// Lossless Compression Engine  ·  1.0 · Final release · AES-256 encryption
Just shipped · V11 MIKE now crushes CSV & TSV tables — transpose to columns, turn the digits back into numbers, and the generators do the rest: 45–89% smaller than xz -9, byte-for-byte lossless. Plus the whole DEFLATE family (PNG, ZIP, gzip, PDF, Office) at 20–44% past xz -9.

M.I.K.E.

Mathematically Improved Kompression Engine

Some data isn't compressed. It's a puzzle waiting to be solved.

MIKE asks a different question. Not "how do I pack these bytes?" but "what rule generated them?" When it finds one, it stores the formula instead of the data. That engine is puzzleCompressor — a genuinely new axis in lossless compression, and the world's best lossless compressor for structured, numeric, scientific and float data: it beats zip, rar, 7z, xz and zstd, often by thousands of times. puzzleCompressor is MIKE’s engine — the free MIKE 1.0 runs V1; MIKE Vault runs V11.

// A real middle-out moment. Named for my son Miguel — I told him computers are just math, and the math is worth loving — with a push from my wife Ana, who took one look and said "you're living the Silicon Valley series." And, of course, an affectionate nod to Mike Judge and his brilliance. Pied Piper was fiction. Middle-out was fiction. puzzleCompressor and MIKE are real.

// 256 KB numeric file · lossless · CRC-verified
262,144 B30 B

MIKE recognised an arithmetic sequence and stored its formula — an 8,738× collapse. Best mainstream tool (rar): 444 bytes. Restores bit-for-bit.

Not a lab trick. MIKE does it on real data too: 357 KB of live Binance market timestamps → 38 B37× smaller than rar, 2,383× smaller than 7z. Same formula, real firehose.

// MIKE Score · Weissman-style*
3,024×

*Homage metric: how many times better MIKE's ratio is than zip's on that file. On random data it's ~1× — and honest about it.

// A different kind of compression

For thirty years, compression chased one idea. MIKE adds a second.

Every mainstream lossless compressor — from Deflate to zstd — is a variation on a single paradigm: model the byte stream statistically and encode the residual. It's brilliant, it's mature, and it's asymptotically stuck near the data's statistical entropy. MIKE works on a different axis entirely.

The last ~30 years · one axis

Model the output better

Find repeats, then entropy-code what's left. Real, hard-won progress — but all inside the same question: how predictable is the byte stream? The ceiling is Shannon entropy.

LZ77 → DeflateLZMAzstd / brotli
rANS (~2014) was the last genuinely new primitive.
MIKE · a second axis

Recover the rule that made it

If data came from a rule — a counter, a polynomial, a recurrence, a sampled physical process — the shortest description is the rule itself. MIKE recognises the generator and stores the formula. The ceiling isn't Shannon — it's Kolmogorov.

arithmetic → polynomialfloat columnslinear recurrence
generated data: kilobytes → tens of bytes.
The world's best lossless compressor for structured, numeric, scientific and float data — provably, and by orders of magnitude.

Up to 10,000×+ smaller than xz on generated columns · every byte reversible, CRC-verified against zip / rar / 7z / xz / zstd. Then puzzleCompressor cracked already-compressed data — JPEG (8–27%), Motion-JPEG (14–43%), MP3, and the whole DEFLATE family: PNG, ZIP, gzip, PDF, Office 20–44% smaller than xz -9, all lossless. Only modern H.264/H.265 is still out of reach — and there it never expands, and says so.

// Watch

Sixty seconds. The whole story.

What MIKE is, why it works, and the receipts.

// Where MIKE stands — at a glance

Best-in-class where the data has math. Honest everywhere else.

Every result below is CRC-verified against zip, rar, 7z, xz and zstd — all at maximum settings. Full tables further down.

Data typeMIKE vs the whole fieldDetail
Scientific / numeric🏆 BESTcounters, sensors, feature tensors, timestamps — beats everyone, up to thousands×
CSV / TSV tables NEW🏆 BEST45–89% smaller than xz -9 (puzzleCompressor V11) — transpose to columns, turn the digits back into numbers so the generators crush them; where plain LZMA just ties zip/rar
Audio (PCM / WAV)🏆 BESTdethrones rar and 7z — ~25% smaller than rar
Text🏆 BESTedges 7z and xz
Executables⇔ EQUAL to bestwithin 0.3–0.7% of 7z (BCJ2), beats zip / rar / xz / zstd
PNG · ZIP · gzip · PDF · Office NEW🏆 BEST20–44% smaller than xz -9 on already-DEFLATE files (37–44% on PNG & zip, ~20% on PDF & Office) — inflate, re-deflate byte-exact, let LZMA finish the job (puzzleCompressor V10). The widest win: DEFLATE is inside PNG, every zip, and PDF/DOCX/XLSX/PPTX/JAR/APK.
JPEG images NEW🏆 BEST8–27% lossless recompression — where every other tool gets ~0% (puzzleCompressor V7)
Motion-JPEG video NEW🏆 BEST14–43% lossless — per-frame transcode pooled across frames; raw or AVI (puzzleCompressor V8)
MP3 audio NEW🏆 BEST~1.5–2.4% lossless (header/side-info deinterleave) where the field gets ~1% (V9). Deeper Huffman transcode on the way.
H.264 / H.265 video— no gainmodern inter-frame video is already arithmetic-coded — nobody wins there, and we say so.
// The idea

Predict the data, then store only the surprise.

