Tech Events in Stuttgart

Dieses Mal sind wir zu Gast bei der Teal Technology Consulting (Location: STEYG Lautenschlagerstraße 16). Sichere dir deinen Platz, lass dich inspirieren und erweitere dein Netzwerk in der Cybersecurity-Community. Sei dabei und gestalte die Zukunft der Cybersicherheit aktiv mit!
Alle Infos und zur Anmeldung gehts HIER

Modellierungs-Fitnessclub is a hands on meeting where researchers build modelling workflows with coding harnesses (such as Claude code, OpenAI Codex, cursor, Open code or Pi). This is a workshop to practice data science skills transferable between scientific domains. We are all learning how agentic programming is disrupting software engineering and our modelling work as a result.

• The 90 minutes workshop chooses and analyses one dataset in 4 phases:

1. Choice Phase 5 minutes. Round of introduction 1 minute per project (with timer). Group decision on the most mature or desirable project among the submitted ones.
2. Specification Phase 15 minutes. On a drawing board or sheet of paper, we specify what we want to achieve during the session.
3. Implementation Phase 1 hour. Implementation with at least one, preferably two or more laptops which load the dataset, tools, and generate software with a coding agent. We push the code to this git repository under a new sub directory corresponding to the day of the event.
4. Wrap-up Phase 10 minutes. What failed, what went well, what can we improve next time.

• Throughout the specification and implementation phase, we can discuss about visualizations such as histogram, scatter plot, line chart, area chart, bar chart, maps, trees, networks and other graphic representations. We can elaborate a data processing pipeline with data structures (such as multidimensional arrays, vectors and tensors), input/output, file storage formats, databases, git version control and issue management, integration tests, CI, security aspects, workflow engines to run models in a dependency graph and about semantics how we name variables and functions.
• The groups goals could be for example:
• Playing with data visualisations on the dataset of the day. Spawning parallel coding agents with personas from different disciplines to enhance the creative process.
• Translating new research questions into back of the envelope calculations. From these questions, we can brainstorm about the implication for model modification or model linking.
• Performing literature research with LLMs that ground their answers in existing publications such as Perplexity. To use context engineering (uploading relevant papers) and prompting techniques to avoid sycophancy (such as grill-me, expert panel, storm review, hostile review).

To participate preferably come with an open dataset already shared on Zenodo or Figshare or a similar open platform. If you cannot share your data, then find a similar dataset from the literature on which we can work on together so that the learning can be transferable to your own data later on.

• Submit your sample dataset in a new issue also called a work item at:
  https://aidaho-edu.uni-hohenheim.de/gitlab/modellierung/modellierungs-fitnessclu/-/work_items
• Make sure your data file is smaller than 10Mb. If the sample data is larger than 10Mb, please post it in a shared repository that can be accessed online, either publicly or through the university network, for example in an idaho repository. Then share that link in the issue.
• Optionally, you can also add modelling code (in the programming language of your choice)
• If you generate code during the meeting's implementation session you are very welcomed to make a merge request to place your generated code directly in the events/ directory.

If the programming language is proprietary and requires a licence, please come with your own laptop or server access so that you can run the model in it's licensed environment.
At the beginning of each meeting we will vote on which project will be analyzed during that me

Modellierungs-Fitnessclub is a hands on meeting where researchers build modelling workflows with coding harnesses (such as Claude code, OpenAI Codex, cursor, Open code or Pi). This is a workshop to practice data science skills transferable between scientific domains. We are all learning how agentic programming is disrupting software engineering and our modelling work as a result.

• The 90 minutes workshop chooses and analyses one dataset in 4 phases:

1. Choice Phase 5 minutes. Round of introduction 1 minute per project (with timer). Group decision on the most mature or desirable project among the submitted ones.
2. Specification Phase 15 minutes. On a drawing board or sheet of paper, we specify what we want to achieve during the session.
3. Implementation Phase 1 hour. Implementation with at least one, preferably two or more laptops which load the dataset, tools, and generate software with a coding agent. We push the code to this git repository under a new sub directory corresponding to the day of the event.
4. Wrap-up Phase 10 minutes. What failed, what went well, what can we improve next time.

