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Australia's New Law: Big Tech to Pay for News or Face 2.25% Tax
The more deals platforms make with media outlets, the less they pay. If enough agreements go through, that effective rate drops to 1.5%, which could generate between A$200 million and A$250 million back into Australian journalism.
Algerian Student Launches AI Platform with 40+ Models
Hello, 20 years old here just got into the Ai platform and launched this last two weeks and here is what I have on it so far. \- **Latest Ai models Comparison**: ChatGPT 5.4 Claude Sonnet 4.6 and many more will be included as well \-**Ai models**: at the moment we have over 40+ different Ai models available for users to compare results from, side by side so its easier for users to compare results. \-**Pricing:** For the pricing I made the monthly plan only $10/mo with limited usage, however on the yearly/Lifetime plan it comes with no limited usage \- **Dark Theme**: lol a developer requested this from me so I added it as well for users specially at night it comes handy. \- **For Future:** I want to include something called mixture AI basically when you enter your prompt it will read all the responses and give you the best one or mix them up to the best use for you. **Please if you have any suggestions/recommendations I would really appreciate it, as I am still learning to develop and improve my abilities.**
SpeakON Dictation Device Review: MagSafe Transcription for iPhone
This $129 device uses MagSafe to stick on the back of an iPhone to power transcription across apps.
Letterboxd Seeks New Owner: Potential Buyers Emerge
Potential buyers of Letterboxd include Versant, the parent company of CNBC and MS NOW, and Hollywood media company The Ankler, according to Semafor.
Preventing AI Model Collapse: The Need for Human-Generated Data
Im all for acceleration. I think the faster we hit AGI the better. but theres a bottleneck nobody here talks about enough-training data. right now we are quietly poisoning the well. More than half of online content is already synthetic. bots talking to bots, articles written by AI, reddit threads generated by LLMs. when the next generation of models trains on this they eat their own tail. model collapse is real. we saw it with image generators. Outputs get blander, weirder, less useful.we need a way to label or filter human-generated data. not because humans are better but because diversity prevents collapse. I know the standard solution sounds like a dystopian meme. biometric scanners, iris codes, hardware verification. and yeah maybe it is dystopian. but so is a dead internet where nothing can be trusted.Reddit CEO Steve Huffman put it simply recently - platforms need to know you're human without knowing your name. Face ID / Touch ID level stuff. im not saying that specific device is the answer. but the category of solution - proof of human that doesnt create a surveillance state - seems necessary if we want to keep scaling past the cliff.what do you think? Is proof-of-personhood just a regulatory speed bump, or is it infrastructure for the next generation of AI?curious where this sub lands.
Self-Taught Developer from Bahrain Launches Multi-Model AI Platform
https://reddit.com/link/1sxotqx/video/xlaqd9i8guxg1/player I'm a self-taught developer, 39 years old, based in Bahrain. Four months ago I started building AskSary - a multi-model AI platform with a persistent memory layer that sits above all the models. The core idea: the model is not the identity. Most AI tools lose your context the moment you switch models. I built the layer that remembers you across all of them. Here's what's shipped so far: **Models & Routing** Every major model in one place - GPT-5.2, Claude Sonnet 4.6, Grok 4, Gemini 3.1 Pro, DeepSeek R1, O1 Reasoning, Gemini Ultra and more - with smart auto-routing or manual override. **Memory & Context** Persistent cross-model memory. Start with Claude on your phone, switch to GPT on your laptop - it already knows what you discussed. Proactive personalisation that messages you first on login before you've typed a word. **Integrations** Google Drive and Notion - connect once, pull files and pages directly into chat or your RAG Knowledge Base. Unlimited uploads up to 500MB per file via OpenAI Vector Store. **Video Analysis** \- Gemini native video understanding for YouTube URL analysis (no download required, processed natively) and direct file upload up to 500MB. Full breakdown of visuals, audio, dialogue, editing style and key moments. **Generation** Image generation and editing, video studio across Luma, Veo and Kling, music generation via ElevenLabs, video analysis via upload or YouTube URL. **Builder Tools** Vision to Code, Web Architect, Game Engine, Code Lab with SQL Architect, Bug Buster, Git Guru and more. Tavily web search across all models. **Voice & Audio** Real-time 2-way voice chat at near-zero latency, AI podcast mode downloadable as MP3, Voiceover, Voice Notes, Voice Tuner. **Platform** Custom agents, 30+ live interactive themes, smart search, media gallery, folder organisation, full RTL support across 26 languages, iOS and Android apps, Apple Vision Pro. **Where it is now** 129 countries. Currently at 40 new signups a day. 1080 Signup's so far after 4 weeks or so. MRR just started. Zero ad spend. All of it built solo, one feature at a time, on a balcony in Bahrain. **The Stack:** Frontend - Next.js, Capacitor (iOS and Android) and Vanilla JS / React Backend - Vercel serverless functions, Firebase / Firestore (database + auth) and Firebase Admin SDK AI Models - OpenAI (GPT, GPT-Image-1), Anthropic (Claude), Google (Gemini), xAI (Grok), DeepSeek Generation APIs - Luma AI (video), Kling via Replicate (video), Veo via Replicate (video), ElevenLabs (music), Flux via Replicate (image editing), Meshy (3D — coming soon) Integrations - Google Drive (OAuth 2.0), Notion (OAuth 2.0), Tavily (web search), OpenAI Vector Store (RAG), Stripe (payments), CloudConvert (document conversion), Sentry (error tracking), Formidable (file handling) Rendering - Mermaid (flow charts) and MathJax Platforms - Web, iOS, Android, Apple Vision Pro (visionOS) Languages - 26 UI languages with full RTL support [asksary.com](http://asksary.com) Happy to answer questions on any part of the build - stack, architecture, API cost management, anything.
