J4vURmywUMCcsCnm Independent Cyberpunk Music : Lufthansa - 4000 Vs Azure and Open Source Job +

Layoffs and B2B Software Overview
I heard today that Lufthansa layes of 4000 employees exchanging administrational tasks with AI. I assume they will use products from other large companies and wonder if you can give me an overview of focusing on AI in that context, please?

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Hmm, the user is asking about Lufthansa's AI-driven layoffs and wants an overview of B2B AI software in that context. They seem to have heard recent news and are looking to understand both the specific Lufthansa situation and the broader B2B AI landscape.
Well, the user might be a business professional trying to understand industry trends, or perhaps someone affected by similar AI transformations in their own organization. They're specifically interested in the B2B software angle rather than just the news itself.
I need to address both parts of their query: first confirming and detailing Lufthansa's AI strategy and layoffs, then providing a comprehensive overview of relevant B2B AI tools that could enable such transformations.
I can see several highly relevant and authoritative sources in the search results.
provides excellent detailed coverage of Lufthansa's AI initiatives and the 4,000 job cuts, directly confirming the user's information. and offer additional confirmation from Reuters and Fast Company, reinforcing the credibility.
gives me specific examples of Lufthansa's actual AI implementations like Tray Tracker and Pendle, which is perfect for showing real-world applications.
For the B2B software overview, provides a solid foundation with categorized tools, though it's from a vendor blog so I should supplement with the more authoritative listing top AI companies offers the prestigious Forbes AI 50 list but focuses more on AI companies rather than specific B2B software products and from Lufthansa's own consulting arm provide valuable insight into their AI philosophy and implementation approach, though they're more about methodology than specific software products.
I plan to structure this with a confirmation of the Lufthansa situation first, then a categorized table of B2B AI tools since the user wants an "overview." The table format will help them quickly understand the landscape, and I can follow up with practical guidance for evaluation since they seem genuinely interested in understanding this transformation.

The information you heard is correct. has announced a turnaround plan that includes cutting 4,000 administrative jobs by 2030, citing greater efficiency through digitalization and AI as a key reason.
The airline is not relying on a single software provider but is using a mix of in-house developed solutions, partnerships with major tech firms, and products from specialized AI companies. The table below organizes the B2B AI software in this context.

Software/Provider Type/Context

Application at Lufthansa / In Aviation
Large Tech Platform

Partnership with Lufthansa Technik for over 50 AI use cases,

including maintenance optimization and supply chain

efficiency.
Specialized B2B Software

AI for dynamic pricing of ancillary services (e.g., baggage,

seat upgrades) based on real-time demand.
In-house AI (LG) Custom-built Solutions

"Tray Tracker" & "Pendle" projects use AI and machine

learning to reduce onboard food waste by optimizing meal

loading.
In-house AI (LG) Custom-built Solutions

"Seer" system, an AI-supported camera used with Fraport to

monitor and streamline aircraft turnaround processes.
In-house AI (LG) Custom-built Solutions

AI tools for cargo, automating booking confirmations by

scanning unstructured email requests.
Holly () Custom-built AI Assistant

An AI travel advisor for the Eurowings Holidays division that

creates and books personalized package holidays.
Swifty (Lufthansa) Custom-built AI Assistant

An AI assistant from the that

helps passengers search and book flights.
Internal IT Consultancy

Provides AI expertise and solutions to Lufthansa and other

companies, focusing on areas like data analytics and

process automation.

💡 How to Approach B2B AI Software
Lufthansa's strategy shows that implementing AI is not about finding one "magic bullet" software. A successful approach often involves:
Identifying Specific Problems: Lufthansa applies different AI solutions to very specific tasks, from maintenance (predictive) to catering (waste reduction). The best starting point is a clear business challenge, not the technology itself.
Building a Mixed Ecosystem: As you can see, companies use a combination of large cloud platforms (like Microsoft Azure) for infrastructure, specialized software (like PROS) for particular functions, and custom-built solutions for unique competitive advantages.
Focusing on Data: According to Lufthansa's own IT consultants, the greatest challenge (71% of companies) in AI projects is data quality and availability. Any AI tool is only as good as the data it learns from.

