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We stopped measuring retrieval quality by how many tokens we could fit into the prompt. When...

Trying to diversify what I listen to and interested to hear what everyone else is enjoying this year!

The fastest, most accurate dictation model in the world Discussion | Link

Automates your existing workflows with a single prompt. Discussion | Link

I've lost count of how many AI side projects I started and abandoned. The pattern was always the same: a spark of excitement, two weeks of frantic coding, then the slow fade into yet another half-finished repo collecting dust on GitHub. But something changed in the last two months. I shipped three AI-powered MVPs to real users. Not all of them made money, but every single one taught me something about what it actually takes to go from "cool idea" to "working product." Here's what I learned. The brutal truth about AI side projects When I started my first real AI project back in February, I had grand ambitions. I was going to build a content summarizer that would pull articles from any URL, analyze sentiment, and generate Twitter threads. I spent three weeks obsessing over the perfect prompt engineering, containerizing the whole stack with Docker, and setting up a complex pipeline using LangChain and Pinecone. Then I showed it to a friend. "Can I just paste a link?" she asked. I had built an entire orchestration layer, but the input field was buried behind two authentication screens. The project died that weekend. Here's the thing I keep rediscovering: AI side projects fail not because the technology doesn't work, but because we over-engineer before we have users. The three MVPs that actually shipped After that failure, I changed my approach. I decided to ship something—anything—every two weeks. No matter how ugly. No matter how incomplete. The goal was to have a URL someone could visit and use. MVP #1: A dead-simple blog title generator I built this in a single afternoon. The entire frontend was a text box and a button. Backend? A single Node.js endpoint that called OpenAI's API with a prompt like: "Generate 5 catchy blog titles about [topic]." Here's the code that powered it (I've simplified it, but this is the gist): import express from ' express ' ; import OpenAI from ' openai ' ; const app = express (); const openai = new OpenAI ({ apiKey : process . env . OPENAI

I built a chiptune song generator and its broadcasting algorithmically generated chiptune music.

Why this series I love writing clean architecture . Not because it looks nice in a diagram, but because it survives change — new requirements, new team members, and now, AI-assisted development , where you want boundaries an AI can respect and tests that catch it when it wanders. The problem in most Flutter stacks is the seam between app and backend. You write Dart on the client, then switch to a different language, a hand-written REST layer, DTOs that drift out of sync, and serialization bugs nobody notices until production. Serverpod removes that seam. You write Dart on the server too, and the client-server communication code is generated for you — type-safe, end to end. What is Serverpod? Serverpod is an open-source backend framework that lets you build the entire stack in Dart. Instead of context-switching between languages, your models, your API, and your database logic all live in one language. What you get out of the box: Endpoints — server methods your Flutter client calls directly. The communication code is generated, so there's no hand-written REST/JSON glue. An ORM — type-safe, statically analyzed database access with migrations and relationships. No raw SQL required. Code generation — define a model once; get serialization and client bindings on both sides automatically. Real-time data — streaming over WebSockets, managed for you. Auth — integrations for Google, Apple, and Firebase. The extras enterprises actually need — file uploads, task scheduling, caching, logging, and error monitoring. And on the "is this serious enough for production?" question: Serverpod says it's battle-tested in real-world apps and secured by over 5,000 automated tests, scaling from hobby projects to millions of users without code changes. That's exactly the property you want in an enterprise foundation. The architecture at a glance Here's how the pieces fit. A Serverpod project is generated as three packages: myapp_server → your backend: endpoints, models, business logic, DB my

A client asked: " After I run a cross-site update check, can each site show — right in the site list — how many plugin updates are still pending? " Visually the answer was obvious: a small red badge on the top-right of the 🔌 plugins button, like an unread-notification count. Easy to specify. The harder question was where the data comes from . We could have added a fresh API endpoint and a new cache to hold "pending count per site." But doing that would have doubled state management , and we already had a cache that knew this. We routed through the existing one. Here's the reasoning behind that decision. Reuse the dashboard cache as the data source The cross-site updates dashboard (the one we wrote about in killing the 24.5-second silence with a cache-first design ) already kept each site's pending plugins in a localStorage-backed state called _updatesDashState . Its shape: _updatesDashState = { sites : [ { site_id : " abc... " , plugins : [ {...}, {...}, {...} ] }, { site_id : " def... " , plugins : [ ... ] }, ], total_pending_count : 12 , loadedAt : 1748600000000 , } Look up by site_id , take plugins.length , and you have the badge's number. No new API, no new cache. The data that powers the cross-site dashboard is also the data that powers the site-list badge. The win of not adding state is quiet but real: When a maintenance run invalidates _updatesDashState , the badge disappears automatically (no sync code to write) The TTL (originally 7 days; later extended to 30 days with partial invalidation ) inherits from the existing design The badge and the underlying count can't drift — there's no second copy to fall out of step There's always a temptation to spin up a new endpoint for a new UI element. The rule we settled on: if the existing state answers it, don't add more. Attaching the badge — consolidate into helpers Both the list view and grid view need the same badge on the 🔌 button, so the logic lives in helpers. function _getPendingPluginCountForSite ( siteId )

We’ve all been there: staring at a stack of printed lab results or a folder full of cryptic report_final_v2_NEW.pdf files, trying to remember if our cholesterol was higher or lower two years ago. For developers, this isn't just a filing problem—it's a data engineering challenge. In the world of healthcare, data is messy, siloed, and often locked in "unstructured" formats. To build a truly personal Electronic Health Record (EHR) system, we need more than just a folder; we need a RAG (Retrieval-Augmented Generation) pipeline that can parse PDFs, map them to the FHIR (Fast Healthcare Interoperability Resources) standard, and provide natural language insights. In this guide, we’ll leverage Unstructured.io , Milvus , and DuckDB to turn chaotic medical PDFs into a queryable, structured knowledge base. The Architecture: From Raw Pixels to Structured Insights Before we dive into the code, let’s look at how the data flows from a messy lab report to a structured answer. graph TD A[Unstructured PDF Reports] --> B[Unstructured.io Partitioning] B --> C{Data Split} C -->|Textual Context| D[Milvus Vector DB] C -->|Tabular Data| E[DuckDB Structured Storage] D --> F[LangChain RAG Engine] E --> F G[User Query: Is my glucose trending up?] --> F F --> H[FHIR-Formatted Response] Why this stack? Unstructured.io : The gold standard for handling "ugly" PDFs (tables, headers, and nested lists). Milvus : A high-performance vector database built for scale. DuckDB : Perfect for running complex analytical SQL queries on the extracted "structured" parts of our medical data. FHIR Standard : To ensure our data follows global healthcare interoperability rules. Prerequisites Make sure you have your environment ready: pip install langchain milvus unstructured[pdf] duckdb openai Step 1: Extraction with Unstructured.io Medical PDFs often contain complex tables. Standard PDF parsers usually fail here. We’ll use unstructured to partition the document into logical elements. from unstructured.partition.pdf

Try it at: ssh krnl.duetbrowser.com No sign up, totally free - no download. Let me know what you think. This is a real TUI browser rendering the web as ANSI with its own layout engine. The backend is Google cloud run, running browserbox and the Krnl TUI browser and it is routed through a simple SSH server endpoint. Prior art exists (brow.sh) but I wasn't satisfied with their graphical take. I wanted the raw, old skool terminal days of the early internet - BBS, and old green phosphor library comp