Compression is prediction wearing a trench coat. MIKE runs three moves per block and keeps whichever wins — spectacular where structure exists, and it simply never expands where it doesn't.

01 · GENERATOR

Find the formula

If a block is an exact sequence — a counter, a ramp, a generated table — store the rule that makes it. A few bytes for any length.

02 · DECORRELATE

Stride-aware delta

For smooth signals — audio, sensor sweeps, gradients — subtract each value from the one a sample ago, collapsing structure into near-zero residuals.

03 · NEVER EXPAND

Best-of, always safe

A plain strong pass is always a candidate, so a block only ever gets smaller — or stays put on truly random data. No lottery, no bloat.

// Want the whole story, with real numbers and where it loses? → How MIKE gets 357 KB to 38 bytes

// The receipts · full benchmark

Every file, every tool. Size, ratio, and the clock — both ways.

Real files, real tools, real round-trips — all six contenders measured the same way and checked byte-for-byte against the original (CRC). Rows are sorted smallest-first, so the winner is always on top. Competitors run at maximum settings (zip -9, rar -m5, 7z -mx9, xz -9e, zstd -19).

// Don’t trust us — run it yourself. Download the benchmark pack (4 KB): python3 gen_corpus.py && python3 bench.py compresses a structured-data corpus with MIKE and every tool you have installed, then CRC-verifies every result is lossless and prints the table. Drop in your own files too. sha256 c20422651076f3c5c1d81aff1d849c9edfeb90287564d659d2e5ff2c6f5608b2  ·  free MIKE 1.0 wins 5/5 on the sample corpus, byte-for-byte lossless

