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In a small forest clearing inside a video game, a creature named Astra is still searching for her friend Quill. She saw him caught in a storm hours ago and hasn't seen him since. What she doesn't know is that Quill found shelter almost immediately and has been perfectly safe the entire time. The game knows this. Astra doesn't. And that gap, deliberately, is the whole point. It's a small moment inside Luminids, an Early Access colony-sim built by a solo developer in Cambridge. But it's also a working demonstration of something Nick Jacob has spent the better part of a year building: a system that lets AI-driven characters hold onto their own version of events, separate from what the game world itself knows, and separate from whichever AI model happens to be generating their dialogue. He calls it CRI, short for Contextual Relational Intelligence, and he's now raising £1.5 million to turn it into a product other studios can use.

The Complete Perplexity AI Course: Free, Advanced, Start to Finish TL;DR This is a full, self-contained course on Perplexity AI - the cited-answer search engine that most people only use for "ChatGPT but with sources." We're not sending you anywhere else to "finish learning it." Every module below - Focus Modes, Deep Research, Spaces, and the free Comet browser - is taught here, in full. It's genuinely free to go deep : unlimited basic searches, several Deep Research queries a day, all six Focus Modes, Spaces, and the fully free Comet agentic browser. By the end of this course, you'll be able to run structured research investigations, build a persistent custom research assistant, and automate repetitive browsing tasks - without paying for anything. Reviewed by Imran Khan Pathan, Editor at AI Tech Safar. I ran real Deep Research sessions, built an actual Space, and used the Comet browser for a genuine multi-step task before writing a single in...

Google Gemini Notebook (NotebookLM) - Advanced Guide and Masterclass TL;DR This is a full, self-contained course on Gemini Notebook , the tool Google renamed from NotebookLM on July 16, 2026. It's the same tool and the same free plan under a new name, and you'll find it at notebook.google.com . We're not sending you anywhere else to "finish learning it." Every module below, from your first notebook to Deep Research reports, Audio Overviews and multi-notebook workflows, is taught here, in full. It's free to start : 100 notebooks and 50 sources each, with no credit card. One thing has changed since this course first went up: since September 2, usage limits are compute-based and refresh every five hours up to a weekly cap, replacing the old fixed daily counts. By the end of this course, you'll be able to build a source-grounded research assistant, run Deep Research investigations, generate podcast-style Audio Overviews and automate recurring research,...

By Imran Khan (AI Tech Safar) In September 2022, a piece called Théâtre D'Opéra Spatial won first place in the digital art category at the Colorado State Fair. The artist, Jason Allen, hadn't touched a brush — he'd typed and refined prompts in Midjourney dozens of times until it produced exactly the composition he wanted. Other entrants were furious. One called it "basically cheating." The story went national, and it did something most explainers never manage: it showed, in one very visible argument, what generative AI actually does and why people are still arguing about it three years later. That argument is really about one question: what happens when a machine can produce something original instead of just sorting, ranking, or retrieving what already exists? This guide walks through what generative AI is, how it works without requiring a computer science background, where it's already sitting inside apps you use, and where the real risks are — not the ...

By Imran Khan (AI Tech Safar) In July 2023, someone ran the United States Constitution through an AI detector. It came back flagged as machine-written. Not a modern AI-assisted essay, not a ChatGPT draft — the actual 1787 Constitution, written by men in wigs with quill pens. That single, slightly absurd result tells you more about how AI detectors actually work than most explainer articles do: they aren't reading for meaning. They're measuring how *predictable* your word choices are, and it turns out 18th-century legal drafting is, statistically, pretty predictable. If you've ever had a detector flag something you wrote yourself, or you're trying to figure out whether these tools are worth trusting at all, here's what's actually happening under the hood — and where it breaks down. Table of Contents Quick Summary & Key Takeaways The Two Core Signals: Perplexity and Burstiness Why Detectors Get It Wrong — Often Watermarking: The Other Approach How th...