标签：#m

While it's not flashy like Apple’s new Siri AI and Apple Intelligence upgrades, there are still a number of additions to iOS 27 worth looking at.

Atlassian details the Forge billing platform built for usage-based pricing across its cloud ecosystem. It processes large-scale usage events with correct attribution, deduplication, and aggregation using a streaming pipeline, idempotent processing, and layered storage to enable accurate billing, near real-time visibility, and reliable reconciliation across distributed services. By Leela Kumili

From its opening minutes, Moves of the Diamond Hand is upfront about what it offers: You're going to have a lot of strange conversations, and you're going to roll a lot of dice. Get on board with this proposition, and the reward is one of the most creative roleplaying games I've seen in years, even […]

I used to think tests were a waste of time. "Ship fast, fix later" was my motto. Until I spent three painful weeks debugging a production issue that a simple test would have caught in 30 seconds. That was the day I became a believer. The Harsh Reality Most Solo Developers Ignore If you're a freelancer or indie hacker building real products for clients, here’s what happens without good tests: You make a "small change" and something unrelated breaks Clients find bugs you should have caught Refactoring becomes terrifying You lose sleep before every deployment Your reputation slowly takes hits A solid test suite changes all of that. What a Test Suite Actually Gives You Confidence to Move Fast You can refactor, add features, or upgrade dependencies without fear. Living Documentation Your tests explain how the system should behave — better than comments ever could. Early Bug Detection Catch issues before they reach the client or production. Better Architecture Writing testable code forces you to write cleaner, more modular code. Professional Credibility When clients or senior devs review your code, a good test suite immediately signals seriousness. The Test Suite Pyramid I Actually Use Unit Tests (70%) → Test individual functions and components Integration Tests (20%) → Test how different parts work together (API + DB) End-to-End Tests (10%) → Critical user flows (login → checkout → etc.) I don't aim for 100% coverage. I aim for high-value coverage — especially around business logic and critical paths. Final Thought Writing tests feels slow at first. But it compounds. Every month you have tests, you move faster and sleep better. The developers who ship reliable software consistently aren't necessarily the smartest — they're usually the ones who learned to respect testing. Have you built a strong test suite habit yet? Or are you still in the "I'll test it manually" phase? Drop your experience below. Let's talk.

Introduction Large Language Models (LLMs) like ChatGPT have transformed how we interact with AI. They can write code, answer questions, summarize documents, and generate creative content. However, they have one major limitation - they only know what they were trained on and can sometimes generate incorrect or outdated information. So, how do modern AI applications answer questions about your company's private documents, recent news, or knowledge that wasn't part of the model's training? The answer is Retrieval-Augmented Generation (RAG). In this blog, we'll explore what RAG is, how it works, its architecture, benefits, challenges, and real-world applications. What is RAG? Retrieval-Augmented Generation (RAG) is an AI architecture that combines a retrieval system with a Large Language Model (LLM). Instead of relying only on the model's internal knowledge, RAG first retrieves relevant information from an external knowledge source and then uses that information to generate a more accurate response. Think of it like an open-book exam. Instead of answering from memory, the AI first searches for the most relevant pages and then writes the answer based on those pages. Why Do We Need RAG? Traditional LLMs have several limitations: Knowledge becomes outdated. They cannot access private company data. They may hallucinate (generate incorrect facts). Retraining models is expensive and time-consuming. RAG solves these problems by allowing the model to retrieve fresh and domain-specific information before generating an answer. RAG Architecture A typical RAG pipeline consists of the following components: User Query Embedding Model Vector Database Retriever Prompt Builder Large Language Model Final Response Step-by-Step Workflow * Step 1: * User asks a question Example: "What is our company's leave policy?" Step 2: Convert the question into embeddings The query is transformed into a vector representation using an embedding model. Example: "What is leave policy?" ↓ [0.12, -0.45, 0.7

In the world of cross-border e-commerce, malicious bot scraping leading to Meta/Google Pixel pollution is a nightmare for every seller. When your store starts gaining traction, these fake traffic sources can "poison" your ad model, causing your ROAS to plummet. To combat this, I’ve developed a robust "Backend Data Isolation" architecture. The Core Defense Strategy Stop triggering ad conversion events directly from the frontend. Instead, build a "firewall" at the backend to ensure that only verified, high-quality conversion data is sent to your ad platforms. Technical Implementation By implementing server-side logic in Python, we can filter out bot requests effectively: def process_pixel_event ( request ): # Filter out bot signatures (User-Agent, IP analysis) if is_bot_signature ( request . headers [ ' User-Agent ' ]): return None # Send only high-quality data to ad platforms if is_real_customer ( request . session ): trigger_pixel_event ( request ) By leveraging this logic, we feed "private, high-quality data" to the AI. This allows the algorithm to learn only from genuine customer behaviors, creating an "immortal pixel" moat around your store. Learn More For a deep dive into full-scale anti-scraping deployments and how to leverage automated translation techniques to scale traffic in blue-ocean markets, check out my full technical guide: 👉 Read the Full Implementation & Troubleshooting Guide Here

Hi, friends! Welcome to Installer No. 133, your guide to the best and Verge-iest stuff in the world. (If you're new here, welcome, happy belated Juneteenth, and also you can read all the old editions at the Installer homepage.) This week, I've been reading about Sam Bankman-Fried and PE Guy and admin nights (which we […]

CMF by Nothing won't be able to release a follow-up to the Phone Pro 2 this year.

Tests of age-verification technology show the risks of life-altering errors.

Steam Next Fest demos, a Virtual Boy-inspired shooter and other new indie games worth checking out.

After adding one to my home, here's why you might want a home battery, how they work, and what to look for, plus some installation tips.

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

We’ve trawled the depths of Amazon to find the best deals on gear we’ve tested.

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]

\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

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

Plus: Gay bars in San Francisco using face scanners, France quits Palantir, Apple plans to change its private email and more.

You have ISTQB Foundation. ISTQB Advanced. Certified ScrumMaster. A cloud cert. A security testing cert. Maybe a Python for Testers badge from a platform. And you still cannot write a test that finds a real bug. I interviewed someone like you last quarter. The resume was a wall of acronyms. The conversation was a wall of theory. "I follow the V-model." "I use equivalence partitioning." "I believe in shift-left." Then I asked: "Show me one test you wrote that caught something the developer missed." Silence. Not because they were nervous. Because they had never written a test that found a bug. They had written tests that passed. They had written tests that covered requirements. They had never written a test that broke something. That is the difference between a certification holder and a tester. Certifications test your memory. Bugs test your thinking. Let me show you what I mean. The Certification Trap Certifications are not useless. They give you vocabulary. They give you structure. They give you something to put on LinkedIn so recruiters stop asking if you know what a test case is. But they do not teach you how to find bugs. Here is why. Every certification exam tests known knowledge. You study a syllabus. You memorize definitions. You answer multiple-choice questions about boundary value analysis. You pass. Then you sit in front of an application. The application does not have a syllabus. It does not have a boundary value analysis section in the documentation. It has a login form that sometimes lets you in with a password that is clearly wrong, but only on Tuesdays, and only if the server clock is behind by exactly four minutes. No certification prepares you for that. The tester with 10 certifications treats testing like a checklist. They write test cases from requirements. They execute them. They mark pass or fail. They report coverage metrics. The tester who finds bugs treats testing like an investigation. They start with a hypothesis. They try to prove the appl