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AI agent toolkit: coding agent CLI, unified LLM API, TUI & web UI libraries, Slack bot, vLLM pods — Trending on GitHub today with 340 new stars.
It'se me, Mario! You may know me from such hits as libGDX (which I created), RoboVM (for which I built a debugger and which is still alive as MobiVM), Spine, and more recently, pi.
You may also know me from exploits like heisse-preise.io. I generally try to use my computer powers for good. I also try to share my findings in form of educational blog posts. Find out more at mariozechner.at.
While I'm financially well off, additional funds to spend on charitable things or other OSS people are always appreciated. Before you sponsor me, consider sponsoring other, younger OSS folks, so we can keep the spirit alive.
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Chinese AI rivals clash over Anthropic’s OpenClaw exit amid global token crunch
Chinese tech companies are engaged in a public war of words as they compete to capitalise on US start-up Anthropic’s decision to pull its industry-leading Claude models from open-source AI agent tool OpenClaw. The development comes as AI agents have triggered a huge increase in demand for AI tokens – the core metric of AI usage – raising questions about the long-term ability of industry players to meet this demand amid a growing global crunch in computational power. On Sunday, Anthropic...
Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
arXiv:2603.24591v2 Announce Type: replace Abstract: While large language models (LLMs) have accelerated 2D software development through intent-driven "vibe coding", prototyping intelligent Extended Reality (XR) experiences remains a major challenge. The fundamental barrier is not just the steep learning curve for human creators, but that low-level sensor APIs and complex game engine hierarchies are ill-suited for LLM reasoning, routinely exceeding context windows and inducing syntax hallucinations. To bridge this gap, we contribute XR Blocks, an open-source, LLM-native WebXR framework. Unlike traditional engines, XR Blocks introduces a semantic "Reality Model" that aligns spatial computing primitives (users, physical environments, and agents) with natural language, providing a robust, conc
Sustainability Analysis of Prompt Strategies for SLM-based Automated Test Generation
arXiv:2604.02761v1 Announce Type: new Abstract: The growing adoption of prompt-based automation in software testing raises important issues regarding its computational and environmental sustainability. Existing sustainability studies in AI-driven testing primarily focus on large language models, leaving the impact of prompt engineering strategies largely unexplored - particularly in the context of Small Language Models (SLMs). This gap is critical, as prompt design directly influences inference behavior, execution cost, and resource utilization, even when model size is fixed. To the best of our knowledge, this paper presents the first systematic sustainability evaluation of prompt engineering strategies for automated test generation using SLMs. We analyze seven prompt strategies across thr
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I tested speculative decoding on my home GPU cluster. Here's why it didn't help.
I spent Saturday night testing n-gram speculative decoding on consumer GPUs. The claim: speculative decoding can speed up LLM inference by 2-3x by predicting future tokens and verifying them in parallel. I wanted to see if that holds up on real hardware running diverse workloads. For the most part, it doesn't. But the journey was worth it, and I caught a benchmarking pitfall that I think a lot of people are falling into. The setup My home lab runs Kubernetes on a machine called Shadowstack. Two NVIDIA RTX 5060 Ti GPUs (16GB VRAM each, 32GB total). I use LLMKube, an open source K8s operator I built, to manage LLM inference workloads with llama.cpp. For this test I deployed two models: Gemma 4 26B-A4B : Google's Mixture of Experts model. 26B total params but only ~4B active per token. Runs a
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