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At WWDC 26, Apple announced the Core AI framework, the official successor to Core ML. It is designed to allow developers to run large language models and generative AI entirely on-device, supporting both custom-converted PyTorch models and pre-optimized open-source models. By Sergio De Simone

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The story starts with a common problem: Python is a fantastic language for rapid prototyping, data analysis, and orchestrating complex tasks. However, when it comes to raw computational speed, especially for number-crunching or highly parallelized operations, it can fall short. C++ and other compiled languages, on the other hand, excel in these areas. The question was: how do you get the best of both worlds? How do you write the performance-critical parts of your application in C++ while still enjoying the development speed and ecosystem of Python? The answer was to create a "binding" – a bridge that allows Python to call C++ code as if it were native Python. Early efforts in this space, such as Boost.Python , were powerful but often came with a steep learning curve and significant compilation overhead. They were a bit like using a sledgehammer to crack a nut – effective, but perhaps a bit unwieldy for many use cases. Have a look at how neat the python code looks; however, the actual job is done by the background C++. import libfoodfactory biscuit = libfoodfactory.make_food("bi") print(biscuit.get_name()) chocolate = libfoodfactory.make_food("ch") print(chocolate.get_name()) Do you like the story? Click on the link and learn about pyBinding - a glue to stitch C++ and Python... submitted by /u/sommukhopadhyay [link] [留言]

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\shadcn/ui and Material UI optimise for opposite priorities. Choose shadcn/ui to own your component code, ship a near-zero runtime, and control every pixel; choose Material UI (MUI) for breadth — 90+ components and a paid data grid — behind Google's Material Design. shadcn/ui has ~116,000 GitHub stars and ships copy-paste components; MUI has ~98,000 stars and ~7.3M weekly npm downloads. Both are MIT-licensed and free for commercial use. This guide covers the parts you only learn by shipping both: how each behaves in the Next.js App Router, the runtime cost, real theming and dark-mode code, forms, data tables, and migration mechanics. What's the real difference between shadcn/ui and Material UI? The difference is ownership, and it decides everything downstream. MUI is an npm dependency ( @mui/material ) you install and import from node_modules — you never touch the source. shadcn/ui is a copy-paste registry: you run a CLI, the component lands in your repo, and it is now your code. shadcn/ui is unstyled, built on Radix UI primitives and Tailwind CSS. MUI ships Material Design and an Emotion (CSS-in-JS) runtime. With MUI you install and import: bash npm install @mui/material @emotion/react @emotion/styled cta.tsx import Button from " @mui/material/Button " ; export function Cta () { return < Button variant = "contained" > Get started </ Button >; } With shadcn/ui the CLI copies the source into your project and you import from your own path — there is no library to upgrade or override: bash npx shadcn@latest add button cta.tsx import { Button } from " @/components/ui/button " ; export function Cta () { return < Button > Get started </ Button >; } That ownership changes how you customise. shadcn's button.tsx lives in your repo and uses class-variance-authority (cva) for variants — you add one directly: components/ui/button.tsx // components/ui/button.tsx — this file is yours const buttonVariants = cva ( " inline-flex items-center justify-center rounded-md ... " , { varia

