How I Automated Sales KPI Reporting with n8n and Cut 99% of Manual Work
Author(s): Apoorvavenkata Originally published on Towards AI. Every sales organization depends on understanding which distributors perform best and which items drive the most volume. Yet in many analytics teams, this information remains trapped in spreadsheets, requiring manual cleanup, complex formulas, pivot tables, …
How to Craft a Strong AI/ML Thesis Statement
Author(s): Ayo Akinkugbe Originally published on Towards AI. Defining Scope, Hypotheses, and Contribution Boundaries for Clarity, Testability, and Impact in AI & ML Research Photo by Omar:. Lopez-Rincon on Unsplash Snapshot A thesis statement is the central claim of your dissertation or …
I Built CommitRecap so Your GitHub Year Reads Like a Story
Author(s): Kushal Banda Originally published on Towards AI. CommitRecap GitHub shows totals and a grid. You already know you wrote code this year; what you want is the story behind it. CommitRecap turns a username into a guided recap that feels personal, …
LLM & AI Agent Applications with LangChain and LangGraph — Part 21: Vector Database and Embeddings
Author(s): Michalzarnecki Originally published on Towards AI. Hi! In this chapter I’ll explain what is the purpose of using vector databases in LLM-based applications and why embeddings are so important in natural language processing. There are multiple database engines that support data …
LLM & AI Agent Applications with LangChain and LangGraph — Part 4 — Components of GPT
Author(s): Michalzarnecki Originally published on Towards AI. Transformers, embeddings and attention: how modern LLMs really think Welcome back in the series related to LLM-based application development. By now you already know the basics of how LLMs are built and what their key …
LLM & AI Agent Applications with LangChain and LangGraph — Part 3: Model capacity, context windows, and what actually makes an LLM “large”
Author(s): Michalzarnecki Originally published on Towards AI. Welcome in next chapter in the series about LLMs-based application development. To this point we already have some basic intuition about how large language models work. Now I want to go one level deeper and …
LLM & AI Agent Applications with LangChain and LangGraph — Part 2: What is a machine learning model and what makes LLMs special?
Author(s): Michalzarnecki Originally published on Towards AI. Welcome in next chapter in the series about LLMs-based application development. In this part I want to clarify two things that appear constantly in any discussion about AI: what a machine learning model actually is, …
LLM & AI Agent Applications with LangChain and LangGraph — Part 1: How LLMs become so important in modern app development
Author(s): Michalzarnecki Originally published on Towards AI. Welcome to the first part of this series. In this part I want to take a step back from LangChain, LangGraph and coding, and focus on the foundations. We will look at the main ideas …
Deep Compression, 2015: How Much More Can We Squeeze in 2025?
Author(s): Vasyl Rakivnenko Originally published on Towards AI. Image generated with ChatGPT-5.2 It may be hard to believe, but compression of Neural Networks was already an important topic more than 25 years ago. Yann LeCun, in his paper Optimal Brain Damage, published …
The AI Industry Is Eating Itself: Nvidia’s $20B Power Play, the End of Scaling, and the $2 Trillion Question
Author(s): Zoom In AI Originally published on Towards AI. Two stories from the past few weeks reveal an AI ecosystem at war with itself. The chips are winning. The economics might not. The Setup: What Just Happened On Christmas Eve 2025, while …
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