Keuzestress in het AI-tool landschap: waarom “meer tools” zelden de beste oplossing is

Steeds meer organisaties willen aan de slag met AI, automatisering en datagedreven werken. Dat is logisch: de kansen zijn groot. Toch ontstaat in de praktijk vaak verwarring. Waarom? Omdat het huidige landschap vol zit met tools die op elkaar lijken, elkaar overlappen of bijna hetzelfde doen, maar zich anders positioneren. De ene tool noemt het “AI-assistent”, de andere “workflow automation”, een derde “copilot”, terwijl de kernfunctionaliteit in veel gevallen sterk overeenkomt.

Het gevolg is herkenbaar: teams testen meerdere oplossingen tegelijk, licenties stapelen zich op, en na enkele maanden blijkt dat slechts een deel echt wordt gebruikt. Dat noemen we ook wel tool-overheat: te veel tooling, te weinig focus. Het kost niet alleen geld, maar ook tijd, adoptiekracht en overzicht.

Het echte probleem is meestal niet technologie, maar fit

De vraag is zelden: “Welke tool is het slimst?” De betere vraag is: “Welke oplossing past bij ons proces, onze mensen en onze doelstelling?” Een goede oplossing sluit aan op hoe jouw organisatie werkt. Zonder die aansluiting ontstaat frictie: extra handwerk, dubbel werk, onduidelijke eigenaarschap en teleurstellende resultaten.

In veel trajecten zien we dezelfde valkuilen:

• Overlapping functionaliteit: meerdere tools voor ongeveer dezelfde taak.
• Onnodige complexiteit: zware enterprise-software voor een relatief eenvoudige behoefte.
• Te snelle aanschaf: eerst kopen, daarna pas nadenken over proces en adoptie.
• Vergeten van alternatieven: betrouwbare freeware of bestaande tools in het huidige landschap worden niet meegewogen.

Pragmatische aanpak: eerst doel en proces, dan pas tooling

Voordat je nieuwe software aanschaft, is het verstandig om één stap terug te doen. Breng eerst het proces in kaart: waar zit tijdverlies, waar ontstaan fouten, welke stappen zijn repetitief, en wat moet de business-uitkomst zijn? Pas daarna vergelijk je tooling.

Een compacte aanpak die goed werkt:

• Stap 1 – Definieer de uitkomst: wat moet concreet verbeteren (tijd, kwaliteit, doorlooptijd, foutreductie)?
• Stap 2 – Inventariseer huidige tools: wat heb je al in huis dat 70–80% kan afdekken?
• Stap 3 – Beoordeel fit: gebruiksgemak, beheerbaarheid, beveiliging, integratie en totale kosten.
• Stap 4 – Start klein: pilot met duidelijke KPI’s in plaats van brede uitrol.
• Stap 5 – Schaal pas op na bewijs: eerst aantonen dat het werkt in de praktijk.

Freeware is niet “goedkoop”, maar vaak gewoon slim

Er bestaat nog steeds het misverstand dat alleen betaalde software professioneel genoeg is. In werkelijkheid kunnen betrouwbare freeware-oplossingen in veel situaties uitstekend functioneren, zeker in combinatie met goede inrichting en duidelijke afspraken. Dat kan licentiekosten aanzienlijk verlagen, zonder in te leveren op kwaliteit.

Natuurlijk is freeware niet altijd de juiste keuze. Voor sommige eisen (compliance, support, specifieke enterprise-integraties) is betaalde software noodzakelijk. Maar het punt is: maak die keuze bewust op basis van requirements, niet op basis van marketingclaims.

Conclusie

AI en automatisering leveren de meeste waarde op wanneer oplossingen passen bij jouw organisatie. Niet de langste toolstack wint, maar de best passende combinatie van proces, mensen en technologie. Door overlap te voorkomen, kritisch te selecteren en waar mogelijk betrouwbare freeware te benutten, voorkom je onnodige kosten en vergroot je de kans op duurzame resultaten.

