Hello Reader!

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There’s a special kind of excitement that hits when a dream-fit client reaches out at the worst possible moment.
That’s exactly what happened last year around this time.

My calendar was overflowing.
Commitments everywhere.
Zero space for anything new.

And then their message landed in my inbox.

Clear. Focused. High-stakes.
Exactly the kind of problem Bluebird Optimization exists to solve.

So I made space where there was none.

Why does the fit of a client change the entire trajectory of a project?
Let me tell you.

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The Sprint That Became a Journey

Our first call made something obvious: their deadline was real, their need was concrete, and their team knew exactly what they wanted. The pressure was high, but the work itself felt right, which made the decision easy.

After the initial sprint, my schedule eased back toward normal. The collaboration settled into a steady rhythm and grew into an almost half-year journey during which we built something with real impact together.

I’ve now written a behind-the-scenes case study about this project — and it’s live today. But I won’t spoil anything here.

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The Lesson Hidden in the Work

This project also reaffirmed an important modeling principle when working with Seeker. Seeker is a primal solver. It doesn’t prove optimality. It explores the search space through extremely smart trial-and-error. Because of that, the way you represent the actual degrees of freedom in your model matters a lot.

Here's a simple way to think about it:

The Piano Metaphor

Your decision variables are like all 88 keys on a piano, but your true degrees of freedom are your fingertips — the few keys you can press at any moment. The rest of the keyboard isn’t something you directly choose; it’s a consequence of the notes your fingers play.

Seeker works best when the model exposes only those fingertips. When the model reflects the actual choices rather than every possible state, Seeker can search much more effectively. Everything else follows naturally.

This principle played a quiet but important role in the project, and if you read the case study, you’ll notice where it shows up — even though I never mention it explicitly.

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Peek Behind the Curtain

If you’re curious what a real optimization engagement looks like from the inside — the calls, the decisions, the modeling path, the collaboration — the full story is now live on the Bluebird blog.

Read the case study here:​
​Success Story: Inventory Optimization Under Uncertainty at Dryft​
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Until the next iteration! Tim Varelmann ​Bluebird Optimization​ Complicated Decisions - Simply Automated! ​ Follow me on LinkedIn​

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