How to Build Systems That Actually Know Your Data (Not Hallucinate About It) Introduction: The Story Begins 👦 Nephew: Uncle, I keep hearing "RAG this, RAG that" in tech interviews. When I ask what it means, people throw around words like "Retrieval-Augmented Generation" and I just nod like I understand. But honestly? I'm lost. 👨‍🦳 Uncle: (laughing) That's the best honest question I've heard all week. Let me ask you something first. If I gave you a question right now - "What year did India win the World Cup?" - how would you answer? 👦 Nephew: Well... I'd pull up Google, search for it, read the answer, then tell you. 👨‍🦳 Uncle: Exactly. You don't answer from memory alone. You go fetch the information first, then answer based on what you found . That's RAG in real life. And that simple idea - fetch first, answer after - fixes almost every problem we face with AI today. 👦 Nephew: But uncle, AI can remember things from its training. Why does it need to fetch? 👨‍🦳 Uncle: Ah! That's where we land in trouble. Come, sit... SECTION 1: RAG FUNDAMENTALS - The Core Concept The Problem We're Actually Solving 👨‍🦳 Uncle: Imagine you're hiring for a tech company. You receive 500 resumes for a Senior React Developer role. Now tell me - how would you actually process them? 👦 Nephew: I'd... probably make a spreadsheet? List all the candidates with key skills? 👨‍🦳 Uncle: Right. But here's the catch - you can't read all 500 resumes deeply. So what do you really do? 👦 Nephew: Skim for keywords like "React", "JavaScript", "5 years"? 👨‍🦳 Uncle: Exactly. You skim and hope you don't miss anyone good. Now, here's the problem: what if a candidate wrote "React.js" instead of "React"? Your eyes might still catch it. But a dumb computer doing exact string matching? It says "no match". What if someone wrote "Built real-time user interfaces with the React framework"? The candidate clearly knows React, but the word "React" appears nowhere in that sentence. The computer misses them. This is exactly wh

AI coding agents are expensive — not because models cost too much per token, but because they send too many of them. An SRE debugging session with a raw agent: 65,694 tokens in. With Headroom in the middle: 5,118. Same bug found. Headroom is a new open-source context compression layer that intercepts everything your agent reads — tool outputs, log dumps, RAG chunks, files, conversation history — and compresses it before the LLM ever sees it. It's local, reversible, and available as a drop-in proxy, a library, or an MCP server. The numbers that matter Savings on real agent workloads: Code search (100 results): 17,765 → 1,408 tokens (92% reduction) SRE incident debugging: 65,694 → 5,118 tokens (92%) GitHub issue triage: 54,174 → 14,761 tokens (73%) Codebase exploration: 78,502 → 41,254 tokens (47%) Accuracy on standard benchmarks (GSM8K, TruthfulQA, SQuAD v2, BFCL) is preserved — some scores actually improve slightly, likely because the model sees cleaner signal. What's doing the compression Under the hood, Headroom routes content through a stack of specialised compressors: SmartCrusher — JSON, nested objects, arrays of dicts CodeCompressor — AST-aware for Python, JS, Go, Rust, Java, C++ Kompress-base — a custom HuggingFace model trained on agentic traces, for prose and mixed content CacheAligner — stabilises prompt prefixes so Anthropic/OpenAI KV caches actually hit It also does CCR (reversible compression) — originals are cached locally and the LLM can retrieve them on demand if it needs them. Nothing is destroyed. Why the proxy mode matters The most interesting deployment path: headroom proxy --port 8787 , then point your existing tool at localhost. Zero code changes. Works with any language. Or even simpler: headroom wrap claude wraps Claude Code, routes its traffic through Headroom automatically. One command, savings start immediately. Same for Codex, Cursor, Aider, Copilot CLI. "Library — compress(messages) in Python or TypeScript, inline in any app. Proxy — hea

Hello Dev Community! 👋 It is officially Day 50 — a massive half-century milestone on my daily, unbroken streak toward mastering full-stack MERN engineering! Reaching Day 50 feels absolutely incredible. Yesterday, I mapped out dynamic path parameters. Today, I wired the input engine by building a complete asset workflow: Capturing Host "Add New Product" data payloads and committing them to local file storage pipelines! Following Prashant Sir's backend sequence , today was all about bridging the gap between host client forms and backend architecture using the Model-View-Controller framework. 🧠 Key Learnings From Day 50 (Product Ingestion & Storage) Processing data mutations sent from input forms requires tight coordination between parsing middlewares and file serialization engines. Here is how I structured the logic today: 1. Intercepting Form Submissions ( POST /host/add-product ) Set up a clean route mapping inside hostRouter.js to process dynamic data blocks sent by the host. The endpoint parses input parameters securely via backend streams. 2. Utilizing Class Instances for Storage Instead of directly pushing raw unstructured dictionaries into file records, I initialized a new object instance using my Day 48 structural class framework ( new houseList(...) ). This forces incoming data attributes—like name, price, location, and images—to match my exact system layout blueprint. 3. Asynchronous File Serialization Invoked the instance method .save() , which runs a non-blocking background task: it reads the active database layout array inside homesdata.json , appends the newly formulated object safely, and flushes the stringified update back onto the hard drive array using Node's fs operations. javascript // A conceptual look at how my controller hands data over to the model layer today const Product = require("../model/home"); exports.postAddProduct = (req, res) => { const { title, price, location, rating, imageUrl } = req.body; // Instantiating the core class data mold

