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Hey folks 👋 If you've ever wanted to save a video lecture, a livestream replay, or a podcast episode for offline listening, you've probably run into the usual options: sketchy "online video parser" websites that ask you to paste your link into their server, or desktop apps that want you to sign up and upload stuff. Neither feels great when the whole point is your content. I went looking for something better and ended up working with FlowPick — an open-source, privacy-first media downloader that runs entirely in your browser. No uploads, no accounts, no telemetry. Everything (sniffing, downloading, merging, transcoding) happens client-side with FFmpeg compiled to WebAssembly. In this tutorial we'll: Clone and run FlowPick locally Download our first HLS ( .m3u8 ) and DASH ( .mpd ) stream Build and deploy it Poke at the internals so we can customize it If you just want to try it without installing anything, there's a hosted version at https://flowpick.net (more below). The full source is on GitHub: https://github.com/ezwebtools/flowpick . 🔗 Repo: https://github.com/ezwebtools/flowpick · Live tools: https://flowpick.net A 30-second primer: what are HLS and DASH? Before we touch code, two words you'll see everywhere in this space: HLS (HTTP Live Streaming) uses a .m3u8 manifest that lists small .ts (or fMP4) segments. Common for live streams and a lot of video platforms. DASH (Dynamic Adaptive Streaming over HTTP) uses a .mpd manifest; video and audio usually travel as separate .m4s tracks. YouTube and Bilibili lean on this. The key idea: the "video" isn't one file. It's a playlist pointing at dozens (sometimes hundreds) of tiny segments. A downloader's job is to fetch all the segments, decrypt them if needed, and stitch them back into one playable file. That's exactly what FlowPick does — in the browser. What FlowPick is, in one paragraph FlowPick is a Nuxt 4 app that ships in two shapes: A browser extension that sniffs media from the current tab's network requests. An

Nobody has ever asked you to upstream your dotfiles. For thirty years that was the deal with every tool we touched: if you didn't like it, you changed it, and the change lived with you. Then the tools started writing the code, and the deal quietly ended. This essay is about why the deal is back on the table. Because the thing that used to make forking expensive — the labour — is exactly what agents just made cheap. In This Article The Workbench Instinct Then the Harness Era Arrived Forking Was Always the Escape Hatch Code Got Cheap What I Shipped Into My Fork A Feature Does Not Have to Be Useful Your Desire Is the Limit The Fork Is the Destination Now the Discipline Part Bring Back the Joy The Workbench Instinct Show me a developer who has never touched their config and I'll show you someone who hasn't started yet. Vim users brag about their init.lua the way woodworkers talk about a hand plane they've had for twenty years. Emacs people wrote a whole operating system inside a text editor because they could. VS Code won partly because it shipped an extension API and got out of the way. Dotfiles repos are public artifacts, starred and forked, because the setup is part of the craft. This isn't productivity theatre. Some of it is genuine need, some of it is fixing a specific annoyance that only you have, and a lot of it is just fun. All three are valid. The workbench is where the joy lives — and nobody ever waited for permission to alias a command. Then the Harness Era Arrived Then agentic coding tools showed up and quietly changed the shape of the deal. The best-in-class agent harnesses are increasingly vendor-controlled. Claude Code is a product, not a repo you can clone and rebuild. Google announced it's retiring Gemini CLI in favour of a closed-source successor. And note where the line falls: Codex CLI is Apache-2.0 and sitting right there on GitHub, but the Codex desktop app — the thing most people actually click on — is not. The terminal stayed open. The interface

