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1001 Threads of Mizan is an action-adventure game with drop-in, drop-out co-op, letting you play solo or with up to 3 people. Developer MBC Game Studio told us how they developed the traversal and combat. Presented by 1001 Threads of Mizan

Section 1001 in the new housing bill "Restricts the purchase of new single-family homes by large institutional investors that directly or indirectly own at least 350 single-family homes." Doesn't this just end up like Durbin where big players are replaced by medium-sized players?

We backed @1001ai_ at seed and are continuing to back the team in their $30M Series A. 1001 builds sovereign AI for the critical infrastructure that moves the physical world: aviation, ports, and energy. It's built and governed locally, so operators stay in control. Congratulations to @b_abughazaleh, Mohammed Fituri, and the entire team.

Vuetify0 v1.2.3 is out. Patch release: useBreakpoints flags follow the matched name (#1003#), resize updates run without the plugin (#1004#), and useStorage falls back when localStorage is refused (#1001#).

This week on Source Material: Zach Lieberman (@zachlieberman), MIT Professor, computational artist and creative coder. 00:00 Intro 00:17 Maeda @ Media — John Maeda, 2000 01:25 The Visual Diaries 1973–1982 — Lance Wyman, 2017 02:09 Design by Numbers — John Maeda, 1999 03:01 Making Things — Issey Miyake, 1999 03:45 An Arbitrary Point P — Masahiko Sato & Euphrates, 2007 04:14 Midnight Animations — Masahiko Sato & Euphrates, 2007 04:53 Visual Music — Kerry Brougher, Jeremy Strick, Ari Wiseman & Judith Zilczer, 2005 05:24 Drawing is Thinking — Milton Glaser & Judith Thurman, 2008 05:49 Arm in Arm — Remy Charlip, 1969 06:22 Siteless: 1001 Building Forms — François Blanciak, 2008 06:51 Cool Tools: A Catalogue OF Possibilities — Kevin Kelly, 2013 07:19 Conditional Design Workbook — Laura Maurer, Jonathan Puckey, Roel Wouters & Edo Paulus, 2013 08:03 Design Writing Research: Writing on Graphic Design — Ellen Lupton & Abbott Miller, 1996 09:45 Figma Config 2026 — Zach Lieberman, Ten Years of Daily Sketching 09:59 Outro

# Elasticsearch Features and Practical Usage 📦 No need to lock down a table schema first — the moment you throw JSON at it, your data is usable. An Elasticsearch index is a document-oriented data store built for search and scale from day one. 🏷️ Title: Index / Document-oriented data store 🔗 URL: 📘 Overview An index is the fundamental unit of storage in Elasticsearch and the level at which you interact with your data. Data is stored one record at a time as JSON "documents," and each document is a set of field key-value pairs plus system metadata such as `_index`, `_id`, and `_version`. ⚙️ How It Works ・Your actual data lives in the `_source` field, while `_index` (owning index), `_id` (unique ID), and `_version` are system-managed metadata. ・A "mapping" defines each field's type and how it is indexed and queried. You pick types like `text` (for full-text search), `keyword` (exact match and aggregations), `integer`, and `date`. ・Even without an explicit mapping, "dynamic mapping" infers types from the incoming JSON, so you can add new fields later and still index them with no schema migration. ・Internally, an index is split into "shards" distributed across nodes. Data inside a shard is written as immutable "segments," and replica shards provide redundancy and scale. ・Settings like `index.number_of_shards` (fixed at creation), `index.number_of_replicas` (adjustable later), and `index.refresh_interval` (default 1s) control index behavior. 🛠️ Practical Usage Indexing a single document is straightforward. `POST products/_doc/p-1001` `{ "name": "Wireless earbuds", "price": 8900, "stock": 120, "category": "audio" }` For large loads, the `_bulk` API batches many operations in one request. `POST products/_bulk` `{ "index": { "_id": "p-1001" } }` `{ "name": "Wireless earbuds", "price": 8900 }` `{ "index": { "_id": "p-1002" } }` `{ "name": "USB-C cable", "price": 1200 }` Adding a brand-new field later (e.g. `sustainability_score`) just works thanks to dynamic mapping. 💡 Use Cases A classic pattern is modeling an e-commerce product catalog as a `products` index, one product = one JSON document (name, price, stock, category, description). A daily batch loads hundreds of thousands of records via `_bulk`, and you can introduce new attributes without waiting on an RDB schema change. ⚠️ Caveats ・A field's type cannot be changed once set. To change a type you must reindex into a new index. ・Use a regular index for frequently updated documents; use a data stream for append-only time-series data. ・Shard count and size directly affect query speed and cluster stability. Avoid huge numbers of tiny shards and aim for sensible shard sizing. #Elasticsearch# #DataModeling#