Towards AI

“Towards AI” is a platform that delves into various aspects of artificial intelligence and machine learning. The content covers topics such as data augmentation for machine learning, Python programming scripts, and the use of Python in web3, blockchain, and smart contracts. Additionally, it explores the challenges and solutions in building AI applications with features like short-term memory and persistent conversation history. The platform also discusses the importance of understanding and utilizing enterprise-grade tools like Spark, EMR on EKS, and Airflow 3 for creating secure and efficient knowledge bases for AI applications.

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Why A/B Testing Fails in B2B Revenue Optimization — And How Causal ML Saves It

Towards AI

How ‘defensive discounting’ masks the true revenue impact of sales strategies in enterprise CRM data, and a Python blueprint to recover it using Causal Inference. Photo by Jason Dent on Unsplash Imag...

MLX vs oMLX vs MTPLX: Apple Silicon’s LLM Stack Explained

Towards AI

A practical guide to choosing between MLX, oMLX, and MTPLX for running local LLMs on Apple Silicon, and why they solve different problems. Continue reading on Towards AI

Three Ways an LLM on Your Warehouse Gets ‘Why Did Revenue Drop?’ Wrong-and How to Fix Each

Towards AI

Connecting a language model to your warehouse is a great demo and a bad diagnostic engine. Here are the three failures you’ll actually hit, each with a fix, in plain SQL. Plug an LLM into your wareho...

Claude Opus 4.8 Is 4x More Honest. That Honesty Is Eating Your Context Window.

Towards AI

Dynamic Workflows and infinite fix loops are turning alignment gains into unit economic losses. Here is the guardrail protocol to stop the… Continue reading on Towards AI

The 30-Day Roadmap to Building a Production RAG System (That Doesn’t Hallucinate)

Towards AI

RAG is the most practical AI pattern for most businesses. Most implementations are broken in ways teams don’t discover until users find them. There’s a demo that plays out in conference rooms and inv...

The Hidden Cost of Multi-Agent AI Systems: Why More Agents Are Not Automatically Better

Towards AI

The current wave of agentic AI has created a strong impression that more agents mean more intelligence .In practive , the opposite can happen once coordination , state sharing , routing and debugging ...

Harness in AI Agents

Towards AI

A harness in AI agents is the runtime and control plane that turns a raw model into something that can actually do work. In Anthropic’s wording, an agent harness or scaffold is the system that enables...

Claude Code Now Spawns 1,000 Subagents — and It Quietly Killed My LangGraph Stack

Towards AI

Last Thursday I deleted a LangGraph orchestration script I’d spent three weekends building. Not because it broke. Because a single Claude… Continue reading on Towards AI

Stop Writing Cost Queries. Just Ask Cortex Code.

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The complete cost-intelligence prompt playbook — every question, what the skill does, and the SQL it generates under the hood. The shift that happened quietly For years, FinOps on Snowflake meant ope...

Exploring the Relationship Between Two Features: A Practical Guide to Finding Meaning in Data

Towards AI

Understanding the relationships between features in data is essential for informed decision-making. This guide presents practical methods and examples for exploring these relationships. The Importanc...

The Evolution of LLM Inference: Decoding algorithms — Part 2

Towards AI

This is the second part of the LLM inference article. For the first part please refer to PART 1 link. This article focuses mainly on decoding algorithms: how we moved from naive autoregressive decodin...

Why Your Cache Breaks The Moment You Scale Beyond One Python Process

Towards AI

Python Caching Explained Through A Production Disaster ☠️ Photo by Growtika on Unsplash In the last blog, we learned something painful: Async wasn’t about making your app faster. It was about helping...