- Changed output to show bytes instead of MB
- Updated header to show 'Minne (bytes)'
- Better for comparing memory usage across all languages
- Memory data already collected in bytes from ps command
- Changed from 10ms to 1ms sampling interval
- Better captures data for fast programs
- Memory already sampled in bytes for consistency
- Will provide more detailed timeline data
- Added charts for 1, 2, 5, 10, 1000, 2000 decimals
- Each decimal level has compiled, JIT-compiled, and interpreted language charts
- Shows time performance scaling across different precision levels
- Demonstrates how languages perform with increasing decimal counts
- All charts use actual test data from benchmarks
- Updated compiled languages chart with real performance data
- Updated JIT-compiled languages chart with actual measurements
- Updated interpreted languages chart with test results
- Updated slowest languages chart with real data
- All charts now reflect actual benchmark results from 100 decimal tests
- Added performance tables for 1, 2, 5, 10, 100, 1000, 2000 decimals
- Shows how each language scales with increasing precision
- Includes memory usage data for all decimal counts
- Added key observations about scaling behavior
- Documented performance leaders and memory efficiency patterns
- Recreated entire README in English
- Preserved all technical details and data
- Improved flow and readability
- Kept Swedish version as README_SV.md for reference
- All charts and analysis now in English
- Scale x-axis to show full program lifetime
- Adjust y-axis per language for clarity
- Add detailed analysis for each diagram
- Group diagrams by language type (compiled, interpreted)
- Add comparison charts for fast and slow languages
- Explain memory patterns and insights
- Added Mermaid XY charts showing memory usage over time
- Individual charts for Elixir, TypeScript, Dart, Haskell, C
- Comparison chart for fast languages
- Shows how memory usage varies during program execution
- Based on actual timeline data from benchmark runs
- 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