Kort gezegd: minder ruis, meer richting. Kies niet voor “meer tools”, maar voor de juiste oplossing op het juiste moment.

Unlocking Python's Data Manipulation Capabilities with Pandas

Pandas is a Python library designed for data manipulation and analysis. It provides highly optimized, flexible, and powerful data structures making data exploration both easy and intuitive. In this article, we'll delve deep into the core functionalities of this library and illustrate how it can be used to perform a wide variety of tasks.

Dataframe: The Cornerstone of Pandas

The DataFrame is arguably the most important object in pandas and is directly derived from the data manipulation language SQL. It's a 2D labeled data structure with columns of potentially different types. A DataFrame allows you to store and manipulate tabular data, where each row corresponds to a measurement or observation, and each column corresponds to a variable or attribute of that observation.

Creating a DataFrame from Scratch

Creating a DataFrame from scratch in pandas is quite simple and can be done in several ways. One way is to create it from a dictionary.

Editing and Retrieving Data

Once our data is in a DataFrame, we can start manipulating it. For example, we can add columns, delete columns, rename columns, and perform various operations on the data.

Handling Missing Data

One essential part of data analysis is handling missing data. Pandas provides the fillna(), dropna(), and interpolate() methods that make dealing with missing data much more comfortable.

Unlock Your Business Potential with Python and Data Science

In an era where data reigns supreme, Python has emerged as a dominant force in data analytics. With its robust libraries and ease of understanding, Python has become the primary language for data scientists across the globe.

Why Choose Python for Data Science?

For starters, Python is a versatile language that is easy to learn and use. It is widely used for its simplicity and readability, enabling developers to construct programs with fewer lines of code than would be possible in languages like C++ or Java.

Python’s Mighty Libraries

Python boasts a number of libraries suited for data science, such as Pandas for data manipulation, Numpy for numeric operations, Scipy for scientific computation, Matplotlib for visualization, and Scikit-learn for machine learning. These libraries make it easier for data scientists to analyze, visualize, and create predictions from data.

Real-World Use Cases

Python’s capabilities extend beyond theory – it is adopted in real-world data science applications, from predicting customer churn to designing dynamic pricing models. For instance, Spotify uses Python to suggest songs you’d like, and Netflix uses it to recommend movies based on your viewing history.

Conclusion

In conclusion, Python, with its easy readability, wide range of libraries, and real-world applications, makes data science accessible and effective. Consider leveraging Python in your next data science project, and unlock your business' potential.

PyTorch 1.10: A New Generation of Deep Learning

Python's popular open-source machine learning library, PyTorch, has released version 1.10, marking significant advancements in its capabilities. This version brings improvements both in functionality and ease of use, offering developers increased flexibility for creating complex models.

What's New in PyTorch 1.10?

PyTorch 1.10 introduces TensorBoard Profiler, which allows developers to profile their PyTorch models in TensorBoard. This new feature provides visual insights into how your model runs on GPU, helping spot bottlenecks in computation and memory usage.

Enhanced Distributed Data Parallel (DDP)

The new bfloat16 support in DDP enables more efficient training on Nvidia's latest Ampere GPUs, reducing memory consumption without a significant impact on training accuracy.

Building More with TorchServe

With this update, TorchServe now supports custom batch handlers, expanding the possibilities for tailoring its functionalities according to your use-case.

Conclusion

With the significant improvements witnessed in PyTorch 1.10, it's clear that deep learning development in Python is on a fast track. This release not only increases PyTorch's versatility but also improves its accessibility, making it easier than ever to implement deep learning models.

Flask has emerged as one of the most popular Python web frameworks, known for its simplicity and flexibility. Unlike Django, which is a full-stack framework, Flask provides the bare minimum for web development, allowing developers to pick and choose the extensions they need for their project.

This lightweight approach makes Flask an excellent choice for small to medium web applications, microservices, and RESTful APIs. Its ease of use and straightforward syntax have garnered a large community of developers, contributing to a rich ecosystem of extensions and tutorials.

