Bootstrapping <Unum.cloud> since 2015.
Investing at <AAL.vc>.
Hosting ML/Systems hackathons around the globe.
Living between London, San Francisco, and Yerevan.
Converting outrageous amounts of coffee into modest amounts of Assembly for Intel, Arm, and Nvidia chips.
Maintaining FOSS libs deployed on ≫ 100M devices.
<ashvardanian.com>
<github.com/ashvardanian>
Submissions
Using AI to build your own software
lemire.me
6 points·by ashvardanian··0 comments
Show HN: ForkUnion v3 – Faster OpenMP-style thread-pool for C, C++, Rust, & Zig
github.com
3 points·by ashvardanian··0 comments
AI Model Co-Design: Hardware-Friendly LLM Design
developer.nvidia.com
2 points·by ashvardanian··0 comments
KinetIQ Ascend: Toward 100% Reliable Manipulation and Superhuman Speed
thehumanoid.ai
5 points·by ashvardanian··2 comments
Nvidia CUDA Python 1.0 and CUDA 13.3 Release
developer.nvidia.com
2 points·by ashvardanian··0 comments
Laguna XS.2 and Laguna M.1 by Poolside
poolside.ai
5 points·by ashvardanian··0 comments
FP8 Search and KV-Caching in USearch
unum.cloud
1 points·by ashvardanian··0 comments
Escaping the Fork: How Meta Modernized WebRTC Across 50 Use Cases
engineering.fb.com
3 points·by ashvardanian··0 comments
Porting Go's io package to C
antonz.org
6 points·by ashvardanian··0 comments
Schema as the Core of Reliability in AI Memory
xmemory.ai
6 points·by ashvardanian··0 comments
NumKong: 2'000 Mixed Precision Kernels for All
ashvardanian.com
47 points·by ashvardanian··6 comments
The State of Allocators in 2026
cetra3.github.io
2 points·by ashvardanian··0 comments
Recraft V4
recraft.ai
2 points·by ashvardanian··0 comments
Nebius to buy AI agent search company Tavily for 275M
nebius.com
2 points·by ashvardanian··1 comments
Running Async WebAssembly on Seastar's Reactor
rockwotj.com
3 points·by ashvardanian··0 comments
Open source USearch library jumpstarts ScyllaDB vector search
thenewstack.io
2 points·by ashvardanian··0 comments
Mixedbread: How We Built Multimodal Late-Interaction at Billion Scale
mixedbread.com
3 points·by ashvardanian··0 comments
Nvidia: Using Context as Training Data Unlocks Models That Learn at Test-Time
developer.nvidia.com
6 points·by ashvardanian··0 comments
Nvidia Kicks Off the Next Generation of AI with Rubin – Six New Chips
Got really excited for this model and asked my Opus planners in 3 pretty different projects to use Sonnets instead of Opus subagents to help me experiment on HPC kernels faster. Not one of them ended up writing a single line of code... Sonnets just kept spinning, wasting tokens. Can't remember the last time it happened with Opus in my codebases. Reverting back.
I really like the speed at which Cloudflare is executing toward becoming a critical infrastructure player with all of those new product offerings. That said, not everything needs to be serverless. Their Gen 13 hardware looks impressive, and it’s a pity you can’t rent it by the hour like AWS EC2 Metal instances.
The README was written by a human. I’ve used models extensively to refine the content, but never accepted more than a couple of lines of edits at a time.
Would it be accurate to say that Meta currently produces more RISC-V chips than other vendors? The specs for those chips look extremely interesting and seem much more programmable than Google's TPUs. It would be cool to see Meta making them available to third parties.
8K QPS is probably quite trivial on their setup and a 10M dataset. I rarely use comparably small instances & datasets in my benchmarks, but on 100M-1B datasets on a larger dual-socket server, 100K QPS was easily achievable in 2023: https://www.unum.cloud/blog/2023-11-07-scaling-vector-search... ;)
Typically, the recipe is to keep the hot parts of the data structure in SRAM in CPU caches and a lot of SIMD. At the time of those measurements, USearch used ~100 custom kernels for different data types, similarity metrics, and hardware platforms. The upcoming release of the underlying SimSIMD micro-kernels project will push this number beyond 1000. So we should be able to squeeze a lot more performance later this year.
PTX is on the GPU side and is already supported on available models. On the CPU side, it must be some form of an Arm ISA extension, I believe, like NEON-FHM or SVE-AES… I'm just not sure what the scope of those extensions would be and how they will coexist with ARM’s other extensions.
Every founder probably dreams of a press release like this — complete with testimonials from the CEOs of OpenAI, Anthropic, Meta, xAI, Microsoft, CoreWeave, AWS, Google, Oracle, Dell, HPE, and Lenovo.
There aren’t many technical details about the new GPUs yet, but the notes on the Vera CPU caught my eye. NVIDIA Spatial Multithreading sounds like their take on SMT — something you don’t usually see on Arm-based designs. Native FP8 support is also notable, though it’s still unclear how it will be exposed to developers in practice.
Overall it looks like an interesting CPU, but it doesn’t feel like it’s in the same league as the rumored Apple M5 Ultra.
My workflow isn't very common. I typically have 3-5 projects open on the local machines and 2 cloud instances - x86 and Arm. Each project has files in many programming languages (primarily C/C++/CUDA, Python, and Rust), and the average file is easily over 1'000 LOC, sometimes over 10'000 LOC.
VS Code glitches all the time, even when I keep most extensions disabled. A few times a day, I need to restart the program, as it just starts blinking/flickering. Diff views are also painfully slow. Zed handles my typical source files with ease, but lacks functionality. Sublime comes into play when I open huge codebases and multi-gigabyte dataset files.
I’m currently using a mix of Zed, Sublime, and VS Code.
The biggest missing piece in Zed for my workflow right now is side-by-side diffs. There’s an open discussion about it, though it hasn’t seen much activity recently: https://github.com/zed-industries/zed/discussions/26770
Stronger support for GDB/LLDB and broader C/C++ tooling would also be a big win.
It’s pretty wild how bloated most software has become. Huge thanks to the people behind Zed and Sublime for actively pushing in the opposite direction!
Not a fan of Windows either, but playing devil’s advocate here: Apple’s Finder has steadily gotten worse over the last ~16 years, at least in my experience. It increasingly struggles with basic functionality.
There seems to be a pattern where higher market cap correlates with worse ~~tech~~ fundamentals.
Yes, CaseFolding.txt. I'm considering using the collation rules for sorting. Now they only target lexicographic comparisons and seem to be 4x faster than Rust's standard quick-sort implementation, but few people use it: https://github.com/ashvardanian/StringWars?tab=readme-ov-fil...
I was just about to ask some friends about it. If I’m not mistaken, Postgres began using ICU for collation, but not string matching yet. Curious if someone here is working in that direction?
Levenshtein distance calculations are a pretty generic string operation, Genomics happens to be one of the domains where they are most used... and a passion of mine :)
This is a very good example! Still, “correct” needs context. You can be 100% “correct with respect to ICU”. It’s definitely not perfect, but it’s the best standard we have. And luckily for me, it also defines the locale-independent rules. I can expand to support locale-specific adjustments in the future, but waiting for the adoption to grow before investing even more engineering effort into this feature. Maybe worth opening a GitHub issue for that :)
<ashvardanian.com>
<github.com/ashvardanian>