AI-Powered Game Recommendations: Gamevibe.us
AI Powered Game Recommendations: Gamevibe.us In the ever expanding world of gaming, finding the perfect game to suit your preferences can be a daunting task. En…
Durable.co: AI Platform for Rapid Website, Brand, and Invoice Creation
AI-driven platform for rapid website, brand, and invoice creation.
Clawdi: Top Platform for AI Agents
Best home for all AI agents
AI and Dune: The Debate on Thinking and AI Assistance
The Globe and Mail's editorial board ran a piece in March titled "AI can be a crutch, or a springboard." To illustrate the crutch half, they offered this: someone asked AI to explain a passage from Dune that warns against delegating thinking to machines. Instead of reading the book. That anecdote is doing more work than the studies the editorial cites. But the studies are real. Researchers at MIT published a paper in June 2025 titled "Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task" (Kosmyna et al., arXiv 2506.08872). The study tracked brain activity across three groups: people writing with ChatGPT, people using search engines, and people working unaided. The LLM group showed the weakest neural connectivity. Over four months, "LLM users consistently underperformed at neural, linguistic, and behavioral levels." The most striking finding: LLM users struggled to accurately quote their own work. They couldn't recall what they had just written. The Globe cites this and similar research to make a point about dependency. The implicit argument: hand enough of your thinking to a machine and you stop doing it yourself. That finding is probably accurate for the way most people use these tools. The question is whether that's the only way they can be used. The Globe's own title contains the counter-argument. Crutch or springboard. They wrote both words. They just didn't develop the second one. Ethan Mollick, a professor at Wharton who has been writing about AI use since the tools became widely available, argued in 2023 that the real challenge AI poses to education isn't that students will stop thinking, it's that the old structures assumed thinking was hard enough to enforce. ("The Homework Apocalypse," [oneusefulthing.org](http://oneusefulthing.org), July 2023.) When AI can do the surface-level cognitive work, the only tasks left worth assigning are the ones that require actual judgment. The tool, in that framing, doesn't reduce the demand for thinking. It raises the floor under it. Nate B. Jones, who writes and consults on what it actually takes to work well with AI, has made a sharper version of this argument. His position: using AI effectively requires more cognitive skill, not less. Specifically, it requires the ability to translate ambiguous intent into a precise, edge-case-aware specification that an AI can execute correctly. It requires detecting errors in output that is fluent and confident-sounding but wrong. It requires recognizing when an AI has drifted from your intent, or is confirming a premise it should be challenging. These are not passive skills. They are harder versions of the same thinking the MIT study found LLM users weren't doing. The difference between the group that lost neural connectivity and the group that doesn't isn't the tool. It's what they decided to do with it. Here's my own evidence. In the past year I built a working web application. Python backend. JavaScript frontend. Deployed on two hosting platforms. Payment processing. User authentication. A full data model. I do not know how to code. Every product decision was mine. Every architectural call. Every tradeoff judgment. I defined what the system needed to do, why, and what done looked like. I reviewed every significant change before it was accepted. When something broke, I identified where the breakdown was and directed the fix. The implementation was handled by AI. The thinking was mine. This mode (call it AI-directed building) is the opposite of the Dune reader. The quality of what gets produced is entirely a function of how clearly you can think, how precisely you can specify, and how critically you can evaluate what comes back. There is no shortcut in that. A vague brief to an AI doesn't produce a confused output. It produces a confident, fluent, wrong one. The discipline that prevents that is yours to supply. Non-coders building functional software with AI is common enough now that it isn't a story. What's less visible is the specificity of judgment underneath the ones that actually work. The practices that force more thinking rather than less are not complicated, but they require a decision to use the tool differently. When I've formed a position on something, I give the AI full context and ask it to make the strongest possible case against me. Ask for the hardest opposing argument it can construct. Then I read it. Sometimes it changes nothing. Sometimes it surfaces something I had dismissed without fully examining. The AI doesn't form my view. It stress-tests one I've already formed. When I'm uncertain between options, I don't ask which is better. I ask: here are two approaches, here is my constraint, now what does each cost me, and what does each require me to give up? I make the call. The AI laid out the shape of the decision. The judgment was mine. The uncomfortable part of thinking is still yours in this mode. The tool makes the work more rigorous, not easier. The MIT researchers and the Globe editorial are almost certainly right about the majority of current use. Passive use produces passive outcomes. That's not a controversial claim. The crutch half and the springboard half use the same interface. The difference is whether the person in front of it decided to think. What are you doing with it that forces more thinking rather than less? Are you using it to skip a step, or to take a harder one? Genuinely asking.
OpenClaw: Cross-Platform Personal AI Assistant
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
PostHog: AI-Powered Platform for Product Success
🦔 PostHog is an all-in-one developer platform for building successful products. We offer product analytics, web analytics, session replay, error tracking, feature flags, experimentation, surveys, data warehouse, a CDP, and an AI product assistant to help debug your code, ship features faster, and keep all your usage and customer data in one stack.