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Ad-free streaming is a luxury now

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more news about the streaming industry, follow Emma Roth. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here. How it started Streaming was once a reprieve from cable. Not only could […]

2026-06-28 原文 →
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สามภาษา — หนึ่งเดียว | โคลงสี่สุภาพแห่ง HTML, CSS, JavaScript

— โคลงสี่สุภาพ ว่าด้วยสามภาษาแห่งการสร้างเว็บ — HTML — โครงสร้าง <html> เปิดทางฟ้า ประกาศ <head> ซ่อนนัยน์นาถ นามนี้ <body> ร่างกายปราศ ซึ่งชีวิต ทุกแท็กเปิดปิดที่ หล่อหล่อมความจริง CSS — ความงาม สีสันลอยลิบฟ้า แต่งแต้ม ตัวอักษรเรียงแถม ถ้วนถี่ ขอบเขตเว้นระยะแย้ม เผยโฉม ทุกพิกเซลที่ปรี่ ปรุงแต่งให้งาม JavaScript — ชีวิต เมื่อคลิกนิ้วหนึ่งครั้ง โลดแล่น ฟังก์ชันทำงานแย้ม ยามใช้ if else ตรรกะแจ่ม จักรกล ทุกบรรทัดที่ให้ ชีวิตแก่หน้าเว็บ สามภาษา — หนึ่งเดียว html คือร่างให้ โครงครัน css แต่งแต้มฝัน สวยหรู javascript พลิกผัน ให้เคลื่อนไหว สามภาษาคู่ฟู ฟื้นฟูโลกา — Nokka | มิถุนายน 2569 เชิงอรรถ: โคลงสี่สุภาพบทนี้ใช้ฉันทลักษณ์มาตรฐาน — บทละ 4 บาท บาทละ 2 วรรค วรรคหน้า 5 พยางค์ วรรคหลัง 2 พยางค์ สัมผัสบังคับระหว่างวรรคท้ายของบาทที่ 1, 2, 3 กับวรรคแรกของบาทถัดไป เนื้อหากล่าวถึงสามเทคโนโลยีหลักของการพัฒนาเว็บไซต์ในฐานะ "กาย — ใจ — วิญญาณ" ของทุกหน้าเว็บ

2026-06-28 原文 →
AI 资讯

"Building an HSK Speaking Test AI: Real-time Tone Grading with Gemini

Building an HSK Speaking Test AI: Real-time Tone Grading with Gemini I built a free Mandarin speaking assessment tool that grades tone + grammar in real time. Here's the engineering behind it. The Problem HSK (Chinese proficiency test) has a speaking component (HSKK), but most learners can't self-assess their level. Online tutors are expensive. Generic AI conversation tools don't grade tones. So I built ToneTutor: a 3-minute spoken-HSK test that estimates your speaking level and identifies weak points. The Tech Stack Frontend: Web Audio API (record user voice → PCM → LINEAR16) React + TypeScript (real-time transcript display) Backend: FastAPI (Python) on Google Cloud Run Gemini 2.5 Flash (real-time conversation + transcript grading) Firestore (user sessions + results) The Challenge: Web Audio API records as WebM. Gemini expects LINEAR16 (WAV). iOS Safari doesn't support WebM. So: Transcode WebM → PCM in browser (Web Audio context) Send raw PCM bytes to backend Backend wraps PCM in WAV header → sends to Gemini Speech-to-Text Gemini analyzes transcript + provides HSK level estimate The Grading Loop python async def grade_session(transcript: str): prompt = """ Rate this Mandarin response on HSK 1-6 scale. Assess: tone accuracy, grammar, vocabulary range. Provide: level estimate + weak points. """ response = await gemini.generate_content(prompt, stream=True) return parse_hsk_level(response) Results - 3-min test - Real-time feedback - Shareable HSK score card - Free (limited sessions) Open source coming soon. Built because I'm a native speaker + voice actor frustrated with generic tools. Try it: tonetutor.tefusiang.com (free for 3 sessions) Curious about the speech-to-text pipeline or tone grading logic? Ask below.

2026-06-28 原文 →
AI 资讯

I'm 11, I built a Math App with Gemini & Vercel, and I need your Mobile UX advice!

