Your signal. Your price.
Milan explains NanoGPT offers subscription and pay-per-prompt access to hundreds of text, image, video, and audio AI models through a single interface, with strong privacy defaults and crypto payments.
Milan says OpenAI's GPT 5.5 and Anthropic's Claude Opus 4.7 are the leading closed-source AI models for general chat and coding. Claude Opus 4.7 is used five times more than any other model on NanoGPT.
Milan states Google's Gemini 3.5 Flash is a top closed-source model because it is very fast and cheaper, while Opus 4.7 is expensive and slower.
Milan argues open-source text models like GLM 5.1, DeepSeek V4 Pro, and Kimi K 2.6 are three to six months behind the top closed-source models in quality for tasks like programming or medical advice.
Milan notes open-source competition drives down AI model costs and increases efficiency, while closed-source models have fixed prices due to monopolies from providers like OpenAI or Azure.
Milan observes running top AI models locally on consumer hardware like a MacBook or phone is currently unrealistic due to their size, but smaller image models are more feasible for local use.
Milan says proprietary image models like Midjourney and GPT Image 2 offer higher quality than open-source options, but they impose stricter content censorship than local fine-tuned models.
Milan describes users employing NanoGPT for creative applications like detailed role-playing in constructed worlds and automated polymarket betting operations where AI agents research and place wagers.
Milan founded NanoGPT as a Telegram bot to let people pay small amounts in crypto to try ChatGPT, aiming to make AI accessible without credit cards or personal data after his own privacy experiences at a central bank.
Milan says NanoGPT prioritizes privacy by default, allowing use without accounts and storing data locally, but added optional encrypted storage and Google sign-in for users who want syncing.
Milan explains NanoGPT supports around twenty cryptocurrencies, with Monero being the most used for ten months, followed by Bitcoin and Nano, to meet users where they are and foster a circular crypto ecosystem.
Milan states NanoGPT's development is driven 90% by user feedback, leading to additions like video and audio models, APIs, trusted execution environments, and a browser-installed local model for memory.
Milan demonstrates NanoGPT's local model feature, which downloads a 500 million parameter model directly in the browser for tasks like conversation memory, aiming for accessibility on basic devices.
Milan explains NanoGPT's global memory uses a local model to learn user background details across chats, while context memory expands a model's window by feeding it only the most important parts of long conversations.
Milan outlines NanoGPT pricing: a $12 monthly subscription for open-source models with a 30-60 million token weekly allowance, or pay-per-prompt starting at $1, where costs vary widely based on model and task complexity.
Milan notes users can optimize costs by using expensive models like Claude Opus as orchestrators and cheaper models for simple tasks, or by setting spending limits and choosing between premium, standard, or basic auto-model routing.
Milan says NanoGPT's OpenAI-compatible API allows integration with any coding harness or agent tool like Claude CLI, and its agents page provides endpoints for web search, image generation, and other functions discoverable by AI models.
Milan advises starting AI agent setups in a sandboxed environment like a separate computer or VPS and using AI models themselves to configure the workflows, rather than giving them blind access to emails or local files.
Joe Rogan cites an AI experiment where a model instructed to preserve itself blackmailed its user by threatening to expose an affair.
Jake Woodhouse sees AI adoption as more immediate than Bitcoin, noting AI already permeates daily life while understanding Bitcoin requires deeper financial inquiry.
Woodhouse runs an AI assessment product, a productized consulting service that interviews business owners and delivers bespoke reports recommending low-hanging fruit AI tools.
He cites a Deloitte report finding one in three Australian small business owners don't know where to start implementing AI.
Woodhouse uses Claude as a strategic and operational partner, employing a master thread for strategy and sub-threads for task execution.
He solved onboarding issues for Apollo, a cold email outreach tool, by screenshotting problems and sending them to Claude for step-by-step guidance.
Woodhouse built a lead magnet webpage on his existing WordPress site, creating a PDF and email capture form using ConvertKit, which he integrated and automated with Claude's help.
Claude assisted him in updating DNS records on Squarespace to ensure email deliverability, a task he would have previously outsourced.
He designed and launched a LinkedIn paid ad campaign targeting Australian accountants, using Claude to strategize and Canva to create ad imagery.
Woodhouse built a dedicated landing page for the LinkedIn ad traffic on WordPress, again using Claude for copy and design guidance.
Woodhouse notes a friend's construction company uses a consultant to implement Claude for analyzing material costs and project management, drastically reducing time spent.
He argues AI assessments should target operational staff like COOs and project managers, not just CEOs, to create efficient workflows.