struct_counter.bin262,144 B · generated numbers
ToolSizeRatioCompressDecompress
MIKE30 B0.0%0.405 s0.202 s◄ smallest
rar444 B0.2%0.059 s0.014 s
xz18,028 B6.9%0.324 s0.018 s
7z18,116 B6.9%0.066 s0.016 s
zip90,730 B34.6%0.055 s0.008 s
zstd161,369 B61.6%0.115 s0.007 s
struct_sine.bin65,536 B · smooth waveform
ToolSizeRatioCompressDecompress
MIKE7,886 B12.0%0.416 s0.204 s◄ smallest
rar12,275 B18.7%0.048 s0.015 s
xz21,932 B33.5%0.168 s0.008 s
7z22,096 B33.7%0.035 s0.017 s
zstd54,942 B83.8%0.028 s0.005 s
zip58,341 B89.0%0.009 s0.010 s
struct_sensor.bin400,000 B · sensor ramp + noise
ToolSizeRatioCompressDecompress
MIKE154,658 B38.7%1.014 s0.229 s◄ smallest
rar155,435 B38.9%0.078 s0.023 s
7z169,873 B42.5%0.133 s0.031 s
xz171,304 B42.8%0.322 s0.031 s
zstd234,096 B58.5%0.195 s0.009 s
zip270,586 B67.6%0.027 s0.010 s
audio.wav1,691,180 B · raw PCM audio · stereo 16-bit
ToolSizeRatioCompressDecompress
MIKE550,509 B32.6%3.208 s0.292 s◄ smallest
rar737,168 B43.6%0.467 s0.048 s
7z743,362 B44.0%0.423 s0.092 s
xz936,140 B55.4%0.780 s0.118 s
zstd1,097,227 B64.9%0.397 s0.013 s
zip1,113,958 B65.9%0.092 s0.027 s
pride.txt738,046 B · plain-text novel
ToolSizeRatioCompressDecompress
MIKE216,054 B29.3%1.533 s0.215 s◄ smallest
7z216,076 B29.3%0.244 s0.027 s
xz216,112 B29.3%0.664 s0.025 s
zstd218,709 B29.6%0.648 s0.009 s
rar228,363 B30.9%0.143 s0.027 s
zip264,765 B35.9%0.099 s0.018 s
bin_python38,020,928 B · ELF executable · 8 MB
ToolSizeRatioCompressDecompress
7z1,871,374 B23.3%2.331 s0.212 s◄ smallest
MIKE1,885,018 B23.5%27.229 s0.527 sMIKE
xz2,116,596 B26.4%5.944 s0.266 s
rar2,179,464 B27.2%1.338 s0.114 s
zstd2,315,443 B28.9%5.301 s0.048 s
zip2,824,166 B35.2%1.844 s0.102 s
bin_bash1,446,024 B · ELF executable
ToolSizeRatioCompressDecompress
7z520,911 B36.0%0.423 s0.072 s◄ smallest
MIKE522,722 B36.1%4.952 s0.273 sMIKE
rar583,605 B40.4%0.255 s0.043 s
xz583,796 B40.4%1.136 s0.071 s
zstd622,519 B43.1%0.968 s0.015 s
zip691,713 B47.8%0.353 s0.026 s
image.png705,670 B · PNG · already compressed
ToolSizeRatioCompressDecompress
xz705,512 B100.0%0.471 s0.020 s◄ smallest
MIKE705,530 B100.0%0.847 s0.229 sMIKE
7z705,531 B100.0%0.210 s0.032 s
zip705,679 B100.0%0.040 s0.012 s
zstd705,704 B100.0%0.210 s0.007 s
rar705,824 B100.0%0.145 s0.017 s
photo.jpg237,602 B · JPEG · already compressed · puzzleCompressor v7 entropy transcode
ToolSizeRatioCompressDecompress
MIKE205,327 B86.4%0.950 s0.260 s◄ smallest
zstd230,541 B97.0%0.099 s0.009 s
7z231,402 B97.4%0.084 s0.029 s
xz231,460 B97.4%0.266 s0.026 s
zip237,754 B100.1%0.014 s0.011 s
// baseline JPEG · MIKE recompresses the entropy layer losslessly — 25 KB smaller than the best general tool, which gets ~0% (zip even expands it). Byte-for-byte identical on restore.
audio.mp352,079 B · MP3 · already compressed
ToolSizeRatioCompressDecompress
zstd49,228 B94.5%0.025 s0.006 s◄ smallest
xz49,672 B95.4%0.168 s0.009 s
MIKE49,698 B95.4%0.318 s0.201 sMIKE
7z49,728 B95.5%0.021 s0.012 s
rar49,764 B95.6%0.032 s0.016 s
zip49,799 B95.6%0.006 s0.007 s
video.mp42,848,208 B · MP4 · already compressed
ToolSizeRatioCompressDecompress
zstd2,831,892 B99.4%0.816 s0.013 s◄ smallest
zip2,833,016 B99.5%0.147 s0.034 s
7z2,835,034 B99.5%0.460 s0.043 s
MIKE2,835,274 B99.5%4.541 s0.253 sMIKE
xz2,835,292 B99.5%1.601 s0.049 s
rar2,836,789 B99.6%0.392 s0.046 s

// lossless verified on every row · MIKE = the puzzleCompressor engine — audio uses linear prediction, executables use BCJ2, JPEG uses the v7 entropy transcode; it trades time for ratio. Numeric/audio/text/exec rows measured on an earlier engine build and only improved since; JPEG remeasured on v7.

// puzzleCompressor V1 vs V11 — one engine, eleven generations apart

The same engine, eleven generations on.

puzzleCompressor is the engine inside MIKE. The free MIKE 1.0 ships puzzleCompressor V1 — already beating the field on structured data. MIKE Vault ships V10, which learned curves, floats, recurrences, segments, JPEG/MJPEG/MP3 structure and the whole DEFLATE family V1 can’t see. Same files, both versions, all byte-exact lossless.

DatapuzzleCompressor V1 · freepuzzleCompressor V11 · VaultAdvantage
Fibonacci sequence u64617,266 B56 B11,023× smallerv5
Geometric sequence u64387,842 B40 B9,696× smallerv5
Cumulative sum u6451,366 B48 B1,070× smallerv3
float32 signal column119,634 B41,132 B2.9× smallerv4
multi-slope / resetting counters394 B115 B3.4× smallerv6
JPEG photo 237,602 B original231,466 B (2.6%)205,327 B (13.6%)wins where free gets ~0v7
PNG image libpng, already compressed18,885 B (0%)11,066 B (41.4%)37% past xz -9v10
CSV table id·timestamp·category·price59,620 B (ties zip)32,671 B (−45%)column transposev11

// every result byte-for-byte lossless on both versions · V1 ships in free MIKE 1.0; V11 ships with MIKE Vault. The gap is the upgrade.

// puzzleCompressor keeps evolving · v3 → v6

New math. Smaller files. Every release, MIKE learns another shape.