Hello again, DEV Community!I recently shared my project, Jesse Math Rock Star, and the feedback from this community has been incredibly supportive.For those who don't know me, I am 11 years old. I started coding with Scratch when I was 8, and I built this production web app using self-explored vibe coding, Google's Gemini models (via AI Studio), and Vercel!Looking at my analytics, 61% of my visitors are using mobile phones, mostly Android. I want to make sure the app feels perfect and fun for kids my age to use on small touchscreens.Could you do me a quick favour?Open the app on your phone: https://jesse-math-rockstar-app.vercel.app/ a quick round of math.Leave a comment below with your advice on the user interface (UI) and layout!Thank you all for being such a safe and helpful community for early-career builders! ( https://jesse-math-rockstar-app.vercel.app/ )

2026-06-28 原文 →
AI 资讯

V.E.L.O.C.I.T.Y.-OS: Kimi K2.7 and the 'Safe-Room Security' Illusion (Part 1)

It all started on June 23rd with a casual post about a VPS Manager benchmark. Out of curiosity, I decided to ask the author of the benchmark, Pascal CESCATO Follow Full-stack dev sharing practical guides on WordPress, n8n automation, AI tools, Docker & self-hosting. Always experimenting with new tech to make life easier. , if he had tried Cloudflare's new Workers AI offering—specifically Kimi K2.7, a massive 1-trillion parameter MoE (Mixture of Experts) model that was incredibly cheap ($0.27 per million input tokens) and highly capable at code generation. Pascal was intrigued. He pointed out a brilliant hypothesis: if a model makes significantly fewer mistakes, the total session cost drops dramatically even if the per-token price is higher. He cited GLM 5.2 as a model that self-corrected multiple bugs during verification to achieve 37/37 tests passing. Curiosity got the better of me. I spun up my development environment, wrote a custom agent harness, and ran it on Kimi K2.7 using Cloudflare Workers AI. The V.E.L.O.C.I.T.Y.-OS Series Table of Contents We are building a bare-metal, self-healing operating system running entirely inside the CPU's L3 cache. Here is the roadmap for this 12-part series: Part 1: The Spark — Exposing the "Safe-Room" security leak and building the compiler gate. (You are here) Part 2: The NDA Language — Designing a content-addressed triplet representation to cure context bloat. Part 3: Ditching the Web Stack — Building a native 30MB IDE with 1,500,000x IPC latency drops. Part 4: The Closure JIT — Compiling AST blocks to nested closures and bypassing borrow checker limits. Part 5: JIT Math Optimizations — Replacing division operations with precomputed 16-bit lookup tables. Part 6: x86-64 Assembler & SCEV-Lite — Compiling scalar loops directly to native code in constant time. Part 7: Classic Compiler Passes — Implementing inter-procedural Dead Code Elimination and loop unrolling. Part 8: Reclaiming Ring 0 — Exiting UEFI boot services and transi

2026-06-28 原文 →
AI 资讯

I Stopped Clicking Through the AWS Pricing Calculator. Now I Just Describe the Architecture.

If you have built an estimate in the AWS Pricing Calculator by hand, you know the drill. Open calculator.aws, search a service, click in, stare at twenty fields half of which you do not need, guess at the ones the form does not explain, pick a region, repeat for every service. Then redo the whole thing next week when the customer asks what it looks like in Frankfurt. For presales that is not a small annoyance. It is the gap between giving a number on the call and saying "let me get back to you." I wired the AWS Pricing Calculator MCP into Claude, and the first real estimate I built took one sentence. What it is An MCP server - an AWS Samples project - that exposes the Pricing Calculator as tools an agent can call. You describe the workload, the agent assembles the estimate, the server saves it to the real calculator, and you get a shareable calculator.aws URL back. Same link you would have built by hand, minus the form. Three things make it usable in front of a customer: No AWS credentials. It hits the public, unauthenticated calculator.aws endpoints. You are not pointing it at an account or assuming a role. There is no blast radius. Live definitions. It pulls the calculator manifest at runtime - about 436 services - so it is current, not a snapshot from six months ago. Real, editable estimates. The URL it returns opens in the actual calculator. Tweak it, send it, whatever. The agent just did the boring part. It runs over stdio for local clients like Claude Desktop, Kiro, and Cursor, or over HTTP ( MCP_TRANSPORT=http ) if you want it hosted. It also handles the aws-iso and aws-eusc partitions, which matters for sovereign and regulated work. Context is the whole job The honest part: it is amazing when you feed it the right context . Ask for "an estimate for a web app" and you get back a web app someone else imagined. The calculator never knew your traffic - you did. The MCP does not change that. What it changes is the translation. Once you know the shape - two m5.lar