Why browser-based? Every other "online tool" site uploads your PDFs and images to their server for processing. That means: Your sensitive documents sit on someone else's machine Processing speed depends on their server load File size limits, watermarks, or forced signups I built everything using client-side processing — Canvas API, pdf-lib, Tesseract.js (WebAssembly), and more. Your files literally never leave your device. What's included PDF Tools (28): Merge, split, compress, convert to Word/Excel, password protect, watermark, page numbers, metadata editor Image Tools (30): Compressor, background remover, crop, resize, DPI changer, EXIF viewer/remover, OCR (image to text), meme generator, QR code generator Developer Tools (42): JSON formatter, JWT decoder, Base64/Base32 encoder, regex tester, cron generator, SQL formatter, CSS/JS/HTML minifier Design Tools (14): CSS gradient, box shadow, border radius generators, color contrast checker (WCAG), Tailwind component builders Calculators (16): EMI, SIP, BMI, compound interest, salary tax, GST/VAT, ROI, fuel cost Plus text tools, unit converters, YouTube tools, utilities, and more. Tech Stack Next.js 15 (static export, deployed on Cloudflare Pages) TypeScript (strict mode) Tailwind CSS (dark mode) pdf-lib, pdfjs-dist (PDF processing) Tesseract.js (OCR — WebAssembly, zero dependency on external APIs) @imgly /background-removal (on-device AI background removal) SEO and Performance Every tool page includes: FAQ + HowTo structured data (visible in Google rich results) BreadcrumbList schema BlogPosting schema for blog articles Keyword-optimized titles and descriptions OG images for social sharing next/image with priority hints for fast LCP Try it toolshubs.app Looking for feedback — especially on the image compressor, PDF merger, and background remover. What tools would you add next?

How synthetic reality may influence human intuition about motion, gravity, and causality AI video generation has reached a point where a model can create scenes that look physically convincing at first glance: A person jumping impossible distances Objects moving without inertia Water flowing upward Animals performing human-like actions Buildings bending like rubber People interacting with impossible environments For decades, humans learned physics by observing the real world. A ball falls. A glass breaks. A person cannot walk through a wall. Heavy objects require more force to move. These observations create what cognitive scientists call intuitive physics : an internal mental model that predicts how objects should behave. But what happens when the majority of visual experiences become synthetic? Could AI-generated videos slowly change how future generations perceive reality? Humans Do Not See Reality Directly A common misconception is that our brain works like a camera: Reality → Eyes → Brain → Understanding The actual process is closer to: Reality ↓ Sensory input ↓ Brain prediction model ↓ Perception The brain is constantly predicting what should happen next. When you see a ball thrown into the air, your brain automatically predicts: trajectory speed gravity collision point acceleration This happens before conscious reasoning. This capability is known as predictive processing . Your brain is not only asking: "What am I seeing?" It is also asking: "Does this match my internal model of how the world works?" The Brain Learns Physics From Experience Young children do not learn physics from equations. They learn by interaction. A baby discovers: Objects continue to exist when hidden Unsupported objects fall Solid objects cannot overlap Larger objects require more effort to move Researchers call these abilities core knowledge systems . Humans appear to have an innate expectation that the physical world follows consistent rules. For example: A child watching a ball roll

The problem Every time I needed to hand someone a quick installable build — a client, a tester, myself on a different machine — I had to either keep a full local toolchain ready (Android Studio, Flutter SDK, Visual Studio...) or spend 20 minutes reinstalling one just for a single build. So I built PhilBuilder : upload a zipped source project, pick a platform, get back an installable app. No local setup required. 🔗 Try it: https://philbuilder.netlify.app What it does You upload a .zip of your project. The tool: Auto-detects the project type (React, Vue, Flutter, React Native, Kotlin, .NET MAUI, Python, Go, Godot, and 14 others — 22 combinations total) Builds it on remote CI Gives you a download link for an APK, AAB, or Windows .exe No account needed for occasional use (3 builds/day). A free account bumps that to 10/day. How it's built The stack is intentionally simple: Frontend : a single static HTML file, no framework, no build step Backend : a Cloudflare Worker handling auth, rate limiting, and dispatching builds Build execution : GitHub Actions — one big workflow with per-language jobs (Cordova for web frameworks, Capacitor for modern web, native Gradle for Kotlin/Java, dotnet publish for MAUI, flutter build for Flutter, briefcase for Python, gomobile for Go, etc.) Storage : Cloudflare R2 for source zips and build artifacts The auto-detection logic walks the extracted zip looking for telltale files — pubspec.yaml → Flutter, *.csproj → .NET MAUI, capacitor.config.* or a @capacitor/core dependency → Capacitor, build.gradle without package.json → native Kotlin/Java, and so on — with fallbacks down to plain HTML. Some technical details Signing : for Android release builds, it can auto-generate a keystore (and let you download it afterward — losing it means you can never update your app on Play Store again, so this is clearly flagged) or accept an uploaded one. Windows builds : this is the newest addition. Flutter and .NET MAUI need windows-latest runners; Go cross-com

