implementation

Feature engineering is the process of formulating the most appropriate features given the data, the model, and the task.

The lesson from cases of people both keeping and losing their jobs is that as long as you keep your boss or bosses happy, performance really does not matter that much and, by contrast, if you upset them, performance won’t save you.

Sierra AI grew from roughly $20M to $150M ARR in about 15 months, charging ~$1.50 per resolution. Decagon grew from ~$6M to ~$35M ARR using perconversation pricing. Intercom's Fin at $0.99 per resolution drove 40% higher adoption. The only other prominent case study I could find is legal tech firm EvenUp which charges per brief generated, but even

This creates what economists call a moral hazard in both directions. Vendors paid per "completed task" face incentives to use cheaper models, minimize compute, skip quality checks, and classify ambiguous outcomes as successes. Customers face incentives to overload systems with low-value tasks or dispute outcomes to avoid

payment. You've rebuilt the

Risk Reducers (Your Safety Net):

• The hiring manager at [Company X] could offer you a job tomorrow

• The investor who funded three companies in your space last year

• The mentor who’s navigated exactly the minefield you’re facing

• The industry veteran whose recommendation opens any door

Steps would be the following:

1. Start with a Central Idea:

   
   
   

   • Begin your mind map with a core concept, like a company name or an industry.




2. Branch Out Naturally:

   
   
   

   • From this center, draw main branches for major categories (e.g., business, competitive advantage, etc.).

   
   
   

   • Each branch can then split into sub-branches.




3. Use Keywords and Images:

   
   
   

   • Replace

“We’re already successful enough to have resources to burn.”

In this exchange I’m clear about my underlying value—having a good relationship with my brother—but I’m still struggling with my metric. I’ve given it another name, “closeness,” but the metric hasn’t really changed:

If he were to listen to Holmes for more than clues as to a suspect’s identity or a case’s line of inquiry, he would find that he might have less need of turning to him in the future. For, if imagination does not enter into the picture—and do so before any deduction takes place—all of those observations, all of that understanding of the prior