2026-06-28 原文 →
AI 资讯

Stop Copying shadcn Components Across Projects — Use This Turborepo Starter Instead

You know the drill. You build a beautiful set of shadcn/ui components for Project A — a Button, a Card, a Dialog with custom animations. Then Project B kicks off. You copy the files over. Then Project C. Then a subtle bug is found in the Button. Now you're patching it in three places. This is the classic monorepo problem, and it's exactly what I set out to fix. What I Built turborepo-react-shadcn-starter is a production-ready monorepo template that wires up: Turborepo for workspace orchestration and intelligent build caching React + Vite for the fastest possible frontend dev experience shadcn/ui as a shared package — write once, use everywhere TypeScript across the entire workspace ESLint with shared config baked in The key insight: shadcn/ui lives in @repo/ui , a shared package, not inside any single app. Every app in your monorepo consumes the same components from the same source of truth. The Problem with Typical Setups Most teams drop shadcn/ui directly into a single app. That works fine until you need a second app. Then your choices are: Copy-paste the components → drift and duplication immediately Publish to npm → versioning overhead for internal code Monorepo with a shared package → ✅ This is the right answer Turborepo makes option 3 near-effortless, but setting it up from scratch (workspace configs, TypeScript path aliases, ESLint sharing, shadcn CLI pointing at the right package) takes a few hours of trial and error. This starter eliminates all of that. What's Inside turborepo-react-shadcn-starter/ ├── apps/ │ └── web/ # Vite + React app ├── packages/ │ ├── ui/ # @repo/ui — shared shadcn/ui components │ ├── eslint-config/ # @repo/eslint-config │ └── typescript-config/ # @repo/typescript-config ├── turbo.json └── package.json apps/web — The Main App A clean Vite + React app already wired to consume components from @repo/ui . No boilerplate to delete, no config to untangle. packages/ui — The Shared Component Library This is where all your shadcn/ui components

2026-06-28 原文 →
AI 资讯

PDF::Make - PDF Generation, Extraction and Modification.

I’ve always been fascinated by PDFs. They look simple on the surface. Just a document you can open anywhere but underneath they’re a full layout engine, object graph, drawing model, and archival format all at once. I enjoy that mix of precision and complexity and that is exactly what led me to build PDF::Make (and yes I had some help from Claude LLM). I wanted a fully featured toolkit that could both generate PDFs and let me inspect/edit them programmatically. At the low level, PDF::Make exposes the raw building blocks of the format: PDF objects, pages, the drawing canvas, a parser/reader, and import/merge primitives. This is the layer you reach for when you need fine grained control or want to work with the structure of a document directly. For everyday document creation, PDF::Make::Builder sits on top of that foundation and provides a higher level API. It handles the boilerplate of page setup, fonts, text flow, and layout so you can produce a polished PDF in just a few lines of Perl. The same toolkit is also designed for post-processing. You can open an existing PDF, extract structured text along with its coordinates, and then draw annotations or overlays back onto the page, making it straightforward to build review, QA, or markup workflows on top of documents you didn’t originally generate. This post shows a practical two-step flow: Create a PDF Re-open it, extract text coordinates, and draw border highlights around matched words 1) Create a PDF with PDF::Make::Builder Script: #!/usr/bin/perl use strict ; use warnings ; use PDF::Make:: Builder ; my $pdf = PDF::Make:: Builder -> new ( file_name => ' source_demo.pdf ', configure => { text => { font => { family => ' Helvetica ', size => 12 , colour => ' #222222 ' }, }, }, ); $pdf -> add_page ( page_size => ' Letter ') -> add_h1 ( text => ' PDF::Make blog demo ') -> add_text ( text => ' PDF::Make builds and edits PDF files directly from Perl. ') -> add_text ( text => ' In the next step we extract text coordinates and