Key Features:

• Minimalist design and easy to learn.
• Extensibility with numerous extensions.
• Integrated support for unit testing.
• RESTful request dispatching.
• Uses Jinja2 templating.

Whether you're starting a new project or maintaining an existing one, Flask's unoppressive nature makes it a versatile tool in your development arsenal.

Python Web Development: A Deep Dive into Redis Queue System

Introduction

Welcome to our tech-savvy deep dive into the world of Python web development, focusing on the Redis Queue System. Redis, known for its lightning speed, is like the espresso shot of web development - quick, powerful, and gives your web applications the kick they need to handle tasks efficiently.

What is Redis?

Redis (Remote Dictionary Server) is an in-memory data structure store, used as a database, cache, and message broker. It's like a Swiss Army knife for web developers - versatile, efficient, and extremely useful in a pinch. Redis supports data structures such as strings, hashes, lists, sets, and more, making it a go-to for handling a variety of data needs.

Redis in Python Web Development

In Python web development, Redis is often used as a queue system. It's like having a personal assistant who organizes your tasks and ensures they're executed efficiently. Python, with its simplicity and elegance, paired with Redis, creates a formidable duo for handling background tasks in web applications.

Implementing Redis Queue

Implementing a Redis Queue in Python is straightforward. You need a Redis server and the Redis Python library. The process goes something like this:

import redis
from rq import Queue

r = redis.Redis()
q = Queue(connection=r)
result = q.enqueue(your_function, arg1, arg2)

This snippet is the "hello world" of Redis Queues. It's like teaching your application to say its first words, and those words are 'efficiency' and 'speed'.

Benefits of Using Redis Queue

• Speed: Redis operates in-memory, making data access lightning-fast.
• Scalability: Easily scale your task handling as your application grows.
• Flexibility: Supports various data types and structures.
• Reliability: Ensures task execution even in case of failures.

Use Cases

Redis Queue shines in scenarios like sending emails, handling file uploads, data processing, or any task that you don't want to slow down your user experience. It's like having a backstage crew in a theatre, ensuring the show goes on smoothly without the audience ever noticing the hustle behind the scenes.

Conclusion

In summary, incorporating Redis Queue into your Python web development toolkit is like adding a secret ingredient to your recipe. It enhances performance, reliability, and efficiency. So why wait? Dive into Redis Queue and watch your web applications perform like never before!

Happy coding!

Embrace the Digital Revolution with Tax Technology

While tax management is traditionally viewed as a complex, intricate field, the advent of tax technology is significantly simplifying this landscape. Tax technology, or taxtech, utilises digital solutions to automate complex tax processes, thereby reducing manual errors and increasing efficiency.

Why Move Towards Tax Technology?

Tax technology stands as a cornerstone of modern tax management. It eases tax compliance, enhances accuracy, and facilitates real-time reporting - all of which contribute to substantial savings in time and resources. Moreover, with data analytics capabilities, tax technology can offer deeper insights into tax data, enabling better decision-making for businesses.

Diving Deep into Tax Technology Tools

Amongst the myriad of tax technology tools, some of the most impactful ones include Automated Tax Software, Data Analytics tools, and AI-based solutions. Automated Tax Software simplifies tax calculations and filings, reducing the chance of manual errors. Data Analytics tools or platforms, on the other hand, facilitate the analysis of vast, complex tax data, providing valuable insights. Artificial Intelligence (AI) solutions, including machine learning and chatbots, can help in predicting tax trends and providing real-time tax advice, respectively.

How Companies are Leveraging Tax Technology

Companies across various sectors are leveraging tax technology to streamline their tax operations. E-commerce giants like Amazon and eBay use AI-based tax solutions to calculate sales tax in real time, ensuring accurate billing. Similarly, many accounting firms implement automated tax software to increase efficiency and improve client service.

The Exciting New Features in Python 3.9

Python 3.9 is here and brings a host of new features and significant improvements. From advanced operators for dictionaries to new string methods, Python 3.9 has much to offer.

