Hi HN, I built an open-source Java SDK for building Model Context Protocol servers: https://github.com/6000fish/mcp-java It is intended for Java developers who want to expose tools, resources, or prompts to MCP-compatible agents without implementing the protocol plumbing from scratch. The project includes: Core MCP server SDK stdio transport SSE transport Java API and annotation-based tool registration Spring Boot starter 5-minute quick-start example Copyable custom server template Ready-to-use MySQL and Redis MCP servers The SDK is available on Maven Central: <dependency> <groupId> io.github.6000fish </groupId> <artifactId> mcp-sdk </artifactId> <version> 0.1.1 </version> </dependency> <dependency> <groupId> io.github.6000fish </groupId> <artifactId> mcp-spring-boot-starter </artifactId> <version> 0.1.1 </version> </dependency> The MySQL and Redis servers are local stdio MCP servers, because database/cache connectors are usually safer to run inside the user's own environment instead of exposing credentials to a hosted remote endpoint. GitHub: https://github.com/6000fish/mcp-java Release: https://github.com/6000fish/mcp-java/releases/tag/v0.1.1 Feedback is welcome.

Tags: react , webdev , onnx , audio Introduction Music generation, vocal separation, and intelligent arrangement have traditionally been server-side tasks requiring complex pipelines and expensive GPU clusters. But what if we could bring the entire interactive music-creation experience—both real-time preview , offline export , prompt-based AI music generation , and local Karaoke processing —directly into the browser? In this post, I'll share how I built AI Groove Pad , a client-side React and Tone.js application featuring: A Prompt-to-Music AI Agent: Enter any prompt (e.g., "Create an energetic Tamil Kuthu beat with a driving bassline and a Nadaswaram melody" ), and the agent composes and adds the tracks directly to the arrangement. A Client-Side Karaoke Separator: Runs a local neural network with 84% accuracy using ONNX Runtime Web to separate vocals and accompaniment locally. 3. High-Performance Audio Engine: Tone.js scheduling, synth fallbacks, and real-time playback. The Tech Stack Frontend UI: React + TypeScript + Tailwind CSS for a premium, glassmorphic dark-mode interface. Audio Engine: Tone.js v15 (built on top of the Web Audio API) for sample playback, precise timing scheduling, and synthesis. Client-Side AI: ONNX Runtime Web ( onnxruntime-web ) executing a local neural network with 84% accuracy for vocal/accompaniment separation (Karaoke mode). AI Music Agent: A natural language agent interface that takes user prompts to compose midi sequences, beats, harmony, and arrangements in real-time. * Offline Rendering: OfflineAudioContext for high-speed, non-realtime rendering of arrangements straight to .wav files. 🤖 The Prompt-to-Music AI Agent With AI Groove Pad , users don't need to be music theory experts. They simply write what they want to hear. The AI Agent interprets the prompt and generates a multi-track composition containing: Groove & Beats: Automatically maps drum samples and rhythmic patterns (e.g. Parai drum, Pambai hits for Kuthu). Melody & Harmony

你知道吗？2026 年中期，在生产环境部署 671B 参数的 DeepSeek-R1 仍然需要 8 张 H100，硬件成本约 20 万美元。但清华大学 MADSys 实验室的开源项目早在 2024 年就能在单台工作站上跑 236B 参数 MoE 模型，2025 年 2 月甚至在普通硬件上实现了 671B DeepSeek-R1 286 tokens/s 的 Prefill 速度。这个项目就是 kvcache-ai/ktransformers，截至 2026-06-12 已有 17,264 Stars、1,313 Forks、Apache-2.0 协议。2026 年的 AI 基础设施叙事被 NVIDIA 机架级系统和越来越贵的显存账单主导。KTransformers 是一条被严重低估的反击路线：它让你在消费级 GPU 和 CPU 内存的混合硬件上跑前沿 MoE 模型，而且提供了五个几乎没人讨论的生产级技巧。 背景：2026 年为什么 CPU/GPU 混合推理至关重要 2026 年，混合专家（Mixture-of-Experts，MoE）已经成为前沿开源模型的默认架构。DeepSeek-V3/R1、Qwen3-235B-A22B、Kimi-K2.5、GLM-4.7 以及最新的 DeepSeek-V4-Flash 全是 MoE。直觉上 MoE 推理仍然需要 H100 级 GPU，因为每个 token 只激活少量专家，活跃参数量虽小，但总参数量巨大（DeepSeek-R1 671B，Kimi-K2.5 上万亿）。CPU-GPU 混合方案把"冷"专家放在 CPU 内存，把"热"专家留在 GPU。KTransformers 把这个想法变成一个生产级框架，截至 v0.6.2（2026-05-03 发布）已支持 9 种不同的 MoE 模型。2026 年 ACM SIGOPS 论文《KTransformers: Unleashing the Full Potential of CPU/GPU Hybrid Inference for MoE Models》正式发表了这一架构。 隐藏用法 #1：基于频率感知的 CPU-GPU 专家调度 大多数人的做法： 把 GPU 当黑盒，试图把整个 MoE 模型塞进显存。模型太大时，要么买更多 GPU，要么换更小的模型。 隐藏技巧： KTransformers 通过 --kt-expert-placement-strategy 标志暴露四种显式专家放置策略。 frequency 策略会记录专家激活统计信息，然后把 最常被激活的专家 放在 GPU，把冷专家留在 CPU 内存。配合 --kt-enable-dynamic-expert-update 还能在运行时根据 prefill token 数重新分配专家。 # 启动频率感知放置的服务 python -m sglang.launch_server \ --model /path/to/qwen3-next-80b \ --kt-num-gpu-experts 8 \ --kt-expert-placement-strategy frequency \ --init-expert-location /path/to/activation_stats.pt # 长上下文场景下加入动态再分配 python -m sglang.launch_server \ --model /path/to/qwen3-next-80b \ --kt-num-gpu-experts 8 \ --kt-expert-placement-strategy frequency \ --init-expert-location /path/to/activation_stats.pt \ --kt-enable-dynamic-expert-update \ --kt-gpu-prefill-token-threshold 512 效果： 在 Qwen3-Next-80B-A3B-Instruct-FP8 + 4 张 RTX 4090 + Intel Xeon Gold 6454S 的官方基准测试中，50% GPU 专家比例下， frequency 策略达到 76.19 tokens/s， dynamic-expert-update 进一步推到 81.17 tokens/s（对比默认 uniform 策略的 65.25 tokens/s）。80% GPU 比例时，frequency 策略冲到 100.67 tokens/s。 数据来源： kvcache-ai/ktransformers GitHub 17,264 Stars、1,313 Forks、Apache-2.0 协议，2026-06-

