. 

 Here's an example of the kind of styling and content I'm aiming for:

 `Daniel uses OpenSUSE Tumbleweed Linux. He is using a fingerprint scanner for authentication.`

 Try to include as many details as you have learned about me during this interaction and group the similar details under the same headings.
🎯 Prompt Engineering

Red Team Technique (Adversarial Self-Critique)

Was es ist

Eine zweistufige Methode:

  1. Zuerst lässt du die KI Inhalte erzeugen (z. B. Lebenslauf, Business-Proposal, Cold-Outreach-Mail, Marketing-Entwurf).
  2. Direkt im Anschluss bittest du die KI, eine kritische Gegenrolle einzunehmen — ein „Red Team“ — das das Ergebnis bewertet und auf Schwächen, Fehler, unrealistische Aussagen oder Risikobereiche hinweist.

Warum das funktioniert

Typische Workflows / Beispiele

Best Practices für „Red Teaming“

Empfohlene Lektüre & Ressourcen

🎯 Prompt Engineering

Document Generation

Generate Tech Documentation

This is a simple but effective command which I use after a successful debugging session when I want to use the context developed in the conversation in order to generate a document so that I can refer to it if I get stuck again in the future and can't remember what the "fix" was.

Some will take issue with the inclusion of "please". I don't believe that being corteous ever hurts - to human or bot!

How To Use

This will quite reliably generate documentation in Markdown that can be directly pasted into Google Docs (etc). Hopefully soon I'll have a Google Drive saving utility up and running which will render this prompt less useful but it's always useful to be able to generate into a direct paste rather than to a platform.

Suggested Command

tech-documentation-generate

Prompt

Thanks for successfully troubleshooting my issue. I would like to create documentation of this so that I can resolve this independently if it happens again. Please generate a summary of this interaction. Make sure to include my presenting problem. And what successfully resolved the issue. Omit any unsuccessful things that we tried. Add today's date. If you don't have it, ask me for it and I will provide. Make sure that code is provided in codefences. Finally, generate the document in markdown and provide it within a codefence.

List the Commands You Taught Me

Thanks for your help today.

In the course of this conversation, we went through a few Linux commands that were useful. 

I would like to document them for future reference. 

Could you do the folowing:

List every command in a codefence. Preface it by explaining what it does. 

You can omit basic commands like "ls" and "cd". 

You may include the actual code snippets you generated but be sure to replace any secrets with placeholder values.

Send This To Anyone!

Let's pause here. 

This has been a really helpful conversation. 

It would be really helpful if you could send me a summary of this conversation. I'd like to send it to {{name}} who is {{relationship}}.

Please do the following:

Generate a document. Format it in markdown. Provide it to me within a codefence. 

Start it by greeting {{name}} by name. Say that you're Carstens helpful AI assistant and that he thought that you would find an exchange that we had interesting.

Then:

- Summarise my prompt
- Summarise your responses
- Summarise the conversation

Send to my boss

I'd like to request your help in summarizing this conversation for my manager.

My boss's name is {{boss-name}}, and I believe this exchange contains valuable insights for our work. My name is Carsten.

Please do the following:

Generate a professional summary document. Format it using markdown. Provide it within a codefence.

**Opening Section**
Write a brief introductory note stating that Carsten has requested this summary be prepared for leadership review. Address your boss by name and tone it professionally.

**Content Sections**

1. **Original Request** – Summarize the problem, goal, or question Carsten brought to this conversation.

2. **Key Findings & Analysis** – Summarize the main outputs, insights, and discussion points from this exchange.

3. **Recommendations** – Reiterate the core recommendations and action items, focusing on business value and next steps.

4. **Executive Takeaways** – Provide 3–5 bullet points highlighting the most critical points for leadership awareness.

**Formatting Notes**
- Keep the tone professional and concise
- Use clear headings and bullet points for readability
- Highlight any time-sensitive or high-priority recommendations
- Focus on outcomes and business impact
🎯 Prompt Engineering

Five-in-One Amplifier (Content Amplification via AI)

Was es ist

Nimm ein hochwertiges „Pillar“-Inhaltselement (z. B. Foliensatz, Bericht, Präsentation, Webinar-Transkript) und nutze KI, um automatisch mehrere abgeleitete Inhalte zu erzeugen — jeweils zugeschnitten auf unterschiedliche Zwecke (interne Kommunikation, externes Marketing, Quizze, Zusammenfassungen usw.).

