TQNN
Fault-tolerant inference for noisy and imperfect data.
Today I'm finally documenting the TQNN journey here.
A little over a year ago, TQNN existed only as notebooks and ideas. Since then I've been building it almost entirely from a Pixel 8 in rural Alberta.
This weekend was a major milestone.
• TQNN Core reached its first stable release.
• The cloud runtime is live.
• The public REST API is operational.
• The official Python SDK has been updated.
• The inference engine has been rebuilt into a modular architecture that's much easier to maintain and extend.
Along the way, the vision evolved as well.
What started as a multi-domain inference engine has become a fault-tolerant inference platform focused on helping applications make confidence-aware decisions when data is noisy, incomplete, or uncertain.
There's still a long road ahead. No revenue yet, plenty left to learn, and lots of benchmarking and validation still to do.
I'm looking forward to sharing both the wins and the failures here as I continue building.
If you're working on AI infrastructure, machine learning, or developer tools, I'd love to connect and learn from what you're building too.
1 Comment
I like that you're describing the evolution of the problem the platform solves instead of treating the architecture itself as the achievement.
I'll be interested to see which kinds of decisions developers consistently trust the platform to make. Those patterns will probably reveal where confidence-aware inference creates the most practical value beyond model performance alone.