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

You asked your AI to help you plan a trip. It gave you a paragraph about packing layers and booking early. You needed a checklist, a hotel shortlist, a flight window, and a rough daily schedule. What you got was a thoughtful non-answer dressed up as advice. That gap — between what AI tells you and what it could actually do for you — is the gap agentic AI is designed to close. And most people don't know it exists. The Difference Between Answering and Acting Standard AI models are trained to respond. You send a prompt, they generate a reply. The entire interaction lives inside a single text exchange. Agentic AI operates differently. Instead of producing one answer, it takes a goal and breaks it into a sequence of steps — then executes them, one after another, checking its own output along the way. It can look things up, organize information, write to a document, revisit a step if something doesn't look right, and deliver a final result that's actually usable. The travel example makes this concrete. A conversational model tells you to pack a rain jacket. An agentic setup builds you the trip: it pulls destination weather data, generates a packing list specific to your travel dates, identifies hotels in your price range, and drops everything into a structured itinerary. Same goal. Completely different level of output. Author's note: The word "agentic" has been overloaded to the point of meaninglessness in tech marketing. For our purposes here, it means one specific thing — an AI that runs a loop: think, act, observe the result, decide the next action. If it's not doing all four of those things in sequence, it's not really an agent. It's just a chatbot with extra steps. Why This Loop Changes Everything The reason agentic AI feels qualitatively different isn't magic — it's architecture. The core mechanic comes from a framework called ReAct (short for Reasoning and Acting), introduced in a 2023 paper by Yao et al. and now foundational to most production agent systems. The l

Exam Guide: Developer - Associate 🏗️ Domain 3: Deployment 📘 Task 4: Deploy Code By Using AWS Continuous Integration And Continuous Delivery (CI/CD) Services This task tests your ability to build and manage CI/CD pipelines using AWS developer tools. You need to understand how CodeCommit, CodeBuild, CodeDeploy, and CodePipeline work together, how to write buildspec and appspec files, how deployment strategies differ, and how to configure automatic rollbacks. Deployment strategies for Lambda and EC2, SAM deployment preferences, and pipeline orchestration. 📘 Concepts AWS CI/CD Pipeline Overview The four AWS developer tools form a complete CI/CD pipeline: Service Role Input Output CodeCommit Source control Git push Source artifact CodeBuild Build and test Source artifact Build artifact CodeDeploy Deploy Build artifact Running application CodePipeline Orchestration Trigger (push, schedule) Coordinated pipeline execution How they connect: CodeCommit (source) → CodeBuild (build/test) → CodeDeploy (deploy) ↑ | └──────── CodePipeline (orchestrates all) ─────┘ 💡CodePipeline is the orchestrator. It doesn't build or deploy anything itself. It connects stages (source, build, test, deploy) and manages transitions between them. Each stage can use different providers (GitHub instead of CodeCommit, Jenkins instead of CodeBuild, etc.). CodeCommit Fundamentals Feature Details What It Is Managed Git repository hosted in AWS Authentication HTTPS (Git credentials or credential helper) or SSH (SSH keys) Encryption Encrypted at rest (AWS managed keys) and in transit (HTTPS/SSH) Triggers SNS notifications or Lambda functions on repository events Cross-account Use IAM roles with AssumeRole for cross-account access Branching Standard Git branching: main, develop, feature branches 💡CodeCommit supports triggers for push events that can invoke Lambda functions or send SNS notifications. This is different from CodePipeline's source stage: triggers are repository-level events, while CodePipeline po

Quick answer: The PromptEval Prompt Quality Report scored over 1,000 real prompts across 12 use cases. The average was 52 out of 100, and only 8% reached "good" (75+). The strongest single predictor of a good prompt is whether it defines its output format, worth 27 points on average. In 9 of 10 prompts, the weakest dimension was robustness. This is the PromptEval Prompt Quality Report . Over 1,000 real prompts have been scored on PromptEval , submitted by real users across use cases from customer support to healthcare to code. Each was scored from 0 to 100 on four structural dimensions: clarity, specificity, structure, and robustness. Every figure below comes from that set. No prompt text is stored; the analysis is anonymous and aggregate. Only 8% of the 1,000+ scored prompts reached "good" (75 or higher). Fewer than 1% reached "excellent." Source: PromptEval Prompt Quality Report, 2026 How the scores break down Here is how the scores spread across the set. The bar for "good" is 75, the point where a prompt is clear, specified, and holds up under variation. Score range Share of prompts 0 to 40 (failing) 25% 41 to 60 (below par) 31% 61 to 74 (functional but mediocre) 36% 75 to 84 (good) 8% 85 to 100 (excellent) under 1% Roughly 92% of prompts never reach "good," and almost none reach "excellent." This includes prompts from people who clearly know the tools. The gap is not talent. It is a few missing pieces that repeat. What separates a good prompt from a bad one For each structural element, we compared the average score of prompts that had it against those that did not. These are averages across the set, not a controlled experiment, so read them as correlation. But the gaps were large and consistent. The prompt... Avg with Avg without Point gap Defines the output format 58 31 +27 Has explicit constraints (what not to do) 63 41 +22 Assigns a role or persona 57 42 +15 Includes at least one example 64 51 +13 Prompts that define their output format score 27 points higher