Web Development: The Rise of JAMstack

In web development, JAMstack is gaining popularity as a modern web development architecture based on client-side JavaScript, reusable APIs, and prebuilt Markup. With great flexibility and speed, it's becoming the new norm for developing and deploying fast, scalable, and secure sites.

Demystifying Deep Learning: New Breakthroughs

In the field of Deep Learning, recent breakthroughs are changing how we approach complex, real-world problems. Techniques like Transfer Learning, GANs, and developments in model interpretability are transforming the landscape – offering more efficient, effective solutions.

Unveiling the Future with Python, Web Development and Deep Learning

Python continues to lead the way in the realm of programming languages. As of 2021, its ubiquity and versatility have solidified its position in the industry. The language’s latest iteration, Python 3.10, introduces pattern matching and enhanced error messages, among other features.

Revolutionizing Virtual Experiences with Modern Web Development Practices

Web Development, on the other hand, is embracing progressive web apps (PWAs) and accelerated mobile pages (AMPs). These technologies provide seamless user experience that's virtually indistinguishable from native mobile applications. Top it off with the increased focus on accessibility and SEO, and it's easy to see why businesses are investing heavily in cutting-edge web development tactics.

Pushing Boundaries with Deep Learning

Deep Learning is the rocket fuel behind advancements in AI. The field has made significant strides in a variety of sectors, including healthcare and autonomous vehicles. This branch of machine learning, powered by neural networks, mimics the human brain and truly opens up a world of opportunities. One of its latest triumphs is GPT-3, an exceptional AI model that exhibits human-like text generation.

Python 3.10: The Future of Web Development and AI

The Python Software Foundation recently released Python 3.10, a major update to its immensely popular programming language. The update brings a slew of features designed to enhance the efficiency and effectiveness of Python in various domains, including web development and Artificial Intelligence (AI).

Amped-Up Web Development

Python 3.10 makes web development sophisticated yet accessible. The introduction of Parental Scope References in Named Expressions is a sparkling feature. It allows developers to access the outer scope’s variables in named expressions - a facility not available in previous versions. This can lead to cleaner and more efficient code.

Boosting AI Capabilities

Python maintains its central position in AI and this latest update reaffirms it. The new Structural Pattern Matching feature carries immense potential for AI programming. It simplifies the parsing of complex data types, leading to rapid prototyping of AI models and more precise data pattern recognition.

Future Directions and Conclusion

Python 3.10 is a testament to Python’s commitment to continuous evolution to cater to the emerging needs of developers all over the world. With new web development features and bolstered AI capabilities, Python is ready to shape the future of technology.

Embrace Python - Embrace the Future

In the realm of programming, new advancements are being made relentlessly. However, one language that has stood the test of time and continues to cause waves in the world of programming is Python. Known for its simplicity and versatility, Python has become the dream assistant of many programmers and data scientists.

The Python-Deep Learning Synergy

Python's compatibility with advanced programming fields like deep learning is a case in point. As a high-level, interpreted programming language, Python makes complex tasks achievable in fewer lines of code which is why it is deemed ideal for deep learning applications.

A New Era of Web Development

And there is more. Python is also increasingly being used for web development – and with good reason. From building simple web applications to creating complex, dynamic websites, Python’s libraries and frameworks – such as Django and Flask – have you covered.

In the programming landscape, Python has distinguished itself as a go-to language for a wide range of applications, from web development to data science. With its newest version, a host of fresh enhancements and exciting features have been introduced, upping the coding game for developers worldwide.

The latest release has underscored Python's commitment to more intuitive coding with the additional pattern matching syntax. This development adds a new dimension to Python programming as it makes code dramatically easier to read and write. Especially when it comes to complex data structures, this enhancement can be a game changer.

Moreover, Python now boasts several improvements in terms of optimization. The performance is significantly boosted with new built-in methods for faster operations. While Python has been known for its easy-to-understand syntax rather than its speed, this update shows Python's progress in becoming a full-breadth programming language maintaining its unique convenience.

Python's latest release also showcases the Python developers' desire to make the language more accessible. Improved error messages, better debugging, and more comprehensive documentation all contribute to a more user-friendly experience.

