Image by Author DeepSeek-R1-0528 is the latest update to DeepSeek’s R1 reasoning model that requires 715GB of disk space, making it one of the largest open-source models available. However, thanks to advanced quantization techniques from Unsloth, the model’s size can be reduced to 162GB, an 80% reduction. This allows users to experience the full …
in an enterprise organization, you’ve probably felt the paradox firsthand. AI dominates your strategic decks, fills your review meetings, and weaves into roadmap discussions. However, when you actually try to turn these AI visions into practical solutions, you’re often left wondering: What’s actually working? Where do we start to see measurable value of AI? Right …
Not Everything Needs Automation: 5 Practical AI Agents That Deliver Enterprise Value Read More »
Image by Author | Canva When it comes to error handling, the first thing we usually learn is how to use try-except blocks. But is that really enough as our codebase grows more complex? I believe not. Relying solely on try-except can lead to repetitive, cluttered, and hard-to-maintain code. In this article, I’ll walk …
5 Error Handling Patterns in Python (Beyond Try-Except) Read More »
is magical — until you’re stuck trying to decide which model to use for your dataset. Should you go with a random forest or logistic regression? What if a naïve Bayes model outperforms both? For most of us, answering that means hours of manual testing, model building, and confusion. But what if you could automate the entire …
How I Automated My Machine Learning Workflow with Just 10 Lines of Python Read More »
Image by Author | Ideogram Reinforcement learning algorithms have been part of the artificial intelligence and machine learning realm for a while. These algorithms aim to pursue a goal by maximizing cumulative rewards through trial-and-error interactions with an environment. Whilst for several decades they have been predominantly applied to simulated environments such as robotics, …
ContentsSafety and responsibilityNative audio capabilities for developers Safety and responsibility We’ve proactively assessed potential risks throughout every stage of the development process for these native audio features, using what we’ve learned to inform our mitigation strategies. We validate these measures through rigorous internal and external safety evaluations, including comprehensive red teaming for responsible deployment. Additionally, …
datasets and are looking for quick insights without too much manual grind, you’ve come to the right place. In 2025, datasets often contain millions of rows and hundreds of columns, which makes manual analysis next to impossible. Local Large Language Models can transform your raw DataFrame statistics into polished, readable reports in seconds — minutes …
LLMs + Pandas: How I Use Generative AI to Generate Pandas DataFrame Summaries Read More »
Join Nolan Fortman and Logan Kilpatrick as they sit down with legendary investor and builder Anjney Midha to chat all things AI. From foundation model progress, to evals, to how to find the next generation of AI enabled products, Anjney shares his deep insights around AI as one of the critical figures in the ecosystem.
I that most companies would have built or implemented their own Rag agents by now. An AI knowledge agent can dig through internal documentation — websites, PDFs, random docs — and answer employees in Slack (or Teams/Discord) within a few seconds. So, these bots should significantly reduce time sifting through information for employees. I’ve seen …
Agentic RAG Applications: Company Knowledge Slack Agents Read More »
is a game changer in Machine Learning. In fact, in the recent history of Deep Learning, the idea of allowing models to focus on the most relevant parts of an input sequence when making a prediction completely revolutionized the way we look at Neural Networks. That being said, there is one controversial take that I …
Hands-On Attention Mechanism for Time Series Classification, with Python Read More »