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Opus 5 runs at 5 and 25 per million tokens against Fable 5 at 10 and 50, so the top tier now costs double for a much smaller gap Thinking is on by default on Opus 5, which silently changes what a tight max_tokens setting does to your output Disabling thinking now returns an error above high effort, so any xhigh or max route that turns it off needs an audit before you migrate Prompt caching starts at 512 tokens on Opus 5, half the Opus 4.8 floor, so short reusable prompts cache with no code change In June I worked through whether Claude Fable 5 was worth double the price of Opus 4.8 and concluded that it usually was, for hard work. Claude Opus 5 landed on July 24 at Opus 4.8's exact price and closed most of that gap. So the answer changed, and a few of the changes will throw errors in code that worked last week. The Price Gap Held, the Capability Gap Closed Opus 5 costs 5 and 25 per million tokens, input and output. That is identical to Opus 4.8 and exactly half of Fable 5 at 10 and 50. Anthropic did not raise the sticker price on the tier it improved, which is the single most consequential fact in this release. What that buys, on the numbers: 79.2 percent on SWE-bench Pro against Fable 5's 80.3, and a CursorBench 3.2 result Anthropic describes as landing within 0.5 percent of Fable 5's peak at max effort, at half the cost per task. On OSWorld 2.0 it goes past Fable 5's best computer-use result at just over a third of the cost. A 1.1 point deficit on the headline coding row, for half the money. Last month the equivalent comparison had an 11 point spread. That is what actually changed, and it flips the default: Fable 5 used to be the reasonable choice for anything hard, and now it has to argue for itself on each task. There is a quieter cost lever too. The minimum cacheable prompt on Opus 5 is 512 tokens, down from 1024 on Opus 4.8. Prompts I had written off as too short to cache now create entries with no code change at all. If you run a lot of small repeated calls,

Three labs shipped flagship models in fifteen days: GPT-5.6 Sol on July 9, Kimi K3 on July 16, Claude Opus 5 on July 24 Opus 5 leads SWE-bench Pro 79.2 to 64.6 over Sol, and ARC-AGI-3 30.2 to 7.8 Sol holds Terminal-Bench 2.1 at 91.9 percent in its top mode and still takes DeepSWE 1.1 and HealthBench Professional Kimi K3 is a 2.8 trillion parameter open-weight model at 3 and 15 per million tokens, roughly 40 percent under Opus 5 on input Fifteen days. That is the gap between OpenAI making GPT-5.6 Sol generally available and Anthropic shipping Claude Opus 5, with Moonshot dropping a 2.8 trillion parameter open-weight model in the middle of it. I wrote a frontier check like this in June and most of it is already out of date, so here is where the three current flagships actually stand. Three Flagships in Fifteen Days Model Lab GA Context Per million (in / out) GPT-5.6 Sol OpenAI 2026-07-09 1.05M 5 / 30 Kimi K3 Moonshot AI 2026-07-16 1M 3 / 15 Claude Opus 5 Anthropic 2026-07-24 1M 5 / 25 The specs have converged to the point where they barely differentiate anything. All three sit at or just above a million tokens of context. All three cap output around 128k. The input prices are within a factor of two of each other. Two years ago a context window was a headline; now it is table stakes, and the interesting differences have moved entirely into behavior under load. Two timing details that get flattened in the coverage. GPT-5.6 Sol was previewed on June 26 and only became generally available on July 9, so some of the earliest benchmark tables were run against a preview build. And Sol is the top of a three-model family alongside Terra and Luna, spanning roughly 1 to 30 per million tokens depending on tier. Comparing Opus 5 to "GPT-5.6" without saying which one is close to meaningless, which is a large share of the comparisons currently circulating. One structural note on Kimi K3, because the parameter count gets quoted carelessly. It is a mixture-of-experts model with 896 exp

