TensorBundle Blog
Notes on building AI systems.
Engineering, product, and strategy writing from the TensorBundle team.
Nothing was built to answer your exact case
A method can be proven somewhere else and still be unproven under your data, workflow, and constraints.
Nothing was built to answer your exact case. That sounds harsher than it is. It does not mean you have to start from nothing, or pretend established methods are useless. They are useful. The problem is that their proof usually belongs somewhere else: someone else's data, someone…
Perfect on paper, wrong in practice
A model can score well on an evaluation and still fail in production when the metric answers the wrong question.
An evaluation usually asks a smaller question than the one people later attach to it. For a customer support assistant, that question is often simple: did the response match the expected answer? The operational question is messier. Did the customer solve the problem? Did they…
You are not choosing a model. You are choosing a theory.
The model you pick quietly decides what your system can learn, what it will miss, and where it will eventually hit a wall.
Picking an architecture usually does not feel like a big decision. Maybe there is a model that is clearly dominant, or a colleague who has already used something, or a benchmark that looks close enough. You make the call and move on. What takes longer to notice is that the…
Don't hire a genius to do a calculator's job
The most capable tool in your technical stack is often the wrong choice for the problem sitting on your desk right now.
We tend to want the most power and flexibility we can get. But the same pattern plays out every time a disruptive technology shows up. We fall in love with what it could do, and before long we reach for it by default. We stop asking what the problem actually needs. We start…