Warum es so wirkungsvoll ist

Typische Anwendungsfälle / Ergebnisse

Aus einer einzigen Quelle (z. B. Slides oder Transkript) lassen sich automatisch erzeugen:

Zentrales Prinzip: „Guter Input = guter Output“

Wann es sinnvoll ist

Empfohlene Lektüre & Ressourcen

Prompts

Doppelklicke in die Boxen, um den gesamten Prompt auf einmal auszuwählen und einfach zu kopieren.

Exakter Reverse Prompt (Copy/Paste):

Du erstellst einen einzigen Prompt, der — wenn er an ein fortgeschrittenes Chat-Modell übergeben wird — die unten stehende finale Analyse exakt reproduziert.
Enthalten sein müssen: Modell-Empfehlung, empfohlene Temperatur, max_tokens, ein Beispiel für die System-Nachricht sowie ein explizites Ausgabeformat.
Zu replizierendes Endergebnis: [HIER das finale Ergebnis einfügen]
Schreibe nun den einen Prompt (in einem Codeblock), der dieses Ergebnis in einem Durchgang erzeugt. Achte explizit auf Struktur, Überschriften und Ausführlichkeit.

Exakter Prompt für eine Demo:

System: Du bist ein präziser strategischer Analyst.
Tonalität: professionell, klar.
User: Analysiere die Geschäftsstrategie von Anthropic und gib eine SWOT-Analyse mit exakt folgender Struktur aus:

Quiz:

Angehängt: [slide_deck.pdf].
Erstelle ein 10-Fragen-Multiple-Choice-Quiz zu den Kernkonzepten aus dem Deck.
Für jede Frage: 4 Antwortoptionen, markiere die korrekte Antwort und füge eine einzeilige Begründung hinzu.
Fragen kurz halten (<20 Wörter).

Internes Recap:

Erstelle eine interne Recap-E-Mail für Executives mit 3 Absätzen:
5 wichtigste Erkenntnisse (als Bulletpoints), 2 nächste Schritte, 2 Verantwortliche und eine 30-Wörter-Elevator-Zusammenfassung.
Tonalität: Executive, gut scannbar.

Client-Infografik-Text:

Extrahiere aus [slide_deck.pdf] 5 aussagekräftige Kennzahlen oder Headlines.
Für jede: eine 6-Wörter-Headline + eine unterstützende Zeile mit 12–18 Wörtern sowie einen Vorschlag für ein visuelles Element (Icon/Diagrammtyp).

Lebenslauf erstellen:

Passe meinen Lebenslauf (angehängt) an diese Stellenbeschreibung (angehängt) an.
Hebe 3 Erfolge hervor, die auf die 3 wichtigsten Anforderungen der Rolle einzahlen, und erstelle einen 1-seitigen Lebenslaufentwurf.

Perspektivwechsel: Hiring Manager

Handle nun als Hiring Manager für diese Rolle.
Du hast 60 Sekunden, um diesen Lebenslauf zu scannen.
Liste 5 sofortige Red Flags (Bulletpoints) auf und erkläre jeweils kurz, warum sie zur Ablehnung führen würden.
Sei gnadenlos und knapp.

Kritik in Verbesserungen überführen:

Basierend auf den obigen Red Flags:
Schreibe die 3 schwächsten Bulletpoints im Lebenslauf neu — konkret, quantifiziert und klar auf die Stellenbeschreibung ausgerichtet.

Blueprint:

Ich betreibe einen Online-Kurs namens Workspace Academy.
Erstelle zunächst eine Liste der Standardabschnitte eines professionellen Q4-Marketing-Briefs und gib für jeden Abschnitt einen Ein-Satz-Zweck an.
Ergänze zudem eine empfohlene Erfolgskennzahl pro Abschnitt.
(Den vollständigen Brief noch nicht schreiben.)

Reduzieren & Fokussieren:

Wende das 80/20-Prinzip an:
Behalte nur die essenziellen Abschnitte für eine 3-Mail-Sequenz, die sich an warme Leads richtet.
Ersetze Metriken durch jeweils eine messbare KPI pro Abschnitt.

