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OpponentEloDiffResultsScoreLOSPerf– Stockfish 15 64-bit 4CPU3622+13−13(+164)6.5 − 13.5(+0−7=13)32.5%6.5 / 200.0%+53– Stockfish 14 64-bit 4CPU3621+13−13(+163)5.5 − 14.5(+0−9=11)27.5%5.5 / 200.0%+14– Dragon by Komodo 3.1 64-bit 4CPU3616+14−14(+158)5 − 15(+0−10=10)25.0%5.0 / 200.0%−8– Fat Fritz 2 64-bit 4CPU3601+10−10(+143)6.5 − 13.5(+0−7=13)32.5%6.5 / 200.0%+32– Berserk 10 64-bit 4CPU3567+13−13(+109)6 − 14(+0−8=12)30.0%6.0 / 200.0%−18– Revenge 3.0 64-bit 4CPU3558+9−9(+100)6.5 − 13.5(+1−8=11)32.5%6.5 / 200.0%−15– Ethereal 13.75 64-bit 4CPU3554+12−12(+96)6.5 − 13.5(+0−7=13)32.5%6.5 / 200.0%−14– Koivisto 8.0 64-bit 4CPU3551+11−11(+93)7 − 13(+0−6=14)35.0%7.0 / 200.0%−2– SlowChess Blitz 2.9 64-bit 4CPU3545+9−9(+87)8.5 − 11.5(+0−3=17)42.5%8.5 / 200.0%+41– Clover 5.0 64-bit 4CPU3544+17−17(+86)12 − 20(+0−8=24)37.5%12.0 / 320.0%+9– Deep Sjeng 3.6 a16 64-bit 4CPU3544+16−16(+86)12 − 20(+0−8=24)37.5%12.0 / 320.0%+9– RubiChess 20220813 64-bit 4CPU3530+15−15(+72)7 − 13(+0−6=14)35.0%7.0 / 200.0%−20– rofChade 3.0 64-bit 4CPU3526+10−10(+68)7 − 13(+0−6=14)35.0%7.0 / 200.0%−24– Clover 4.0 64-bit 4CPU3525+16−16(+67)11 − 19(+0−8=22)36.7%11.0 / 300.0%−16– Minic 3.32 64-bit 4CPU3520+14−14(+62)8.5 − 15.5(+0−7=17)35.4%8.5 / 240.0%−28– Minic 3.30 64-bit 4CPU3515+14−14(+57)6.5 − 13.5(+0−7=13)32.5%6.5 / 200.0%−52– Caissa 1.8 64-bit 4CPU3513+17−17(+55)11 − 15(+0−4=22)42.3%11.0 / 260.0%+8– Seer 2.5.0 64-bit 4CPU3510+13−13(+52)9.5 − 10.5(+1−2=17)47.5%9.5 / 200.0%+38– Carp 3.0.0 64-bit 4CPU3501+16−16(+43)10.5 − 11.5(+0−1=21)47.7%10.5 / 220.0%+29– Arasan 23.4 64-bit 4CPU3499+13−13(+41)8.5 − 11.5(+0−3=17)42.5%8.5 / 200.0%−4– Uralochka 3.38c 64-bit 4CPU3493+15−15(+35)10 − 10(+2−2=16)50.0%10.0 / 200.0%+35– Rebel 15.1a 64-bit 4CPU3490+16−16(+32)9 − 11(+0−2=18)45.0%9.0 / 200.1%+4– Arasan 23.5 64-bit 4CPU3488+15−15(+30)10.5 − 11.5(+1−2=19)47.7%10.5 / 220.2%+17– Igel 3.1.0 64-bit 4CPU3484+12−12(+26)9.5 − 10.5(+0−1=19)47.5%9.5 / 200.3%+13– Black Marlin 7.0 64-bit 4CPU3466+14−14(+8)8.5 − 11.5(+1−4=15)42.5%8.5 / 2018.7%−41– Houdini 6 64-bit 4CPU3456+7−7(−2)9 − 11(+1−3=16)45.0%9.0 / 2060.1%−31– Velvet 5.1.0 64-bit 4CPU3454+17−17(−4)16.5 − 13.5(+7−4=19)55.0%16.5 / 3063.2%+29– Marvin 6.1.0 64-bit 4CPU3448+15−15(−10)11.5 − 12.5(+0−1=23)47.9%11.5 / 2482.7%−20– Wasp 6.00 64-bit 4CPU3440+15−15(−18)12.5 − 7.5(+5−0=15)62.5%12.5 / 2096.4%+55– Nemorino 6.05 64-bit 4CPU3432+16−16(−26)10 − 10(+3−3=14)50.0%10.0 / 2099.2%−26– Booot 7.0 64-bit 4CPU3428+16−16(−30)10 − 10(+2−2=16)50.0%10.0 / 2099.7%−33– Velvet 4.1.0 64-bit 4CPU3423+15−15(−35)11 − 9(+4−2=14)55.0%11.0 / 20100.0%−4– Mantissa 3.7.2 64-bit 4CPU3381+15−15(−77)12 − 8(+4−0=16)60.0%12.0 / 20100.0%−17– Marvin 6.0.0 64-bit 4CPU3377+16−16(−81)11 − 