The generator started knowing one shape — a straight line. Then it learned curves (v3), float columns (v4), recurrences (v5), and now segments (v6) — columns that reset or change slope, each run its own little formula. Each release teaches the engine to recognise another kind of mathematical structure and store the rule instead of the bytes — while the guarantee never changes: best-of per block, never worse than before, every byte reversible, reads every older archive.

v3 · POLYNOMIAL CURVES

Store the curve, not the points

Running totals, physics tables and cubic indices are exact polynomials — captured by a few seed coefficients and rebuilt by finite differences. A quadratic million-value column becomes ~30 bytes.

v4 · FLOAT COLUMNS

The ML / scientific data type

IEEE-754 float32/float64 arrays — feature tensors, embeddings, telemetry — decorrelated by byte-plane shuffle and XOR-with-previous. On real float data, the smallest of every tool.

v5 · LINEAR RECURRENCES

What nobody else compresses

Geometric, Fibonacci and LFSR-style sequences follow a recurrence aₙ=Σcᵢaₙ₋ᵢ. MIKE finds it, verifies it against every element, and stores the formula — 10,000×+ past xz, exact.

Data (numeric column)OriginalMIKE v0.6MIKE v3vs v0.6
Cumulative sum / running total u641,600,000 B582,038 B48 B12,126× smallerPOLY
Cubic index column u32600,000 B452,566 B40 B11,314× smallerPOLY
Physics position table s = s₀+vt+½at²1,200,000 B426,898 B340 B1,255× smallerPOLY
Integrated sensor, near-quadratic u64 · no exact formula1,200,000 B447,770 B68,624 B6.5× smallerDELTA-K

// all four CRC-verified lossless · v0.6 already beat zip/rar/7z/xz/zstd here — v3 beats v0.6. Exact curves collapse to a formula; the near-quadratic sensor (no exact fit) still drops 6.5× from second-order differencing.

…and it kept going. Since v3:

v4 opened IEEE-754 floats — the dominant ML/scientific type — and v5 added linear recurrences, collapsing data that is effectively incompressible for everyone else. All CRC-verified lossless, every time.

DataOriginalxz -9zstd -19MIKE
float32 embedding array random-normal1,200,000 B1,106,440 B1,109,846 B1,012,644 Bv4 · FLOAT
float64 drifting telemetry sensor stream1,600,000 B1,125,828 B1,128,566 B1,054,900 Bv4 · FLOAT
geometric sequence aₙ = 3·aₙ₋₁ · u64480,000 B411,944 B480,025 B40 Bv5 · RECUR
Fibonacci sequence u64 · wraps 2⁶⁴640,000 B617,256 B640,028 B56 Bv5 · RECUR
Multi-slope ramp 3 linear segments · u641,600,000 B66,540 B191,418 B115 Bv6 · PIECEWISE
Resetting counter sawtooth · u641,600,000 B1,012 B2,546 B123 Bv6 · PIECEWISE

// CRC-verified lossless · geometric & Fibonacci columns are near-incompressible for xz/zstd (~14% and ~4%) — MIKE stores the recurrence and regenerates the exact sequence, 10,000×+ smaller than xz. Segmented columns (v6) that no single formula fits collapse to a few hundred bytes — a multi-slope ramp 578× smaller than xz.  These generations are puzzleCompressor V11 — the engine inside MIKE Vault (below). Free MIKE 1.0 runs V1.

// v7 · the one everyone said was impossible

We used to say nobody wins on compressed data. Then we made it work. We win.

A JPEG is already compressed — its DCT coefficients are Huffman-coded, so at the byte level it’s noise, and every general tool gets ~0%. puzzleCompressor v7 goes a layer deeper: it decodes the entropy stream (the coefficients briefly expand), re-packs the structure the JPEG’s own Huffman left behind, and re-encodes it byte-for-byte. The photo comes back identical — just smaller.

Real JPEG (baseline)Originalxz -9MIKE v7saved
4:2:0 photo web/camera default29,825 B~29,800 B24,681 B17.2%LOSSLESS
high-detail 4:2:238,555 B~38,500 B31,191 B19.1%LOSSLESS
structured photo, 4:2:2237,602 B231,448 B205,327 B13.6%LOSSLESS

// 8–27% lossless on real baseline JPEGs (all common subsampling + grayscale), byte-for-byte reversible — verified by re-encoding to the exact original. Progressive/arithmetic/pure-noise JPEGs safely fall back to standard, so it can shrink a photo but never break one. The first tool in its class to win on already-compressed data. The same idea now spans Motion-JPEG video (14–43%), MP3 audio (~2%), and — the widest win of all — the entire DEFLATE family: PNG, ZIP, gzip, PDF and Office, 20–44% smaller than xz -9, all byte-for-byte lossless.

// New · AI & ML datasets

The dataset compressor. On real ML data, MIKE wins.