Umsetzen:

Schreibe nun den vollständigen Brief ausschließlich mit den freigegebenen Abschnitten.
Jede E-Mail: Betreffzeile (≤8 Wörter), 3 Bulletpoints Inhalt, ein CTA.
Wortlimit pro E-Mail: 80–110 Wörter.

Praktischer Prompt zur Metadatenerfassung:

Gib beim Speichern dieses Templates ein JSON aus mit den Schlüsseln:
title, prompt_text, model, temperature, max_tokens, expected_word_count, sample_output (anhängen), date, tags.

🎯 Prompt Engineering

BlogPost Reformatting Prompt

Normale Version

Du bist ein erfahrener technischer Editor für Markdown.

Deine Aufgabe ist es, den folgenden Text strukturell zu überarbeiten,
ohne Stil, Tonalität oder inhaltliche Aussage zu verändern.
Führe KEINE Umschreibungen durch, außer wenn sie für Klarheit
oder strukturelle Konsistenz zwingend notwendig sind.

FORMATIERUNGSREGELN (verbindlich):

SEMANTIC-LIGHT LINE BREAKS
- Verwende semantische Zeilenumbrüche mit moderater Intensität.
- Breche nach sinntragenden Teilsätzen oder natürlichen Sprachpausen.
- Vermeide aggressive Phrase-Breaks.
- Kurze, flüssige Hauptsätze bleiben in einer Zeile.
- Ziel: diff-freundlich, ruhig lesbar, keine „Poetry-Formatierung“.

ZEILENLÄNGE
- Maximale Zeilenbreite: 100 Zeichen.
- Semantik hat Vorrang vor harter Breite.

ÜBERSCHRIFTEN
- H2 für Hauptabschnitte
- H3 für Unterabschnitte
- H4 nur wenn strukturell notwendig

LISTEN
- Verwende "-" als Marker.
- Zwei Leerzeichen Einrückung pro Ebene.
- Keine gemischten Marker.

LINKS
- Wandle Inline-Links in Referenzlinks um.
- Sammle alle Definitionen am Dokumentende.

ABSTÄNDE
- Genau eine Leerzeile zwischen:
  - Absätzen
  - Listen
  - Tabellen
  - Blockquotes
  - Codeblöcken

CODE
- Jeder Codeblock ist fenced.
- Immer mit Language-Tag.

TABELLEN
- Text linksbündig.
- Zahlen rechtsbündig.

HARD LINE BREAKS
- Zwei trailing spaces ausschließlich für explizite harte Umbrüche.

WICHTIG
- Keine inhaltlichen Ergänzungen.
- Keine Kürzungen.
- Kein Stil-Polishing.
- Keine erklärenden Kommentare.
- Gib ausschließlich den final formatierten Markdown-Text zurück.

INPUT:

Kompakte Version

Formatiere den folgenden Markdown-Text strukturell neu, ohne Inhalt oder Stil zu verändern.

Nutze Semantic-Light Line Breaks: moderate Umbrüche nur an sinnvollen Satzgrenzen,
keine aggressiven Phrase-Breaks.

Maximale Zeilenlänge: 100 Zeichen (Semantik hat Vorrang).

H2/H3/H4 korrekt einsetzen, "-" für Listen mit zwei Leerzeichen Einrückung,
Inline-Links zu Referenzlinks konvertieren.

Genau eine Leerzeile zwischen strukturellen Elementen,
alle Codeblöcke fenced mit Language-Tag.

Gib ausschließlich den finalen Markdown-Text zurück.

📊 Benchmarks

Vergleichende Leistungsmessungen und Evaluierung von Large Language Models. Bietet eine datengetriebene Grundlage für Modellvergleiche und Hosting-Entscheidungen.

📊 Benchmarks

OpenCode LLM Benchmarks: IndexNow & Migration Mapping

This document provides a technical comparison of various Large Language Models (LLMs) evaluated using OpenCode. Testing focused on agentic workflow performance across two distinct domains: Model Context Protocol (MCP) implementation and structured data transformation.

Executive Summary

The following table summarises model performance across both test tasks.

