Write down which model you are using for which purpose?

For Anyone who is working on Microsoft Power BI and is tired of Doing DAX and data modeling, Check this out...

Its an MCP that you can Connect to your Ai model locally or on Cloud (like Claude) and just text what you want to do! It will literally Connect to you Power BI, do analysis and execute the code live and even do debug if wrong!!!

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Next step is to build the MCP to do the design for you, and you will already be doing a fully Done Power BI

For Anyone interested in VLM check this blog it will help you understand the implications simply

https://towardsdatascience.com/how-to-apply-vision-language-models-to-long-documents/

𝕋𝕙𝕚𝕤 𝕨𝕖𝕖𝕜 𝕛𝕦𝕤𝕥 𝕜𝕖𝕖𝕡𝕤 𝕘𝕖𝕥𝕥𝕚𝕟𝕘 𝕓𝕖𝕥𝕥𝕖𝕣🔥

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Credit: Miklol

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The thing about Financial Services in Ethiopia is that Consistency is key...

Was about to pay using Santim pay at a Cafe and the Waiters just told me that it won't work... bec customers has experienced system errors and lags....

Now just bec of few bad exp people won't even try... 🤷‍♂

Anybody tried to pay using Santim Pay recently...what was your experience...

Devtopia is back.

We talked about what we were up to for the last 6 months. We are planning to do one episode per two weeks.

We also have an editor now, Bisrat, so hopefully this will help us maintain our release schedule.

[Devtopia Interlude E04]

When did CBE start Digital Marketing😂 seriously...?

I think we are done for today...
All tests passed ✅️

Hode Yabawn Chatgpt Yawetawal

~ YG

Agentic Gov Era 🤔

Even the Government is planning to apply Agentic systems in Abu Dhabi...

$2.3B🤯

✅ 25 AI & Machine Learning Abbreviations You Should Know 🤖🧠

1️⃣ AI – Artificial Intelligence: The big umbrella for machines mimicking human smarts, from chatbots to self-driving cars.

2️⃣ ML – Machine Learning: AI subset where models learn from data without explicit programming—think predictive analytics.

3️⃣ DL – Deep Learning: ML using multi-layered neural nets for complex tasks like image recognition.

4️⃣ NLP – Natural Language Processing: Handling human language for chatbots or sentiment analysis.

5️⃣ CV – Computer Vision: AI that "sees" and interprets visuals, powering facial recognition.

6️⃣ ANN – Artificial Neural Network: Brain-inspired structures for pattern detection in data.

7️⃣ CNN – Convolutional Neural Network: DL for images/videos, excels at feature extraction like edges in photos.

8️⃣ RNN – Recurrent Neural Network: Handles sequences like time series or text, remembering past inputs.

9️⃣ GAN – Generative Adversarial Network: Two nets competing to create realistic data, like fake images.

🔟 RL – Reinforcement Learning: Agents learn via rewards/punishments, used in games like AlphaGo.

1️⃣1️⃣ SVM – Support Vector Machine: Classification algo drawing hyperplanes to separate data classes.

1️⃣2️⃣ KNN – K-Nearest Neighbors: Simple ML for grouping based on closest data points—lazy learner!

1️⃣3️⃣ PCA – Principal Component Analysis: Dimensionality reduction to simplify datasets without losing info.

1️⃣4️⃣ API – Application Programming Interface: Bridges software, like calling OpenAI's models in your app.

1️⃣5️⃣ GPU – Graphics Processing Unit: Hardware accelerating parallel computations for training big models.

1️⃣6️⃣ TPU – Tensor Processing Unit: Google's custom chips optimized for tensor ops in DL.

1️⃣7️⃣ IoT – Internet of Things: Networked devices collecting data, feeding into AI for smart homes.

1️⃣8️⃣ BERT – Bidirectional Encoder Representations from Transformers: Google's NLP model understanding context both ways.

1️⃣9️⃣ LSTM – Long Short-Term Memory: RNN variant fixing vanishing gradients for long sequences.

2️⃣0️⃣ ASR – Automatic Speech Recognition: Converts voice to text, like Siri or trannoscription tools.

2️⃣1️⃣ OCR – Optical Character Recognition: Extracts text from images, e.g., scanning docs.

2️⃣2️⃣ Q-Learning – Q-Learning: A model-free RL algorithm estimating action values for optimal decisions.

2️⃣3️⃣ MLP – Multilayer Perceptron: Feedforward ANN with hidden layers for non-linear problems.

2️⃣4️⃣ LLM – Large Language Model: Massive text-trained nets like GPT for generating human-like responses (swapped the repeat API for this essential one!).

2️⃣5️⃣ TF-IDF – Term Frequency-Inverse Document Frequency: Scores word importance in text docs for search/retrieval.

@datawithsimon