Nufar Gaspar

speaker
2,488 appearances 11 recordings 1 series first heard Oct 2025 last heard 3 Sep

Nufar Gaspar’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months
5 · Jun OctJan 26AprJulnow

Recordings per month over the last 12 months — 11 in all, peaking in Jun 2026 with 5.

Appearances

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So that's for identifying spin.
And now the habits that we should all adopt to mine our tokens.
These are several things that anybody can do immediately that typically improves the token consumption without reducing the business value.
New task is a new session.
This one is an interesting one because we will talk in a minute about also model routers.
But at least for now, for the most part, be intentional about which model you use for what task.
Sometimes it's actually going up to like an Opus or even Fable class models because they will get the job done in one iteration and overall reduce the spend.
In some other cases, it's not doing a web search with Fable, but rather going to the Haiku or the lower cost of models.
Right-size your context.
Tell your AI what it needs to know.
This is a classical Goldilocks, not too much, not too little, but sufficient such that it will not go into endless internal reasoning token loops just to try and understand what you're talking about.
Build reusable capabilities.
Often when we're
Just vibing with our model and trying to use it ad hoc rather than sitting down and creating the skills, creating the proper automation, creating the proper agents.
We're just wasting a ton of tokens to re-ask the tools to do something again and again.
So seeing and building proper systems often is one of the best levers that you have to use their tokens wisely and filter everything that you can.
Tell it in which rows of the table the data exists, in which parts of the project board the data accounts for, which Slack channels and so on.
The more you point the model to the right place, the better the results that you will get.
And lastly, in many cases, we start doing the work.
We realize that the model is completely off.
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