Every few weeks I'd end up rewriting the same 10 things from scratch: rate limiter middleware, webhook signature check, retry-with-backoff, connection pool config. So I built AutoSnippets. 50 snippets across Python, JS, TS, Java, C#, C++, Go, PHP, Rust, and SQL. All production-ready, and even more are being made. Favorites of mine: Go channel-based worker pool (snippet #32) Rust Arc + Mutex safe counter (#41) SQL recursive CTE for org charts (#50) PHP RBAC in like 8 lines (#38) Free, no signup. Bookmark it if you find it useful.

AI-Driven Development: How Machine Learning is Reshaping Software Workflows in 2026 The software development landscape of 2026 looks almost unrecognizable compared to just a few years ago. Artificial intelligence has moved from being a novel assistant to a core pillar of the development workflow. Today, AI doesn't just autocomplete a line of code; it helps architect entire systems, automatically detects and fixes bugs before they reach production, and continuously learns from the organization's codebase to accelerate every phase of delivery. This article explores the key transformations and practical examples of how AI is reshaping software development in 2026. AI-Powered Code Generation and Completion By 2026, AI-powered code assistants have evolved far beyond simple autocomplete. Modern systems understand natural language requirements, project architecture, and even business logic. Developers can describe complex features in plain English, and the AI generates multi-file implementations, including dependency management, configuration, and tests. Example: Generating a REST API with AI A developer might request: "Create a FastAPI endpoint for user registration with email verification, rate limiting, and an asynchronous database call." The AI would produce: from fastapi import APIRouter , HTTPException , Depends from sqlalchemy.ext.asyncio import AsyncSession from app.database import get_async_session from app.models import User from app.schemas import UserCreate , UserResponse from app.services import create_user , send_verification_email from app.rate_limiter import rate_limit router = APIRouter ( prefix = " /auth " , tags = [ " auth " ]) @router.post ( " /register " , response_model = UserResponse ) @rate_limit ( max_requests = 5 , window_seconds = 60 ) async def register ( user_data : UserCreate , db : AsyncSession = Depends ( get_async_session )): existing_user = await User . find_by_email ( db , user_data . email ) if existing_user : raise HTTPException ( statu

Video transitions are one of those details that quietly shape the feel of an edit. In Reel Quick issue #13 , the goal was simple: let users control how long a scene transition lasts instead of forcing a fixed value. Issue URL: https://github.com/ronin1770/reel-quick/issues/13 The problem The app already supported transition effects between scenes, but the duration was fixed. That meant creators could choose what transition to use, but not how long it should run. For short-form video, that matters a lot: fast transitions create a snappier pace longer transitions feel smoother or more cinematic some edits need no transition at all The feature The new behavior adds a configurable transition duration: minimum: 0.0 seconds maximum: 4.0 seconds step: 0.5 seconds A slider in the frontend lets the user choose the duration, and that value is sent to the backend for FFmpeg video generation. If the value is 0.0 , transitions are disabled entirely. The FFmpeg math When two clips are joined with a transition, the transition overlaps the end of the first clip and the start of the second clip. So the final duration is: final length = clip 1 + clip 2 - transition duration Example 1 Clip 1 = 7 seconds Clip 2 = 8 seconds Transition duration = 4 seconds Math: 7 + 8 - 4 = 11 seconds Final video length: 11 seconds Example 2 Clip 1 = 7 seconds Clip 2 = 8 seconds Transition duration = 0.5 seconds Math: 7 + 8 - 0.5 = 14.5 seconds Final video length: 14.5 seconds Why validation matters This feature also needs guardrails. The backend validates that: the duration is between 0 and 4 the duration is a multiple of 0.5 clips are long enough for the selected transition That last point is important. A 4 second transition cannot work safely if a clip itself is only 3 seconds long. Implementation notes The implementation touches both frontend and backend: Frontend add a transition duration slider show the selected value beside it send transition_duration in the video creation request show inline vali

