The Problem Nobody Talks About Picture this: You're trying to find a place to meet up with friends. Someone suggests a coffee shop. It's 8 minutes from their house. It's 45 minutes from yours. You say yes anyway, because suggesting a different place feels awkward. This happens all the time — with friends, with remote teams, with family scattered across a city. And the worst part? Most "meet in the middle" suggestions aren't actually in the middle. They're just the geographic midpoint, which completely ignores traffic, transit options, and the fact that roads don't go in straight lines. I got frustrated enough to build something about it. Meet Meetle Meetle is a free web app that finds the fairest meeting spot for any group of people — based on real travel times , not just distance. A Chrome Extension is coming soon so you'll have it one click away in your toolbar. You add everyone's starting location, choose how each person is traveling (driving, walking, or transit), hit Find Meeting Point , and Meetle does the math across every person simultaneously. It then surfaces the best nearby cafés, restaurants, parks, gyms, or whatever venue type you're looking for — ranked by actual fairness. No more "it's fine, I don't mind the drive." Now you have data. How It Actually Works Under the hood, Meetle uses three Google Maps APIs working together: Distance Matrix API calculates travel time from every person's location to every candidate venue, simultaneously. This is the core of the fairness scoring — you can't rank venues fairly without knowing everyone's actual travel time to each one. Places API finds candidate venues near the calculated center point. You can filter by type (coffee, food, parks, gyms, etc.), price level, minimum rating, and whether they're open right now. Maps JavaScript API renders everything visually — the map, the travel zones (isochrones), and the markers for each suggested venue. The scoring works two ways and you can toggle between them: Fairness mo

Most developers building "chat with your data" apps hit the exact same wall. You chunk the text, embed it, dump it in a vector database, and the retrieval is still terrible. The model hallucinates or completely scrambles tables. People think data ingestion is just text extraction. It isn't. In 2026, text extraction is a solved, boring problem. The actual hard part is layout. If your ingestion layer doesn't know that a bold header implies hierarchy, or that a two-column page isn't just one long string of text read left-to-right, your LLM is reading garbage. Markdown won the ingestion war We've mostly stopped treating PDFs as plain text. Markdown is now the default format for document ingestion, simply because it preserves structure. Modern ingestion tools don't just dump strings. They output Markdown where headers, lists, and tables actually mean something. This gives the LLM the context it needs to figure out where a piece of information lived in the original document, which makes citations and retrieval significantly more accurate. Local engines vs. Vision models Right now, there are basically two ways to handle this layout problem. First, you have local deterministic engines like IBM's Docling or OpenDataLoader PDF. Docling has quietly become a standard for enterprise RAG because it natively handles the whole Office suite and spits out clean Markdown. It runs locally without a GPU. OpenDataLoader does something similar. If you have a massive volume of private documents, this is the realistic path. Then you have the Vision-Language Model (VLM) approach. Instead of trying to parse messy PDF code, tools like Mistral OCR and LlamaParse just look at the document as an image. They see it the way we do. This completely bypasses the nightmare of multi-column layouts and nested tables that broke older parsers. The tradeoff VLM parsing feels like magic, but it's expensive. If you process millions of pages, running everything through a cloud vision API will destroy your budg

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Tech leaders debate whether the Anthropic episode is a wake-up call for India’s AI ambitions.

I've made a drawing app based on my physical sketching practice, using fluid sim and some shader tricks to mimic watercolor-style ink washes. Best used on iPad or with a drawing tablet. The linked article shows how the core engine works, with plenty of little interactive demos. It was fun to make, sharing in hopes others find it fun too :)

Review your kid's texts. Nothing leaves your Mac Discussion | Link

Native quick access and SSH agent for Proton Pass for macOS Discussion | Link