LLM & AI Agent Applications with LangChain and LangGraph — Part 12: Reasoning, ReAct, and Agents
Author(s): Michalzarnecki Originally published on Towards AI. In this chapter we will zoom in on “reasoning” in language models. My goal is that after this article it’s clear: which models actually plan and infer better, how that differs from the ReAct approach …
Your Brain Already Does Multimodal AI. It Took Us 10 Years And 7 Breakthroughs To Copy It.
Author(s): DrSwarnenduAI Originally published on Towards AI. See cat. Hear “cat”. Read “cat”. Same concept. Here’s every innovation that made GPT-4V possible. Close your eyes. I say “cat.” mimic human sensoryThe article discusses the advancements in AI, particularly focusing on the development …
LLM & AI Agent Applications with LangChain and LangGraph — Part 13: Multimodal Models
Author(s): Michalzarnecki Originally published on Towards AI. Hi! This time we’ll tackle a topic that has become massively important in the recent time: multimodal models. A lot of “classic” language models — like GPT-3 or early versions of LLaMA — work only …
The ML Evaluation Math You Can Actually Trust
Author(s): Akshat shah Originally published on Towards AI. Train/Val/Test, Cross-Validation, and Data Leakage Photo by Thomas T on Unsplash Machine learning isn’t just “a model that predicts things.” In the real world it’s a measurement process. You build a pipeline, and you …
The $14 vs $2 Plot Twist: Why GLM-4.7 Just Broke the AI Leaderboard
Author(s): Adham Khaled Originally published on Towards AI. Ranking #6 on the Artificial Analysis Index, this open-weight model just made “Agentic AI” affordable for the rest of us. Two months ago, I wrote about how GLM-4.6 was the “Claude-level AI at 1/7th …
The Truth About LLM Evals: Why Your AI Model Might Be Better (or Worse) Than You Think
Author(s): Nikhil Originally published on Towards AI. When you’re building or deploying a large language model (LLM), one critical question emerges: how do you know if it’s actually good? Unlike traditional software where you can measure success with clear metrics like “did …
A Practical Guide to Building RAG Systems: Series Introduction
Author(s): Angela & Kezhan Shi Originally published on Towards AI. A shared framework behind diverse use cases If you try to build a RAG system, you may face these concrete problems very quickly: Document QA — all images by authorThis article serves …
The Ghost Teacher: Why Yann LeCun Says “Generative” AI might be a Dead End
Author(s): Siddharth M Originally published on Towards AI. Meta’s latest 7B-parameter vision model (DINOv3) proves that “labels” are the bottleneck of intelligence. Here is the definitive engineering deep dive into Gram Anchoring, the Ghost Teacher, and the future of Objective-Driven AI. Yann …
I Thought Private Variables Were Actually Private (Then I Accessed Them From Outside the Class)
Author(s): Dua Asif Originally published on Towards AI. AI GENERATED class BankAccount: def __init__(self, balance): self.__balance = balance # Private variable def get_balance(self): return self.__balance def withdraw(self, amount): if amount <= self.__balance: self.__balance -= amount return True return False Two underscores. That …
This Python Package Makes Differentiable Physics Simulations Practical
Author(s): Gowtham Boyina Originally published on Towards AI. It’s from NVIDIA, it's not CUDA I’ve spent way too long fighting with CUDA just to prototype a simple physics simulation. You either hand-roll low-level kernels in C++ — which breaks your Python workflow …
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