标签：#IDE

I’m building Euro Toolhub , a German-first index of European software, SaaS, cloud and AI alternatives. The idea is simple: many companies, agencies, developers and privacy-conscious users want alternatives to common tools when they care about things like data residency, open source, self-hosting, European jurisdiction or reducing dependency on non-European providers. But most existing lists stop at listing tools. I want Euro Toolhub to go one step further. Each provider profile can include: country and jurisdiction category alternative-to mappings open source status self-hosting availability EU data residency DPA availability certifications where known target audience strengths and limitations a transparent sovereignty score embeddable badges for providers The project starts in German because the first target market is DACH, but the structure is prepared for more languages later. The long-term vision is to build a practical decision platform for digital sovereignty: not just “which European alternatives exist?”, but “which one fits my actual use case?” Current categories include web analytics, cloud and hosting, newsletter tools, CRM, email, password managers, AI APIs, project management, video conferencing and e-signatures. I’m looking for feedback from developers, SaaS founders, privacy people and self-hosting communities: Which European tools should be added? Which categories matter most? What would make a sovereignty score trustworthy? Should the provider dataset be opened through GitHub for corrections and submissions? Project: https://www.euro-toolhub.eu/de Provider submission: https://www.euro-toolhub.eu/de/anbieter-eintragen ``

Slides have an odd status in engineering: we make them constantly, but we treat them as disposable. They rarely live in a repo. They rarely go through review. The diagram you spent twenty minutes aligning in Keynote is gone the moment the talk ends. Dinghy's Slide Builder treats a slide like any other artifact: a folder of source files that compiles to a single deliverable. What it is A presentation builder layered on top of RevealJS , wired into the same Dinghy CLI you already have: YAML DSL: recognized keys map to semantic blocks, and any other key becomes the matching HTML element, so you describe slides in a structured, indented form. Markdown and HTML, auto-loaded: drop a .md or .html file in the slide folder and it becomes a section. Self-contained HTML output: assets inlined, a single file you can share or host anywhere. Live reload dev server: run dinghy slide start and edit-to-reload feedback. Prezi-style zoom and pan: exclusive to Dinghy, covered below. A YAML slide The core idea is that a slide is a tree of HTML elements, and YAML is a tidy way to describe nested structure: sections : - badge : Show Case title : Slide Builder subtitle : author RevealJS presentations in YAML ul : li : - YAML DSL maps keys to HTML elements - Self-contained HTML output - Live reload development server Some keys are recognized aliases: badge becomes <div class="badge"> , title becomes <h2 class="title"> , subtitle becomes <p class="subtitle"> . Any other key, such as p , h3 , ul , or li , becomes that HTML element directly. Multi-file slides are just multiple YAML files in the same folder, picked up in filename order. A Markdown slide If you want to add a section quickly, plain Markdown works: ## Slide Builder - YAML DSL maps keys to HTML elements - Self-contained HTML output - Live reload dev server Markdown files become slides automatically. You can mix YAML, Markdown, and raw HTML in the same slide and use whichever fits each section. The single-file output Run dinghy slid

Hey developers, I've been thinking about a problem and wanted to get some feedback from the community. We have search engines like Google, Bing, and others that help us find websites through keywords. We also have directories and archives, but I haven't found a place that attempts to catalog every active website on the internet in a structured and discoverable way. So my first question is: Does a platform already exist where I can browse or search through a massive database of active websites, regardless of whether they're popular or not? The Idea Imagine a project called "Library of Websites." Instead of ranking sites primarily through SEO and search algorithms, the goal would be to build a continuously growing database of active websites across the internet. Website owners could install a small script or verification snippet on their sites, similar to how Google Search Console verification works. Once verified, the website would automatically become part of the Library of Websites database. The platform could then: Categorize websites by industry, niche, and technology. Track whether sites are still active. Allow users to browse websites like books in a library. Discover small, independent websites that search engines rarely surface. Create a searchable index of the web that focuses on discovery rather than ranking. Over time, this could become a living map of the internet, helping people explore websites they would never normally find. Does something like this already exist? What are the biggest technical challenges in building such a database? Would website owners actually be willing to install a verification script? Is there a better approach than relying on voluntary website registration? What would you personally want from a "Library of Websites" platform? I'd love to hear your thoughts, criticism, and suggestions. Thanks!

Top up your Qi2 Android phone or MagSafe iPhone with a magnetic wireless charging stand, pad, car charger, or power bank.

