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One of the most notable changes is that Meta plans to implement a daily two-hour time limit for teens that can only be disabled with parental permission.

TL;DR SVG attributes like stroke-width become strokeWidth in JSX. class → className . Numeric values become {expressions} . Inline styles become objects. xmlns and XML comments are removed. The converter outputs either JSX or TSX with SVGProps . Use automation (SVGR or SVGCode) for large icon sets. Import only what you need to keep bundle sizes small. Converting an SVG file into a React component is more than just pasting markup into a .jsx or .tsx file. React uses JSX, which is stricter than HTML/XML and requires specific changes to ensure your SVG renders correctly and remains maintainable. In this post, we’ll explore every transformation that takes place—from attribute casing to TypeScript typing—so you understand exactly what our free SVG to React converter does under the hood. What Actually Changes? Kebab‑case Attributes Become camelCase SVG uses attributes like stroke-width , fill-rule , and clip-path . JSX requires property names that are valid JavaScript identifiers, so these become: SVG Attribute React JSX stroke-width strokeWidth stroke-linecap strokeLinecap stroke-linejoin strokeLinejoin fill-rule fillRule clip-path clipPath font-size fontSize stroke-dasharray strokeDasharray class Becomes className In SVG you write class="icon" , but in JSX you must use className="icon" because class is a reserved word in JavaScript. Numeric Attributes Are Converted to Expressions React treats string values differently from numbers. For numeric SVG attributes like width , height , x , y , cx , r , etc., the converter outputs {value} instead of "value" . <circle cx="12" cy="12" r="10" /> becomes: < circle cx = { 12 } cy = { 12 } r = { 10 } /> Inline Styles Become Objects If your SVG uses style="fill: red; stroke: blue;" , it must be converted to a JavaScript object: style = {{ fill : ' red ' , stroke : ' blue ' }} xmlns and Namespace Declarations Are Removed React automatically uses the correct SVG namespace, so xmlns and other XML namespace declarations are unnecessary a

Particle’s new podcast intelligence platform transcribes and analyzes more than 130,000 podcasts, making their conversations searchable on the web and accessible to AI agents through an API and MCP.

File sizes have also been increased and uploads should be faster.

The company called on YouTube and TikTok to join it in setting time limits for teens.

SoundCloud will let you buy music directly from artists.

The lawsuit alleged that Meta knowingly designed platforms like Instagram and Facebook to addict children, despite knowing about the harms the platforms could pose to young users.

Meta settled its latest kids online safety trial with a group of 29 state attorneys general, sparing it from the remainder of a trial that could have cost it hundreds of billions of dollars. Under the terms of the settlement, which resolves claims by a larger group of 47 states and several districts and territories, […]

Bluesky's new 10-minute video support includes faster upload speeds, too.

Building a Serverless Weather Pipeline on AWS: A Step-by-Step Walkthrough This is a build log for someone who's used AWS a bit — deployed a Lambda from the console, poked around S3 — but hasn't touched CDK, Step Functions, EventBridge Scheduler, or GitHub's OIDC setup before. I'll explain each concept the first time it comes up, and show the actual code behind every piece, roughly in the order I built it. Here's what it ends up doing: every 10 minutes, EventBridge Scheduler kicks off a Step Functions workflow that pulls current weather for five cities in parallel from a free public API, reshapes the results into JSON Lines, drops them into S3 in a partitioned layout, and makes them queryable in Athena with plain SQL. No crawler, and no AWS credentials sitting anywhere in the GitHub repo that deploys it. kasukur / serverless-weather-pipeline AWS Serverless Weather Pipeline Serverless Weather Data Pipeline A small but complete serverless data pipeline on AWS walkthrough: EventBridge Scheduler → Step Functions → Lambda → S3 → Glue/Athena , deployed by GitHub Actions with no AWS access keys stored anywhere (authentication is via GitHub's OIDC provider). flowchart TD A["EventBridge Scheduler (every 10 min)"] --> B["Step Functions state machine"] B --> C["PrepareCities (Pass)"] C --> D["ForEachCity (Map, concurrency 4)"] D --> E["FetchWeather (Lambda -> Open-Meteo public API)"] E -.-> F["retries transient errors (up to 2 attempts)"] E -.-> G["FetchFailed (Pass): per-city failure absorbed here, other cities continue"] E --> H["TransformWeatherData (Lambda, pure function, no AWS calls)"] H -.-> I["splits successes vs failures"] H -.-> J["builds JSON-Lines body + partitioned S3 key"] H --> K["LoadToS3 (Lambda, writes to S3 via boto3)"] K --> L["S3 (processed/dt=YYYY-MM-DD/hour=HH/*.jsonl)"] L --> M["Glue Data Catalog table (partition projection -- no crawler)"] M --> N["Athena (query with plain SQL)"] D -.-> … View on GitHub Table of Contents What we're building, and why eac

Pet owners, rescue agencies, and wildlife groups are calling for new safeguards as AI makes it harder to tell whether animals, from polar bears to house cats, are real or fake.

Runable says 60%–70% of its 1 trillion-plus token usage in the last 90 days came from paying customers.

