The Future of AI for Sales Is Diverse and Distributed
True creativity and innovation will come from human-agent collaboration. One human, millions of agents.
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Democratizing Marketing Mix Models (MMM) with Open Source and Gen AI
A practical system design combining open-source Bayesian MMM and GenAI for transparent, vendor independent marketing analytics insights.
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From 4 Weeks to 45 Minutes: Designing a Document Extraction System for 4,700+ PDFs
How a hybrid PyMuPDF + GPT-4 Vision pipeline replaced £8,000 in manual engineering effort, and why the latest models weren’t the answer
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How to optimize context, a precious finite resource for AI agents
The post Context Engineering for AI Agents: A Deep Dive appeared first on Towards Data Science.
The Geometry Behind the Dot Product: Unit Vectors, Projections, and Intuition
The geometric foundations you need to understand the dot product
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Proxy-Pointer RAG: Achieving Vectorless Accuracy at Vector RAG Scale and Cost
A new way to build vector RAG—structure-aware and reasoning-capable
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Building a Python Workflow That Catches Bugs Before Production
Using modern tooling to identify defects earlier in the software lifecycle.
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A Practical Guide to Measuring Relationships between Variables for Feature Selection in a Credit Scoring.
The post Building Robust Credit Scoring Models with Python appeared first on Towards Data Science.
When we try to train a very deep neural network model, one issue that we might encounter is the vanishing gradient problem. This is essentially a problem where the weight update of a model during training slows down or even stops, hence causing the model not to improve. When a network is very deep, ...
I Replaced Vector DBs with Google’s Memory Agent Pattern for my notes in Obsidian
Persistent AI memory without embeddings, Pinecone, or a PhD in similarity search.
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Why thinking longer can matter more than being bigger
The post How Can A Model 10,000× Smaller Outsmart ChatGPT? appeared first on Towards Data Science.
The Map of Meaning: How Embedding Models “Understand” Human Language
Learn why embedding models are like a GPS for meaning. Instead of searching for exact words, it navigates a "Map of Ideas" to find concepts that share the same vibe. From battery types to soda flavors, learn how to fine-tune these digital fingerprints for pinpoint accuracy in your next AI project.
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I’ve been so surprised by how fast individual builders can now ship real and useful prototypes. Tools like Claude Code, Google AntiGravity, and the growing ecosystem around them have crossed a threshold: you can inspect what others are building online and realize just how fast you can build today. O...
Explainable AI in Production: A Neuro-Symbolic Model for Real-Time Fraud Detection
SHAP needs 30 ms to explain a fraud prediction. That explanation is stochastic, runs after the decision, and requires a background dataset you have to maintain at inference time. This article benchmarks a neuro-symbolic model that produces a deterministic, human-readable explanation in 0.9 ms — as a...
Self-Healing Neural Networks in PyTorch: Fix Model Drift in Real Time Without Retraining
What happens when your production model drifts and retraining isn’t an option? This article shows how a self-healing neural network detects drift, adapts in real time using a lightweight adapter, and recovers 27.8% accuracy—without retraining or downtime.
The post Self-Healing Neural Networks in PyT...
Using OpenClaw as a Force Multiplier: What One Person Can Ship with Autonomous Agents
It's easier than ever to 10x your output with agentic AI.
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From NetCDF to Insights: A Practical Pipeline for City-Level Climate Risk Analysis
Integrating CMIP6 projections, ERA5 reanalysis, and impact models into a lightweight, interpretable workflow
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Building a Production-Grade Multi-Node Training Pipeline with PyTorch DDP
A practical, code-driven guide to scaling deep learning across machines — from NCCL process groups to gradient synchronization
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What the Bits-over-Random Metric Changed in How I Think About RAG and Agents
Why retrieval that looks excellent on paper can still behave like noise in real RAG and agent workflows
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Following Up on Like-for-Like for Stores: Handling PY
My last article was about implementing Like-for-Like (L4L) for Stores. After discussing my solution with my peers and clients, I encountered an interesting issue that brought additional requirements to my first solution. This is what I want to discuss here.
The post Following Up on Like-for-Like for...
Understanding how to set up human-in-the-loop (HITL) agentic workflows in LangGraph
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My Models Failed. That’s How I Became a Better Data Scientist.
Data Leakage, Real-World Models, and the Path to Production AI in Healthcare
The post My Models Failed. That’s How I Became a Better Data Scientist. appeared first on Towards Data Science.
From Dashboards to Decisions: Rethinking Data & Analytics in the Age of AI
How AI agents, data foundations, and human-centered analytics are reshaping the future of decision-making
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Production-Ready LLM Agents: A Comprehensive Framework for Offline Evaluation
We’ve become remarkably good at building sophisticated agent systems, but we haven’t developed the same rigor around proving they work.
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