• Throughout the specification and implementation phase, we can discuss about visualizations such as histogram, scatter plot, line chart, area chart, bar chart, maps, trees, networks and other graphic representations. We can elaborate a data processing pipeline with data structures (such as multidimensional arrays, vectors and tensors), input/output, file storage formats, databases, git version control and issue management, integration tests, CI, security aspects, workflow engines to run models in a dependency graph and about semantics how we name variables and functions.
• The groups goals could be for example:
• Playing with data visualisations on the dataset of the day. Spawning parallel coding agents with personas from different disciplines to enhance the creative process.
• Translating new research questions into back of the envelope calculations. From these questions, we can brainstorm about the implication for model modification or model linking.
• Performing literature research with LLMs that ground their answers in existing publications such as Perplexity. To use context engineering (uploading relevant papers) and prompting techniques to avoid sycophancy (such as grill-me, expert panel, storm review, hostile review).

To participate preferably come with an open dataset already shared on Zenodo or Figshare or a similar open platform. If you cannot share your data, then find a similar dataset from the literature on which we can work on together so that the learning can be transferable to your own data later on.

• Submit your sample dataset in a new issue also called a work item at:
  https://aidaho-edu.uni-hohenheim.de/gitlab/modellierung/modellierungs-fitnessclu/-/work_items
• Make sure your data file is smaller than 10Mb. If the sample data is larger than 10Mb, please post it in a shared repository that can be accessed online, either publicly or through the university network, for example in an idaho repository. Then share that link in the issue.
• Optionally, you can also add modelling code (in the programming language of your choice)
• If you generate code during the meeting's implementation session you are very welcomed to make a merge request to place your generated code directly in the events/ directory.

If the programming language is proprietary and requires a licence, please come with your own laptop or server access so that you can run the model in it's licensed environment.
At the beginning of each meeting we will vote on which project will be analyzed during that me

Physical AI meets Industry: Experience robotics, autonomous systems & AI-driven manufacturing live at ARENA2036 in Stuttgart.

Physical AI is transforming industry faster than ever before.
‍From autonomous systems and humanoid robotics to AI-driven manufacturing and intelligent automation - the next industrial race has already begun.

At the Summit, leading innovators, industrial players, robotics pioneers, and investors come together at ARENA2036 in Stuttgart to explore the technologies, use cases, and business opportunities shaping the future of Physical AI.

Experience live demonstrations, real-world applications, and the people building the next generation of intelligent industry.

This is not just another tech conference.
This is where Physical AI Meets Industry.

Be Quick and Join now - Tickets are limited!

Get more information on our website!

🇬🇧👇
🇩🇪 Jeden ersten Dienstag im Monat veranstalten wir im codecentric-Büro in Stuttgart mit dem Hackergarten einen unterhaltsamen und offenen Workspace, bei dem wir gemeinsam in einer geselligen Runde an verschiedenen Open-Source-Projekten arbeiten.

Wir freuen uns darauf, euch in lockerer Atmosphäre mit ausreichend Essen und Verpflegung begrüßen zu dürfen und gemeinsam an tollen Projekte zu arbeiten und unser Wissen auszutauschen. Ihr benötigt lediglich einen eigenen Laptop zum Arbeiten.

Was ist ein Hackergarten?
Ein Hackergarten ist eine Mischung aus Softwarewerkstatt, Labor, Klassenzimmer, Spielplatz, geselliger Runde und Studio. Ziel ist es, Neues zu schaffen, Bestehendes zu erweitern, Fehler zu beheben, Dokumentationen oder Tutorials zu schreiben. Wir wollen etwas erarbeiten, was andere nutzen können – indem das Ergebnis am Ende als Patch, Contribution oder auf ähnlichem Wege einem Open-Source-Projekt zugeführt wird. Man lernt neue Leute kennen, bekommt Einblick in Projekte oder Technologien, kann Erfahrungen und Wissen austauschen. Dazu ist jeder willkommen. Egal, ob du studierst oder schon lange dabei bist – jeder kann etwas beitragen, sofern er bereit ist, einen Laptop und Zeit mitzubringen. Bringt Eure eigenen Ideen oder auch Probleme mit, und gemeinsam kann daraus etwas wachsen.