If you run GitOps with Flux, a broken manifest usually gets caught the slow way: it merges, the reconciler chokes, and you find out from a failing Kustomization. Flux Schema, the plugin that shipped with Flux 2.9, moves that check left into CI. It validates every YAML document against JSON Schema and CEL rules using the same evaluation logic as the Kubernetes API server, so a bad field fails the pull request instead of the cluster. Install and run it Flux Schema is a CLI plugin, not part of the core binary. Install it through the plugin system: $ flux plugin install schema $ flux schema --help Pin a version in CI so a new release never changes your gate's behavior mid-sprint: $ flux plugin install schema@0.5.0 Point it at a directory of manifests and it validates each document: $ flux schema validate ./manifests It ships with built-in schemas for Kubernetes, OpenShift, Gateway API, and the Flux CRDs, so a fresh install already knows your HelmRelease and Kustomization kinds without any setup. Strict validation flags unknown fields, wrong types, and missing required properties as hard errors, which catches the typos kubectl apply --dry-run=client quietly ignores. What CEL adds over plain schema checks JSON Schema catches shape problems: a string where an int belongs, a misspelled key. CEL rules catch logic problems. Because Flux Schema runs the x-kubernetes-validations rules embedded in CRDs through the same CEL engine the API server uses, a manifest that violates a cross-field constraint (say, a replica count that must stay below a limit, or two mutually exclusive fields both set) fails in CI with the exact message the cluster would have returned. You are testing against the real admission logic, not a stale copy of it. Wire it into a config file Drop a .fluxschema.yml at your repo root to control what gets checked. The file uses the schema.plugin.fluxcd.io/v1beta1 API and a Config kind: apiVersion : schema.plugin.fluxcd.io/v1beta1 kind : Config skipKind : - Secret s

The most expensive part of running AI-powered customer support isn't the token cost or the infrastructure—it's the maintenance of truth. You deploy a chatbot, it works brilliantly for three days, then your product team pushes a breaking change to your API or shifts your refund policy. Suddenly, that 'intelligent' agent is hallucinating outdated information with extreme confidence. This is where most developers fail: they treat AI support as a static RAG (Retrieance-Augmented Generation) problem when it should be treated as an observability and orchestration problem. I've spent years building systems where the drift between documentation and reality was the primary cause of production incidents. The MCP (Model Context Protocol) changes this trajectory because, for the first time, we have a standardized way to move beyond 'read-only' agents. When I looked at how Aidbase implements its MCP server, I didn't see just another way to query an FAQ. I saw the blueprint for a self-healing support loop. The Shift from Reading to Operating Most people use MCP to give Claude or Cursor access to their codebase or some documentation files. It's useful, but it's passive. You ask a question; the agent finds an answer. With the Aidbase implementation, the capabilities are fundamentally different because they include 'write' operations via tools like add_aidbase_faq_item and add_aidbase_website_knowledge . This shifts the LLM from being a passive librarian to an active Support Engineer. Think about your current workflow. You find a bug, you fix it, you update the PR, and then... you remember you need to go into the Aidbase dashboard (or Zendesk, or Intercom) to manually update the FAQ so the bot doesn't keep telling customers the old way is correct. That manual step is where human error lives. With this MCP server, your workflow looks like this: You finish the PR in Cursor. You point at the new documentation URL or a snippet of code. You tell Claude: "Update our Aidbase knowledge base