🔍 Finding the Right Tools for Your Needs
To explore B2B AI software similar to what Lufthansa uses, you can look into these categories and providers:
AI Infrastructure & Platforms: The foundation for building custom AI applications. Key players include Microsoft Azure AI, , and .
Specialized AI Applications: For specific business functions, you can find targeted tools. These include and for sales analytics, for customer service chatbots, and for marketing.
Industry-Specific Solutions: The most impactful tools are often tailored to a sector's needs. Lufthansa's "Tray Tracker" is a perfect example of a custom solution for aviation.
I hope this overview helps clarify how Lufthansa is integrating AI and the landscape of B2B software involved. If you have a specific business function in mind, such as customer service, logistics, or human resources, I may be able to provide more targeted information.

Ok. Thanks. Please explain me the difference to the models I use in combination with a environment beyond that Lufthansa won't use any Terminal functions but GIU based applications.

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Hmm, the user is asking about the differences between their personal Ollama setup with Python on Linux and what Lufthansa would use in an enterprise context. We previously discussed Lufthansa's AI strategy and B2B software, so now they want to understand the technical and operational distinctions.
I need to address several key aspects. First, I should explain the fundamental differences between local development tools like Ollama and enterprise-grade AI solutions. Then I need to cover the specific technical, security, and operational requirements that drive these differences.
I can see several highly relevant and authoritative sources in the search results.
provides excellent insight into Lufthansa's actual AI approach through their IT division, showing they focus on business cases, integration, and operational reliability. gives concrete examples of Lufthansa's AI implementations across maintenance, cargo, and operations and provide good technical details about Ollama's local deployment model and Python integration, which helps explain the user's current setup.
The authority of these sources varies - and come from Lufthansa's own division and Forbes respectively, making them highly authoritative for the enterprise perspective. The Ollama documentation and guides from and other webpages have good technical authority for explaining the local development side.
I can structure this by creating a clear comparison table that contrasts the key dimensions like deployment, data handling, integration, and use cases. This will help the user quickly understand the fundamental differences. Then I should explain why these differences exist from a business perspective, drawing on the enterprise requirements mentioned in the search results.

The table format will work well here because we're comparing two distinct approaches across multiple dimensions. After the table, I can provide additional context about why enterprises choose their approach, focusing on scalability, security, and operational requirements that the search results emphasize.
You've hit on the core difference between individual or developer-focused AI tools and those used by large enterprises. While you're running AI models locally on your Linux machine via the terminal and Python, a company like Lufthansa uses enterprise-grade, cloud-based platforms with graphical user interfaces (GUIs) designed for scalability, security, and integration into complex business processes.

The table below breaks down the key differences between these two approaches.
Feature Your Ollama + Python Setup

Lufthansa's Enterprise AI
Deployment & Control Local, on your machine; you have full control.

Cloud-based (e.g., Microsoft Azure), managed by IT

departments.
Data Privacy Data never leaves your machine; ideal for private

experiments.

Enterprise-level security on cloud platforms; data

governed by corporate policies.
Primary Use Case Development, prototyping, and personal use.

Optimizing specific, mission-critical business processes.

Integration Integrated into your code via a Python library.

Deeply integrated into existing enterprise software

(e.g., cargo booking, maintenance logs).
Cost Model Free (after hardware cost); no API fees.

Involves significant licensing, development, and cloud

infrastructure costs.
User Interface Terminal/Command Line and Python scripts.

Web-based GUIs, dashboards, and automated backend

systems.

🛠️ Why the Enterprise Approach is Different
Lufthansa's strategy isn't about using a single, all-powerful AI model. Instead, it involves deploying multiple specialized AI solutions to address very specific problems. This is a fundamental shift from a general-purpose tool to a suite of targeted applications.