Siehe auch: http://hackergarten.net/

🇬🇧
We host the Hackergarten at the codecentric office in Stuttgart, an entertaining and open workspace where we work together on various open source projects in a relaxed setting.

You can bring your own projects, which we will be happy to support you with and which we can discuss and work on together.

We are looking forward to welcoming you in a relaxed atmosphere with enough food and drinks to work together on great projects and share our knowledge. All you need is your own laptop to work with.

What is a Hackergarten?

Hackergarten is a crafter's workshop, classroom, a laboratory, a social circle, a writing group, a playground, and an artist's studio. Our goal is to create something that others can use; whether it be working software, improved documentation, or better educational materials. Our intent is to end each meeting with a patch or similar contribution submitted to an open and public project. Membership is open to anyone willing to contribute their time.

See also: http://hackergarten.net/

THEMA: Wer hat dieses Plugin verbrochen?!

Egal was wir darüber denken, Vibe Coding hat seinen Weg in WordPress-Workflows gefunden. Plugins und Themes können per Prompt ins Leben getippt werden oder aus losen Ideen und Gedankenfetzen zusammengesetzt. Aus Absichten werden halbwegs vorzeigbare Produkte, ohne dass jemand den Code wirklich einmal vollständig durchgelesen hat. Das ist nicht zwingend ein Problem. Aber es verschiebt etwas darin, wie Verantwortung verteilt wird. Und über diese Verschiebung wird bisher zu wenig gesprochen.
Dieser Vortrag nähert sich der Frage aus drei verschiedenen Perspektiven: als aus der Sicht eines Produktmenschen, eines Entwicklers und eines Security-Unternehmens und lässt KI-gestützte Entwicklung mit CMS-Sicherheit und neuen Regulierungen zur Cyber-Security zusammenkommen.
Schauen wir uns also gemeinsam an, wie es richtig schiefgehen kann. Und noch wichtiger: was wir dagegen tun können.

ALLGEMEINES: Die Teilnahme am WordPress-Meetup ist kostenlos.
LOCATION: Das WordPress Meetup Stuttgart findet ab sofort wieder als Präsenz-Meetup statt.
Weitere Infos:
wp0711.de
#wp0711 #wpmeetupstgt

Modellierungs-Fitnessclub is a hands on meeting where researchers build modelling workflows with coding harnesses (such as Claude code, OpenAI Codex, cursor, Open code or Pi). This is a workshop to practice data science skills transferable between scientific domains. We are all learning how agentic programming is disrupting software engineering and our modelling work as a result.

• The 90 minutes workshop chooses and analyses one dataset in 4 phases:

1. Choice Phase 5 minutes. Round of introduction 1 minute per project (with timer). Group decision on the most mature or desirable project among the submitted ones.
2. Specification Phase 15 minutes. On a drawing board or sheet of paper, we specify what we want to achieve during the session.
3. Implementation Phase 1 hour. Implementation with at least one, preferably two or more laptops which load the dataset, tools, and generate software with a coding agent. We push the code to this git repository under a new sub directory corresponding to the day of the event.
4. Wrap-up Phase 10 minutes. What failed, what went well, what can we improve next time.

• Throughout the specification and implementation phase, we can discuss about visualizations such as histogram, scatter plot, line chart, area chart, bar chart, maps, trees, networks and other graphic representations. We can elaborate a data processing pipeline with data structures (such as multidimensional arrays, vectors and tensors), input/output, file storage formats, databases, git version control and issue management, integration tests, CI, security aspects, workflow engines to run models in a dependency graph and about semantics how we name variables and functions.
• The groups goals could be for example:
• Playing with data visualisations on the dataset of the day. Spawning parallel coding agents with personas from different disciplines to enhance the creative process.
• Translating new research questions into back of the envelope calculations. From these questions, we can brainstorm about the implication for model modification or model linking.
• Performing literature research with LLMs that ground their answers in existing publications such as Perplexity. To use context engineering (uploading relevant papers) and prompting techniques to avoid sycophancy (such as grill-me, expert panel, storm review, hostile review).

