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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.**
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.
Comparing AI Models: Surprising Differences in Responses
I’ve been experimenting with different AI models lately (ChatGPT, Claude, etc.), and I tried something simple: Using the exact same prompt across multiple models and comparing the results. What surprised me most wasn’t that they were different — it’s *how* different they were depending on the task. For example: * Some models are much better at structured writing * Others explain concepts more clearly * Some give more “creative” responses, but less accuracy It made me realize there isn’t really a “best” AI — it depends heavily on what you're trying to do. One thing I did notice though is that manually comparing them is kind of a pain (copying prompts, switching tabs, etc.). Curious how others approach this: Do you stick to one model, or actually test multiple before deciding? And if you do compare — what’s your process like?
Unsloth Gemma 4-26B: A4B-it-GGUF AI Model on Hugging Face
Unsloth Gemma 4 26B: A4B it GGUF AI Model on Hugging Face Unsloth Gemma 4 26B: A4B it GGUF is an innovative AI model available on Hugging Face, designed to push…
AI Models Stack Polyominoes in New Challenge
ChatGPT GPT 5.5 was DOA. Write up [here](https://aicc.rayonnant.ai/challenges/stackmaxxing/).
AI Industry Shifts: The End of All-You-Can-Eat AI Plans?
I am a GitHub Copilot Pro+ user. I have been enjoying 39 dollars plan that actually is worth 60 dollars compute with 1500 premium prompts to models count based. Given the availability of free tier models and model switching option, It has felt like never ending. It will be turned into token based after June. This corresponds to the projections about "the death of the ai buffet" I think. Less bundled memberships, more token based costs. As all these foundational model providers crave for profit, I think this is the natural step we are heading. They need to be able to measure and limit the use for profit. I am just curious how fast that will happen? Should we not take cheap & free AI for granted? Or can open-source models actually create a balance? If we are heading for less accessibility, how should average user be prepared?
Google's Gemma 4 26B-A4B IT: New AI Model on Hugging Face
Revolutionary AI Model: Google's Gemma 4 26B A4B on Hugging Face Google's latest offering, the Gemma 4 26B A4B, a sophisticated AI model, is now seamlessly inte…
Jackrong/Qwopus-GLM-18B-Merged-GGUF: New AI Model on Hugging Face
Jackrong/Qwopus GLM 18B Merged GGUF: The Latest AI Tool on Hugging Face Discover the innovative potential of Jackrong/Qwopus GLM 18B Merged GGUF , a cutting edg…
Google's Gemma 4 31B IT: New AI Model on Hugging Face
Gemma 4 31B IT: Revolutionizing AI Tools on Hugging Face Google's Gemma 4 31B IT is a transformative AI model set to revolutionize the landscape of AI tools on …
Google's Gemma 4 26B-A4B IT: New AI Model on Hugging Face
Revolutionary AI Model: Google's Gemma 4 26B A4B on Hugging Face Google's latest offering, the Gemma 4 26B A4B, a sophisticated AI model, is now seamlessly inte…
Jackrong/Qwopus-GLM-18B-Merged-GGUF: New AI Model on Hugging Face
Jackrong/Qwopus GLM 18B Merged GGUF: The Latest AI Tool on Hugging Face Discover the innovative potential of Jackrong/Qwopus GLM 18B Merged GGUF , a cutting edg…
Google's Gemma 4 31B IT: New AI Model on Hugging Face
Gemma 4 31B IT: Revolutionizing AI Tools on Hugging Face Google's Gemma 4 31B IT is a transformative AI model set to revolutionize the landscape of AI tools on …
Qwen 3.6-27B: New AI Model on Hugging Face
Qwen 3.6 27B: Revolutionizing AI with New Capabilities Qwen 3.6 27B, the latest addition to the rapidly evolving AI landscape, has been introduced on Hugging Fa…
Qwen 3.6-27B-FP8: New AI Tool on Hugging Face
Qwen/Qwen3.6 27B FP8: Revolutionizing Text Generation and Understanding Qwen/Qwen3.6 27B FP8 is a state of the art language model designed to push the boundarie…
Tencent's New AI Tool: Hy3-Preview on Hugging Face
Unlocking Innovation with Tencent HY3 Preview Tencent's HY3 Preview, part of the innovative Tencent Game Development platform, is designed to revolutionize the …
Qwen 3.6-35B-A3B: Hugging Face's New AI Tool
Discover Qwen/Qwen3.6 35B A3B: Revolutionizing AI Language Models In the rapidly evolving world of artificial intelligence, the Qwen/Qwen3.6 35B A3B model stand…
Qwen 3.6-27B: Advanced AI Tool on Hugging Face
Qwen/Qwen3.6 27B: Revolutionizing Natural Language Processing Qwen/Qwen3.6 27B represents a groundbreaking advancement in natural language processing (NLP), dev…