= Unprecedented • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising • AI Usage + Cost + Loss Growth = Unprecedented • AI Monetization China USA – LLM #2 AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising 3 Cost of Key Technologies Relative to Launch Year % of competitive. Breakthroughs in large models, cost-per-token declines, open-source proliferation and chip performance improvements are making new tech advances increasingly more powerful, accessible, and economically

or small-medium-large as units for assigning story points. Over time, as the teams accumulate performance data, this iterative and incremental4 process improves accuracy in allocating points. Point team to plan the amount of work to accomplish in the next sprint and continually measure its performance. Teams use burn down charts (Figure 3) to track progress during a sprint. Figure 3: Example mitigation strategy, since early working software products reduce risk by validating requirements and performance characteristics rather than by conducting exhaustive paper analysis. The requirements process

with backtrace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 34 Performance Tips 416 34.1 Performance critical code should be inside a function . . . . . . . . . . . . . . . . . 416 untyped global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 34.3 Measure performance with @time and pay attention to memory allocation . . . . . . 417 34.4 Tools . . . . . . . . Tweaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 34.23 Performance Annotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 34.24 Treat Subnormal

with backtrace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 34 Performance Tips 416 34.1 Performance critical code should be inside a function . . . . . . . . . . . . . . . . . 416 untyped global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 34.3 Measure performance with @time and pay attention to memory allocation . . . . . . 417 34.4 Tools . . . . . . . . Tweaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 34.23 Performance Annotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 34.24 Treat Subnormal

with backtrace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 35 Performance Tips 444 35.1 Performance critical code should be inside a function . . . . . . . . . . . . . . . . . 444 untyped global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 35.3 Measure performance with @time and pay attention to memory allocation . . . . . . 445 35.4 Tools . . . . . . . . Tweaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 35.25 Performance Annotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468 35.26 Treat Subnormal

with backtrace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 35 Performance Tips 444 35.1 Performance critical code should be inside a function . . . . . . . . . . . . . . . . . 444 untyped global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 35.3 Measure performance with @time and pay attention to memory allocation . . . . . . 445 35.4 Tools . . . . . . . . Tweaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 35.25 Performance Annotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468 35.26 Treat Subnormal

with backtrace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 35 Performance Tips 444 35.1 Performance critical code should be inside a function . . . . . . . . . . . . . . . . . 444 untyped global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 35.3 Measure performance with @time and pay attention to memory allocation . . . . . . 445 35.4 Tools . . . . . . . . Tweaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 35.25 Performance Annotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468 35.26 Treat Subnormal

Comparison with backtrace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 35 Performance Tips 454 35.1 Table of contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1182 51.4 Implementation notes and performance . . . . . . . . . . . . . . . . . . . . . . . . 1185 51.5 Design inspiration . . . . . . . . blogs on Julia 1.2 Introduction Scientific computing has traditionally required the highest performance, yet domain experts have largely moved to slower dynamic languages for daily work. We believe

Comparison with backtrace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 35 Performance Tips 455 35.1 Table of contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1180 51.4 Implementation notes and performance . . . . . . . . . . . . . . . . . . . . . . . . 1183 51.5 Design inspiration . . . . . . . . blogs on Julia 1.2 Introduction Scientific computing has traditionally required the highest performance, yet domain experts have largely moved to slower dynamic languages for daily work. We believe