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We Are No Longer Building a Canadian Legal AI Model
Yesterday I published the post-mortem: we asked flash-1-mini ten questions any Canadian lawyer would consider basic, and it invented seven citations. That post was about what broke. This one is about what it changed. Because two weeks ago I was telling people we were building a Canadian legal AI model - and today we decided we're not. I want to walk through why, because the answer changed how I think about what "building AI" actually means. The original scope The original scope made sense
Read storyFounder Reality is written by George Pu — $10M+ portfolio built by 27, no investors, no co-founders.
Latest Essays
What I'm thinking about right now.
What We're Building: An Open-Weight Canadian Model Series
The model is the smallest part of the story. Here's what it is, what it isn't, and what comes next. Today we shipped flash-1-mini. It's a 4-billion-parameter open-weight model, fine-tuned for Canadian context, bilingual in English and French, that runs on a laptop with no cloud dependency. You can download it, run it offline, and own it. The weights are yours. I want to write about what it is, what it isn't, and what comes after — because the model itself is the smallest part of the story.
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While You Were Watching the Chatbots
Over the past two years, Canada quietly rebuilt the question of who controls its artificial intelligence. Not in one announcement. That's the point. There was never a single moment loud enough to make you look up. The decisions arrived in fragments — a Christmas Eve letter, a contribution agreement with a file number, a press release at a university most people don't follow, an MOU with the important parts blacked out. Each fragment was, on its own, small enough to ignore. Together they red
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Canada Is Renting Its Sovereignty
In the last piece I wrote about the anchor: Canada designated Cohere as its national champion at the foundation model layer — $240 million, a 24-year term, an MOU that called the company "the only sovereign, cloud-agnostic large language model operating in Canada," and a fund Cohere itself proposed. That designation is settled for a generation. This piece is the opposite kind of writing. The last one was analysis of public records, and I tried to stay out of it. This one carries a point of
Read essayFrom the series · 90-Day Action Plan to Surviving AI
The Acceptance
There's a moment I remember clearly.
More on Announcements
Three essays from the archive on a different angle.
We're Shutting Down Our Cloud-Hosted Apps
Effective immediately, we're sunsetting the cloud-hosted versions of the following apps: * Founder Simulation Game * SimpleDirect Changelog * SimpleDirect Chat * SimpleDirect Roadmap SimpleDirect Financing, our legacy fintech application, is not affected. Let's be real These apps have no moat. We know that. AI can rebuild any of them in an afternoon. Pretending otherwise - keeping servers running, maintaining hosted vers
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Why I'm fine-tuning a small model (and why it runs on your laptop)
I'm training an AI model. It's going to run on a laptop. Three weeks ago I would have told you I was training a 70-billion-parameter model, the kind of thing that needs a data center to breathe. I'm not. I'm training a 4-billion-parameter model that runs on a Mac Mini. If the smaller one works, a larger companion model may follow. But the 4B is the bet. This is the first post in a series where I'll share what I'm building, why,
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Announcing Ghost Narrator: Self-Hosted AI Narration Stack
We publish about 200 blog posts a month on Founder Reality. Every post gets a narrated audio version — you can listen instead of read. The obvious solution was ElevenLabs. Best-in-class voice cloning, simple API, great output. $330/month for the scale we needed. We almost signed up. Then we tried something else. We ran an open-source model on a laptop. Qwen 3.5 14B for script rewriting, Qwen TTS for voice cloning. The whole pi
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One Rack Is a Cloud
What colocation is, and why most AI founders have never heard of it
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I Tried to Buy a Mac Studio This Week. Here's What Happened.
I've been running our AI stack on a rented L4 GPU on Google Cloud. $700 a month. Serves a Qwen 14B model. Fine for production. Not fine for what I want to do next. I want to self-host a 70B open-source model on my own hardware. Run it locally. Write about the whole process in public. Eventually help other founders do the same. The plan was simple. Mac Studio. M4 Max. 128GB unified memory. Quiet enough to sit on a desk. Fast en
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Recent threads
The latest from @TheGeorgePu.
Canada's AI hardware reality check — what's actually available vs. what founders think they can buy.
GPU shipping is the tell. If you can't physically own the compute, you don't own your AI stack.
I only write code when it's 10/10 important. Slowing down is the real productivity move in 2026.
Mac Studio supply is crunched. Apple's quietly rationing M3 Ultra — AI builders feel it first.
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