The Problem That Wouldn't Leave Me Alone Pakistan has 220 million people. A functioning legal system. Hundreds of Acts, ordinances, and constitutional provisions that technically protect every citizen. Almost nobody can use them. The median lawyer's consultation fee in Karachi is more than what many families earn in a week. Legal aid is understaffed and geographically concentrated in major cities. And the laws themselves? Written in English — a language most of the population reads functionally at best, and doesn't speak at home at all. So when a landlord illegally locks someone out. When a factory worker gets fired without severance. When a woman wants to know her inheritance rights. When a tenant needs to understand what "Section 16 of the Rent Restriction Ordinance" actually means for their specific situation — they either find a lawyer they can't afford, ask someone who doesn't really know, or quietly give up. This isn't a knowledge problem. It's an access problem. I'm a CS student at Sukkur IBA University in interior Sindh — not Karachi, not Islamabad. The kind of city where you feel the gap between what the law says and what people actually know it says every single day. That gap is where HAQ started. HAQ is an Arabic and Urdu word. It means right — as in, what is rightfully yours. The name felt important. The Core Idea: Ask the Law, Get the Law There's a specific failure mode with AI and legal questions that drove every design decision I made, and it's worth naming clearly. Standard LLMs — any of them — will answer legal questions confidently. They'll cite "Section 144" or "the Transfer of Property Act" with total authority. They are often wrong. Sometimes subtly: the section exists but doesn't say what the model claims. Sometimes obviously: the Act doesn't apply in that province. Always uncitable: the user has no way to verify without finding the source themselves. For an accessibility tool, a confidently wrong answer isn't neutral. It's actively dangerous.

Everyone needs a safe way to listen to music on outdoor runs. We’ve found the bone conduction headphones to grab on your way out the door.

Avoid phone FOMO with our favorite smartphones that aren’t officially sold stateside but are available in markets like the UK and Europe.

Lighten your pack this summer—skip the sleeping bag and carry one of our favorite ultralight backpacking quilts instead.

Google added two new models to the Gemini API today: Nano Banana 2 Lite (image generation) and Gemini Omni Flash (video generation + editing). Neither is the Gemini 3.5 Pro release people have been waiting for, so it's easy to miss. Here's what's actually in them. TL;DR Nano Banana 2 Lite: gemini-3.1-flash-lite-image = text-to-image in ~4s, $0.034/1K images Gemini Omni Flash: gemini-omni-flash-preview = video gen + conversational editing, $0.10/sec Both are built to be chained: generate an image fast, then animate it into video Neither model is positioned as a quality upgrade = both are cost/speed plays Nano Banana 2 Lite Model ID: gemini-3.1-flash-lite-image Text-to-image output in about 4 seconds $0.034 per 1K-resolution image Positioned as the direct replacement for the original Nano Banana ( gemini-2.5-flash-image ) - if you're on that model, this is a drop-in upgrade Available in Google AI Studio, Gemini API, Gemini Enterprise Agent Platform, and consumer surfaces (Search AI Mode, Gemini app, Photos, NotebookLM, Flow, Google Ads) Gemini Omni Flash Model ID: gemini-omni-flash-preview Public preview in Google AI Studio and the Gemini API Conversational editing - refine a generated video using plain-language instructions instead of re-prompting from zero Multimodal referencing - combine text, image, and video inputs to keep a scene consistent $0.10 per second of video output (same rate as Veo 3.1 Fast) Known limitations right now Generations capped at 10 seconds No audio reference uploads yet No scene extension yet Video references under 3 seconds are accepted by the API schema but not correctly processed yet Character consistency across scene changes/pans still has rough edges Google says longer durations are coming. The part worth paying attention to: chaining them Generate an image with Nano Banana 2 Lite (fast, cheap) Pass that image as a reference into Omni Flash Omni Flash animates it into a video Both models are optimized for throughput and cost, not for to

Digital Signatures: Format, Certificate, and Validation Policy Are Not the Same Thing The right move when a digital signature fails validation is don't look at the cryptography first . I know that sounds backwards. If the algorithm is RSA-2048 and the certificate chain is intact, why would validation fail? Because a signature can be cryptographically perfect and still get rejected by the validator. The problem isn't the hash or the private key — it's the format, the wrong certificate, or the validation policy the system is applying. My thesis is simple: most errors that look cryptographic in digital signatures are actually upper-layer errors — incompatible format, a certificate that doesn't meet the required profile, or a validation policy that the issuer and receiver never aligned on. And confusing those three layers has a real cost: debugging time wasted in the wrong place. The Real Mess: Three Layers People Keep Mixing Up When a digital signature fails validation, the typical mental sequence is: "Is the algorithm correct? Does the private key match the public one? Did the certificate expire?" Those are reasonable questions, but they're all in the same layer. The problem is there are three distinct layers, and each one can fail independently. Layer 1 — Signature Format The format defines how the signature is packaged together with the signed data. CMS/PKCS#7 is not the same as XAdES, PAdES, or JAdES. Each has variants: BASELINE-B , BASELINE-T , BASELINE-LT , BASELINE-LTA . Choosing CAdES-BASELINE-B when the receiver expects XAdES-BASELINE-LT produces a rejection that has nothing to do with the cryptographic algorithm. The public documentation for DSS (Digital Signature Service) from the European Commission describes these variants in detail. DSS is the reference library for eIDAS-compliant signatures, and its documentation is one of the most complete and verifiable resources publicly available. Layer 2 — Certificate The certificate is the signer's identity, but it

When going abroad, the right plugs are essential to keep your gadgets charged. These are my favorite travel adapters and chargers.