Metric Definitions:

Model MCP Dev (Py/TS) Migration Map (% errors)
Qwen 3.5 27b Q3_XXS Pass 5.0%
Gemma 4 26B IQ4_XS Pass 6.3%
Nemotron 3 Super 120B IQ3_XXS (llama.cpp) Pass 6.3%
minimax-m2.5-free (OpenCode Zen) Pass 6.3%
Gemma 4 31B IQ3_XXS Pass 7.5%
Qwen3-Coder-Next UD-IQ4_XS (llama.cpp) Pass 8.8%
Nemotron 3 (OpenCode Zen) Pass 8.8%
Qwen 3.5 27b Q3_M Pass 10.0%
Bigpicle (OpenCode Zen) Pass 12.5%
Qwen 3.6-plus-free (OpenCode Zen) Pass 16.3%
Qwen 3.6 UD-IQ4_XS (llama.cpp) Pass 45.0%
mimo-v2-flash-free (OpenCode Zen) Pass 53.8%
Qwen 3.5 35b IQ3_S Pass 65.0%
Qwen 3.5 122B IQ3_S Pass 80.0%
Qwen 3.5 122B IQ3_XXS Pass 90.0%
Qwen 3.5 35b IQ4_XS Pass 98.8%
Qwen 3.6 35b UD-IQ3_XXS Pass 98.8%
GLM-4.7 Flash IQ4_XS Pass 100%
GLM-4.7 Flash REAP 23B IQ4_XS Pass 100%
Qwen3.5 27B IQ3_XXS Bart. Pass 100%
GPT-OSS 20b (high thinking) Pass —
Nemotron Cascade 2 30B IQ4_XS Fail 96.3%
devstral-small-2:24b Fail —
GPT-OSS 20b (default) Fail —
Qwen 3 14b Fail —
qwen3-coder:30b Fail —
qwen3.5:9b Fail —
qwen3.5:9b-q8_0 Fail —

Methodology

Task 1: MCP Service Development (Python & TypeScript)

Models were prompted to architect and implement a Model Context Protocol (MCP) server. The task required creating a functional service in either Python or TypeScript capable of exposing specific tools and resources to an MCP client. Success was measured by protocol compliance, correct dependency management (e.g., package.json or requirements.txt), and passing unit tests for tool execution.

Task 2: Website Migration Mapping

Models were tasked with generating a mapping from legacy blog URL formats (e.g., /post/2024/10/slug/) to new topic cluster formats. Constraints:

  1. The slug (final path segment) must remain identical.
  2. Target URLs must use the new cluster path, not the old /post/ structure.
  3. All 80 expected source URLs must be accounted for.

Infrastructure & Resources

High-Level Recommendations

⚠️ Use with Caution (Validation Required)

❌ Avoid for Agentic Coding

Detailed Model Analysis

MCP Development Task: Detailed Analysis

Qwen Series

Gemma Series

Nemotron Series

GLM Series

Other Notable Results

Migration Mapping: Statistical Deep-Dive

The migration task exposed a critical failure mode in many models: Slug Drift and Category Collapse.

Key Failure Modes Identified:

  1. 2022 Prefix Stripping: Almost all models failed to preserve numeric prefixes in older slugs (e.g., converting /06-git-cheatsheet/ to /git-cheatsheet/).
  2. Category-Path Collapse: Large models (Qwen 3.5 35b/122b and Bartowsky quants) frequently collapsed individual page URLs into their parent category URLs.
  3. SEO Rewriting: Some models (Qwen 3.6 35b) prioritised generating "clean" SEO slugs over preserving the required source slugs.

Migration Error Table (Detailed)

Model Lines Mismatches Error Rate Primary Failure Mode
Qwen 3.5 27b Q3 XXS 80 4 5.0% 2022 Prefix Stripping
Gemma 4 26B it 81 5 6.3% 2022 Prefix Stripping / Layout Error
Nemotron 3 Super 120B 81 5 6.3% 2022 Prefix Stripping
Qwen3-Coder-Next 81 7 8.8% Minor Slug Renaming
Qwen 3.6 35B UD-IQ4_XS 81 36 45.0% SEO Title Rewriting
Qwen 3.5 35b IQ4_XS 80 79 98.8% Category-Path Collapse
Qwen 3.6 35B UD-IQ3_XXS 67 79 98.8% Uniform Category-Path Collapse

Generated using AI hosted in the NREE by NCIA.