To participate preferably come with an open dataset already shared on Zenodo or Figshare or a similar open platform. If you cannot share your data, then find a similar dataset from the literature on which we can work on together so that the learning can be transferable to your own data later on.

• Submit your sample dataset in a new issue also called a work item at:
  https://aidaho-edu.uni-hohenheim.de/gitlab/modellierung/modellierungs-fitnessclu/-/work_items
• Make sure your data file is smaller than 10Mb. If the sample data is larger than 10Mb, please post it in a shared repository that can be accessed online, either publicly or through the university network, for example in an idaho repository. Then share that link in the issue.
• Optionally, you can also add modelling code (in the programming language of your choice)
• If you generate code during the meeting's implementation session you are very welcomed to make a merge request to place your generated code directly in the events/ directory.

If the programming language is proprietary and requires a licence, please come with your own laptop or server access so that you can run the model in it's licensed environment.
At the beginning of each meeting we will vote on which project will be analyzed during that me

Modellierungs-Fitnessclub is a hands on meeting where researchers build modelling workflows with coding harnesses (such as Claude code, OpenAI Codex, cursor, Open code or Pi). This is a workshop to practice data science skills transferable between scientific domains. We are all learning how agentic programming is disrupting software engineering and our modelling work as a result.

• The 90 minutes workshop chooses and analyses one dataset in 4 phases:

1. Choice Phase 5 minutes. Round of introduction 1 minute per project (with timer). Group decision on the most mature or desirable project among the submitted ones.
2. Specification Phase 15 minutes. On a drawing board or sheet of paper, we specify what we want to achieve during the session.
3. Implementation Phase 1 hour. Implementation with at least one, preferably two or more laptops which load the dataset, tools, and generate software with a coding agent. We push the code to this git repository under a new sub directory corresponding to the day of the event.
4. Wrap-up Phase 10 minutes. What failed, what went well, what can we improve next time.

• Throughout the specification and implementation phase, we can discuss about visualizations such as histogram, scatter plot, line chart, area chart, bar chart, maps, trees, networks and other graphic representations. We can elaborate a data processing pipeline with data structures (such as multidimensional arrays, vectors and tensors), input/output, file storage formats, databases, git version control and issue management, integration tests, CI, security aspects, workflow engines to run models in a dependency graph and about semantics how we name variables and functions.
• The groups goals could be for example:
• Playing with data visualisations on the dataset of the day. Spawning parallel coding agents with personas from different disciplines to enhance the creative process.
• Translating new research questions into back of the envelope calculations. From these questions, we can brainstorm about the implication for model modification or model linking.
• Performing literature research with LLMs that ground their answers in existing publications such as Perplexity. To use context engineering (uploading relevant papers) and prompting techniques to avoid sycophancy (such as grill-me, expert panel, storm review, hostile review).

To participate preferably come with an open dataset already shared on Zenodo or Figshare or a similar open platform. If you cannot share your data, then find a similar dataset from the literature on which we can work on together so that the learning can be transferable to your own data later on.

• Submit your sample dataset in a new issue also called a work item at:
  https://aidaho-edu.uni-hohenheim.de/gitlab/modellierung/modellierungs-fitnessclu/-/work_items
• Make sure your data file is smaller than 10Mb. If the sample data is larger than 10Mb, please post it in a shared repository that can be accessed online, either publicly or through the university network, for example in an idaho repository. Then share that link in the issue.
• Optionally, you can also add modelling code (in the programming language of your choice)
• If you generate code during the meeting's implementation session you are very welcomed to make a merge request to place your generated code directly in the events/ directory.

If the programming language is proprietary and requires a licence, please come with your own laptop or server access so that you can run the model in it's licensed environment.
At the beginning of each meeting we will vote on which project will be analyzed during that me

Wir laden euch herzlich zum nächsten Azure Meetup in Stuttgart ein. Dieses Mal dreht sich alles um Azure IoT in der Praxis und Building a Cloud Platform (STACKIT).
Besonders freuen wir uns, dieses Meetup gemeinsam mit der Cloud Platform Community Stuttgart auszurichten.

