GitHub
Ai zip compression
What it does
Adaptive Neural Compression (ANC) -- AI-powered compression at every level: Gestalt prompt shortening, ZIP-RAM communication, Synapse Indexing - danielmigrow-alt/zip-ai
Does the same job
all alternatives →- AIAn interactive guide to compression basics2017 · unwttng.com · ▲256
- MAmisa77 - a codec that decodes 2x faster than LZ4 (at better ratios)Jul 2026 · github.com · ▲164
I've spent the last few months working on this codec. It has the following characteristics: - SOTA decompression throughput in its ratio class - Decent ratios (comparable to LZ4 at high effort levels) - Slow compression Most of the gains can be attributed to reducing branches and making decompression very friendly to out-of-order cores, by using a smart format. Results on the tarred Silesia corpus on Intel x86-64 follow: codec decode ratio encode misa77 -0 5219 MB/s 42.64% 54.5 MB/s misa77 -1 4274 MB/s 39.65% 51.2 MB/s lz4 2505 MB/s 47.59% 371 MB/s lz4hc -12…
- MAMinimax – A Compressed-First, Microcoded RISC-V CPU2022 · github.com · ▲171
RISC-V's compressed instruction (RVC) extension is intended as an add-on to the regular, 32-bit instruction set, not a replacement or competitor. Its designers intended RVC instructions to be expanded into regular 32-bit RV32I equivalents via a pre-decoder. What happens if we explicitly architect a RISC-V CPU to execute RVC instructions, and "mop up" any RV32I instructions that aren't convenient via a microcode layer? What architectural optimizations are unlocked as a result? "Minimax" is an experimental RISC-V implementation intended to establish if an RVC-optimized CPU is, in practice, any…
- OAOverfitted a 900KB Transformer to Compress a 100MB CSV into 7MBJun 2026 · ▲112
I built an experiment that uses an overfitted transformer and arithmetic coding to compress individual files. Instead of training the model to generalize, I train a 900KB transformer to memorize a single file and predict the next byte. Those predictions are fed into an arithmetic coder to produce the compressed output. On a 100MB NYC taxi CSV, it compresses to about 7MB (~0.5 bits/byte). On a 100MB slice of enwik9, it compresses to about 21MB (~1.68 bits/byte). It's pretty slow right now (roughly 20–30 minutes of training and 45 minutes each for compression and decompression on my…
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Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
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Dev tools · May 2026 · github.com