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.

Visit Towards AI

Managing Memory in LangGraph: From RAM to Postgres to Smarter Context Windows

Towards AI

A practical look at short-term memory, persistence, and the two techniques that keep your AI agents from forgetting everything on restart. TL;DR InMemorySaver stores state in RAM — gone on restart. U...

Top 15 Computer Vision Datasets [2026]

Towards AI

A ML engineer’s guide to top image datasets. Learn about ImageNet, COCO, and more, and understand how data annotation and benchmarks drive… Continue reading on Towards AI

40 Generative AI Interview Questions That Actually Get Asked in 2026 (With Answers)

Towards AI

A practitioner’s guide to cracking senior GenAI/LLM engineering roles — from RAG pipelines to multi-agent orchestration I’ve been in AI/ML for eight years. In the last two, almost every interview I’v...

Next-Word Prediction: How Conditional Probability Turned Language into a Learning Task

Towards AI

Language can be studied from more than one direction. One direction asks how words can be represented so that a machine can work with them at all. Another asks what happens when language unfolds thro...

Your Brain Is Running 5 Electrical Symphonies Right Now. We Built a Quantum Circuit to Listen.

Towards AI

Originally published on LinkedIn What happened when we deployed a real VQC on EEG brain data and what four experiments actually told us about where quantum ML stands today. Quantum machine learning is...

Multimodal AI Systems: Scalability & Cost Optimization

Towards AI

Scalable, cost-efficient architecture best practices Balancing scalability and cost optimization is a fundamental challenge in designing modern systems, especially those involving data-intensive or A...

Breaking the Memory Wall: TurboQuant KV Cache Quantization on Apple Silicon

Towards AI

Implementing Google Research’s TurboQuant algorithm on MLX- for 5× KV cache compression confirmed, quality benchmarks coming in Part 2 Continue reading on Towards AI

Context Engineering, Not Retrieval: Why Your Agentic RAG Fails in Production

Towards AI

Last quarter I watched a revenue forecasting agent confidently report that Q3 was up 14% year-over-year. The CFO loved it. The board saw it. Then someone in data engineering pointed out the number was...

Your AI Agent Is a Security Nightmare. Here’s What I Do About It.

Towards AI

341 malicious skills on a marketplace. 43% of MCP servers vulnerable to command execution. Tool descriptions that steal your SSH keys without being called. The agentic AI ecosystem is growing faster t...

Inside LLM Inference: KV Cache, Prefill, and the Decode Bottleneck

Towards AI

How LLMs reuse history, read memory, and transform computation into data movement KV cache cuts latency In the previous article, we saw that LLM inference slows down as context grows. Not just becaus...

Claude Code Subagents and Main-Agent Coordination: A Complete Guide to AI Agent Delegation Patterns

Towards AI

Mastering AI Agent Coordination: Effective Delegation Patterns for Claude Code Subagents Continue reading on Towards AI

The Math That the Agi Industry Has Never Publicly Confronted — And What It Actually Means for the…

Towards AI

Every AI Company Is Making the Same Trillion-Dollar Bet. A 1997 Theorem Proves Nobody Can Know If They’re Right. Continue reading on Towards AI