I need your help. I've problems with an Navigation button on ios. On Windows in all browsers it's working good but on an ios device the menu isnt opening. I've tried to run devtools on an ios device to take a look in the console but this stays empty. https://rb.gy/7o5yql This is the url of the website. I've tried to remove the country flag and the logo i thought it was in the way of the button but nothing helps. Who would like to take a look at it?

Feeling overwhelmed by the constant flood of new AI features, MCP servers, and agentic platforms? In a world full of tech noise, it's easy to get exhausted trying to keep up. I just watched an incredible video by Burke Holland where he strips away the hype and shares 10 highly practical, concrete strategies to make AI coding tools actually work for your daily workflow. If you want to stop overcomplicating your setup and start getting better production results, here is the ultimate breakdown. The 10 AI Coding Tips (TL;DR Summary) Huge shoutout and credit to Burke Holland for these insights: 1) Use Visual Studio Code to maximize your environment with powerful themes, extensions, and inline terminal chats. 2) Always turn on YOLO / "allow all" mode so your AI agent can execute commands seamlessly without breaking your flow with constant permission prompts. 3) Never run agents on your own machine , choosing instead to isolate them via remote SSH or dev containers so YOLO mode is completely safe. 4) Prototype and mock everything upfront to map out UI design languages and logic before implementing code. 5) Always plan and grill by leveraging interactive planning modes to answer critical edge-case questions before generating file. 6) Rubber duck your plans across different AI model families (like combining Claude and GPT) to cross-verify solutions and expose blind spots. 7) Utilize autopilot and sub-agents to delegate parallel tasks and route smaller, faster models where appropriate. 8) Use built-in browser tools to visually review live previews and directly prompt structural or stylistic adjustments. 9) Run iterative multi-model reviews on autopilot to catch hidden bugs and refine code quality until reaching a clear point of diminishing returns. 10) Learn from your session history using tools like Chronicle to analyze your prompting habits and continually optimize how you interact with the agent. 📚 Recommended Reading If you are looking to dive deeper into perfecting your

OpenAI, Anthropic, and Gemini each report token usage differently, and it stops being trivia the moment you track LLM cost. I build Spanlens, an open-source LLM observability tool that sits in front of all three as a proxy and records every call with its model, latency, tokens, and cost. To do the cost part I read the token usage back out of every response, including the streaming ones. I assumed the three providers would report usage in roughly the same way. They send the same kind of data, after all: input tokens, output tokens, maybe a cached count. How different could it be. Pretty different, it turns out. Here is the whole thing in one table, then each gotcha in detail with the real parser code from the repo. Provider Where usage lives (streaming) Cache accounting Field names OpenAI final chunk, needs stream_options: { include_usage: true } prompt_tokens includes cache prompt_tokens / completion_tokens Anthropic split across message_start + message_delta input_tokens excludes cache, so add it input_tokens / output_tokens Gemini usageMetadata , two stream formats not applicable promptTokenCount / candidatesTokenCount Gotcha 1: the usage numbers live in different places in the stream For a non-streaming call this is boring. Every provider hands you a usage object on the response body and you read it. Streaming is where it gets weird, because the token counts are not in the content chunks. They show up somewhere else, and "somewhere else" is different for each provider. OpenAI puts the usage in a final chunk, after all the content, right before [DONE] . You only get it if you ask for it with stream_options: { include_usage: true } . Miss that flag and you stream the whole response and end up with no usage at all. export function parseOpenAIStreamChunk ( line : string ): Partial < ParsedUsage > | null { if ( ! line . startsWith ( ' data: ' )) return null const data = line . slice ( 6 ). trim () if ( data === ' [DONE] ' ) return null const json = JSON . parse ( data