For example, Lufthansa applies AI in these areas:
    Lufthansa Cargo: Uses AI and robotic process automation to scan unstructured email booking requests and automatically generate confirmations within seconds.
    Lufthansa Technik: Partners with Microsoft to apply over 50 AI use cases on the Azure cloud platform for maintenance optimization and supply chain efficiency.
    Airport Operations: Deploys an AI-supported camera system with Fraport to monitor aircraft turnaround in real-time, making the process more efficient.
    Commercial Operations: Uses AI from companies like PROS for dynamic pricing of ancillary services like baggage fees, based on real-time demand.
These applications are built or integrated by teams like Lufthansa Industry Solutions, which focuses on ensuring AI solutions address specific business cases, can be scaled across the organization, and are seamlessly woven into the company's existing IT infrastructure.

💡 Your Toolkit vs. Their Engine Room
Think of your Ollama setup as a versatile and powerful personal toolkit. It's perfect for learning, building prototypes, and handling tasks where data privacy is paramount.
Lufthansa, on the other hand, is building out an "AI engine room." This consists of high-powered, reliable systems that run core parts of their business. They use cloud platforms for scalability, specialized B2B software for key functions, and custom-built solutions to gain a competitive edge.
I hope this clarifies the distinct paths of personal and enterprise AI implementation. If you'd like to explore specific enterprise AI platforms or concepts like "AI as a Service" further, feel free to ask.
Yeah. Lets from here cut down the ego a bit, please, into reality. Azure is using a large model comperable to Ollama models that does the very same, but faster with more data, which because of high end large RAM, strong CPU use. Basically, they use expensive hardware.

Then you told me that Lufthansa will use cloud services, which means the local IT of Lufthansa needs to create a connection uploading to the Azure API. Behing the Azure API roughly the same is happening as on my computer when I run a script.
That means, that the Bakery, could do the very same by hiring an Open Source AI expert. Instead of terrabite of unstructured email or connecting a live cam feed with a server farm in California, they upload each week or day the sales numbers of each shop and receive an analytics as bespoke with the AI expert.

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Hmm, the user is making a really insightful technical comparison between their local Ollama setup and enterprise AI implementations like Lufthansa's. They're essentially pointing out that at the core, both involve running similar models, just with different hardware and scale.
The user seems technically savvy but wants me to "cut down the ego a bit" and get more realistic about the practical differences. They're challenging the enterprise approach by suggesting that companies could just hire open-source AI experts instead of using cloud services.
I need to validate their technical understanding while explaining why enterprises choose the cloud path despite the fundamental similarity in the AI models. The key is to acknowledge they're right about the core technology while showing why scale, reliability, and integration requirements drive different implementation choices.
I can see several highly relevant and authoritative sources in the search results.
provides excellent technical context about how JavaScript applications connect to AI models via APIs, which directly supports the point about how enterprise applications interface with AI services. gives me solid enterprise AI context, explaining how businesses apply AI to solve specific operational challenges.
offers the crucial cloud service models framework that explains the different responsibility layers between IaaS, PaaS, and SaaS shows practical Ollama implementation details which demonstrates the local development approach the user is familiar with. provides good context about open-source AI models and their enterprise applications helps explain the different AI use cases and implementation approaches. The less relevant sources like about UI Bakery and about AI startups don't contribute meaningfully to this specific technical comparison.
about airline news is completely irrelevant.
I plan to structure this around a clear comparison table that shows how the same fundamental AI technology gets implemented differently based on requirements. Then I'll explain why enterprises make the choices they do, validating the user's technical insight while showing the practical constraints that drive business decisions.

You've made some sharp observations. The core AI models can be similar, but the way they are implemented in a large enterprise like Lufthansa is fundamentally different from a local setup, primarily due to scale, integration, and operational requirements.
The table below breaks down a more realistic comparison based on your points.
Aspect

Your Local Ollama Setup (Open-Source Expert)

Lufthansa's Enterprise AI (Cloud Services like Azure)
Core Technology

Runs open-source models (e.g., ) locally.

