- Save timeline data for each run (time, memory, CPU)
- Create timelines directory structure
- Add Python visualization script for charts
- Fix integer comparison errors in profiling
- Collect samples throughout program lifetime
- Track memory and CPU usage over program lifetime
- Store elapsed time with each sample
- Generate timeline data for visualization
- Enable detailed performance analysis
- Added XY charts for compiled, JIT, and interpreted languages
- Charts show execution time and memory usage from real tests
- Data collected from actual benchmark runs on Apple A18 Pro
- All measurements from 100 decimal test runs
- Memory profiling shows 0-5MB usage across languages
- CPU profiling shows 0% (processes too fast to sample)
- Added get_cpu_usage() function to sample CPU percentage
- Updated profile_resources() to profile both memory and CPU
- Modified run_program() to track CPU usage alongside memory
- Updated output format to show CPU statistics
- Summary now displays: Time, Memory (avg/peak), CPU (avg/peak)
- Both memory and CPU are sampled every 10ms during execution
- Translated beginning of AGENT.md to English
- Added project status section with current languages (34 total)
- Added test environment specifications (Apple A18 Pro, 8GB RAM)
- Added performance results summary
- Added binary size information
- Partial translation in progress
- Added binary size section to README with actual file sizes
- Updated run_all.sh to profile memory usage during execution
- Memory profiling shows average and peak memory consumption
- All data from actual measurements on Apple A18 Pro
- Binary sizes range from 103B (wrapper scripts) to 13M (Haskell)
- Added system specifications (Apple A18 Pro, 8 GB RAM)
- Included actual benchmark results for 100, 1000, and 10000 decimals
- Scientific analysis of performance differences
- Detailed technical explanations for each language
- All data from real test runs on actual hardware
- Removed hypothetical examples, using only measured data
Ein Anderssono