I ripped open $892 worth of Pokémon packs on my phone in under 15 minutes and walked away with 62 cents. My adrenaline rush felt like the future of gambling.

Video AI systems consistently fail to track what happens when the camera looks away: when a scene pans away from an object in motion and returns, current models re-render the object in its original position rather than showing the logical result of off-screen change. Scaling to more parameters makes this failure worse, not better, according to WRBench , a new benchmark that tests what researchers call "world model reliability." The benchmark presents AI video systems with scenes where something happens off-screen — the camera pans away while an object is in motion, or while a light changes, or while an open door should stay open — then pans back to see what the system believes should have happened. A system that genuinely models the world would track what occurred during the off-screen interval. Current systems mostly don't. Key facts What: A new benchmark tests whether video AI systems can track what happens to parts of a scene the camera isn't currently showing. Across 23 models, the answer is mostly no — and making the models larger made the problem worse, not better. When: 2026-06-19 Primary source: read the source (arXiv 2606.20545) The benchmark covers twenty-three different video generation models and nearly ten thousand video clips across six categories of off-screen change, each designed to test a different aspect of world continuity: objects in motion, light sources changing, object states such as open or closed doors, and several others. This gives a comprehensive picture rather than a single narrow test. The most striking finding is the scaling result. The researchers tested one of the more capable video generation systems at two different sizes: a smaller version and one with more than ten times as many parameters. More parameters didn't help. Scaling made the off-screen tracking problem measurably worse. The larger model produced more realistic-looking frames, but it was less accurate about what should have happened to the parts of the scene it wasn't

Sony will stop producing physical discs for all new PlayStation games beginning in 2028, as the company embraces an all-digital future.

With these high-tech automatic litter boxes, gone are the days of scooping and smells. Welcome to the future.

If you've ever received a .pages or .numbers file on a Windows PC, you know the pain — you can't open it. No preview, no converter built in, and Apple's iCloud web tools are slow and clunky. So I built iworkviewer.com — a free, browser-based iWork file viewer and converter. No signup, no upload to any server. Everything happens in your browser. What it does Open .pages files → view them instantly, export to PDF or .docx Open .numbers files → view spreadsheets, export to .xlsx or PDF Open .keynote files → view presentations, export to PDF or .pptx Batch convert multiple iWork files at once The tech Built with Next.js, Cloudflare Pages, and pure client-side JavaScript. All file processing happens in the browser — your files never leave your computer. Zero server costs, zero privacy concerns. Why I built it I kept seeing Reddit threads and Quora questions: "How do I open a Pages file on Windows?" The answers were always the same — use iCloud.com (slow), download some sketchy converter (risky), or ask the sender to export as PDF first (annoying). I figured: if the browser can read a file, it can convert it. And it turns out, it can. Try it 👉 iworkviewer.com Open a .pages, .numbers, or .keynote file right in your browser. Free, forever, no account needed.

3 platforms, one queue, zero editorial control. That was the state of my content automation before I sat down to spec the dashboard. LinkedIn, X, and Threads each had their own generator, their own state files, their own publishing loop. Drafts got generated, passed a quality gate, and fired into the void. If the draft was mediocre or the timing was wrong, I found out after the fact. The problem is not the automation. Automation is why I can run three platform engines without spending two hours a day managing content. The problem is that zero editorial visibility means you cannot catch the bad ones before they post. What I wanted: see every draft before it goes out. Edit inline if needed. Post immediately or schedule for the next slot. Compose something manually when I have a specific take to push. Keep the comment automation untouched because that runs high frequency, low stakes, and babysitting individual replies defeats the point. The spec came out to three core flows. Review queue. Every pregenerated draft surfaces here with full context: platform, topic, generation timestamp, quality score. One click to edit inline, one to approve for the next slot, one to post immediately. The goal is a 30 second review per draft, not a full editing session. Manual compose. Sometimes I know exactly what I want to say. A text area, platform selector, and post button. No generation, no queue, just publish. This is the escape hatch for when something is happening in real time and the pregenerated queue is irrelevant. Schedule view. A simple calendar showing what is queued for which slot across all three platforms. The generator already handles slot logic and quiet hours. The dashboard just needs to surface the state so I can see gaps and move things around without touching JSON files directly. What I deliberately left out: comment automation. That pipeline runs separately, fires frequently, and does not benefit from human review on every reply. Adding it to the dashboard would cr

Google is updating its image generator to make it faster and cheaper, making it a more useful tool for creators looking to make AI content.

Peptide drugs are popular, but FDA scientists warn they're untested, may be harmful.