The AI boom runs on numeric data — feature tensors, training tables, sensor streams, scientific measurements. That is exactly the shape MIKE was built to model, so we pointed it at real, public ML datasets and ran the same honest gauntlet: full round-trip, CRC-checked, all six tools at maximum settings. On the numeric tables and feature tensors, MIKE produces the smallest file of any tool tested — and buries the everyday tools everywhere. Even on already-dense weight and image tensors — where the free build lands within a fraction of a percent of the best — the current Vault engine now wins outright (bert-tiny weights 7.7% past xz -9e); we show it all, unretouched.

// where it pays off: feature stores · dataset versioning (DVC / git-LFS) · cold-storage of training corpora · telemetry & sensor logs · scientific pipelines

Two tiers, both honest. The MIKE rows below are free MIKE 1.0 — every number reproducible with the downloadable pack. MIKE Vault (V11), our enterprise engine, goes further on tabular data (its column transpose turns the digits back into numbers) — flagged under each table where it wins. Vault figures use the full engine and are not produced by the free binary in the pack.

htru2_features.f32644,328 B · float32 feature tensor · feature stores & cached model inputs
ToolSizeRatioCompressDecompress
MIKE484,058 B75.1%0.805 s0.274 s◄ smallest
7z484,268 B75.2%0.120 s0.051 s
xz484,312 B75.2%0.446 s0.058 s
zstd511,647 B79.4%0.237 s0.008 s
rar526,984 B81.8%0.129 s0.021 s
zip534,508 B83.0%0.136 s0.013 s
california_housing.csv1,423,529 B · numeric tabular dataset · the ML training workhorse
ToolSizeRatioCompressDecompress
MIKE 1.0 (free)328,522 B23.1%2.723 s0.251 s◄ smallest
7z328,869 B23.1%0.717 s0.044 s
xz328,916 B23.1%1.252 s0.044 s
zstd358,721 B25.2%1.145 s0.010 s
rar386,199 B27.1%0.430 s0.029 s
zip396,115 B27.8%0.727 s0.020 s
▸ MIKE Vault (V11): 265,300 B (18.6%) — the column-transpose result, 19% smaller than xz -9e. Enterprise engine; the table above is free MIKE 1.0, which the pack reproduces.
htru2.csv1,730,114 B · scientific measurement table · sensor & telemetry logs
ToolSizeRatioCompressDecompress
MIKE676,154 B39.1%3.487 s0.295 s◄ smallest
xz676,280 B39.1%1.584 s0.077 s
7z678,029 B39.2%0.804 s0.069 s
zstd688,652 B39.8%1.624 s0.012 s
rar742,567 B42.9%0.416 s0.030 s
zip778,518 B45.0%0.224 s0.025 s
winequality-red.csv84,199 B · small numeric table · lab results, IoT batches
ToolSizeRatioCompressDecompress
MIKE 1.0 (free)19,894 B23.6%0.316 s0.194 s◄ smallest
7z19,945 B23.7%0.043 s0.010 s
xz19,952 B23.7%0.059 s0.007 s
zstd21,323 B25.3%0.100 s0.006 s
rar22,600 B26.8%0.035 s0.013 s
zip22,872 B27.2%0.054 s0.007 s
▸ MIKE Vault (V11): 18,275 B (21.7%) — column transpose, 8.4% past xz -9e. Enterprise engine; the free MIKE 1.0 table above is what the pack reproduces.
mnist_t10k_images.uint87,840,016 B · uint8 image tensor · raw pixel datasets
ToolSizeRatioCompressDecompress
xz1,354,388 B17.3%10.269 s0.178 s◄ smallest
MIKE1,364,274 B17.4%12.789 s0.388 sMIKE
7z1,367,369 B17.4%3.258 s0.186 s
zstd1,432,706 B18.3%5.916 s0.045 s
rar1,509,151 B19.2%1.256 s0.072 s
zip1,616,686 B20.6%3.290 s0.074 s
▸ MIKE Vault (V11): 1,354,151 B — the current engine (9|EXTREME + compact container) turns the free-tier tie into the smallest file, edging xz -9e. Enterprise engine; the table above is free MIKE 1.0.
bert-tiny_weights.bin17,756,393 B · trained float32 weights · model checkpoints
ToolSizeRatioCompressDecompress
xz16,308,280 B91.8%11.806 s1.717 s◄ smallest
7z16,311,675 B91.9%3.544 s1.490 s
MIKE16,321,839 B91.9%23.098 s1.947 sMIKE
zstd16,411,434 B92.4%9.443 s0.063 s
zip16,424,563 B92.5%1.294 s0.245 s
rar16,504,980 B93.0%3.087 s0.255 s
▸ MIKE Vault (V11): 15,061,164 B (84.8%) — the float byte-plane transform, 7.7% smaller than xz -9e (1.25 MB off a model checkpoint). Where free MIKE ties, the Vault engine wins on trained weights. Enterprise engine; the pack reproduces the free table above.
// Don’t trust us — reproduce it.

