And here is the problem: the encryption performs 2 passes over the data: first to encrypt, then to compute the authentication tag.

As we've seen before, it's bad because you will pay huge penalties when loading / unloading your data to / from memory to / from SIMD registers multiple times, even if you AES-CTR and GHash implementation are optimized to the mooooon.

It reduces the noise of the input and the AI consumes 90% less tokens. These tokens are less noise for the AI to compute.

Source: https://github.com/chopratejas/headroom
The documentation: https://headroom-docs.vercel.app/docs

As always serving raw HTML and CSS for the win. In comparison to NextJS, Astro delivered the same features for 5% of the original bundle size.

A Finite State Transducer seems to be the best algorithm instead of a full index search.
The data don't need to be stored in a database indeed. They only need to be searched as text.

The bun single binary performs better!

64-bits pointer address can be compressed to 32 bits

Passer les PNG/JPEG qualité 90 à AVIF qualité 50 permet d'économiser au moins 75% de bande passante.

L'idée plus innovante est de compresser au préalable les ressources avant qu'elles soient utilisées.

[Précompresser avant de déployer] veut dire qu’on peut les compresser une seule fois, avec le niveau maximum, et demander à nginx de servir directement les fichiers pré-compressés. Zéro CPU à chaque requête, mais surtout un meilleur ratio au final, car on peut compresser plus fort.

En outre, Zopfli permet de compresser en .zip avec 3 à 8% d'efficacité en plus.

# Serve pre-compressed files generated at build time
gzip_static on;
brotli_static on;  # nécessite libnginx-mod-http-brotli-static

# Fallback pour les contenus non pré-compressés
gzip on;
gzip_vary on;
gzip_min_length 1024;
gzip_types text/plain text/css text/xml text/javascript
           application/javascript application/json
           application/xml image/svg+xml;

La compression Brotli permet de compresser à hauteur de 81% le HTML. La score des Web Core Vitals est passé de 70-85 à 99%.

Ok, FreeType renders font on LCD screens 40% faster

Reading a file is actually slow.

getCurrentThreadUserTime() uses many syscalls because it reads from /proc.
clock_gettime(CLOCK_THREAD_CPUTIME_ID) has only one syscall and a direct function call chain.

The optimisation can be done, but:

1. The kernel policy is clear: don't break userspace
2. It's undocumented anywhere!
3. Author's take: if glibc depends on it, it's not going away.

This is why I like browsing commits of large open source projects. A 40-line deletion eliminated a 400x performance gap. The fix required no new kernel features, just knowledge of a stable-but-obscure Linux ABI detail.

The lessons:

• read the kernel source. POSIX tells what's portable; the kernel source code tells what's possible.
• check the old assumptions: revisiting them occasionally pays off.

Optimizations that don't need Rust:

• HTTP range requests for metadata
• Parallel downloads
• Global cache with hardlinks
• Python-free resolution
• PubGrub resolver algorithm

Rust has benefits though:

• zero-copy deserialization
• Thread-level parallelism
• No interpreter startup
• compact version representation: uv packs version into u64 integers. The micro-optimization compounds across millions of comparisons

uv is possible because of many PEP that came since 2016 (so too soon for me): PEP 518, 517, 621, and 658. There are the low-handing fruits: static metadata, no code execution to discover dependencies, and the ability to resolve everything upfront before downloading

How to optimize a rust program to squeeze maximum performance and as little RAM as possible

But again there is tool online for it:

• SVGOMG
• ILoveIMG

There are obvious for me, but they are good.

I see some are totally useless for Rust in comparison. Both have different targets though. It is moreover awesome to see 100x improvements.

The heap is a performance killer in Rust. One woraround is to swap to a more efficient memory allocator such as jemalloc.

In Cargo.toml:

[dependencies]
mimalloc = "0.1"

In main.rs:

#[global_allocator]
static GLOBAL: mimalloc::MiMalloc = mimalloc::MiMalloc;

The best performance optimisation is to avoid the heap. There is the heapless create for that. "The only thing to know is that the size of heapless types and collections needs to be known at compile-time."

A minimalistic UI and a minimal page weight