Offline navigation is a lifeline for travelers, adventurers, and everyday commuters. We demand speed, accuracy, and the flexibility to tailor routes to our specific needs. For years, OsmAnd has championed powerful, feature-rich offline maps that fit in your pocket. But as maps grew more detailed and user demands for complex routing increased, our trusty A* algorithm, despite its flexibility, started hitting a performance wall. How could we deliver a 100x speed boost without bloating map sizes or sacrificing the deep customization our users love?
I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.
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16 February 2026ShareSave
services.AddSingleton();We leveraged this existing dependency injection structure to properly set up the AOT DLL build. By defining a custom IoC container and injecting it with the concrete implementations required for offline play we were able to minimize the amount of refactoring necessary to make everything work. For the previous telemetry client example, we simply inject a no-op implementation in the serverless code.
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