#performance

The Fifth Kind of Optimisation https://lobste.rs/s/1xj2y2 #performance
https://tratt.net/laurie/blog/2025/the_fifth_kind_of_optimisation.html

The Fifth Kind of Optimisation https://lobste.rs/s/1xj2y2 #performance
https://tratt.net/laurie/blog/2025/the_fifth_kind_of_optimisation.html

Imagine caring about #browser #performance & being mindful of data usage & then proceeding to ship all of your “styles” in the form of 1 MB of #Tailwind #CSS classes in the markup that TUI browser & screen readers will never need to use while also not having *any* semantic class names for behaved scrapers, filter lists, & userStyles.

I swear, friends don’t let friends use base Tailwind in this way.

Coming soon in #Fedify 1.5.0: Smart fan-out for efficient activity delivery!

After getting feedback about our queue design, we're excited to introduce a significant improvement for accounts with large follower counts.

As we discussed in our previous post, Fedify currently creates separate queue messages for each recipient. While this approach offers excellent reliability and individual retry capabilities, it causes performance issues when sending activities to thousands of followers.

Our solution? A new two-stage “fan-out” approach:

1. When you call Context.sendActivity(), we'll now enqueue just one consolidated message containing your activity payload and recipient list
2. A background worker then processes this message and re-enqueues individual delivery tasks

The benefits are substantial:

• Context.sendActivity() returns almost instantly, even for massive follower counts
• Memory usage is dramatically reduced by avoiding payload duplication
• UI responsiveness improves since web requests complete quickly
• The same reliability for individual deliveries is maintained

For developers with specific needs, we're adding a fanout option with three settings:

• "auto" (default): Uses fanout for large recipient lists, direct delivery for small ones
• "skip": Bypasses fanout when you need different payload per recipient
• "force": Always uses fanout even with few recipients

// Example with custom fanout setting
await ctx.sendActivity(
  { identifier: "alice" },
  recipients,
  activity,
  { fanout: "skip" }  // Directly enqueues individual messages
);

This change represents months of performance testing and should make Fedify work beautifully even for extremely popular accounts!

For more details, check out our docs.

What other #performance optimizations would you like to see in future Fedify releases?

#ActivityPub #fedidev

Coming soon in #Fedify 1.5.0: Smart fan-out for efficient activity delivery!

After getting feedback about our queue design, we're excited to introduce a significant improvement for accounts with large follower counts.

As we discussed in our previous post, Fedify currently creates separate queue messages for each recipient. While this approach offers excellent reliability and individual retry capabilities, it causes performance issues when sending activities to thousands of followers.

Our solution? A new two-stage “fan-out” approach:

1. When you call Context.sendActivity(), we'll now enqueue just one consolidated message containing your activity payload and recipient list
2. A background worker then processes this message and re-enqueues individual delivery tasks

The benefits are substantial:

• Context.sendActivity() returns almost instantly, even for massive follower counts
• Memory usage is dramatically reduced by avoiding payload duplication
• UI responsiveness improves since web requests complete quickly
• The same reliability for individual deliveries is maintained

For developers with specific needs, we're adding a fanout option with three settings:

• "auto" (default): Uses fanout for large recipient lists, direct delivery for small ones
• "skip": Bypasses fanout when you need different payload per recipient
• "force": Always uses fanout even with few recipients

// Example with custom fanout setting
await ctx.sendActivity(
  { identifier: "alice" },
  recipients,
  activity,
  { fanout: "skip" }  // Directly enqueues individual messages
);

This change represents months of performance testing and should make Fedify work beautifully even for extremely popular accounts!

For more details, check out our docs.

What other #performance optimizations would you like to see in future Fedify releases?

#ActivityPub #fedidev

Coming soon in #Fedify 1.5.0: Smart fan-out for efficient activity delivery!

After getting feedback about our queue design, we're excited to introduce a significant improvement for accounts with large follower counts.

As we discussed in our previous post, Fedify currently creates separate queue messages for each recipient. While this approach offers excellent reliability and individual retry capabilities, it causes performance issues when sending activities to thousands of followers.

Our solution? A new two-stage “fan-out” approach:

1. When you call Context.sendActivity(), we'll now enqueue just one consolidated message containing your activity payload and recipient list
2. A background worker then processes this message and re-enqueues individual delivery tasks

The benefits are substantial:

• Context.sendActivity() returns almost instantly, even for massive follower counts
• Memory usage is dramatically reduced by avoiding payload duplication
• UI responsiveness improves since web requests complete quickly
• The same reliability for individual deliveries is maintained

For developers with specific needs, we're adding a fanout option with three settings:

• "auto" (default): Uses fanout for large recipient lists, direct delivery for small ones
• "skip": Bypasses fanout when you need different payload per recipient
• "force": Always uses fanout even with few recipients

// Example with custom fanout setting
await ctx.sendActivity(
  { identifier: "alice" },
  recipients,
  activity,
  { fanout: "skip" }  // Directly enqueues individual messages
);

This change represents months of performance testing and should make Fedify work beautifully even for extremely popular accounts!