9(+2−0=18)55.0%11.0 / 20100.0%−53– Expositor 2BR17 64-bit 4CPU3376+16−16(−82)12.5 − 7.5(+6−1=13)62.5%12.5 / 20100.0%0– Counter 5.0 64-bit 4CPU3373+18−18(−85)14.5 − 9.5(+7−2=15)60.4%14.5 / 24100.0%−17– Smallbrain 6.0 64-bit 4CPU3370+16−16(−88)13.5 − 10.5(+3−0=21)56.3%13.5 / 24100.0%−52– Stash 34.0 64-bit 4CPU3364+18−18(−94)16.5 − 7.5(+10−1=13)68.8%16.5 / 24100.0%+28– Drofa 4.0.0 64-bit 4CPU3318+20−20(−140)15 − 3(+12−0=6)83.3%15.0 / 18100.0%+102– Winter 1.0 64-bit 4CPU3306+18−18(−152)12.5 − 3.5(+9−0=7)78.1%12.5 / 16100.0%+40– Drofa 3.3.22 64-bit 4CPU3300+21−21(−158)13.5 − 6.5(+7−0=13)67.5%13.5 / 20100.0%−47

Query");console.log(results);Example of a binary hamming distance query over BigUint64Array (64 bits processed at a time using pure JS):(Insanely Fast) Query Binary Embeddings Using Hamming Distance Over WebAssembly SIMD (+128 bits at a time, shorter CPU cicles)The WebAssembly SIMD implementation processes 128 bits per iteration (via v128.xor) compared to 64 bits per iteration in the JavaScript implementation using BigUint64Array. This alone gives a theoretical 2x speedup.However SIMD instructions execute XOR, popcount, and similar operations on multiple data lanes in parallel. This reduces the number of CPU cycles required for the same amount of work compared to sequential bitwise operations in JavaScript. SIMD in WebAssembly is likely 2x to 4x faster or more over big vectors.Check haming_distance_simd.wat for the WASM source code. Compiled using wat2wasm.const results = await db.queryBinarySIMD( "Find similar texts based on this query");console.log(results);Example of a binary hamming distance query over WebAssembly SIMD (+128 bits at a time, shorter CPU cicles):The logs show offsets (0, 16, 32), which means the code processes 128 bits (16 bytes) at a time. Since the total vector is 384 bits, it takes 3 steps (384 ÷ 128 = 3), confirming 128-bit SIMD processing.For NextjsIf you're using Next.js, you may need to configure Webpack to work with Transformers.js. Add the following to your next.config.js file: { // Override the default webpack configuration config.resolve.alias = { ...config.resolve.alias, "onnxruntime-node$": false, // Disable onnxruntime-node for browser environments "sharp$": false, // optional - Disable sharp package (used by some image processing packages) }; return config; },"> webpack: (config) => { // Override the default webpack configuration config.resolve.alias = { ...config.resolve.alias, "onnxruntime-node$": false, // Disable onnxruntime-node for browser environments "sharp$": false, // optional - Disable sharp package (used by some image processing packages) }; return config; },ContributingFeel free to fork the repository, create issues, and submit pull requests. We welcome