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I just finished something that took 2 years and 12 failed drafts to get right...
Most AI books tell you what to think. This one teaches you how to OWN the entire stack — from hardware to models to deployment. Volume 2 is complete. 300+ pages on building your own AI infrastructure. No cloud dependencies. No API keys. No monthly fees. Just you, your hardware, and total control. (If you know what it's like to watch your AI budget evaporate on token charges, you know why this matters.) But that's just the beginning. There's a WHOLE UNIVERSE coming. 12 follow-up books. Each targeting a specific vertical. Sales. Marketing. Operations. Healthcare. Real Estate. Legal. 12 companion playbooks. The exact frameworks for implementation. A certification program. For consultants who want to build businesses around the methodology. 15 revenue streams. Conservative projection: $1.2M-$6M annually. I've been building this quietly. And now it's time to open the doors. The Be Practical Series is going live. Very, very soon. Want first access to the playbooks, the beta certification, and the behind-the-scenes build? Drop a "OWN IT" below.
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👋 Meet Our Founder, Hamish Aman Prakash!
Hey Open Source AI Builders, If you're new here, it's time for an intro! Our community is powered by Hamish Aman Prakash – a recognized force in Australia's AI and tech ecosystem. Here's why you can trust the guidance, insights, and vision you'll find in this group: - Hamish is a *4-time award-nominated entrepreneur* in technology and artificial intelligence, known for pioneering projects in automation, open source, and local AI solutions. - He's built A-Tech from the ground up – delivering real AI products, consulting for businesses regionally and nationally, and championing a privacy-first, "own your AI" approach through Project Infra. - Hamish is constantly featured across platforms (including LinkedIn: linkedin.com/in/hamishfromatech), sharing innovations, practical demos, and leadership in AI adoption. - His influence and impact have made him a thought leader in Australia's AI community—bridging the world of no-code/builders and real technical mastery. - As a founder, mentor, and teacher, Hamish helps others scale with AI, break down technical barriers, and access life-changing opportunities in an AI-driven future. Let's welcome new members, share our stories, and always aim higher—knowing we're learning from the best in the business. Leave your questions for Hamish below, or connect with him directly to learn more about his journey! 🚀
👋 Meet Our Founder, Hamish Aman Prakash!
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Open‑Source AI > Paid AI – Here’s Why
Hey builders 👋 I’ve been watching the paid‑AI hype for a while, and every time someone talks about “the next big thing” from a giant, I’m reminded of why we love the open‑source movement. 1️⃣ You own it. With a paid model you’re locked into the vendor’s pricing, limits and data policy. With an open‑source LLM you can run it on your own GPU, tweak the weights, and keep 100 % of the data you feed it. Ownership = freedom. 2️⃣ No price hikes. Open‑source doesn’t have a quarterly “upgrade” bill. If you want more compute, buy hardware or move to the cloud – you decide the cost. Paid AI can suddenly bump your token price and hit your budget like a surprise tax. 3️⃣ Community‑driven innovation. Every bug fix, new feature or model release is a community effort. That means faster iteration and more diverse use‑cases than any single company can ship on its own. 4️⃣ Security & privacy. Running locally means no data leaves your network unless you choose to share it. That’s a game‑changer for regulated industries and privacy‑concerned founders. 5️⃣ Learning & growth. Open source lets you dive into the code, experiment with architectures and build a portfolio that shows real engineering skill. Paid APIs are black boxes – great for quick prototypes, but not a path to deep expertise. Bottom line: Open‑source AI gives you control, cost‑efficiency and a community that’s building the future together. Paid AI is fine for one‑off projects or when you need instant scaling, but if you’re serious about building something that lasts, the open‑source route is the smart move. Drop a 👇 if you’re already on the open‑source side or if you’ve got questions about setting up a local LLM. Let’s keep the conversation going!
[For Hire] AI Engineer | Automation, VoiceAI, Agent
I'm a full stack/AI engineer with 10 years of experience. I help businesses and individuals turn AI ideas into real, production-ready solutions. These are what I can build for you. • AI Agents & Multi-Agent Systems • RAG Chatbots & Knowledge Assistants • AI Automation Workflows •Voice AI Agents • Document & PDF Data Extraction • Custom GPT, Claude, Gemini Integrations • Internal Company Copilots Recent projects: • Built an AI research agent that automated data gathering and report generation • Developed a RAG chatbot connected to company documents and knowledge bases • Created AI workflows for email handling, lead qualification, and data processing • Built document extraction systems for invoices, contracts, and PDFs • Integrated AI into existing business tools and internal platforms Tech Stacks : React/Next.js | Node.js/Python/Django | n8n/ OpenAI / OpenClaw / Claude / Gemini / LangGraph / FastAPI/Airtable and modern AI agent tools. Happy to connect with builders and discuss their projects together. Let's build something useful👍
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DiffusionGemma is here. But what is Diffusion?
If you've used image or video gen models then you kind of already know what Diffusion is, without actually knowing what it is. Diffusion is the art of processing your request in patterns rather than linearly/sequentially. When you talk to say GPT 5.5, your prompt/message is being processed word for word, token by token. To ensure GPT can respond properly. Quite an expensive way to process prompts. It's like talking to a PHD level expert and asking them how planes stay in the air, then immediately ask how to make a cheese sandwich. Your expert will answer both questions properly but it will cost the expert it's brain cells and capacity. In plain english this means diffusion models don't need to process each word/token in a sequence. Which saves A LOT on compute power. What's the real upside? Over 1,000 tokens per second. On consumer hardware like @Mason Page 's. Mind boggling speeds right? And we're just getting started. This is the 3rd generation of Diffusion LLMs on the market and the first from Google. Can't wait to see other providers building in the diffusion space soon because damn guys we're going into hyperspace mode!! What could you do with 1,000 tokens per second speeds? How many websites, apps, softwares, or client solutions could you build IN A DAY now? Loads I say.
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DiffusionGemma is here. But what is Diffusion?
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