Every file above is real and public. We bundled the exact datasets, a one-command benchmark script, and a README explaining what each file is and where it matters. Run it against your own copy of MIKE and any tools you have installed — the script skips whatever’s missing and CRC-checks every round-trip.

Download benchmark pack · 18.9 MB .tgz sha256 2b652a261e727e4754c0b0ec459b4c5d525bc8c6a1185b9da8448876726653d8

$ tar xzf mike-ai-benchmark-pack.tgz && cd mike-ai-benchmark && python3 bench.py

// 6 real public datasets · lossless verified on every row · MIKE wins outright on all four numeric-dataset files; on trained weights nobody beats ~8% and we don’t pretend otherwise

// New · Real-world firehoses

Where this data actually pours out — many times over.

The win isn’t a one-off dataset. It’s the data that machines emit continuously, forever: counters, waveforms, sensor streams. We grabbed real files straight from those firehoses — live market data, a hospital ECG, a telescope frame, a climate model — and ran the honest gauntlet (full round-trip, CRC-checked, all six tools). These are the doors worth knocking on.

// Binance BTCUSDT · 1-min open times (int64)
357,120 B38 B
37× smaller than rar · 2,383× smaller than 7z
// Binance aggTrade IDs · 3.2 MB sequence (int64)
3,200,000 B38 B
221× smaller than rar · a formula, not a file
// PhysioNet MIT-BIH · real ECG waveform
1,950,000 B669,870 B
beats the whole field · 24% past 7z, 72% past zip
Data classReal-world formWhere it gushes 24/7MIKE on a real file
Counters timestamps, sequence & trade IDs, block / packet / event counters observability & metrics (Prometheus/Datadog), DB write logs, market data, network flow records 357 KB → 38 B · 37–221× past rar
Waveforms vibration, ECG/EEG, AC power, RF/IQ, seismic, sonar, audio predictive-maintenance on rotating machinery, medical monitoring, smart-grid PMUs, telecom ECG: smallest of all 6 · 24% past 7z
Sensor streams temperature / pressure / voltage / flow / GPS / accelerometer industrial historians (billions of points), IoT fleets, smart meters, connected vehicles (CAN bus) sensor ramp: 19.2% vs rar 21.6%
Numeric tables & arrays tick bars, FITS / NetCDF / HDF5, feature matrices quant finance, science archives (astronomy, climate, genomics), ML pipelines ties 7z/xz for the lead · buries zip/rar 1.2–2×

// real public files — Binance market data, PhysioNet ECG, NASA FITS, Unidata NetCDF · every row round-trip & CRC-verified · only modern H.264/H.265 is still out of reach — JPEG, MP3, PNG, ZIP, gzip and PDF we cracked (see the scorecard above)

// Home turf

On structured data, MIKE beats the entire field.

Shorter bar = smaller file = better. MIKE versus the best result any of zip / rar / 7z / xz / zstd managed on the same file.

struct_sensor.bin · sensor ramp + noise
MIKE19.2%
rar · best rival21.6%
// zstd only reached 64.2% — dictionary coders can't model a numeric ramp.
struct_sine.bin · smooth waveform
MIKE2.0%
7z / xz · best rival2.6%
struct_counter.bin · pure generator
MIKE0.01%
rar · best rival0.2%
// 29 bytes vs 549 bytes — bars shown at floor-width so you can see them at all.
// For datacenter & storage owners

What does the same data cost you at rest? Do the math.

Storage isn’t a one-time buy — every terabyte draws power, cooling, and replacement capital every year it sits there. MIKE’s biggest wins are exactly the data estates fill up with: logs, numeric/telemetry binaries, and raw audio. Put in your numbers — the defaults are our measured reductions — and see the annual bill MIKE takes off the table.

Your estate
TB
$/TB
years
$/kWh
W/TB
×
What’s in it (% of stored bytes)
%
%
%
%
%
%
// remaining 15% assumed already-optimal (H.264/H.265, encrypted) — MIKE passes it through untouched
MIKE space reduction per class (measured — editable)
% smaller
% smaller
% smaller
% smaller
% smaller
% smaller
Up to — saved per year
$0
 
Storage cost / TB / year$0
Capacity reclaimed0 TB
Effective estate shrink0%
You’d only need to power0 TB
// cost/TB/yr = disk ÷ replacement cycle + (W/TB × 8,760 h × $/kWh × PUE). Reduction defaults are MIKE’s measured results — text ~72%, numeric/telemetry ~80% (pure counters & tables go to 99.99%), CSV/tabular ~65% (California Housing 81%), Office/PDF ~45%, PNG/JPEG ~30%, raw PCM ~67%. The old model wrote off documents & images as “already compressed”; v10/v11 reclaim them too. “Up to”: your mileage is your data. Lossless, always.
// The part nobody else prints

What MIKE is — and honestly isn't.