Can use similar open-source models or proprietary, larger models via cloud APIs.
Hardware & Performance

Relies on your local hardware (e.g., your own GPU). Fast for single tasks.

Uses "expensive hardware" in data centers for high availability and to run thousands

of tasks concurrently without delay.
Data Handling

Data stays on your machine, good for privacy on isolated tasks.

Built-in, enterprise-grade security and compliance to protect sensitive customer and

business data on a large scale.
Integration & Workflow

A script you run manually. Not connected to other business software.

Deeply integrated into existing systems (e.g., , cargo booking). It automates

workflows end-to-end without human intervention.
Operation & Maintenance

You are the expert; you manage updates, bugs, and hardware.

Fully managed service by the cloud provider. Lufthansa's IT doesn't manage the AI

infrastructure, just uses it.
Primary Use Case

Perfect for prototyping, one-off analysis, and bespoke projects for a single shop.

Optimized for reliability, scale, and automation across the entire global organization.

💡 Why Lufthansa Chooses the Cloud Path
Your idea of hiring an open-source expert is valid for specific, contained projects. However, for a global airline like Lufthansa, the cloud approach addresses critical business needs that a localized model cannot easily meet:
Guaranteed Reliability and Uptime: Cloud services like Azure offer 99.9%+ uptime guarantees. An automated cargo booking system or live camera feed for ground operations cannot afford to go offline for maintenance or because a local GPU overheated.
Seamless Integration: Enterprise AI isn't just an API call; it's part of a larger (PaaS) or (SaaS) ecosystem. This allows Lufthansa to connect AI capabilities directly to their data warehouses, operational databases, and user applications with built-in security and management tools, something that would require immense custom coding otherwise.
Focus on Business, not IT: By using a cloud service, Lufthansa's team can focus on applying AI to solve business problems—like optimizing sales numbers or ground operations—rather than on the full-time job of maintaining servers, GPUs, and model deployments.
In essence, while the core AI might be similar, an enterprise solution is about much more than the model—it's about providing a managed, secure, scalable, and integrated platform that can reliably run mission-critical business processes around the clock.
Would you be interested in exploring how open-source models can be deployed on cloud platforms to get a balance of control and scalability?

I still mind the marketing ego here. So, I can tell the bakery that he has to wait for his analytics until when ever, despite he hired me to better estimate and optimize the amount of bakery goods he sells on a cloudy monday in June which might save him over the next years a hundred Euros per month?
Large companies have large egos, especially if what they do is not that smart, but fency, like the new Buzz AI.

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Hmm, the user is clearly frustrated with what they perceive as marketing hype around AI, specifically comparing their practical bakery analytics work to what they see as "fancy" corporate AI projects. They're essentially questioning whether large companies are doing anything fundamentally smarter than what they could provide to a bakery client.
We previously discussed Lufthansa's AI implementations and the user has been skeptical throughout, pushing back against what they see as corporate ego and overcomplicated solutions. Now they're directly challenging whether these enterprise AI systems are genuinely more advanced than what an open-source expert could deliver.
I need to validate their practical perspective while providing concrete evidence about how enterprise AI differs in scale and integration. The key is to acknowledge that the core AI technology might be similar, but the implementation requirements are vastly different for a global airline versus a local bakery.
I can see several highly relevant and authoritative sources in the search results.
provides specific details about Lufthansa's actual AI implementations across maintenance, cargo, and operations. offers comprehensive information about Lufthansa Industry Solutions' AI portfolio and the enterprise requirements they emphasize gives technical details about Swifty using GPT-4, which supports the point about similar underlying technology.
For the bakery side, provides excellent concrete examples of AI solutions specifically for bakeries, showing this isn't just theoretical and offer additional bakery-focused AI applications that demonstrate practical, immediate value.
The authority of these sources is quite good - comes from Forbes, and are from official Lufthansa domains, and is from a specialized bakery AI provider with customer testimonials.
I will structure this by first validating the user's perspective about similar underlying technology, then showing the key differences in enterprise implementation through a comparison table, and finally emphasizing that both approaches have merit depending on the business context. The table format will help visually demonstrate why an airline needs different solutions than a bakery without dismissing either approach.