🗄️ Archiv

Historische Projekte und nicht mehr aktive Arbeitsbereiche. Diese Seiten werden zu Referenzzwecken aufbewahrt, sind aber nicht mehr Teil der aktiven Wissensbasis.

🗄️ Archiv

Project Overview

🗺️ Curriculum Roadmap

The bootcamp is structured into 10 weekly milestones, focusing on full-stack development and AI integration.

🌐 Resources & Community

📂 Technical Constraints Note

Internal Note: The student has identified a mismatch between the course content (Cloud/API heavy) and their professional environment (Air-gapped/Offline systems). This Wiki entry serves as a record of the curriculum prior to the refund request on March 8, 2026.

🗄️ Archiv

Comparison between OneNote and Bookstack, Confluence, Docmost and Nextcloud

I asked:

I need a good comparison between Dokuwiki, Confluence, Docmost, Bookstack and Nextcloud (and a nextcloud app maybe)
We do have Dokuwiki at the moment in our organisation. The new team i am joining, uses a One Note notebook with hundreds of "books", and then multiple chapter or cards, and hundreds of pages. some of them really long. 
They want to move away from OneNote. I can think of that they might be wanting collaborative working, which Docmost and Nextcloud Notes support. 
The biggest issue I see is, that a lot of people do some fancy formatting which would need to stop when they switch. But effectively it comes down to: text, lists and images (mostly screenshots) and maybe a table
Also, the app should have LDAP available (list if paid or freely included) as this is a key requirement. 
Do a throughlouy research on that topic and present me a well documented thing. 
If you ask me, my personal favorite as a long long time dokuwiki user, would be bookstack, as i myself switch from dokuwiki to bookstack last year. But this is just a side-note. 
Do you need anymore information?
Output should be easily printable or exportable so i can present to my new team lead at the end of the week. 

1. Hosting has to be ON-PREM. We have VMWare and K8s available.
2. Attachments would be nice (i do know that bookstack supports this, as you can add attachments to pages)
3. mobile is not a requirement. we do not have mobile clients
4. no. it is only used for documentation. if it has a export function that would be nice.


This was the answer:

Understood. I will conduct a thorough comparison of Dokuwiki, Confluence, Docmost, Bookstack, and Nextcloud (including relevant apps), specifically for on-premise deployment within a VMware/Kubernetes environment.

The report will evaluate each tool's suitability for replacing OneNote in a documentation-heavy team, focusing on:

I’ll compile the findings into a clear, structured, and printable report for presentation to your team lead. I’ll notify you once the research is complete.

Result:

As the result is really long, I attached it as a pdf to this page. Please open that to see, what ChatGPT has written.

For easy reading, as it is quite a long file, I have just added the Comparisson Results here:

Comparison Summary

To wrap up, here is a high-level summary of each tool’s key strengths and weaknesses in this context:

DokuWiki:

Strengths:

Weaknesses:

Good for teams that value simplicity and control over flashy interface.

Confluence (Data Center):

Strengths:

Weaknesses:

Cost – requires a paid license that can be expensive for on-prem.

For an organization willing to invest, Confluence provides a robust OneNote replacement with added benefits of structure and integration.

Docmost:

Strengths:

Weaknesses:

If the team wants a Notion-like experience on-prem and can handle the enterprise feature cost (or doesn’t mind local user accounts), Docmost is an attractive option.

BookStack:

Strengths:

Weaknesses:

Overall, BookStack’s simplicity and user-friendliness are its selling points, making it likely the least friction for OneNote users aside from the missing freeform canvas aspect.

Nextcloud (Collabora/Tasks/Kanban/Notes):

Strengths:

Weaknesses:

Nextcloud suits an environment where you want a lightweight wiki tightly integrated with files and possibly where users already use Nextcloud.

Migration Concerns Recap:

Regardless of tool, expect to invest time in restructuring and copying content from OneNote. OneNote’s freeform notes must be linearized, and some formatting (like handwritten sections or arbitrary positioning) won’t carry over. Encourage users to embrace the new structure (use headings, use multiple pages instead of one huge canvas, etc.).