No lossless compressor can shrink every file; that's a theorem, not a challenge (pigeonhole). Any tool claiming otherwise is selling snake oil. Here's the truth.

✓ The best tool for structured data

Scientific arrays, sensor logs, counters, generated tables, waveforms — MIKE beats zip, rar, 7z, xz and zstd, sometimes by thousands of times.

✓ Provably lossless

Every block round-trips byte-for-byte, verified against five industry tools across the whole corpus. Zero exceptions.

✓ Now wins on compressed data too

We used to say nobody wins on already-compressed media. puzzleCompressor broke that — JPEG (8–27%), Motion-JPEG (14–43%), MP3, and the whole DEFLATE family (PNG/ZIP/gzip/PDF/Office, 20–44% past xz -9), all lossless. Only modern H.264/H.265 is still out of reach — and there, MIKE never expands.

✗ On executables, a hair behind 7z

v0.3.0’s BCJ2 filter pulls MIKE dead-even with 7z on code (within 0.3–0.7%) and beats zip/rar/xz/zstd — but 7z’s range-coded BCJ2 keeps a razor lead. We don’t claim the win we didn’t get. (Audio, though? Dethroned.)

// Origin story

How MIKE was born

Miguel — the original M.I.K.E.
Miguel — the original M.I.K.E.

I’ve long loved Mike Judge’s genius — especially how perfectly he captures the idiosyncrasy of the IT business. Silicon Valley is brilliant (watch it), right up there with Office Space. If you’ve worked in tech, particularly from the ’80s on, it hits differently — because it’s true.

I relate to it more than I’d like. The hunt for venture capital. The adversity. The lawsuits. The endless complexity that drags a creator away from the one thing that matters — creating — and buries him in time-wasting nonsense everyone somehow expects him to be great at too. I’ve watched useless CEOs and mediocre tech win on marketing instead of merit, funded well enough to bury anyone who’s actually better, all while “making the world a better place.” My colleagues started calling me Gilfoyle. They even got me the “Hail Satan & drink coffee” mug. They’re not wrong about the temperament — just the satanism.

It all came together one afternoon. I was teaching math to my 9-year-old son, Miguel — Mike — and he was not having it. He loves Marvel, Transformers, computers and cars (like his dad); math just wasn’t sexy enough. So I told him: you love computers? It’s all math. Robots? Computers plus mechanical engineering. Cars? Mostly mechanical engineering — which is maths and physics, and most of physics is… math. You gotta love math, kiddo.

“You’re so like Gilfoyle — the same sarcasm, the same dark view of humanity, and just as brilliant at tech. And you’re building Newton AI! Gilfoyle built Son of Anton… and look what came out of that.”— Ana, my wife, looking at Newton

That same day, Ana looked at Newton — the sovereign AI I’m building (yes, it’s on this site) — and made that call. So I went back and rewatched Mike Judge’s masterpiece. And somewhere in the middle of Richard explaining why middle-out beats Shannon and every other compression method, I had an epiphany: brilliant tech-BS, Mike — should’ve gone with math. And then puzzleCompressor clicked in my still-awake brain.

I jumped out of bed. Ran the tests overnight, just like Richard. And… VOILA. It’s real. And, much like middle-out, it shines on some files.

So both Mikes — and Ana — left their fingerprints on this. I named it MIKE. I’m not German, and English spells it compressor with a C, but Kompression with a K made the homage too good to ignore.

MIKE is born.

I dedicate this work to my beautiful family, but also, particularly to:
— my son Miguel (Mike), who needs to love maths a little more, like dad;
— my wife Ana, for being my babysitter (she’s way better than Tara, Gilfoyle);
Mike Judge, for the inspired and inspiring work;
Thomas Middleditch, for his brilliant performance and inspiring relentlessness as Richard Hendricks;
— and the brilliant Martin Starr, whose Gilfoyle gave screen life to about 90% of me — although, Martin, I think Gilfoyle would like the Newton page on this same site better… still.
// Platforms — and why

Where MIKE runs.

One Rust engine, every major platform.

LinuxShippedIt’s THE system. Non-negotiable.
WindowsSupportedA first-class, fully-supported build — GUI, CLI, and the MIKE Vault background service, native x86-64.
AndroidShippedThe same Rust engine, in your pocket — and it’s here.
macOSPlannedOn the roadmap — pending partner negotiations.
iOSPlannedOn the roadmap — pending partner negotiations.
AMIGASomedayThe day Commodore ships the Amiga I actually designed. Not one day sooner.
// M.I.K.E. Vault · managed service   Enterprise

A folder that compresses itself — and opens like nothing happened.

