🚀 Introducing BubblSpace — Voice-to-Voice AI Personas for Enterprise in just 3 easy clicks: 1️⃣ Select an AI Persona trained on your internal docs 2️⃣ Schedule a secure, on-demand session—no code 3️⃣ Engage in real-time, voice-to-voice AI-powered Q&A 🔒 Launch your 3-month

🧘‍♀️Epic Post- I had to ask Grok to put this in story form The Tale of Marin 32B: The Bad Spike and the Redemption of QK Norm In the bustling labs of the Marin community, nestled in the digital ether of 2025, the air was thick with anticipation. It was late May when Percy Liang,

🎨 What if you could create stunning visual animations just by describing them in plain English? Introducing SketchPad-SLM - an Open Source AI-powered creative coding studio that runs entirely in YOUR browser! ✨ Simply type: "Create a particle system with gravity effects" 🤖

We at BubblSpace are addressing this exact challenge. While horizontal AI technologies offer impressive UI and UX, they often fall short in domain-specific depth. Our solution empowers each enterprise—and even individual teams within it—to design their own AI workflows,

🔬💊 TimeCapsule-SLM is LIVE! - Complete AI Deep Research & creative Open Source platform that runs 100% in your browser! 🎯 Try it now: timecapsule.bubblspace.com 🚀 Revolutionary Features: 🧠 In-Browser RAG - Upload PDFs, get AI insights (no server needed!) 🔗 TimeCapsule

We are totally trending 🚀on ollama r/ollama & github. Deepresearch + RAG all in browser timecapsule.bubblspace.com reddit.com/r/ollama/comme…

🔥1+ month of effort and first signs of success! Final Product: TimeCapsule-SLM An Open Source Deepresearch that works in browser with Qwen 3 0.6b(ollama) that has semantic understanding , provide insights & generate novel ideas . Privacy first local Deep Research. 👽

🎯 Milestone Unlocked I’m excited to share that I’ve completed the “Scratch to Scale: Large-Scale Training in the Modern World” course by Zach Mueller on maven ! Scratch to Scale has been one of the most practical and insightful courses I’ve taken — it goes far beyond

🎛️Way Points- My Journey: 1. We started with the fundamentals of distributed training (operations such as all-reduce and broadcast). The custom nbdistributed package made it incredibly easy to get hands-on with multi-GPU training in notebooks right from week one.I know notebooks

🎛️Another major highlight was the keynote and spotlight sessions — featuring exceptional speakers from top AI labs and startups. These are people who’ve actually built the tools, frameworks, and innovations we use today — from model design and scaling pipelines to

🎛️Zachary is a brilliant instructor — laser-focused on helping us learn how AI pros truly work at scale.This course genuinely bridges the gap between academic theory and real-world distributed training. 🎛️What I’ll Apply Next 🔹 Build Expert Parallelism (MoE) from scratch using

FireHacker Zach Mueller Love how you unpacked the real mechanics behind scaling AI — clarity like this drives stronger teams and smarter systems.

Use your favourite AI coding agent to create AI frames. What if you could connect everything—your PDFs, videos, notes, code, and research—into one seamless flow that actually makes sense? AI-Frames: Open Source Knowledge-to-Action Platform:timecapsule.bubblspace.com ✨ Annotate •

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Use your favourite AI coding agent to create AI frames. What if you could connect everything—your PDFs, videos, notes, code, and research—into one seamless flow that actually makes sense? AI-Frames: Open Source Knowledge-to-Action Platform:timecapsule.bubblspace.com ✨ Annotate •

The community's response towards Cowork gives us a strong positive signal towards what we have been building timecapsule.bubblspace.com Our product works in your browser & solves a different problem. The Problem: How do you manage a knowledge base, user or team expertise,

One of the biggest hidden pains of building products with AI coding agents is regression testing. A new feature written by an agent can quietly break existing functionality and wipe out days of effort. I’ve run into this multiple times. The problem isn’t feature velocity. It’s

Bartosz Idzik I’m trying to build this from first principles instead of focusing on any specific framework. My goal is test effectiveness rather than test density — what you assert, where you assert it, and how fast it runs. I’m trying to provide enough data to agents so they can generate

Terence Exactly right. And the scary part — most founders have no idea because their site looks fine in Search Console. I ran Google Analytics on my own site. Barely any activity. Then I built FetchLens and pointed it at the same site. 93 AI agent visits and 298 vulnerability scan