For more details, check out our docs.

What other #performance optimizations would you like to see in future Fedify releases?

#ActivityPub #fedidev

Coming soon in #Fedify 1.5.0: Smart fan-out for efficient activity delivery!

After getting feedback about our queue design, we're excited to introduce a significant improvement for accounts with large follower counts.

As we discussed in our previous post, Fedify currently creates separate queue messages for each recipient. While this approach offers excellent reliability and individual retry capabilities, it causes performance issues when sending activities to thousands of followers.

Our solution? A new two-stage “fan-out” approach:

1. When you call Context.sendActivity(), we'll now enqueue just one consolidated message containing your activity payload and recipient list
2. A background worker then processes this message and re-enqueues individual delivery tasks

The benefits are substantial:

• Context.sendActivity() returns almost instantly, even for massive follower counts
• Memory usage is dramatically reduced by avoiding payload duplication
• UI responsiveness improves since web requests complete quickly
• The same reliability for individual deliveries is maintained

For developers with specific needs, we're adding a fanout option with three settings:

• "auto" (default): Uses fanout for large recipient lists, direct delivery for small ones
• "skip": Bypasses fanout when you need different payload per recipient
• "force": Always uses fanout even with few recipients

// Example with custom fanout setting
await ctx.sendActivity(
  { identifier: "alice" },
  recipients,
  activity,
  { fanout: "skip" }  // Directly enqueues individual messages
);

This change represents months of performance testing and should make Fedify work beautifully even for extremely popular accounts!

For more details, check out our docs.

What other #performance optimizations would you like to see in future Fedify releases?

#ActivityPub #fedidev

Coming soon in #Fedify 1.5.0: Smart fan-out for efficient activity delivery!

After getting feedback about our queue design, we're excited to introduce a significant improvement for accounts with large follower counts.

As we discussed in our previous post, Fedify currently creates separate queue messages for each recipient. While this approach offers excellent reliability and individual retry capabilities, it causes performance issues when sending activities to thousands of followers.

Our solution? A new two-stage “fan-out” approach:

1. When you call Context.sendActivity(), we'll now enqueue just one consolidated message containing your activity payload and recipient list
2. A background worker then processes this message and re-enqueues individual delivery tasks

The benefits are substantial:

• Context.sendActivity() returns almost instantly, even for massive follower counts
• Memory usage is dramatically reduced by avoiding payload duplication
• UI responsiveness improves since web requests complete quickly
• The same reliability for individual deliveries is maintained

For developers with specific needs, we're adding a fanout option with three settings:

• "auto" (default): Uses fanout for large recipient lists, direct delivery for small ones
• "skip": Bypasses fanout when you need different payload per recipient
• "force": Always uses fanout even with few recipients

// Example with custom fanout setting
await ctx.sendActivity(
  { identifier: "alice" },
  recipients,
  activity,
  { fanout: "skip" }  // Directly enqueues individual messages
);

This change represents months of performance testing and should make Fedify work beautifully even for extremely popular accounts!

For more details, check out our docs.

What other #performance optimizations would you like to see in future Fedify releases?

#ActivityPub #fedidev

Coming soon in #Fedify 1.5.0: Smart fan-out for efficient activity delivery!

After getting feedback about our queue design, we're excited to introduce a significant improvement for accounts with large follower counts.

As we discussed in our previous post, Fedify currently creates separate queue messages for each recipient. While this approach offers excellent reliability and individual retry capabilities, it causes performance issues when sending activities to thousands of followers.

Our solution? A new two-stage “fan-out” approach:

1. When you call Context.sendActivity(), we'll now enqueue just one consolidated message containing your activity payload and recipient list
2. A background worker then processes this message and re-enqueues individual delivery tasks

The benefits are substantial:

• Context.sendActivity() returns almost instantly, even for massive follower counts
• Memory usage is dramatically reduced by avoiding payload duplication
• UI responsiveness improves since web requests complete quickly
• The same reliability for individual deliveries is maintained

For developers with specific needs, we're adding a fanout option with three settings:

• "auto" (default): Uses fanout for large recipient lists, direct delivery for small ones
• "skip": Bypasses fanout when you need different payload per recipient
• "force": Always uses fanout even with few recipients

// Example with custom fanout setting
await ctx.sendActivity(
  { identifier: "alice" },
  recipients,
  activity,
  { fanout: "skip" }  // Directly enqueues individual messages
);

This change represents months of performance testing and should make Fedify work beautifully even for extremely popular accounts!

For more details, check out our docs.

What other #performance optimizations would you like to see in future Fedify releases?

