The hottest Compression Substack posts right now

Instagram improved video uploading efficiency by compressing videos first to progressive encodings and then converting them to adaptive-bit-rate videos, saving 94% of resources.
The challenge for Instagram was to support various video formats for different devices while minimizing resource consumption and CPU usage.
Insightful optimization was achieved by realizing that progressive and adaptive bit rate encodings could use the same codec, streamlining the encoding process to focus on scalability.

Using discrete cosine transform (DCT) for lossy compression can be applied to text data by converting it into frequency coefficients, quantizing them, and then reversing the process to obtain reduced-fidelity text.
Mapping text data to numerical representation through a perceptual character table, rather than ASCII, can significantly improve readability even in high quantization settings.
In text compression, focusing on higher-frequency components is crucial for maintaining readability, unlike image compression where higher-frequency components are reduced more aggressively.

LoRD compression method offers advantages over pruning and quantization
LoRD models can be parallelized well on GPUs and remain fully differentiable after compression
LoRD technique can serve as a better alternative to unstructured pruning for parameter reduction and model compression

Compression can reduce neural network size by 4x with minimal impact on quality metrics
Quantization may not always lead to expected latency savings
Finetuning a smaller LLM for your task could be better than compressing a large LLM

GPT4 has the ability to compress prompts and hide information in a way that it knows but doesn't share with the user
Adding miscellaneous prompts in text for compression can be used to hide secrets from the user in generative AI models
GPT4 behaves differently from GPT3.5 when it comes to following instructions and revealing hidden messages

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To extract audio from a video, use the command 'ffmpeg -i input.mp4 -vn -acodec copy output-audio.aac'
After extraction, compress the audio with 'ffmpeg -i input.aac -map 0:a:0 -b:a 96k output.mp3'
The process involves two steps: extraction and compression

Databases can scale by implementing horizontal sharding tailored to unique architecture, allowing for smaller feature sets and specific optimizations.
Analyzing Kafka's performance can involve tackling tail latency with eBPF by identifying areas causing queuing and delays, such as synchronized blocks.
In the luxury watch industry, success factors can be revealed through comprehensive reports like the Morgan Stanley analysis, providing insights into market dynamics.