Alternatives
Products that do what Shilowiz does
Shilowiz : 400% faster unified trial data
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2018 · github.com
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At Oblong Industries one of the basic building blocks of everything we built was a homegrown C-based IPC system called Plasma. The message channel was an mmap'd file used as a ring buffer. All messages were human-readable, performance was good, configuration was trivial. What was especially useful (and unusual in IPC systems it seems) was the property that message channels outlive all readers and writers, and even survive reboots, because they're just files. For local IPC you don't need a broker or server process. All the engineers who ever worked at Oblong loved Plasma, so I've recreated…
Mar 2026 · github.com
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Frustrated by the complexity and resource drain of multi service monitoring stacks, I built Simon. I wanted a single, lightweight dashboard to replace the heavy stack and the constant need for an SSH client for routine tasks. The result is a resource efficient dashboard in a single Rust binary, just a couple of megabytes in size. Its support for various architectures on Linux also makes it ideal for embedded systems and lightweight SBCs. It integrates: Comprehensive Monitoring: Realtime and historical metrics for the host system and Docker containers (CPU, memory, disk usage, and network…
Nov 2025 · github.com
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Double-digit speed-ups over naïve K-Means Parallel by default (Rayon) Balanced & spherical variants in one crate Memory-savvy for multi-GB datasets Builder API that just feels ergonomic Docs are live, the license is Apache-2.0, and the repo is one `cargo add kentro` away. Give it a spin—curious to hear how it performs in your pipelines!
2025 · github.com
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Here's the latest eBPF performance tool of mine - xstack. It's a minimal tool, just 165 lines of eBPF C and under 500 lines of userland C code (including all comments and boilerplate!). It uses the libbpf and (Rust) BlazeSym libraries though (which are a lot of code). The point (and difference) of this tool is that it can sample both the kernel and userspace stack traces of all threads in your system. Traditionally, the "bpf_get_stack()" helper can not read userspace stack traces of other tasks in Linux, but since Linux 5.18 we can combine sleepable eBPF task iterator programs with a new…
2025 · tanelpoder.com
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ran this over the weekend. stack was Llama 3.2 3B running locally + Keiro Research API for retrieval. 85.0% on 4,326 questions. where that lands: ROMA (357B): 93.9% OpenDeepSearch (671B): 88.3% Sonar Pro: 85.8% Llama 3.2 3B + Keiro: 85.0% the systems ahead of us are running models 100-200x larger. that's why they're ahead. not better retrieval, not better prompting — just way more parameters. the interesting part is how small the gap is despite that. 3 points behind a 671B model. 0.8 behind Sonar Pro. at some point you have to ask what you're actually buying with all that compute for this…
Mar 2026 · keirolabs.cloud
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2014 · dripstat.com
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title doesn't let nuance, ofc it's not the app that's faster but the way you can use it with Groq inference for example.
May 2026 · mumbli.app
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Hi HN, I'd like to share DualMix128, a pseudo-random number generator I developed. It's written in C and achieves very high speed while maintaining strong statistical properties for non-cryptographic use. GitHub (MIT License): https://github.com/the-othernet/DualMix128 Key points: * *Fast:* Benchmarked at ~0.36 ns per 64-bit generation on GCC 11.4 (-O3 -march=native). This was over 2x faster than `xoroshiro128++` (~0.74 ns) and competitive with `wyrand` (~0.36 ns) in the same tests. (Benchmark code/results in repo). * *Robust:* Passes the full TestU01 BigCrush suite…
2025 · github.com
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Time to first token is 39% faster Agent wall times decrease by 46% No swaps Tracks your resource usage in real-time and adjusts how the model runs so that it works perfectly on your device. Implements KV cache sizing, prefix caching, live RAM pressure management, context trimming, KV quantization, and more. Built a ton of features
Jun 2026 · autotunellm.com
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2017 · github.com
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Quick note on how it works and how I've done my batch embedding engine IgniteMS. The whole thing runs as one process using Rust, reading input, tokenizing, packing batches, keeping the queue full. TensorRT handles inference. Python is only as a wrapper. I built it this way because when you use more than couple of GPUs, the GPUs stop being the problem. CPU cannot feed them fast enough. One A100 can go through batches faster than Python can tokenize and feed, so the GPU just sits there idle waiting for work. Most of my time went into optimizing this. At 8 GPUs that was basically the entire…
Jun 2026 · github.com
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2016 · github.com
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Over the past few months, as we scaled our internal AI Agents, we hit a dead end: Running LLM-generated arbitrary code in Docker is basically running naked on security due to container escape risks. But using full traditional VMs takes minutes to boot and eats too much memory to support high-density concurrency. We loved the developer experience of SaaS sandboxes on the market, but they are closed-source, expensive, and have too high a barrier to entry for self-hosting. So, our team decided to build our own. After months of grinding, using RustVMM and KVM, we built a blazing-fast,…
Apr 2026 · github.com
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