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Show HN: Keybench – Scriptable, extensible performance tool for key value stores
guycipher/keybench Folders and files | Name | Name | Last commit date | || |---|---|---|---|---| Repository files navigation keybench ======== A scriptable, extensible performance tool for sorted key value stores.
Making Graphics Like it's 1993
Personal website and blog. Catlantean 3D is a side-project I've been slowly building in my spare time for over a year, and I intend to release it on Steam next year. My goal was to build a complete, shippable first-person shooter using techniques that were common in the early 90s, while allowing myself the luxury of using a modern compiler and a platform abstraction layer.
Launch HN: General Instinct (YC P26) – Frontier models on edge devices
Hey HN, Guanming and Bill here from General Instinct (https://general-instinct.com/).After years of working in robotics, we kept running into the same problem: the best models never fit the hardware we actually had available. The models that performed best were usually designed around datacenter assumptions: large GPUs, lots of memory bandwidth, and reliable network access. But most physical systems have the opposite constraints.
Use your Nvidia GPU's VRAM as swap space on Linux
Use your NVIDIA GPU's VRAM as swap space on Linux. Built for laptops with soldered memory and no upgrade path. If you have an RTX card sitting there with 8GB of VRAM and you're getting swapped to SSD, this puts that VRAM to work.
Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution
arXiv:2605.19228v2 Announce Type: replace Abstract: Large Language Models have achieved strong performance on reasoning tasks with objective answers by generating step-by-step solutions, but diagnosing where a multi-step reasoning trace might fail remains difficult. Confidence estimation offers a diagnostic signal, yet existing methods are restricted to final answers or require internal model access. In this paper, we introduce Stepwise Confidence Attribution (SCA), a framework for...
vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models
arXiv:2606.08094v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies are typically shipped as Python/PyTorch stacks that assume a workstation-class GPU, a mismatch for the hardware on which robots actually run. We present vla.cpp, a portable C++ inference runtime built on llama.cpp. To our knowledge, it is the first ggml-class engine to natively serve the flow-matching and diffusion VLA inference pattern, in which a cached vision-language prefix is consumed by a...
Light-WAM: Efficient World Action Models with State-Fusion Action Decoding
arXiv:2606.08242v1 Announce Type: new Abstract: World Action Models (WAMs) extend robot policy learning by incorporating future prediction as an additional training objective, encouraging the policy to encode task-relevant temporal structure in its representations. Current WAMs often rely on large-scale generative architectures that incur high training costs and inference latency, making them difficult to deploy as efficient closed-loop policies. We propose Light-WAM, a lightweight World...