I went looking for a simple answer to a simple question: How do you give an agent access to Google Calendar? Not a demo. Not a screenshot. A real agent, running unattended, with enough access to be useful and enough guardrails that it won’t turn into a security incident. While researching OpenClaw setups, I found a thread on r/openclaw where someone asked what looked like a tiny question: what do I need to add Google Calendar to OpenClaw? One reply said: "Look into gog cli." That answer is way more revealing than it looks. Because the hard part usually isn’t Google Calendar itself. The hard part is everything hidden behind the phrase "connect Google" . And if you’re building agents in n8n, Make, Zapier, OpenClaw, or a custom OpenAI-compatible loop, auth is only half the problem anyway. Once the workflow runs 24/7, you also need to think about retries, quota limits, caching, and how many LLM calls the thing is quietly making in the background. That’s where a lot of teams hit the same wall: the integration works, but the operational shape of it is bad. Security is fuzzy. Request volume is noisy. And AI costs get weird fast if every poll and retry triggers more model calls. The demo version is lying to you If you’ve used something like n8n Cloud, you’ve seen the polished version: Click Google Calendar Sign in Approve access Done That flow is real inside a managed product. But the minute you leave the managed garden — self-hosted n8n, OpenClaw, a custom MCP server, a Python worker on Ubuntu, or your own app using the OpenAI SDK against an OpenAI-compatible endpoint — you inherit the boring parts. Now "connect Google" actually means: create a Google Cloud project configure the OAuth consent screen choose the right OAuth client type enable the Google Calendar API pick the right scopes store credentials safely handle refresh tokens deal with quota errors later That’s not setup trivia. That’s infrastructure. One user in that same OpenClaw discussion realized it immediately:

JFrog Security Research revealed "PixelSmash," a vulnerability in the FFmpeg media framework, allowing for Remote Code Execution and Denial of Service attacks. Present for sixteen years, it affects numerous applications using the MagicYUV decoder. Exploitation requires only a crafted media file. Users are advised to check for the vulnerability and apply patches or disable the decoder if necessary. By Olimpiu Pop

Opus 5 posts 79.2 percent on SWE-bench Pro against Opus 4.8 at 69.2, a 10 point jump with no change in per-token price Anthropic published most gains as ratios (three times ARC-AGI-3, more than double Frontier-Bench) rather than absolute scores On CursorBench 3.2 at max effort it lands within 0.5 percent of Fable 5's peak at half the cost per task Public GDPval-AA figures disagree across sources by up to 117 Elo, so I left that row out entirely Anthropic shipped Claude Opus 5 on July 24, and the coverage filled up with ratios instead of scores. Three times the next-best model. More than double the previous Opus. Just over a third of the cost. I went looking for the actual numbers behind those phrases. What I found says as much about how model launches get reported as it does about the model. The Numbers That Are Actually Comparable The cleanest row is SWE-bench Pro, which runs a model against real GitHub issues and checks whether the patch passes the repository's own tests. It is harder than the older SWE-bench Verified set and it is the row the whole industry now quotes. Model SWE-bench Pro Released Claude Fable 5 80.3 2026-06-09 Claude Opus 5 79.2 2026-07-24 Claude Opus 4.8 69.2 2026-05-29 GPT-5.6 Sol 64.6 2026-07-09 That is a 10 point jump from Opus 4.8 to Opus 5 inside two months, and the per-token price did not move (both tiers run at 5 and 25 per million tokens). Fable 5 keeps a 1.1 point lead and charges double for it. Those two facts together are the actual story of this release, and neither one is a ratio. On SWE-bench Verified, the older and easier set, Opus 5 reports 96.0 percent averaged over five trials. The averaging matters. A single run on a set that saturated above 90 percent tells you very little, because the spread between runs starts to rival the gap between models. Five trials is better practice than most launch tables bother with, and it is worth noticing when a lab does it. It is worth being precise about why those two rows behave differently,