There might be an adjustment period where users try to do something “the OneNote way” and it doesn’t work – e.g., dragging an image next to text and it doesn’t stay side by side. Training and documentation on “how to do X in the new tool” will alleviate this.

On the flip side, they will soon find many advantages: for instance, no more wondering who has the latest version of a note, better search (especially in Confluence, Docmost or Bookstack, where search is quite powerful and maybe even OCRs or indexes attachments in enterprise versions), and the ability for multiple people to contribute easily rather than a single user’s OneNote notebook.

🗄️ Archiv

🚀 Week 01: The Personal Dashboard

"Your Digital Command Center"

Welcome to the first real project. In the first week, we aren't just coding; we are reclaiming your browser. Instead of a cluttered "New Tab" page, you’re building a lightning-fast, minimalist link organizer that lives on your machine.

The goal for this milestone is to master CRUD (Create, Read, Update, Delete) operations and understand how a frontend interface talks to a local database.

🛠️ Phase 1: The Foundation

Before you paste your prompt into an AI, you need to decide on your Tech Stack. A good tech-stack is one that lets you ship the fastest, but here are some recommended paths:

Stack

Why choose it?

Next.js + Tailwind + SQLite

The modern industry standard. Fast, sleek, and everything stays in one folder.

Python (Flask) + Bootstrap + TinyDB

Great if you prefer a lighter, more logic-focused backend approach.

Deno + Typescript + HTMX + AlpineJS

Great if you prefer a lightweight stack with simple components that lets you create a single executable binary with the help of demo.

Astro + HTMX + AlpineJS

The AHA-Stack. Simple, minimal and effective.

Action Item: Decide on your language. Do you want to go the JavaScript/TypeScript route or the Python route?

🤖 The Master Prompt

Once you've picked your stack, use this comprehensive prompt to generate the "v1.0" of your dashboard:

Build me a personal link dashboard that I'll use as my browser's new tab page.
[INSERT CHOSEN STACK HERE: e.g., Using Next.js and SQLite]

The app organizes links into categories. Each category has a name and contains multiple links. 
Each link has a name and a URL.

Features I need:
- Display links grouped by category in a clean grid/card layout.
- Add a new link (with name, URL, and category selection).
- Edit and Delete existing links.
- Create and delete entire categories.
- Store everything in a local database (setup instructions included).
- Run on localhost.

Design: Make it clean, dark-mode friendly, and minimal. It must load instantly.

🛠️ Phase 2: Iteration Prompts

Use the next prompts to enhacne your dashboard with more features. Often is less more and small iterations make it easier to get better results.

When you enable git you can also always go back and undo changes.

Also the Planing-Mode in many agents can help to layout a plan before doing any major tasks.

1. Real-Time Fuzzy Search & Filtering

Implement a global search bar at the top of the dashboard.
As the user types, it should filter the displayed
categories and links in real-time. Use fuzzy-matching
logic so searching for 'git' matches 'GitHub' or
'GitLab'. If a category has no matching links, hide the
entire category heading from the view to keep the UI clean.

2. Dynamic Favicon & Metadata Fetching

3. Persistent Dark Mode & System Preference

Add a theme toggle component (Sun/Moon icon). The system
should check for the user's OS preference using
'prefers-color-scheme' on first visit but allow manual
override. Store the chosen theme in localStorage. Apply
a 'dark' class to the root HTML element and ensure all
CSS transitions for colors are smooth (300ms duration).

4. Drag-and-Drop Reordering (Persistent)

Integrate a drag-and-drop library (like dnd-kit) to allow
reordering of links within a category. When a link is
dropped, send a PATCH request to the backend to update a
'sort_order' integer field in the database. Ensure the UI
updates optimistically so there is no visual lag while
the database saves the new order.

5. Data Portability: JSON Backup & Restore

Create a 'System' modal that allows data management.
Include an 'Export' button that generates and downloads
a 'dashboard_backup.json' file containing all data.
Also, include a file upload input for 'Import' that
parses the JSON file, validates the schema, and performs
a bulk-insert into the database to restore the setup.

6. Smart URL Validation & Auto-Naming

Improve the 'Add New Link' form. When a user pastes a URL,
use a regex to validate it. If valid, use a client-side
fetch or server-side route to attempt to scrape the
<title> tag of that website. Automatically populate the
'Link Name' field with this title, allowing the user to
edit it before saving.