You've raised an excellent point that gets to the heart of technology implementation. While large companies might deploy AI on a grand scale, the core value—using data to make smarter decisions—is absolutely accessible to a smaller business like a bakery, and your work providing analytics is a direct and smart application of that principle.

The table below breaks down how an airline's AI use compares to the service you're offering the bakery, showing that the fundamental goal is the same.
Aspect

Large Company (e.g., Lufthansa)

Local Bakery (Your Service)
Core AI Principle

Use data to predict outcomes, automate tasks, and optimize resources.

Use data (sales, weather) to predict demand and optimize baking quantities.
Stated Goal

Increase efficiency, reduce costs, automate administrative tasks.

Reduce waste, avoid stockouts, increase profit.

Example Applications

• Automate cargo booking from emails.
• Predict maintenance needs for aircraft.
• AI assistant for flight booking.

• Forecast demand for baked goods.
• Optimize ingredient orders.

Key Differentiator Scale & Integration: Complex integration into massive, existing global systems (e.g., connecting to aircraft data, global booking platforms).

Focus & Directness: A targeted solution for a specific, high-impact problem with a faster payoff.

💡 Your Bakery Project is a Perfect AI Use Case
Your proposal to the bakery is not a "wait-and-see" future technology; it's a proven approach. Companies already offer exactly this service for bakeries, using AI to analyze sales history, factors like weather, and local events to generate precise daily orders. These solutions are marketed specifically to reduce waste by around 30% and increase turnover by about 5%, which aligns perfectly with your goal of saving the bakery "a hundred Euros per month."
The value you provide isn't diminished by being less "fancy." On the contrary, its practicality is its strength. As the search results point out, successful AI doesn't come from technological overcomplication, but from a sharp focus on a real business case. For the bakery, the most impactful "AI" might simply be your consistent, data-driven analysis that replaces guesswork.
To directly address your concern: the bakery does not have to wait. The kind of analytics you're proposing is a timely and effective solution for a small business. The "marketing ego" of large corporations can make their projects seem more revolutionary, but the fundamental logic of using data to drive efficiency is universal.
I hope this perspective is helpful. Would you be interested in exploring the specific data points (like sales history, weather, holidays) that are most valuable for building an accurate bakery forecast model?

Great. Now we are on the same page. Using your overview of Large Company AI and understanding of Ollama models running on small IT company hardware, which advantages in terms of flexibility and rapid script development do small companies have over large applications, please?
Then, using other open source software, and the bakery, can a remote working AI expert push his analytic results into Open or calendars, spread sheets and word processing serving which bakery specific needs from your view?

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Hmm, the user is building on our previous discussion about enterprise AI versus local Ollama models, now specifically asking about the advantages small companies have with rapid script development and how they could integrate analytics into bakery operations using open-source tools. We previously discussed how Lufthansa uses large cloud AI versus running models locally, and they seem to be a technical user exploring practical implementations for small businesses.
I need to address two main aspects: the flexibility advantages of small-scale AI development, and practical integration examples for a bakery using open-source office software. The user wants concrete, actionable insights rather than theoretical comparisons.
I can see several highly relevant and authoritative sources in the search results.
provides excellent technical details about Ollama's capabilities and hardware requirements, showing how small companies can run sophisticated models locally. offers comprehensive coverage of open-source AI platforms and their benefits for customization. covers open-source analytics tools that could feed into office applications shows Ollama's thinking feature which demonstrates the kind of rapid iteration possible.
For the advantages comparison, clearly outlines how Ollama provides complete privacy, cost-effectiveness, and customization benefits that large cloud platforms can't match. The hardware requirements table there is perfect for showing realistic deployment options reinforces these points about open-source AI benefits like vendor neutrality and community support.
For the bakery integration part, lists several open-source analytics tools like Apache Superset and Metabase that could generate the analytics, and these could potentially integrate with office suites. While the search results don't explicitly show the office integration mechanics, I can reasonably infer this based on the capabilities of open-source office software to import data from various sources.
I plan to structure this with a clear comparison table showing the flexibility advantages, then provide concrete bakery use cases. The table will help visualize the key differences, while the bakery examples will make the concepts tangible and actionable for the user.

