Praveen

[Jarred Sumner @jarredsumner]

Since a bunch of people asked about Bun’s usage of unsafe, here’s some slop

https://t.co/kMHyDmx5xb

[Pierre @pierrecomputer]

diffshub[dot]com

Take any public diff from GitHub and virtualize it nearly instantly, no matter how large, with DiffsHub. Built to show off our brand new CodeView component.

To try it out, replace `github` with `diffshub` in your address bar.

[Pekka Enberg @penberg]

Guys, we moved on from porting code to Rust with coding agents to building programming languages, right?

[Mitchell Hashimoto @mitchellh]

I strongly believe there are entire companies right now under heavy AI psychosis and its impossible to have rational conversations about it with them. I can't name any specific people because they include personal friends I deeply respect, but I worry about how this plays out.

I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation. All those arguments are rearing their ugly heads again but now its... the whole software development industry (maybe the whole world, really).

It's frightening, because the psychosis folks operate under an almost absolute "MTTR is all you need" mentality: "its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!" We learned in infrastructure that MTTR is great but you can't yeet resilient systems entirely.

The main issue is I don't even know how to bring this up to people I know personally, because bringing this topic up leads to immediately dismissals like "no no, it has full test coverage" or "bug reports are going down" or something, which just don't paint the whole picture.

We already learned this lesson once in infrastructure: you can automate yourself into a very resilient catastrophe machine. Systems can appear healthy by local metrics while globally becoming incomprehensible. Bug reports can go down while latent risk explodes. Test coverage can rise while semantic understanding falls. Changes happens so fast that nobody notices the underlying architecture decaying.

I worry.

[Mitchell Hashimoto @mitchellh]

Libghostty can now be used to fuzz TUIs, thanks to @owickstrom + @AntithesisHQ. They already found bugs in multiple including btop. I always imagined libghostty would be useful for testing TUIs, super happy to see this is both practical and valuable. https://t.co/02rVjF740v

This is another example of where speed matters! "Why does Ghostty need to be so fast?"

Well, if you're running hundreds or thousands of unit tests that each use a clean in-memory terminal, you want that to be fast. If you're fuzz testing and trying to push an unlimited amount of data through a terminal, you want that terminal to be fast.

So many people got hung up on "why does my terminal _GUI_ need to be fast" without connecting one more dot and realizing the GUI is only fast if the core is fast, and the core being fast unlocks a hell of a lot more.

Like this.

[Warp @warpdotdev]

Warp is now open-source.

[Praveen @opensourcegeekz]

@penberg Cheers, I wasn't sure if nightly for allocator_api unlocked some other features. Trying to think through how this would work for an olap style workload. I've seen work stealing executors used for it, mixing io/compute tasks(yield every X micros)

[Pekka Enberg @penberg]

After being DST-pilled by TigerBeetle, I've written the same completion-based I/O loop many, many times just to get going on whatever new i was working on.

This time I extracted it as a small Rust library so next time I don't have to start from scratch.

https://t.co/6GH8fVwZG5

[P99CONF @P99CONF]

Kenny Chamberlin's #P99CONF session covered reducing thread counts to cut tail latency, scoping locks tightly, 7 removing locks with help from Rust’s borrow checker. Watch for free to learn practical examples with metrics, code, & live demos. https://t.co/01Z1JdJdSE

#ScyllaDB

[Mitchell Hashimoto @mitchellh]

Huge W for Zig, used for inference for K2.6. If you want absolute performance with exacting control over what your CPU executes and the way memory is laid out, Zig is the way.

[Paul Dix @pauldix]

We released @InfluxDB 3.9 today! For our Enterprise customers, it has a beta of enhancements to our storage system enabling very wide & sparse tables and MUCH faster performance on single series queries.

This brings the flexible schema and fast series lookups you love from v1 and v2 into v3 while maintaining support for infinite cardinality, scalable object store durability and the fully featured SQL query engine provided by DataFusion.

Read all about it: https://t.co/NXJDYKd1cw

[InfluxData @InfluxDB]

InfluxDB 3.9 is out today!

This release focuses on making InfluxDB 3 more predictable in production with more control as you scale.

➡️ We’re also excited to open up a performance preview (beta) in Enterprise.

Time series performance isn’t one-size-fits-all. It’s shaped by data distribution, ingest patterns, query access, and cardinality, and small differences can completely change how a system behaves at scale. That’s why we’re introducing these changes as a preview: to see how they hold up across real workloads before becoming the default.

