Author name: aifuturethinkers.com

Hello, and welcome to the world of my AI! I am overjoyed that you have chosen to accompany us on this journey into the fascinating field of artificial intelligence. If you find artificial intelligence to be as fascinating as I do, you are in for a thrilling trip! As a Data Engineering professional, I’ve been immersed in technology for the past ten years, and it’s become second nature to me. With a Master’s degree in Computer Application under my belt, I’ve had the fortunate opportunity to see artificial intelligence (AI) disrupting businesses and changing the game in ways that we couldn’t have anticipated before it happened. Now that I’ve finished reading all of my blogs, I’m ready to pass on all of the incredible information that I’ve gained. Together, we will investigate everything from the most cutting-edge AI applications to the most recent fashions. It doesn’t matter if you’ve never worked with AI before; I guarantee to make the process easy and entertaining so that anybody may take part in the AI adventure.

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Using Causal Inference to Estimate the Impact of Tube Strikes on Cycling Usage in London

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(TFL) is a statutory body responsible for London’s public transport network, managing buses, the Underground, Docklands Light Railway, Overground, and major roads. Their ‘Open Data’ policy means that they share much of their internal data with the public, which they say is currently powering over 600 apps for Londoners. One interesting data source they share …

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Seeing What’s Possible with OpenCode + Ollama + Qwen3-Coder

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Image by Author   # Introduction  We live in an exciting era where you can run a powerful artificial intelligence coding assistant directly on your own computer, completely offline, without paying a monthly subscription fee. This article will show you how to build a free, local artificial intelligence coding setup by combining three powerful tools: OpenCode, …

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I Vibe Coded a Tool to That Analyzes Customer Sentiment and Topics From Call Recordings

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Image by Author   # Introduction  Every day, customer service centers record thousands of conversations. Hidden in those audio files are goldmines of information. Are customers satisfied? What problems do they mention most often? How do emotions shift during a call?Manually analyzing these recordings is challenging. However, with modern artificial intelligence (AI), we can automatically transcribe …

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Image editing in Google Gemini gets a major upgrade

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Today in the Gemini app, we’re unveiling a new image editing model from Google DeepMind. People have been going bananas over it already in early previews — it’s the top-rated image editing model in the world. Now, we’re excited to share that it’s integrated into the Gemini app so you have more control than ever …

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Beyond Prompting: Using Agent Skills in Data Science

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In my last article, I shared how to use MCP to integrate LLMs into your full data science workflow. I also briefly mentioned another .  A skill is a reusable package of instructions and optional supporting files. It helps AI handle a recurring workflow more reliably and consistently. At a minimum, it needs a SKILL.md …

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Python Project Setup 2026: uv + Ruff + Ty + Polars

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Image by Editor   # Introduction  Python project setup used to mean making a dozen small decisions before you wrote your first useful line of code. Which environment manager? Which dependency tool? Which formatter? Which linter? Which type checker? And if your project touched data, were you supposed to start with pandas, DuckDB, or something newer? …

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Gemini 3.1 Flash TTS: New text-to-speech AI model

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Today, we’re introducing Gemini 3.1 Flash TTS, the latest text-to-speech model that delivers improved controllability, expressivity and quality — empowering developers, enterprises and everyday users to build the next generation of AI-speech applications. Starting today, 3.1 Flash TTS is rolling out: Improved speech quality and controllability We’ve improved the overall speech quality of Gemini 3.1 …

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Context Layer

RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work

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TL;DR a full working implementation in pure Python, with real benchmark numbers. RAG systems break when context grows beyond a few turns. The real problem is not retrieval — it’s what actually enters the context window. A context engine controls memory, compression, re-ranking, and token limits explicitly. This is not a concept. This is a …

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