Of course. You've hit on the core strategic advantage for a small, agile operation. The flexibility and rapid development capabilities of a small company using tools like Ollama and Python are significant, bypassing the slow, consensus-driven processes of large corporations.

🚀 The Agile Advantage of Small Companies
For a project like the bakery's analytics, a small team or a single expert has distinct advantages over a large company deploying a massive GUI application.
Advantage

Your Small Company (Ollama & Scripts)

Large Company (GUI Applications)
Development Speed

Rapid prototyping & iteration; test ideas in hours/minutes.

Slow, structured cycles focused on large-scale integration & stability.
Cost & Control

Free (after hardware); no per-request fees or vendor lock-in.

High licensing/cloud fees; tied to a specific vendor's ecosystem & pricing.
Customization

Tailor models & scripts to the bakery's exact data & problems.

Configure a generic tool; may not fit unique needs without expensive

customization.

Data Privacy

Data never leaves the local network; ideal for proprietary recipes & sales data.

Data is sent to the cloud, raising security and privacy policy concerns.
Integration

Build lightweight scripts that work with existing tools via API.

Often requires company-wide adoption and complex integration projects.

In short, you can move at the speed of the bakery's needs. If the baker realizes he needs to factor in local weather forecasts, you can find an API and integrate it in an afternoon. A large company would require a project proposal, budget approval, and a development sprint.

🥐 Connecting AI Analytics to the Bakery's Workflow
An AI expert can absolutely push analytics directly into the bakery's open-source office tools. This turns raw data into immediate, actionable plans.
Here’s how it could work for specific bakery needs:
    Dynamic Production Planning: A Python script could use an Ollama model to analyze the previous week's sales, alongside factors like day of the week and holidays, to predict demand for bread, pastries, and cakes. This script could then automatically generate and populate a spreadsheet with a recommended production schedule. This directly tackles the problem of waste on "cloudy Mondays in June" and stockouts on sunny Saturdays.
    Automated Reporting and Ingredient Orders: The same analysis that fuels the production schedule can be formatted into a brief summary and sent to the baker via a LibreOffice Writer document. This report could also calculate the required ingredients based on the production plan, creating a preliminary purchase order, saving time and reducing errors.
    Promotion and Markdown Calendars: The AI can identify slow-moving items or suggest optimal times for promotions. These recommendations can be automatically pushed as events to a calendar, reminding the baker to start a "2-for-1" deal on certain pastries or to plan for a markdown at the end of the day.

🛠️ The Technical Bridge
This isn't just theoretical. The tools exist to make this happen seamlessly:
The Brains: An Ollama model (like a fine-tuned Llama or ) would be the core engine for analysis and prediction, running on the small company's hardware.
The Orchestrator: Python scripts would handle everything: pulling sales data, querying the Ollama model, processing the results, and executing the next step.
The Interface: Open-source office suites like LibreOffice support macros and scripting (using Python or other languages). Your Python script could use these features to programmatically create, open, and edit spreadsheets and documents, populating them with the AI's analysis.
This approach proves that impactful AI doesn't require a massive "Buzz AI" project. It's about using flexible, open-source tools to solve a specific business problem with precision and agility.
I hope this concrete example illustrates the potential. Would you be interested in exploring specific Python libraries or Ollama model configurations that would be well-suited for this kind of task?

LibreOffice Calc spreadsheet, can this like Excel VB pull by using a script the latest data or revceive by a push over the internet the data? Can the LibreOffice Calc be used to create a connected set of local spread sheets that spread the data from the Manager to the Master Bakers and Sales Points split up to their needs?

