AI Tools & Frameworks Directory

Your RAG system isn’t failing at retrieval — it’s failing at reasoning. This article shows how I built a lightweight self-healing layer that detects and corrects hallucinations before they reach users. The post RAG Hallucinates — I Built a Self-Healing Layer That Fixes It in Real Time appeared first...

Part 2. Building scale-invariant agents that seamlessly change contexts The post Surviving High Uncertainty in Logistics with MARL appeared first on Towards Data Science.

AI tools speed up IoT development — but closer to the hardware, the same code that looks correct can silently break thousands of devices at once. The post How AI Tools Generate Technical Debt in IoT Systems — and What to Do About It appeared first on Towards Data Science.

A practitioner's decision framework for Ridge, Lasso, and ElasticNet based on three quantities you can compute before fitting a model The post Which Regularizer Should You Actually Use? Lessons from 134,400 Simulations appeared first on Towards Data Science.

One scale parameter determines accuracy in rotation-based vector quantization. The post How a 2021 Quantization Algorithm Quietly Outperforms Its 2026 Successor appeared first on Towards Data Science.

The first database built for AI Agents The post Ghost: A Database for Our Times? appeared first on Towards Data Science.

How we replaced Python pipelines with dlt, dbt, and Trino — and cut delivery time from weeks to one day. The post 4 YAML Files Instead of PySpark: How We Let Analysts Build Data Pipelines Without Engineers appeared first on Towards Data Science.

A deep dive into how Apache Flink works, why it exists, and learning it while building a real-time recommendation engine The post System Design Series: Apache Flink from 10,000 Feet, and Building a Flink-powered Recommendation Engine appeared first on Towards Data Science.

Blast-radius control tells you how much to break. Intent tells you what breaking it will teach. Only one of these has mature tooling. The post The Next Frontier of AI in Production Is Chaos Engineering appeared first on Towards Data Science.

NaNs don’t crash your training — they quietly destroy it. After losing hours to a silent failure in a ResNet training run, I built a lightweight detector that pinpoints the exact layer and batch where things break. Using forward hooks and gradient checks, it catches issues early with minimal overhea...

A simulation of how a single forecast change moves through five planning teams, and why most retailers lose money in the gap between Sales and Stores. The post How Spreadsheets Quietly Cost Supply Chains Millions appeared first on Towards Data Science.

With the advent of UDFs and their combination with calculation groups, I see a lot of discussion about not creating explicit measures but instead offering calculation groups to report creators. The post Comparing Explicit Measures to Calculation Groups in Tabular Models appeared first on Towards Dat...

Most slow Pandas code "works", until it doesn't. Learn how to spot hidden bottlenecks, avoid costly row-wise operations, and know when Pandas is no longer enough. The post I Reduced My Pandas Runtime by 95% — Here’s What I Was Doing Wrong appeared first on Towards Data Science.

A local, zero-cost project that cleans, structures, and summarizes your reading automatically The post I Built an AI Pipeline for Kindle Highlights appeared first on Towards Data Science.

A practical pipeline for classifying messy free-text data into meaningful categories using a locally hosted LLM, no labeled training data required. The post Using a Local LLM as a Zero-Shot Classifier appeared first on Towards Data Science.

Turning free-to-use data into a hypothesis-ready dataset The post Using Causal Inference to Estimate the Impact of Tube Strikes on Cycling Usage in London appeared first on Towards Data Science.

How I turned LLM persona interviews into a repeatable customer research workflow The post From Ad Hoc Prompting to Repeatable AI Workflows with Claude Code Skills appeared first on Towards Data Science.

How you can build your own Thompson Sampling Algorithm object in Python and apply it to a hypothetical yet real-life example The post DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling appeared first on Towards Data Science.

As memory grows in RAG systems, accuracy quietly drops while confidence rises — creating a failure that most monitoring systems never detect. This article walks through a reproducible experiment showing why this happens and how a simple memory architecture fix restores reliability. The post Your RAG...

Conceptual overview and practical guidance The post Context Payload Optimization for ICL-Based Tabular Foundation Models appeared first on Towards Data Science.

Why it tickles your brain to use an LLM, and what that means for the AI industry The post The LLM Gamble appeared first on Towards Data Science.

Open source. 5-minute setup. Vector RAG done right—try it yourself. The post Proxy-Pointer RAG: Structure Meets Scale at 100% Accuracy with Smarter Retrieval appeared first on Towards Data Science.

Explore the end-to-end pipeline of TurboQuant, a novel KV cache quantization framework. This overview breaks down how multi-stage compression achieves near-lossless storage through PolarQuant and QJL residuals, enabling massive context windows with minimal memory overhead The post KV Cache Is Eating...

Git worktrees, parallel agentic coding sessions, and the setup tax you should be aware of The post AI Agents Need Their Own Desk, and Git Worktrees Give Them One appeared first on Towards Data Science.