As Python continues to evolve and disrupt the world of web development, data science, and beyond, its developers around the globe eagerly anticipate the next set of enhancements in the future releases.

Python is a high-level, interpreted scripting language developed in the late 1980s by Guido van Rossum at the National Research Institute for Mathematics and Computer Science in the Netherlands. It is a language designed to prioritize code readability, with clear, simple syntax and a robust standard library.

Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming. Due to its comprehensive standard library, Python is often described as a 'batteries included' language.

What makes Python incredibly powerful is its readability and simplicity, which make it the perfect language for beginners. It's also used by plenty of professionals in various fields, including web development, data science, artificial intelligence, and more. It's also heavily used in scientific computing, finance, and academic research.

Python Flask is a web framework that's gaining popularity for its simplicity and flexibility in the realm of web development. It's used for building small and large scale web applications. One of Flask’s main features is that it's lightweight and easy to get started with, making it increasingly popular among programming beginners and startups.

Flask’s simplicity doesn't imply that it lacks functionality, however. Its extensions are easily available that can add a myriad of functions, making it nearly as powerful as its competitor, Django. The Flask-SQLAlchemy, Flask-login, Flask-Bootstrap, and Flask-restful are some of the main extensions that make this transformation possible.

Why choose Flask?

Flask gives you more control over your project as it allows you to choose your own tools and libraries to include in the project. Its simplicity, flexibility and control make it a suitable choice for startups and smaller projects, and even for certain large, complex projects.

This (guide) / short story with information is made for somewhat advanced programmers and interested individuals. But feel free to send me an email if you have an idea, time and or money to invest. Let's start.

As always we start of with some basic Flask / PIP Python packages. The below set should be sufficient.

Flask==2.0.2
Flask-Admin==1.5.8
Flask-CKEditor==0.4.6
Flask-CacheControl==0.3.0
Flask-Login==0.5.0
Flask-Migrate==3.1.0
Flask-SQLAlchemy==2.5.1
openai==0.27.4
requests==2.28.1
werkzeug==2.0.3

1. Initiate your environment and install the above packages. Hereafter, create a new web application and create a view for your index page. 
2. Create your __init__.py and initiate the SQL db, CSRF, Login_Manager, and migrate your DB.
3. Configure your .env / app.config file 
4. Create a blueprint for at least the OpenAI GPT part. Register the blueprint in your __init__.py
5. Configure either an app.json + Procfile for Heroku or ZIP your stuff incl. a .git folder to PythonAnywhere. DigitalOcean should also be able to host your flask app. I am assuming you are able to deploy your application on a platform using GIT or another repository system/tool.
6. Think about an idea that could be handled through GPT.
7. Create a prompt that works well and come up with relevant questions
8. Create a page with a form, or use a third-party like JotForms
9. Handle the form request and gather the data from the user (use some front-end validation, etc. (ofc)). Tip: transform to json (python dict) and use .get(x, '').
10. Create your prompt from the form data
11. Create a view / function that posts your prompt to GPT, something like below;

 

def generate_gpt_response(prompt):
    openai.organization = app.config.get('OPENAI', False)
    openai.api_key = app.config.get('OPENKEY', False)

    try:
        response = openai.Completion.create(
            engine="text-davinci-003",
            prompt=prompt,
            max_tokens=1500,
            n=1,
            stop=None,
            temperature=0.0,
    )
    except Exception as e:
        print(f'something is going very wrong here... {e}')
        return 'error in response'

    print(response)

    try:
        text = response.choices[0].text.strip()
    except Exception as e:
        print(f'something is going very wrong here... {e}')
        return 'error in response'

    return text

The Why
Within Den Bosch (Netherlands), there is a community for young people interested in several subjects. Such as e-sports, making music / jamming, etc. For the e-sports community, I wanted to create a bot that serves as a ‘looking for a group’ in Discord. We all can have our own server and still hook up with people within the community for any game we want. After finishing the bot, I realized that sharing this could be helpful for training purposes, but also for other communities that would like to use this bot in a similar manner.

