6 Open-Source AI Tools That Actually Make Your Life Easier
Author(s): AbhinayaPinreddy Originally published on Towards AI. Why You Should Care About These Projects Right Now Picture this: You’re drowning in company documents, trying to build an AI chatbot, or wanting to fine-tune a model for your specific needs. You Google “AI …
Part 4: How to Know If Your AI Agents Are Actually Working
Author(s): Rittika Jindal Originally published on Towards AI. This is Part 4 of a 4-part series: “The Honest Guide to Building AI Agents That Actually Work.” In Part 1, we fixed context loss. In Part 2, we designed tools agents can actually …
Hybrid Search RAG That Actually Works: BM25 + Vectors + Reranking in Python
Author(s): Tarun Singh Originally published on Towards AI. Fix “dumb RAG” using hybrid retrieval and a lightweight reranker pipeline. If your RAG app is “kind of okay” but randomly wrong, you don’t have an LLM problem. The fix is simple and powerful:This …
AI’s Cold War: The Infrastructure Race from Greenland to Orbit
Author(s): Eray Alguzey Originally published on Towards AI. The Hidden Energy Bill of Artificial Intelligence In a hyperscale data center in rural Virginia, forty cents of every dollar spent goes to a single task: keeping the machines from melting. This isn’t a …
Deploying a TensorFlow Model with TensorFlow Serving and Docker
Author(s): Samith Chimminiyan Originally published on Towards AI. TensorFlow Serving is a powerful tool for deploying machine learning models in a production environment. It allows for easy scaling and management of models, as well as the ability to serve multiple models at …
Writing Tools for Your Agents: A Complete Guide
Author(s): Yashod Perera Originally published on Towards AI. This is the era of Agentic AI, where everyone is writing their agents with tools. But are we writing tools correctly? What is a tool? What are the best practices? If you are having …
Prompt Repetition Boosts LLM Accuracy 76% Without Latency Increase
Author(s): MKWriteshere Originally published on Towards AI. How repeating prompts twice improves the non-reasoning model accuracy from 21% to 97% while maintaining zero latency overhead I avoid reasoning models in production. Latency kills user experience, and the token costs add up quickly …
Building a Self-Updating Knowledge Graph From Meeting Notes With LLM Extraction and Neo4j
Author(s): Cocoindex Originally published on Towards AI. Transform unstructured meeting notes into a queryable knowledge graph with incremental updates — no full reprocessing required. Meeting notes are goldmines of organizational intelligence. They capture decisions, action items, participant information, and the relationships between …
Why Most RAG Projects Fail in Production (and How to Build One That Doesn’t)
Author(s): Bran Kop, Engineer @Conformal, Founder of aiHQ Originally published on Towards AI. Enterprise AI discussions often begin with a diagram that looks almost perfect in its simplicity: Documents → Vector Database → LLM → Answers It is not wrong. It is …
How to Run AI Agents Fully Locally: Memory, Tools, and Models on Your Laptop
Author(s): Luna Originally published on Towards AI. How to Run AI Agents Fully Locally: Memory, Tools, and Models on Your Laptop If you’ve ever tried to build an “agent” that helps with real work (not just a demo), you usually hit the …
Ralph Wiggum vs Chain-of-Verification: How LLMs Can Fact-Check Themselves
Author(s): Digvijay Mahapatra Originally published on Towards AI. Implementing the “Factored” approach to reduce hallucinations without external tools. We have all been there. You ask an LLM for a bio of a semi-famous engineer, and it confidently tells you they invented the …
V. FastAPI — Leverage The Async : When and Why
Author(s): Mahimai Raja J Originally published on Towards AI. Who else will like to wait, when there is a better option? Hi, Welcome back, we talked about building a FastAPI app with production ready architecture patterns and structure previously. Today we are …
The Builder’s Notes: No-Show Rate Costs Practices $150K/Year — Here’s the Automation That Pays Back in 2 Months
Author(s): Piyoosh Rai Originally published on Towards AI. Manual reminder calls: 260 hours of staff time quarterly, 38% failure rate, $6,500 cost. Automated SMS reminders: 3 seconds per patient, 98% delivery rate, $0.02 cost. The family practice in this case study recovered …
Google Just Launched a Protocol That Could Change E-Commerce Forever
Author(s): Gowtham Boyina Originally published on Towards AI. Google Just Launched a Protocol That Could Change E-Commerce Forever Last Sunday at the National Retail Federation conference, Google announced the Universal Commerce Protocol (UCP), an open-source standard designed to power the next generation …
Synthetic Data That Behaves: A Practical Guide to Generating Realistic Healthcare-Like Data Without Violating Privacy
Author(s): Abhishek Yadav Originally published on Towards AI. A hands-on guide to building synthetic data that looks, feels, and behaves like the real world without privacy risk Photo by Luke Chesser on Unsplash Healthcare organizations sit on treasure chests of data be …
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