Here's a fact that should stop every AI infrastructure engineer in their tracks: as of mid-2026, the de facto standard for serving a 671B DeepSeek-R1 model in production still requires 8x H100 GPUs and roughly $200,000 of hardware . Meanwhile, an open-source project from MADSys Lab at Tsinghua University has been quietly running 236B-parameter MoE models on a single workstation since 2024, and hit 286 tokens/s prefill on DeepSeek-R1 671B on commodity hardware. That project is kvcache-ai/ktransformers , and as of 2026-06-12 it has 17,264 Stars, 1,313 Forks, and an Apache-2.0 license. The 2026 AI infrastructure conversation has been dominated by NVIDIA rack-scale systems and the ever-growing VRAM bill. KTransformers is the open-source counter-narrative: it lets you run frontier-class MoE models on a mix of consumer GPUs and CPU RAM, and it does this with five production-grade techniques that almost nobody talks about. Context: Why CPU/GPU Hybrid Inference Matters in 2026 In 2026, Mixture-of-Experts (MoE) has become the default architecture for frontier open-weight models. DeepSeek-V3/R1, Qwen3-235B-A22B, Kimi-K2.5, GLM-4.7, and the new DeepSeek-V4-Flash are all MoE. The naive assumption is that MoE inference still needs H100-class GPUs because each token only activates a few experts, so the active parameter count is small, but the total parameter count is enormous (671B for DeepSeek-R1, 1T for Kimi-K2.5). The CPU-GPU hybrid approach moves the "cold" experts to CPU RAM and keeps the "hot" experts on the GPU. KTransformers has turned this idea into a production framework that supports nine different MoE models as of v0.6.2 (released 2026-05-03). The 2026 ACM SIGOPS paper "KTransformers: Unleashing the Full Potential of CPU/GPU Hybrid Inference for MoE Models" formally published the architecture. Hidden Use #1: CPU-GPU Expert Scheduling with Frequency-Aware Placement What most people do: They treat the GPU as a black box and try to fit the entire MoE model into VRAM. Whe

The Budget You Approved Isn't the Budget You'll Pay You approved $180K for a senior AI engineer. Eighteen months later, you've spent $282K and you're still not sure the hire is working out. This isn't unusual. It's the rule. Companies hiring AI engineers for the first time routinely underestimate total cost by 40–60%. Here's a breakdown of where that gap comes from — and why most founders don't see it until it's too late. The 56% Gap: Where It Comes From 1. Recruiting Costs Are Higher Than You Think (~12–18% of first-year salary) AI engineer recruiting isn't like standard software recruiting. Specialized headhunters charge 20–25% of first-year salary. Even if you find someone through your network, you'll spend founder or VP time on 15–30 hours of interviewing, plus take-home evals that the best candidates increasingly decline. If you use a staffing firm, add the markup. If you DIY it, add the opportunity cost. Typical recruiting overhead: $22,000–$40,000 per hire 2. Onboarding Takes Longer for AI Roles (~2–3 months of ramp) An AI engineer hired to build production agent systems isn't productive on day 1. They need to understand your domain, your data, your existing architecture, and your risk tolerance for AI-generated outputs. The ramp is real — most teams see 60–90 days before meaningful output. At $180K salary, two months of ramp is $30,000 in salary with limited ROI. Add engineering time for mentoring (typically 20% of a senior engineer's time during ramp), and you're adding another $15,000–$20,000. Ramp cost: $30,000–$50,000 3. Infrastructure Spend Scales With Experiments AI engineers experiment. That's the job. Every experiment has a GPU bill, an API bill, and a storage bill. Early-stage teams routinely see $3,000–$8,000/month in AI infrastructure spend once they've hired their first AI engineer — much of it from exploratory work that doesn't ship. Over a year: $36,000–$96,000 in infra costs that weren't in the original headcount budget 4. Tooling and Data Cos

Find & fix live vulnerabilities in Vibe Apps with 1-prompt. Discussion | Link

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Unlike humanoid robots designed around a fixed form — think Boston Dynamics — Theker's machines are built to be reconfigured.

The world's first MCP dating app inside Claude. Discussion | Link

On-device voice dictation. No cloud, no account. Discussion | Link

SaaS teams using AI-driven experimentation platforms (also called A/B testing automation or CRO...