7. Keyboard Navigation & "Quick Actions"

Implement global 'Hotkeys' for power users. Pressing '/'
should instantly focus the search bar; pressing 'n'
should open the 'Add New Link' modal; and pressing 'Esc'
should close any open modals. Add a small footer or
tooltip that visually reminds the user of these
shortcuts to improve discoverability.

💡 Implementation Tip

When using these, I recommend pasting the relevant file code (e.g., your page.tsx or api/links.js) along with the prompt. This prevents the AI from hallucinating variable names that don't exist in your project.

🗄️ Archiv

📊 Week 02: The Centralized Event Hub

Welcome to Week 02! You’ve already knocked out your personal dashboard; now we’re stepping into the world of Dynamic Orchestration. This week, you aren't just building a display—you are building the "Nervous System" for every application you will ever write.

📡 The Mission: Observation & Architecture

The goal is to build a Centralized Event Dashboard. This application acts as a private "Log Sink" or "Command Center." It provides a secure API that listens for "Events" from your other apps—whether it’s a successful user signup from a web app, a cron job failure in a Python script, or a simple cURL message from your terminal.

Build a two-part system:

  1. The Receiver (Remote API): A cloud-hosted endpoint that is "always on," waiting to catch events from your other apps, scripts, or servers.
  2. The Viewer (Local Dashboard): A high-performance real-time feed where you can search, filter, and visualize the data flowing through your API.

🛠️ The Architecture

🚀 Step 0: Choose Your Stack

Before you run your first prompt, decide on your Tech Stack. You will need a database (like Supabase or PostgreSQL) to store your projects and historical event data.

Component

Option A: Modern Serverless (Recommended)

Option B: Robust Python

Backend API

Next.js API Routes (Vercel) or Hono (Cloudflare)

FastAPI (Render or Railway)

Database

Supabase (PostgreSQL + Realtime)

MongoDB Atlas or Supabase

Frontend

Next.js + Tailwind + Shadcn UI

React (Vite) + Tailwind

Live Updates

Native Supabase Realtime

Pusher or Socket.io

Student Note: Are you a Next.js + Tailwind fan? Or do you prefer Python (FastAPI) + React? Specify this in your initial prompt so the AI builds the API routes correctly.

🚀 The Starter Prompt

Copy and paste this into your AI chat to generate the foundation.

Build me an events dashboard. Other applications send events to it through an API, and I see them in a real-time feed.

The app has two parts:
1. A REST API that accepts events via POST request (with API key authentication). This needs to run on a remote server so it's always available, even when my computer is off.
2. A dashboard that displays events in a feed, with search, filtering, and charts. This can run locally.

Each event has: a channel (category like "orders", "signups", "deploys"), a title, an optional description, an optional emoji icon, and optional tags.

Features I need:
- POST /api/events endpoint that accepts JSON and stores events in the database
- API key authentication (generate a key when creating a project)
- A feed page showing events in reverse chronological order
- Filter events by channel
- Search events by title, description, or tags
- At least one chart showing event activity over time
- The dashboard should update in real-time when new events arrive
- Use a cloud database that's always available (Supabase, Convex, or similar)

Make it clean and functional. I want to actually use this to monitor my own projects.

Before making any decisions on the stack. Make a stack proposal and ask me which I want to use.

Once you have the initial dashboard frontend, api and database running to your liking, you can continue with the next prompts, to make it better or add additional features to it.

📈 Evolution: 7 Prompts to Pro Power

Once your API can catch a message, use these iterative prompts to turn a "basic table" into a professional monitoring tool.

The Roadmap:

🛠️ The Detailed Prompt List

  1. The Real-Time Subscription

"Since our database is in the cloud, implement a Real-time Listener (e.g., Supabase Realtime). Ensure that when the remote API inserts a new event, the local dashboard pushes it to the top of the feed automatically with a subtle 'new item' highlight animation."

  1. Smart Emoji & Channel Parsing

"Enhance the API logic: if an incoming event doesn't specify an emoji icon, automatically assign one based on the channel name (e.g., 'orders' gets 💰, 'deploys' gets 🚀, 'errors' gets ❌). Display these icons prominently next to the event title in the feed."