Short introduction to the bot
The bot is created to serve as a looking for a group function. Please refer to the README.md for the initial setup requirements. You should know how to run a Python script online, e.g. at Amazon, Google, PythonAnywhere, Heroku, Azure and so fort. Once setup, the bot can be invited to the different servers of people’s private communities that exists within your bigger community. Whenever a group/user is looking for players to join him, he can command the bot by using : ‘/zoek <game> <search for X players> <max of players>. The bot will post a Message in each servers ‘find-gamers’ channel with a link to the main server in which the bot will create a temporary voice channel for you. For more technical specs (e.g. what temporary means) refer to the README.md.

Keeping it short
You will need to have some knowledge on Discord and running an anonymous Python script online. I will however not explain this step-by-step. It feels like an overload on info looking at all the current articles / youtube clips and of course the official documentation. Also, I really find it boring, sorry😐. You could refer to:

• https://discord.com/developers/docs/getting-started
• https://medium.com/python-in-plain-english/how-to-make-a-discord-bot-using-python-4fee60648d3d

The code is made with help of the discord.py and discord versions=2.0.2 libraries that require a Python version >= 3.8.1/3. Here’s the link to it’s documentation:

• https://discordpy.readthedocs.io/en/stable/api.html#

Please note, that this is a 0.0.1 version. There are probably some exceptions left to cover, but it works fine for now!

Open source
You are free to modify and use the package to your own liking. You can download the package via GitHub.

• GitHub: https://github.com/CTAdevelopment/DBE-Discord-Bot
• Read:ttps://github.com/CTAdevelopment/os_discord_looking_for_group_bot/blob/main/README.md

Mastering BigCommerce Beta V3 Import: A User-Friendly Guide

Vulcan.eu who uses BigCommerce as their e-commerce platform recently transitioned to BigCommerce and wanted to import their extensive product database with over 500 articles. However, as luck would have it, BigCommerce had just switched to a beta V3 version of their product import system, and we encountered some hurdles. Unfortunately, due to a short timeframe, Vulcan was unable to reach the IT company that migrated them to BigCommerce, and BigCommerce support was also not available. This job would have taken several workdays to perform manually. This post will detail our experience, the solutions we found, and a step-by-step tutorial to help others facing similar challenges. So buckle up and enjoy the ride!

The Challenge: Importing Products in BigCommerce Beta V3

Our client's product database had to be imported into BigCommerce, which was easier said than done. The new beta V3 version of the product import system had limited documentation and lacked a comprehensive error log system. As a result, we had to manually debug the entire file, identifying and fixing over 400 bugs before finally succeeding in importing all products, complete with YouTube material and other specifications.

The Solution: A Step-By-Step Tutorial

To help others overcome similar challenges, we've put together a user-friendly tutorial to navigate the BigCommerce beta V3 import process. Here are the key steps to follow:

• Remove special characters: In name/string fields, make sure to remove all |, "", and other special characters.
• Rename duplicate rows: In the columns SKU and Name, ensure that duplicate rows are renamed.
• Include essential columns: At a minimum, include the following columns in your import file: ID, SKU, Name, Type, Categories, and Description.
• Be aware of column changes: Some standard columns may have changed from the V2 to the V3 batch import. Double-check your columns to avoid confusion.
• Define and cumulate custom column fields: Define your custom column fields, cumulate the data, and join the list item with a ' ; '.
• Optional YouTube link injection: If desired, create a separate function to inject a row for each product that contains a valid YouTube link in a defined column (e.g., YouTube ID, Item: Video).
• Custom field order: The order of custom fields (top to bottom, 0-end) determines how they will appear in the front-end. Arrange them accordingly.

Conclusion

Tackling the BigCommerce beta V3 import process may seem daunting, but with the right approach and a touch of patience, you can successfully import your products, complete with all the bells and whistles. We hope this user-friendly guide, sprinkled with a pinch of fun, helps you navigate the challenges and ensures a smooth import experience. Happy importing!