Puzzles and games have been central to AI development since the very beginning. Just as we humans like to test our smarts with crosswords or logic puzzles, developers can test how far models have advanced with a gaming gauntlet. The term “machine learning” was popularized in a 1959 article by the IBM computer scientist Arthur…

When my oldest child was born, I immediately set up Gmail and Twitter accounts in her name. I broadly announced her birth online and proceeded to plaster her photo across all sorts of platforms. In short, I began creating her digital footprint long before she could stand on her own two feet. Fast-forward a couple…

It’s a glorious day in Kirkland, Washington, an affluent Seattle suburb on the eastern shore of Lake Washington. The temperature is in the mid-80s, and the sky is incapable of being any more blue. The view from the Gates Ventures conference room overlooks the Carillon Point Marina, where a flotilla of expensive boats bob in…

TL;DR An agentic RAG pipeline treats retrieval as a tool the AI agent can call, evaluate, and call again rather than as a fixed step. The pipeline can search an internal knowledge base first, then use real-time web search when the available evidence is missing, weak, or outdated. Internal documents and web results should be converted into a shared evidence format before the model generates an answer. A reliable system must preserve URLs, publication dates, document identifiers, and the claims supported by each source. Retrieval quality, web-search precision, citation correctness, latency, cost, and stopping behaviour should all be evaluated. A basic RAG pipeline works well until the answer is not in the knowledge base. Imagine an enterprise copilot that can answer questions about internal product documentation. It performs semantic search against a vector database, retrieves several relevant passages, and passes them to a language model. For questions covered by the indexed documents, the system may work remarkably well. Then a user asks about a release announced yesterday, a recently changed regulation, or how the company’s product compares with a new competitor. The vector database cannot retrieve information it has never indexed. A conventional pipeline may return no answer, but it may also produce a confident response from incomplete or outdated context. Adding a Web Search API helps solve the freshness problem, but it introduces another decision: when should the system trust its internal knowledge, and when should it search the open web? An agentic RAG pipeline places that decision inside the retrieval workflow. What Makes a RAG Pipeline Agentic? A traditional RAG pipeline usually follows a fixed path: transform the question into a search query, retrieve the most similar passages, add those passages to the prompt, and generate an answer. An agentic RAG pipeline allows the model to make decisions between those stages. Retrieval becomes a tool rather than a manda

Ringg has raised $10 million from Peak XV as a part of its Series A extension.

Why are AI scientists so adamant that frontier model LLMs are NOT conscious or sentient? submitted by /u/DoublePassRadiator [link] [留言]

Honestly, when I saw the headline "Someone in Korea used AI to cut the prep time for a dose of Korean herbal medicine from 300 minutes to 5," the first thing that caught my eye wasn't "whoa, robots can make herbal medicine now." It was how they did it—because they happened to get right the one thing most people get wrong when they think about applying AI. What Onerve Did Let's start with the facts. There's a Korean startup called Onerve (오너브), backed by the Korea Institute of Oriental Medicine, working on automating the manufacturing of Korean herbal medicine (한약). Their system is called HAP. It connects AI with electronic medical records (EMR) to automate the entire flow—from prescription input, to manufacturing, cleaning, packaging, and inventory management. The key is the raw material: they use standardized, freeze-dried herbs in a "cartridge" format—turning herbs that used to require on-site boiling and heavy manual labor into uniform, standardized modules. The result: prep time for a single dose of Korean herbal medicine dropped from around 300 minutes to around 5. They won a CES Innovation Award and closed a Series A round of roughly 6.2 billion won. And they're not alone—another Korean company, Camelotech (with its Cameleon system), is doing almost the same thing and also showed up at CES. So "Korean herbal medicine automation" is turning from a one-off experiment into an actual category. What I'm Actually Paying Attention To Isn't the Speed—It's Which Layer They Automated If all you take away from this is "300 minutes became 5," you're missing the most important part. When people see AI moving into an industry with a thousand-plus years of tradition behind it, the gut reaction is usually panic: "Are even Korean medicine doctors about to get replaced by AI?" But if you look closely at what Onerve actually automated—it's the manufacturing , not the diagnosis and prescribing . Deciding which medicine a person should take, how to adjust the dosage, how to read t

📝 Originally published (in Japanese) at forge.workstyle.tech . When building voice-based AI interactions in the browser (avatars, voice bots, streaming AI), you’ll inevitably hit pitfalls stemming from audio physics and browser implementation quirks. This article compiles 16 traps I encountered during product development , organized in a symptom → cause → solution lookup format . No need to read from top to bottom—jump straight to the symptom you’re facing. Echo and Self-Response Issues 1. Avatar Responds to Its Own Voice (Despite echoCancellation: true ) Symptom : TTS audio is picked up by the mic, and STT recognizes it as user speech, creating a self-response loop. Cause : AEC (Acoustic Echo Cancellation) requires a reference signal (the "sound to cancel"). Only the browser's official playback paths ( <audio> / WebRTC receiver tracks) serve as references. Custom playback via Web Audio API does not reliably function as a reference . Solution : Return TTS audio from the server as a WebRTC remote track and play it via an <audio> element. This eliminates echoes without text-matching workarounds (tested: 99 seconds of continuous speech with speakers on, zero false user turn detections). 2. Echoes Are Gone, but Speaking Simultaneously with the Avatar Distorts My Voice and Causes Misrecognition Symptom : Only during dual speech, proper nouns get mangled (e.g., "社員数" → "シャインズ"), especially at word beginnings. Cause : Fundamental AEC trade-off. To cancel echoes, AEC suppresses/distorts near-end (user) audio during dual speech. Solution : Mitigate in three layers: ① Increase mic Opus bitrate and enable FEC (see Pitfall 13) ② Provide vocabulary hints to STT (see separate article: use "recent avatar speech" as initial_prompt , not a dictionary) ③ Instruct LLM: "Input is STT transcription with potential errors. Interpret unnatural words as phonetically similar terms and add confirmation prompts." 3. Can’t Suppress Audio from Other Apps (Music, Videos) Symptom : Audio/lyrics fr