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118 commits this week
Dec 17, 2025
-
Dec 24, 2025
Deploying to gh-pages from @ IntersectMBO/plutus@79b0431690d19985d1e8ab558dd46657fc0a1edb 🚀
Deploying to gh-pages from @ IntersectMBO/plutus@d08fc0c048594325886b7b7e7cdd3b810e431046 🚀
Deploying to gh-pages from @ IntersectMBO/plutus@d08fc0c048594325886b7b7e7cdd3b810e431046 🚀
add Plutus Benchmarks (customSmallerIsBetter) benchmark result for d08fc0c048594325886b7b7e7cdd3b810e431046
Update documentation of plutus-benchmark (#7495)
Up `mdast-util-to-hast` version to 13.2.1 in docusaurus (#7503)
Deploying to gh-pages from @ IntersectMBO/plutus@921420b8215d1b6cca968f564e67cb9b11454816 🚀
Deploying to gh-pages from @ IntersectMBO/plutus@921420b8215d1b6cca968f564e67cb9b11454816 🚀
add Plutus Benchmarks (customSmallerIsBetter) benchmark result for 921420b8215d1b6cca968f564e67cb9b11454816
Deploying to gh-pages from @ IntersectMBO/plutus@921420b8215d1b6cca968f564e67cb9b11454816 🚀
Fix instance FromData PlutusTx.Data.List (#7492)
Co-authored-by: Nikolaos Bezirgiannis <[email protected]>
Up mdast-util-to-hast version to 13.2.1 in docusaurus
Deploying to gh-pages from @ IntersectMBO/plutus@2aaccb0dc7095df37df8909c63a694a2fa960a4d 🚀
Deploying to gh-pages from @ IntersectMBO/plutus@2aaccb0dc7095df37df8909c63a694a2fa960a4d 🚀
Deploying to gh-pages from @ IntersectMBO/plutus@2aaccb0dc7095df37df8909c63a694a2fa960a4d 🚀
Add a phantom-type test for deriveEnum (#7499)
Co-authored-by: Nikolaos Bezirgiannis <[email protected]>
feat(costing): update Value↔Data memory models with linear coefficients
Replace constant memory costs with linear models derived from empirical measurements: - ValueData: memory = 38×size + 6 (was constant 1) - UnValueData: memory = 8×nodes + 0 (was constant 1) CPU: 290658×nodes + 1000 (was 43200×arg + 1000) The linear models better reflect actual memory behavior: ValueData scales with serialized size, UnValueData scales with node count. Benchmark data regenerated with new memory measurement approach.
refactor(benchmark): adapt Value↔Data benchmarks for memory wrappers
Update ValueData and UnValueData benchmarks to use createOneTermBuiltinBenchWithWrapper with appropriate memory measurement wrappers (ValueTotalSize and DataNodeCount). This ensures benchmarks measure the same memory behavior as production builtins.
feat(costing): use specialized wrappers for Value↔Data builtins
Apply ValueTotalSize to ValueData and DataNodeCount to UnValueData, replacing plain Value/Data types. This enables accurate memory accounting: ValueData uses total serialized size, UnValueData uses node count for measuring input Data complexity.
feat(costing): update Value↔Data memory models with linear coefficients
Replace constant memory costs with linear models derived from empirical measurements: - ValueData: memory = 38×size + 6 (was constant 1) - UnValueData: memory = 8×nodes + 0 (was constant 1) CPU: 290658×nodes + 1000 (was 43200×arg + 1000) The linear models better reflect actual memory behavior: ValueData scales with serialized size, UnValueData scales with node count. Benchmark data regenerated with new memory measurement approach.