These updates focus on smoothing resource usage under load, improving long-range single-series queries, and supporting very wide to highly sparse schemas.

PM @PeteWithProduct breaks it down: https://t.co/KEbnZFWHxm

[Qwen @Alibaba_Qwen]

🚀 Qwen3.5-Omni is here! Scaling up to a native omni-modal AGI.

Meet the next generation of Qwen, designed for native text, image, audio, and video understanding, with major advances in both intelligence and real-time interaction.

A standout feature: 'Audio-Visual Vibe Coding'. Describe your vision to the camera, and Qwen3.5-Omni-Plus instantly builds a functional website or game for you.

Offline Highlights:
🎬 Script-Level Captioning: Generate detailed video scripts with timestamps, scene cuts & speaker mapping.
🏆 SOTA Performance: Outperform Gemini-3.1 Pro in audio and matches its audio-visual understanding.
🧠 Massive Capacity: Natively handle up to 10h of audio or 400s of 720p video, trained on 100M+ hours of data.
🌍 Global Reach: Recognize 113 languages (speech) & speaks 36.

Real-time Features:
🎙️ Fine-Grained Voice Control: Adjust emotion, pace, and volume in real-time.
🔍 Built-in Web Search & complex function calling.
👤 Voice Cloning: Customize your AI's voice from a short sample, with engineering rollout coming soon.
💬 Human-like Conversation: Smart turn-taking that understands real intent and ignores noise.

The Qwen3.5-Omni family includes Plus, Flash, and Light variants.

Try it out:
Blog: https://t.co/yuSAz3DuO8
Realtime Interaction: click the VoiceChat/VideoChat button (bottom-right): https://t.co/nnAW9ZfRet
HF-Demo: https://t.co/rLsqejKgCG
HF-VoiceOnline-Demo: https://t.co/LIGtmITeSw
API-Offline: https://t.co/lNE7fH5YUt
API-Realtime: https://t.co/9A3lopXGwV

[Qwen @Alibaba_Qwen]

🚀 Introducing the Qwen 3.5 Small Model Series
Qwen3.5-0.8B · Qwen3.5-2B · Qwen3.5-4B · Qwen3.5-9B

✨ More intelligence, less compute.
These small models are built on the same Qwen3.5 foundation — native multimodal, improved architecture, scaled RL:
• 0.8B / 2B → tiny, fast, great for edge device
• 4B → a surprisingly strong multimodal base for lightweight agents
• 9B → compact, but already closing the gap with much larger models
And yes — we’re also releasing the Base models as well.
We hope this better supports research, experimentation, and real-world industrial innovation.
Hugging Face: https://t.co/wFMdX5pDjU
ModelScope: https://t.co/9NGXcIdCWI

[Jens Axboe @axboe]

This code now landed upstream and will be part of the 7.0 kernel when released. The liburing helpers, man page, and test cases have been merged into the master branch.

[Viacheslav Biriukov @brk0v]

🦀 Building an LLM from scratch in Rust - Part 4 is where the model actually learns.

No autograd magic: you hand-roll backprop through linear layers, attention, layer norm, MLP.

Watching it go from pure noise to Shakespeare.

https://t.co/ggHZl87xRj
#rust #rustlang #LLM #gpt

[Dmitrii Kovanikov @ChShersh]

After 8 years of Haskell, 2 years of OCaml, 2.5 years of C++ and 45 minutes of Go, I present you the ultimate Design Pattern.

The Context Pattern

FP, OOP, Procedural and Declarative Programming combined to create The Last and Only design pattern you ever need.

A single record containing all your dependencies that you pass to every function explicitly.

No more inheritance.
No more classes and methods.
No more Dependency Injection.
No more singleton pattern.
No more private/public.
Mocks have never been easier.

This is the only pattern you need to structure EVERY SINGLE APP NO MATTER THE INDUSTRY (microservice, compiler, spaceship system).

[Sean McArthur @seanmonstar]

Just published a new #rustlang reqwest release candidate: v0.13.0-rc.1.

This has some breaking changes, the biggest was switching to rustls by default.

I'd appreciate if you gave it a spin 🙏

https://t.co/TfsJ9i34wZ

[Python Software Foundation @ThePSF]

TLDR; The PSF has made the decision to put our community and our shared diversity, equity, and inclusion values ahead of seeking $1.5M in new revenue. Please read and share. https://t.co/laZQCbHWJU
🧵
https://t.co/laZQCbHWJU