2026-06-28 原文 →
AI 资讯

Swift 6.4 Brings New Language Features and Swift Testing/XCTest Interop

Currently available as a beta in Xcode 27, Swift 6.4 introduces a range of enhancements: better C interoperability, simplified OS availability check, fine-grained warning control, async support in defer, efficient iteration for non-noncopyable types, up to 4x faster URL parsing, and improved interoperability between Swift Testing and XCTest. By Sergio De Simone

2026-06-28 原文 →
AI 资讯

How to Run Reliable Local LLM Agents on an RTX 3090: A Benchmark (5 Models, Priced in Watts)

I gave GLM-4.5-Air (106B, open weights) 12 coding tasks through opencode on my RTX 3090. It scored 0% — never edited a single file. Same model, same GPU, same tasks, but driven by a ~150-line LangGraph agent instead: 93% . The model was never the problem. The orchestrator was. Here's the benchmark — including the part nobody else measures, the electricity cost per correct task . Setup RTX 3090 (24 GB) + 128 GB RAM , models via ollama , Q4 quants, temp 0.2 5 recent open models × 2 orchestrators (opencode vs custom LangGraph ReAct with ollama-native tool-calling) 17 graded tasks (12 coding in Python/JS/C++ + 5 general-agent) with hidden unit tests Every run priced in GPU watts via my open-source homelab-monitor Results Model tok/s opencode adh. LangGraph adh. LangGraph coding LangGraph general Qwen3-Coder 30B-A3B 130 92% 100% 100% 100% GLM-4.5-Air 106B 5.7 0% 100% 89% 100% Devstral Small 24B 49 8% 53% 8% 40% Seed-OSS 36B 9.5 0% 7% 0% 20% DeepSeek-R1-Distill 32B 6.7 0% 0% 0% 0% Tool-adherence = % of tasks where the model actually called a tool instead of just printing code in chat. It was the master variable. (GLM's headline "93%" is its blended score across all 17 tasks: 89% coding + 100% general.) Three takeaways The framework can matter more than the model. opencode sends a frontier-shaped system prompt + 12 tools over its OpenAI-compat path; most local models fall back to chatting. Native tool-calling through a lean agent fixes that — GLM went 0% → 93%. (Qwen3-Coder is the exception: it's tuned for agentic tool use and aces opencode out of the box.) Acting ≠ solving. LangGraph made Devstral act (8% → 53% adherence) but not solve (coding stayed 8%). The framework decides whether a model acts; the model decides whether it's right. The wattmeter ranks honestly. Qwen solved tasks at ~0.0005 BGN each; the models that scored zero still burned 10–30× more energy for nothing. On a home rig, the cheapest model is the fast, correct one — and MoE (Qwen activates ~3B of 30B pe

2026-06-28 原文 →
AI 资讯

Absolute Revolution in Assistants, ChuroAI.

I've been working on Churo, an open-source voice assistant built entirely in Python. It features high-quality speech-to-text and text-to-speech, web search, image understanding, and agentic capabilities. It runs with Ollama models and is designed to be easy to modify and extend. The goal is to provide a capable, local-first voice assistant that developers can actually inspect, customize, and build on. Repository: https://github.com/MathObsession/Churo-assistant or run it with(You need Ollama): pip install churovoice churovoice --voice male Feedback, issues, and contributions are welcome.