#ActivityPub #fedidev

Coming soon in #Fedify 1.5.0: Smart fan-out for efficient activity delivery!

After getting feedback about our queue design, we're excited to introduce a significant improvement for accounts with large follower counts.

As we discussed in our previous post, Fedify currently creates separate queue messages for each recipient. While this approach offers excellent reliability and individual retry capabilities, it causes performance issues when sending activities to thousands of followers.

Our solution? A new two-stage “fan-out” approach:

1. When you call Context.sendActivity(), we'll now enqueue just one consolidated message containing your activity payload and recipient list
2. A background worker then processes this message and re-enqueues individual delivery tasks

The benefits are substantial:

• Context.sendActivity() returns almost instantly, even for massive follower counts
• Memory usage is dramatically reduced by avoiding payload duplication
• UI responsiveness improves since web requests complete quickly
• The same reliability for individual deliveries is maintained

For developers with specific needs, we're adding a fanout option with three settings:

• "auto" (default): Uses fanout for large recipient lists, direct delivery for small ones
• "skip": Bypasses fanout when you need different payload per recipient
• "force": Always uses fanout even with few recipients

// Example with custom fanout setting
await ctx.sendActivity(
  { identifier: "alice" },
  recipients,
  activity,
  { fanout: "skip" }  // Directly enqueues individual messages
);

This change represents months of performance testing and should make Fedify work beautifully even for extremely popular accounts!

For more details, check out our docs.

What other #performance optimizations would you like to see in future Fedify releases?

#ActivityPub #fedidev

Coming soon in #Fedify 1.5.0: Smart fan-out for efficient activity delivery!

After getting feedback about our queue design, we're excited to introduce a significant improvement for accounts with large follower counts.

As we discussed in our previous post, Fedify currently creates separate queue messages for each recipient. While this approach offers excellent reliability and individual retry capabilities, it causes performance issues when sending activities to thousands of followers.

Our solution? A new two-stage “fan-out” approach:

1. When you call Context.sendActivity(), we'll now enqueue just one consolidated message containing your activity payload and recipient list
2. A background worker then processes this message and re-enqueues individual delivery tasks

The benefits are substantial:

• Context.sendActivity() returns almost instantly, even for massive follower counts
• Memory usage is dramatically reduced by avoiding payload duplication
• UI responsiveness improves since web requests complete quickly
• The same reliability for individual deliveries is maintained

For developers with specific needs, we're adding a fanout option with three settings:

• "auto" (default): Uses fanout for large recipient lists, direct delivery for small ones
• "skip": Bypasses fanout when you need different payload per recipient
• "force": Always uses fanout even with few recipients

// Example with custom fanout setting
await ctx.sendActivity(
  { identifier: "alice" },
  recipients,
  activity,
  { fanout: "skip" }  // Directly enqueues individual messages
);

This change represents months of performance testing and should make Fedify work beautifully even for extremely popular accounts!

For more details, check out our docs.

What other #performance optimizations would you like to see in future Fedify releases?

#ActivityPub #fedidev

Made some progress in investigating the likely cause of slowness for some #GNOMECalendar users who have a metric shitton of events to display: https://gitlab.gnome.org/GNOME/gnome-calendar/-/issues/1381#note_2375819

#Sysprof #performance #GNOME

Made some progress in investigating the likely cause of slowness for some #GNOMECalendar users who have a metric shitton of events to display: https://gitlab.gnome.org/GNOME/gnome-calendar/-/issues/1381#note_2375819

#Sysprof #performance #GNOME

finally setup our organisation's (#Emanat) user on our #PeerTube (vid.pretok.tv) and uploaded a video trailer from december's premiere which I finished today.

if you want, you can follow Emanat via this fediverse handle @emanat

(also direct URL: https://vid.pretok.tv/a/emanat/ and more info at https://emanat.si)

#StageArt #performance #music #publishing #ContemporaryDance

Experimenting with some flamegraph stuff!

Goal was to understand the format for the input files for future ports to other environments...

#performance #flamegraph #linux

«Great things about Rust that aren't just performance»
– from @nicole

🦀 https://ntietz.com/blog/great-things-about-rust-beyond-perf/

#rust #rustlang #code #performance #it #itsec #learnrust

CloudFrontでどんな形式のファイルでもgzip圧縮して配信するトリック / 転送量50%減！
https://qiita.com/LostMyCode/items/5a7e25b60a23bb62873d?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

#qiita #JavaScript #AWS #CloudFront #performance #個人開発

FreeBSD 14.2 is here! New ZFS, Firecracker VMM, AIM for UDP, rtw89(4) driver, & AddressSanitizer. Explore performance & security updates! 🔗https://buff.ly/4f5bZG5
#FreeBSD #OpenSource #Networking #Virtualization #Security #Performance

The TUXEDO Stellaris 16 strikes the perfect balance between portable dimensions, cooling performance for the fastest high-end hardware and quiet fan noise with very high work and gaming performance.