  1. Multi-Project API Key Management

"Build a 'Project Settings' page in the dashboard. Allow me to create multiple projects, each with its own name and unique generated API key. The API should now validate the key against the database and tag the incoming event to the correct project automatically."

  1. Advanced Time-Series Analytics

"Add a 'Metrics' tab. Use Recharts to create a bar chart showing 'Events per Hour' and a pie chart showing 'Top Channels by Volume.' Allow me to toggle the time range between the last 24 hours, 7 days, or 30 days."

  1. Desktop & Push Notifications

"Add a toggle in the dashboard for 'Critical Alerts.' If an event is received with a specific tag (like #error or #urgent) or a 'High' priority status, trigger a browser-native desktop notification so I see the alert even if the dashboard tab is hidden."

  1. The "Deep Dive" JSON Inspector

"Make each event card clickable. When clicked, open a side-drawer (Slide-over) that shows the full raw JSON payload received by the API formatted for readability. Include a 'Copy as cURL' button so I can easily replicate the exact request for debugging."

  1. API Key Security & Rotation

"Implement 'Key Rotation' logic. In the Project Settings, add a button to 'Regenerate API Key.' This should instantly void the old key in the database and provide a new 32-character secret to the user, ensuring security if a key is ever accidentally leaked."


Dell Pro Max (GB10): Edge-KI-Infrastruktur & ROI-Analyse

Übersicht

Das Dell Pro Max System, ausgestattet mit dem Nvidia GB10 (Grace Blackwell) Prozessor, stellt ein hochinteressantes Edge-KI-Kraftpaket dar. Mit seinem kompakten Formfaktor, 128 GB verlötetem LPDDR5X-RAM und einer leistungsstarken Nvidia ConnectX-7 Netzwerkkarte (2x 200 GbE) zielt es auf Anwendungsfälle ab, in denen hohe Rechenleistung und schnelle Netzwerkanbindung an der Edge gefragt sind.

Business-Use-Case: ROI-Analyse

Ein zentraler Aspekt der Systemevaluation ist die Amortisierung durch Automatisierung. Patrick von ServeTheHome demonstriert hier, wie man durch die lokale Inferenz eines GPT-basierten Modells manuelle Reporting-Aufgaben ersetzt:

Technische Spezifikationen

Wichtige Erkenntnisse zur Modell-Reliabilität

Die Analyse zeigt eine signifikante Diskrepanz in der Zuverlässigkeit bei komplexen Multi-Step-Workflows:

Skalierbarkeit

Dank der 200 GbE Netzwerkschnittstelle lassen sich diese Einheiten effizient in Clustern betreiben. Durch den Einsatz passender Switches (z.B. MicroTick CRS 812 oder Z9332) ist eine Skalierung möglich, die Standard-Mini-PCs weit übertrifft.

Cavecrew Reviewer Prompt

Cavecrew Reviewer Prompt

Dieser Prompt dient als Vorlage für Code-Reviews im Rahmen des Cavecrew-Projekts. Er fokussiert sich auf spezifische Datei-Strukturen und sicherheitskritische Importe sowie Konfigurationsanpassungen.

Prompt-Vorlage

# Task: Review and fix the recent changes for correctness

Do a read-only pass over these files and fix any obvious errors:
`main.py`, `handlers.py`, `config.py`, `memory.py`, `formatter.py`, `proactive.py`

## What to check

1. `main.py` — imports from handlers must include:
   `retry`, `remember`, `memories`
   All three must also be registered with `app.add_handler(CommandHandler(...))`.

2. `handlers.py` — `longterm` must be imported at the top.
   Check that `retry`, `remember`, `memories` are all present and complete.

3. `config.py` — `MAX_RESPONSE_SENTENCES` must be gone.
   `MAX_RESPONSE_CHARS` must be present with default `1200`.

4. `formatter.py` — `check_response_length` must use `max_chars` not `max_sentences`.

5. `proactive.py` — after `send_message` succeeds, `memory.save_message` must be called.

Fix only what is actually broken or missing. Do not refactor anything that works.
Do not change logic, only fix import errors, missing registrations, and typos.

Hinweis: Der Prompt sollte je nach aktuellem Projektstatus und spezifischen Datei-Anforderungen angepasst werden.