Freut euch auf einen technischen Abend mit praxisnahen Einblicken, Architektur-Diskussionen und echtem Erfahrungsaustausch aus dem Projektalltag.

Agenda:
Die Sprecher an diesem Abend werden sein:

• Patrick Koss (STACKIT)
• Felix Burkhard (Lunaris Digital Solutions)

​Building a Cloud Platform Where Everything is Just Another Kubernetes Resource
​At STACKIT, we took the Kubernetes API and turned it into our entire platform control plane. Not just for running containers. For everything. S3 buckets, databases, DNS records, IAM credentials, even entire child Kubernetes clusters. All defined as YAML manifests. All managed via GitOps. All continuously reconciled by controllers.
​We started with Terraform like everyone else. It worked fine until our infrastructure got complex. Monolithic state files that locked the whole team. Slow applies that recalculated everything when we only needed to change one thing. Drift that only surfaced when someone remembered to run a plan.
​So we rebuilt the platform on Crossplane and ArgoCD. One management cluster provisions and orchestrates cloud infrastructure per environment. Developers get self-service APIs by applying Kubernetes resources. Ops teams enforce policies through admission webhooks. Everything reconciles in real-time. No external state to manage. No waiting for tickets to get unblocked.
​This is the production architecture. How it works, why we designed it this way, what went wrong during the migration, and what we'd change if we started over today.

​Azure IoT in der Praxis: Von der Edge bis zur Auswertung
​IoT-Lösungen wirken auf den ersten Blick oft geradlinig. Ein Gerät sendet Daten, in der Cloud werden sie verarbeitet und am Ende irgendwo ausgewertet. In der Praxis entsteht aber schnell eine deutlich komplexere Landschaft mit Anforderungen rund um Anbindung, Entkopplung, Vorverarbeitung, Skalierung, Betrieb und Analyse.
​Der Vortrag gibt einen praxisnahen Überblick darüber, wie sich solche IoT-Architekturen in Azure sauber aufbauen und einordnen lassen. Im Mittelpunkt stehen typische Bausteine und Entscheidungsmuster entlang des gesamten Datenpfads, von der Edge über die Übertragung und Verarbeitung bis zur Speicherung und Auswertung.

Das Meetup findet in deutscher Sprache statt.
Essen und Trinken ist auf Selbstzahler-Basis.

Wann:
​17:45 Uhr - Ankunft & Socializing (Essensbestellung)
​18:30 Uhr - Abendessen
​19:15 Uhr - Session #1 (Patrick Koss)
​20:00 Uhr - Pause
​20:15 Uhr - Session #2 (Felix Burkhard)
​21:00 Uhr - Networking

Ort:
​Kursaal Gastronomie & Eventlocation
Königsplatz 1
70372 Stuttgart
​📄 Speisekarte - Kursaal Stuttgart Bad Cannstatt
​Raum "König Karl"

Parkmöglichkeit

Tiefgarage Am Kursaal
Königsplatz 1
70372 Stuttgart
​Pro angefangene Stunde: 1,20 €
Tageshöchstsatz: 9,70 €

​​ÖPNV
​Mit der S-Bahn S2 oder S3 vom Stuttgarter Hauptbahnhof in Richtung Schorndorf bzw. Backnang bis Bad Cannstatt (nur 1 Haltestelle). Von dort sind es etwa 12 Minuten zu Fuß zum Restaurant.

Neben 2 technisch fokussierten Vorträgen steht insbesondere das persönliche Networking im Vordergrund. Die Veranstaltung richtet sich an IT-Architekten, Engineers und technisch orientierte Professionals, die sich mit Cloud Architecture, Platform Engineering, Governance und Security im Unternehmenskontext beschäftigen.