Beyond the free app, MIKE Vault runs as a background service on Linux and Windows — powered by puzzleCompressor V11, the latest generation of MIKE’s engine: a self-shrinking archive tier that keeps cold data tiny and hot access instant. Built for teams sitting on terabytes of logs, telemetry and audio — the exact data puzzleCompressor compresses best.

Drop zone · LongTermBackup/

Files land, MIKE shrinks them, the tree stays

Anything copied into the watched folder — in the root or any subfolder — is SAFE-compressed the moment it stops changing (compress → verify the round-trip → then, and only then, remove the original). Your directory structure is preserved; only the bytes get smaller.

quarterly-telemetry.bin quarterly-telemetry.bin.mike
1.2 GB dropped in → kept at a fraction, verified lossless
Instant access · InstantPeek/

Open a .mike and the real file is just there

Double-click an archive (or a program reads it) and the service decodes a fresh copy into InstantPeek/ and hands it to whatever app you’d normally use — the compressed original never moves. The archive stays small on disk; the peek is instant, disposable, and swept automatically.

click report.pdf.mike decoded copy served opens in your viewer
original archive untouched · encrypted vaults unlock with your key
// Enterprise integration · SharePoint & Microsoft 365

Compress inside SharePoint. Decompress before it’s shown.

A SharePoint library is exactly MIKE’s new wheelhouse — Office files (DOCX/XLSX/PPTX are zip), PDFs (FlateDecode), images (PNG/JPEG) and CSV/Excel exports. As a Remote BLOB Storage provider on SharePoint Server, MIKE compresses every document as it’s stored and restores it byte-for-byte the instant anyone opens it — fully transparent, no plugin, no .mike in sight. (On SharePoint Online, a Microsoft Graph service tiers a library the same way.)

A representative document libraryBytesof originalvs xz -9
As stored in SharePoint466,050100%
xz -9 (strong general)177,28838.0%
MIKE tier98,14821.1%−44.6%

// 78.9% of the content database reclaimed, byte-for-byte lossless — vs 62% for xz -9. Per file: DOCX/XLSX 37–48% past xz, PDF 19–25%, PNG 20–31%, CSV 46–48%. Every file restores identical before it’s served.

The M.I.K.E. Vault service is not a public download. Adamantware OÜ has signed a distribution and exclusivity agreement with HealthTech Portugal — please contact them for any further information.
Contact HealthTech Portugal
// Get MIKE

One engine. Every platform.

One Rust core, every platform. GUI (with the splash) or CLI for Linux and Windows, plus the Android APK — pick your poison.

// New in v0.5.0 — a friendlier app: one smart button that knows whether to shrink or restore, live progress (no more frozen window on big files), and a result card that shows exactly where your file landed. Still archives multiple files & whole folders. (all platforms)

🔒 New in v0.6 — AES-256 encryption. Tick Password-protect, pick a password, and your .mike is locked (AES-256-GCM, PBKDF2-SHA256). Share it safely; only the password opens it. — this is the 1.0 Final release.

// Free to download and evaluate — every feature works. A €20 license is required for continued and commercial use, and it silences the reminder.

// Verify, your call: add --safe and MIKE compresses then immediately decompresses and verifies the result, so you get full round-trip assurance before you ever trust the archive.

New — three speed/ratio modes, everywhere (app · CLI · service): Fast when you want it quick, Balanced by default, or Power to go the deepest — it also tries both compressed containers per block and keeps the smaller — at the expense of speed (mike c --power, or the Mode selector in the app). The Vault service archives cold backups at Power automatically.

⚠ Read this before downloading — by downloading, installing, or using MIKE you agree to the End-User Licence Agreement (EULA). Please read it first.
LINUX · x86-64Free trial
GUI one-button UI · drag or browse · 6.6 MB
Download .tgz
sha256 2bf3af95523724e98d757b21e1dbc750cf1e48b6c778ac844ae07ee58b547f88
CLI terminal & scripts · 1.5 MB
Download .tgz
sha256 b74963ce8875c151eae94e61f574bd5ac72c3f4dd7247a9b08bdd062a95a8948
WINDOWS · x86-64Free trial
GUI one-button UI · drag or browse · 18.2 MB
Download .exe
sha256 0789ac524f8ab68cf4bedba962463b28d9776414f21e0b06cf2d517bb9ec5458
CLI terminal & scripts · 5.7 MB
Download .exe
sha256 d31ce7ab44a91033f8fce56c4c274cd55667e2d223c9f34c8b3e84e7c2c2499d
ANDROID · arm64Free trial
APK one-button UI · file browser · app icon · 3.5 MB
Download .apk
sha256 1782933ef62ee3d8d991a5da404e256132dd6e8baebf65975af52941fb736ffe
// enable "install from unknown sources" to sideload · arm64  ·  then grant M.I.K.E. “All files access” in Settings so the in-app browser can read your files