💪https://www.tuxedocomputers.com/en/TUXEDO-Stellaris-16-Gen6.tuxedo

#tuxedo #laptop #linux #performance

Tools written in #Rust are so fast you think they're broken.¯\_(ツ)_/¯

#Performance #RustLang

Hey all! I am a self described #feralresearch -er (see bio for link), #artist, #designer, recovering #academic and internet old person.

I write, teach, lecture about and build things (software mostly, hardware sometimes) that overlap technology and culture, #digitalscenography and #narrative and #performance. I am currently senior systems #designer for #vrchat. I am occasionally (and professionally) a fox on the internet (#queer subcultures will save what’s left of us)

Most recently I am working on #maps and #mapping software for #dh and beyond, a locative #ar project about the history of #MTV, and #digitalphotography and image-making, especially in VR.

Interested in: #videogames #deadtech #zines #queer #subcultures, #furry, #lowfi #lowtech #interactivefiction #locative #media #ar #vr #mr #music #sound and #food

Time for re-#introductions —

I am an #artist on the border between comics and #contemporaryart. My current work is #mixedmedia #collage and #monoprint, and is also a main source for my #artisticresearch.

I give #audiovisual #jamsession #performance with musician Allan Grønvall, link in bio.

Did my MA in #visualculture on #abstractcomics and #nonlinear reading schema as #ergodicliterature. That's quite a mouthful, so I rolled it up in a neologism — #uncomics, which I am currently exploring in a print anthology, podcast series and website.

On this account I'm prone to toot unsolicited thoughts on whatever #popculture or #genrefiction I let wash over me at any given time, or just the latest shiny thing I saw on the internet... Apologies in advance!

My posts on @uncomics are usually less scattershot.

Electronic Disturbance Theater 3.0 is proud to be part of the 2022-23 MexiCali Biennial: Land of Milk and Honey (BORDERLANDS)

Our performance is with the poetry, drones and antennas on both sides of the border fence in Mexicali/Calexico.

https://www.youtube.com/watch?v=NqoD0woQbt4

#borderart #performance

I'm a computer musician making:

⇒ various forms of electro-funk and bass #music

⇒ ambiental drone, noise and sonic experiments

⇒ expanded cinema & #video projects

I also:
⇒ do #queer #performance
⇒ run a small local #CreativeCommons #netlabel "Kamizdat" w/ boutique physical releases
⇒ use #FLOSS & #Linux

support art-making via my www (see support link in bio)

#SuperCollider #renoise #processing #ArtWithOpenSource #GlitchArt #ardour #kdenlive #ubuntustudio #reintroduction #introduction

#Nightmares #acoustic #performance #live in the #studio

https://youtu.be/v-WLT4uGa04

#musician #songwriter #SingerSongwriter #IndieMusician #IndependentMusician #AcousticGuitar #singing #singer #guitar

Hello, fediverse! 👋🐘

We’re Cloud Four, a small but mighty agency founded in the Pacific Northwest. 🌥🌲

We solve complex responsive web design and development challenges for ecommerce, healthcare, fashion, B2B, SaaS, and nonprofit organizations. ✨📱

https://cloudfour.com/

#Introduction #WebDesign #WebDev #Responsive #Ecommerce #Performance #CSS #JavaScript

For those of you who don't know me, I'm a queer, cisgender British software engineer living in #Barcelona working for an #OSS company & am mostly known for my work in the #Java / #JVM / #Performance / #Observability / #Architecture spaces.

I've written a bunch of books about software. However, this account is much more likely to talk about random stuff, society, #queer and #leftist politics, #antifascism, #cats, #green issues & food more than tech specifically.

Posts in EN & sometimes ES.

Box Plots, Or Better!

5 Reasons Why Box Plots are the Better Default Choice for Visualizing Performance

https://stefan-marr.de/2024/06/5-reasons-for-box-plots-as-default/

#performance #benchmarking #visualization #paperWriting

finally setup our organisation's (#Emanat) user on our #PeerTube (vid.pretok.tv) and uploaded a video trailer from december's premiere which I finished today.

if you want, you can follow Emanat via this fediverse handle @emanat

(also direct URL: https://vid.pretok.tv/a/emanat/ and more info at https://emanat.si)

#StageArt #performance #music #publishing #ContemporaryDance

We are a little late to the party. How about we do an #introduction?

Hi Fediverse, we are SpiderMonkey, @mozilla’s #opensource engine for #JavaScript and #WebAssembly.
SpiderMonkey is used in Firefox, Servo and various other projects.

This account is run by our engineers, and none of us know how social media works. We were told to use hashtags.
#firefox #opensource #compilers #wasm #foss #privacy #security #performance #community

Nice to meet you!