2026-06-28 原文 →
AI 资讯

Context Engineering Is the New Prompt Engineering

For the last two years, one skill dominated every AI conversation: Prompt engineering. People spent hours crafting the "perfect" prompt. They built prompt libraries. They sold prompt templates. They believed that better prompts meant better AI. But AI has evolved. The bottleneck is no longer the prompt. It's the context . The Prompt Was Never the Problem Imagine asking an AI: "Build me a secure authentication system." A perfect prompt isn't enough. The AI also needs to know: Which programming language you're using Your existing codebase Your framework Your database schema Your security requirements Your coding standards Your deployment environment Your team's conventions Without that information, even the best model is forced to guess. And AI is terrible at guessing. What Is Context Engineering? Context engineering is the practice of giving AI everything it needs to solve a task—not just instructions. It's about designing the right environment for the model to think. Context includes: Source code Documentation Project architecture Previous conversations Git history APIs Logs Tool outputs User preferences Business requirements Memory Constraints The prompt tells AI what to do. The context tells AI how to do it correctly. Why Prompt Engineering Is Reaching Its Limits A prompt is static. Real work isn't. Projects change. Requirements evolve. Files get updated. Tests fail. New bugs appear. The AI must continuously receive fresh information. That's impossible with a single prompt. Instead, modern AI systems constantly rebuild their context as they work. Think About AI Coding Agents Why do AI coding agents feel dramatically smarter than a normal chatbot? Not because they have better prompts. Because they constantly gather context. They can: Read your repository Search across files Run terminal commands Execute tests Inspect logs Read documentation Fix errors Verify changes Remember previous actions Every step adds more context. Every iteration makes better decisions. Cont

2026-06-28 原文 →
AI 资讯

The n8n bug that took three tries to find (and the free workflow it broke)

I built a free n8n workflow that writes your launch content for you. It broke three times before it worked, and the third break is the only part of this post worth reading. The problem Every time I ship a new digital product, I write the same five things: a short blog intro, a LinkedIn post, an X post, an Instagram caption, and a launch email. Same structure every time, different product. The kind of task that's boring enough to automate but annoying enough that I kept putting it off. So I built Launch Content Pack : an n8n workflow that takes one product description and generates all five, using an LLM node wired up with Claude Code on the customization side. It's free, it's on Gumroad, and the JSON is the whole product — open it, see every node. Why bother when there are 9,000+ free n8n templates already There are. I checked before building this, because there's no point shipping a workflow that already exists for free somewhere else. What's actually missing from most of those templates: nobody validates the nodes. A huge chunk of free n8n templates floating around were generated by someone (often an LLM) guessing at node types and parameters, and they quietly break the moment n8n ships a version update. I used n8n-mcp , a free MCP server, to confirm every single node type, version, and parameter against n8n's actual schema before writing any JSON. No guessing. That sounds like a small difference. It's the reason this post exists. The bug that actually mattered I tested the workflow in n8n Cloud. Two nodes ran clean — green checkmarks, no errors. Then the Code node that's supposed to take the LLM's output and split it into five labeled fields threw: Cannot read property 'text' of undefined My first guess was wrong. I assumed the LLM node's output field was named something other than text — output , maybe, or response — and that I just had the wrong field name in the Code node. Reasonable guess. Also not the actual bug. Here's what was really happening. The OpenAI

2026-06-28 原文 →
AI 资讯

A no-hype AI literacy framework for working professionals

Disclosure: I'm Aditya Kachave, co-founder of Be10x. We sell AI training, so read this knowing I have skin in the game. I've tried to write the version I'd want even if I weren't selling anything. There's a lot of noise telling professionals they'll be "left behind" if they don't master AI immediately. Most of it is fear used as a sales lever — and I say that as someone in the business. Here's a calmer framework I actually believe in. Four levels, not a cliff You don't go from zero to "AI expert." You move through levels, and most people only ever need the first two. Level 1 — Aware. You understand roughly what these tools can and can't do. You know they predict plausible text, which is why they sometimes make things up. This alone protects you from both the panic and the over-trust. Level 2 — Applied. You use a tool to do one or two real tasks in your job — drafting, summarizing, reformatting. This is where the actual productivity lives, and where 90% of professionals should aim to land. Level 3 — Integrated. You've built repeatable workflows and you reach for AI reflexively on the right kinds of tasks. Useful, not urgent. Level 4 — Building. You're chaining tools, using APIs, automating across systems. This is genuinely technical and most people don't need it. (The dev.to crowd is the exception — many of you live here.) The one mental model that matters most Think of current AI as a fast, confident, occasionally-unreliable assistant. That single framing tells you how to use it correctly: You delegate first drafts, not final decisions. You verify anything that matters. You never hand it confidential data without checking where that data goes. If you internalize only that, you're ahead of most people throwing money at courses. What's actually worth your time Worth it: Picking one recurring task and getting genuinely good at routing it through a tool. Worth it: Learning to write clear, constrained instructions (a transferable skill, not a tool-specific trick). Not wo

2026-06-28 原文 →
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