​Freut euch auf einen Abend mit fachlichem Tiefgang, offenen Diskussionen und wertvollem Austausch innerhalb der Community

Grüße, das Azure Stuttgart Team

🇩🇪 Der Talk wird auf Deutsch gehalten.

👉 „Ich nutze Claude Code." – „Wir haben Copilot eingeführt." Was solche Sätze kosten, weiß meistens niemand genau. Ralf Brauchler zeigt, dass AI-Kosten auf fünf Ebenen entstehen – Umgebung, Harness, Modell, Laufzeitort und Zugang – und warum die größten Hebel nicht dort liegen, wo man zuerst sucht. Mit konkreten Zahlen aus eigenen Messungen und einem Vorher-Nachher-Vergleich derselben Aufgabe.

🗓 Agenda
17.45 Uhr Ankommen
18.00 Uhr Begrüßung
18:05 Uhr Was kostet dein Agent? AI (Cost) Optimization auf fünf Ebenen
18:45 Uhr Offener Austausch (Netzwerken & Fingerfood)

📣 Was kostet dein Agent? AI (Cost) Optimization auf fünf Ebenen (Ralf Brauchler)
„Ich nutze Claude Code." oder „Wir haben Copilot eingeführt." Was solche Sätze kosten, weiß meistens niemand genau. Die Rechnung kommt monatlich, die Ursachen bleiben im Dunkeln, und die erste Reaktion ist fast immer dieselbe: ein günstigeres Modell einstellen.

Das greift zu kurz. AI-Kosten entstehen auf fünf Ebenen: in der Umgebung, im Agenten-Harness, beim Modell, am Laufzeitort und beim Zugang. Und die größten Hebel liegen nicht dort, wo man zuerst sucht. Das Harness entscheidet, wie viele Token überhaupt losgeschickt werden, inklusive der kompletten Session-History bei jedem einzelnen Schritt. Der Zugang entscheidet, was ein Token kostet und ob man es überhaupt messen kann. Das Modell ist nur einer von fünf Faktoren, wenn auch ein sichtbarer: Zwischen dem teuersten und dem günstigsten Cloud-Modell liegt ungefähr Faktor 100 im Preis, bei wenigen Prozentpunkten Unterschied in gängigen Coding-Benchmarks.

Der Talk geht die fünf Ebenen nach Hebelgröße durch, mit konkreten Zahlen aus eigenen Messungen und einem Vorher-Nachher-Vergleich derselben Aufgabe. Am Ende steht kein Sparappell, sondern eine brauchbare Reihenfolge: erst messen, dann eine Ebene nach der anderen anfassen. Und eine Warnung: Die Entwicklerstunde ist weiterhin der teuerste Posten. Wer 20 Euro Tokenkosten spart und eine Stunde Nacharbeit erzeugt, hat draufgezahlt. Deshalb zählt am Ende nur eine Kennzahl: Die Kosten pro erledigter Aufgabe.

Das B2B Event für den Mittelstand. Echte Cases, starke Thesen und Vernetzung.

Tech & Tell bringt Entscheiderinnen und Entscheider aus dem B2B-Mittelstand der Region Stuttgart zusammen, um Digitalisierung und KI vom Hype in die Anwendung zu bringen – mit echten Impulsen, ehrlichem Praxistransfer und vertraulichem Austausch auf Augenhöhe.

Der Mittelstand der Region baut exzellente Produkte – doch oft fehlen Impulse und Praxistransfer für den beschleunigten Wandel: Daten stecken in Silos zwischen ERP, Excel und Postfach, Ressourcen sind in Routineaufgaben gebunden, und Veranstaltungswissen bleibt zu oft folgenlos.

Genau das soll Tech & Tell ändern – mit drei Zielen:

• Befähigen – echte Impulse plus Starter-Kit: Problembefund, Prioritäten-Matrix, erste Schritte, neue Kontakte
• Austauschen & Verbinden – vertrauliche Gruppenarbeit mit Branchenkollegen, die vor denselben operativen Hürden stehen
• Modernisieren – Chancen aufzeigen, das Thema entmystifizieren, auch ohne langwieriges Softwareprojekt

Open Source conference in Stuttgart, Germany