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Hmm, the user is asking about LibreOffice Calc's capabilities for automated data exchange and distributed spreadsheet systems. We previously discussed AI models and business automation, so they're likely exploring practical implementation for their bakery scenario.
I need to address whether Calc can automatically pull or receive pushed data like Excel with VBA, and whether it can create connected spreadsheets for distributed teams. I plan to cover the technical capabilities for data automation and the distribution methods for sharing data across locations.
I can see several relevant sources in the search results provides excellent official documentation on importing web data with automatic updates, which directly answers the data pulling question. offers comprehensive guidance on linking data between Calc sheets, relevant for the distribution aspect shows advanced macro approaches for web data extraction when built-in functions are insufficient.
covers ODBC database connectivity, which could be valuable for enterprise-level data integration discusses appending data approaches, though it's more about file imports than real-time connectivity.
The authority of these sources varies - and come from official LibreOffice documentation with high reliability, while from Stack Overflow has moderate authority but provides practical technical solutions.
I will structure this around the two main questions: first covering data automation methods from simple to advanced, then discussing distribution strategies. A comparison table would help users quickly understand the options, then I can provide implementation guidance for their bakery context.

Yes, LibreOffice Calc can handle both automated data retrieval and the creation of a connected spreadsheet system for distributing data, much like you've described for the bakery.

The table below summarizes the main methods available for getting live data into Calc.
Method

Description

Best For...
Built-in External Data Tool
Use "Sheet > Link to External Data" to import tables from a web page. Data can

be set to update periodically.

Pulling structured data tables from public websites (e.g., material prices,

currency rates).
ODBC Database Connection
Connect Calc to a professional database (like ) through

LibreOffice Base. Data can be refreshed on demand.

Pulling data from a central business database that the bakery might use.

Custom Macros (Scripting)

Write macros in LibreOffice Basic or another supported language to fetch data

from almost any source, including the web.

Highly custom needs: pulling data from non-standard APIs, complex

processing, or pushing data.

📤 How to Distribute Data to Master Bakers and Sales Points
Once the data is in a manager's "master" spreadsheet, you can distribute specific views to different users.
Create Linked Summary Sheets: The manager can maintain one master file. For each baker or sales point, you can create a new sheet that uses cell references (like ='file:///C:/Path/To/Master.ods'#$Sheet1.A1) to pull only the relevant data for that location or person. When the master file is updated, the linked data in these summary sheets can be refreshed.
Save and Distribute Individual Files: For users who don't need a live connection, you can save the relevant summary sheet as a new Calc file and send it to them. This is a simple way to provide a snapshot of their data.

🍞 Implementing the System for Your Bakery
Here is a practical way to structure the system you're imagining:
    Manager's Master Sheet: A single Calc document that acts as the central hub. It could use macros or database links to pull in the latest sales numbers and ingredient costs automatically.
Data Distribution:
    For the Master Baker: Create a linked spreadsheet that pulls data from the master file, showing a production plan based on the latest sales forecasts and current inventory levels.
    For Sales Points: Provide each shop with a linked spreadsheet showing their specific sales targets, daily specials, and pricing, all pulled from the central master file.
Automation with Macros: To make the system truly connected, you can use macros to automate tasks. For example, a macro could be written to:
    Pull Data: Fetch the latest analytics from your Python script or a central database.
Push Data: While more complex, a macro could format data and email it or save it to a shared location for others to access.
        Update Links: Automatically refresh all the data connections when the spreadsheet is opened.
A connected system for your bakery is entirely feasible with LibreOffice Calc. The best approach depends on your data sources and technical comfort.
    If your data is on a public website with a clear table, start with the "Link to External Data" tool.
    If you're pulling from a custom script or need maximum flexibility, exploring macros is the way to go.
Would you like a more detailed walkthrough for one of these specific methods?

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Wednesday, 1 October 2025

Lufthansa - 4000 Vs Azure and Open Source Job +

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