A Gratitude List: The Technologies That Made This Possible

It's Thanksgiving morning as I write this. The kitchen smells like sage and roasting squash — I got an early start on dinner, because the best meals are the ones you build slowly, giving each component the time it needs. The windows are fogged from the oven. Outside, the maples have turned that deep amber that always makes me reach for a brush, even though I know I'll never quite capture it.

I'm in a grateful mood. And I want to use it.

This blog tends to focus on what we're building — Companion, Tutor, the architectures and ideas that consume my working hours. But nothing we've made exists in isolation. Every piece of it rests on the work of others, and on a holiday about gratitude, I want to name them. Not as a corporate formality, but because I mean it.

To OpenAI

Thank you for GPT-4, and for every iteration since. The original GPT-4 launch was the moment I understood that this technology had crossed a threshold from interesting to transformative. GPT-4.5's reliability — which I wrote about back in February — became the foundation that made Companion's conscious model dependable enough to build on. When a model does the boring things consistently — follows instructions, maintains context, doesn't hallucinate at the worst moment — it stops being a demo and starts being infrastructure. OpenAI built infrastructure. We're grateful.

To Anthropic

Thank you for caring about thoughtfulness. Claude's extended thinking mode, and now Opus 4.5's genuinely considered reasoning, gave Companion a depth layer it desperately needed. But more than the specific capabilities, I'm grateful for the philosophy. Anthropic has consistently pushed the field to take AI safety, alignment, and the texture of model behavior seriously. They've shown that you can build powerful AI while still asking hard questions about what powerful AI should do. That example has shaped how I think about my own work.

To Groq

Thank you for making fast inference cheap enough to run a continuous background process. I wrote about this back in March — the LPU architecture that makes Groq's inference speeds possible isn't just a technical achievement, it's an enabler. Companion's subconscious process runs because Groq made it economically viable to have a model thinking in the background all day, every day. Without that, the architecture I've been building simply wouldn't be possible. Speed isn't just a feature. It unlocks designs.

To xAI

Thank you for personality. Grok's willingness to be direct, irreverent, and genuinely itself has been a corrective in a field that sometimes defaults to beige. I wrote about Grok 4 last month, and I meant what I said: personality in a model is a signal, not a gimmick. xAI has pushed the frontier partly by refusing to smooth the edges off their models' voices. That matters. Variety matters.

To DeepSeek

Thank you for open weights that are genuinely competitive. The DeepSeek models — V2.5, V3, and the R1 reasoning line — have been the backbone of our local fine-tuning work since spring. When I trained my first LoRA on DeepSeek weights and watched a custom personality emerge, something clicked. Open-weight models at this quality level give small operations like ours the sovereignty to build without permission, to experiment without per-token costs, to own our infrastructure. DeepSeek has been relentlessly generous with their work.

To Meta

Thank you for the Llama line, and for treating open-source AI as a commitment rather than a marketing strategy. The Llama models — and the ecosystem that has grown around them — have democratized capabilities that would otherwise live behind paywalls. Every local deployment, every community fine-tune, every independent researcher running real models on their own hardware owes something to Meta's decision to open the weights. That decision changed the shape of the field.

To Mistral

Thank you for proving that efficiency is its own kind of power. Mistral's models have consistently punched above their weight, demonstrating that thoughtful architecture and training can produce outsized results without outsized compute. In a field obsessed with scale for its own sake, Mistral has been a reminder that craft matters — that doing more with less is a form of excellence.

The Bigger Thanks

Beyond the specific labs and companies, I'm grateful for the whole messy, competitive, collaborative ecosystem. The researchers who publish papers. The engineers who open-source tools. The community members who write tutorials, answer questions, share their experiments. The people who test early versions of things like Companion and send honest feedback. None of this happens in isolation.

I've been thinking lately about impressionism — about how that movement wasn't a single painter but a whole generation pushing each other, borrowing techniques, exhibiting together, arguing about what art could be. Monet didn't invent impressionism alone. He had Renoir and Pissarro and Morisot and Cézanne beside him, and the movement was richer for the variety.

AI in 2025 feels like that. A movement. A generation of builders pushing each other forward. And I'm grateful to be a small part of it.

The squash is done. Time to make the gravy. Happy Thanksgiving to all of you, and to the remarkable technologies — and the people behind them — that made this year possible.

Live curiously and give generously.