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海外机会工作台
把工作机会、创业线索、GitHub 趋势和模型工具发布压成一个可复核队列。先分层,再行动。
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OpenAI This repository contains Swift community-maintained implementation over OpenAI public API. Installation Swift Package Manager Usage Initialization Using the SDK for other providers except OpenAI Cancelling requests Text and prompting Responses Chat Completions Function calling Tools MCP (Model Context Protocol) MCP Tool Integration Images Create Image Create Image Edit Create Image Variation Audio Audio Create Speech Audio Transcriptions Audio Translations Structured Outputs Specialized models Embeddings Moderations Assistants (Beta) Create Assistant Modify Assistant List Assistants Threads Create Thread Create and Run Thread Get Threads Messages Add Message to Thread Runs Create Run…
README这条内容主要是一条趋势线索线索,正文前几句在说:CLI tool for configuring and monitoring Claude Code language:Python stars:30854 open source github developer tool github trending fallback recent repository momentum。先判断它是不是你要找的方向,再决定要不要继续打开原文。
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趋势线索:davila7/claude-code-templates
这条内容主要是一条趋势线索线索,正文前几句在说:CLI tool for configuring and monitoring Claude Code language:Python stars:30854 open source github developer tool github trending fallback recent repository momentum。先判断它是不是你要找的方向,再决定要不要继续打开原文。
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Expressive HTTP middleware framework for node.js to make web applications and APIs more enjoyable to write. Koa's middleware stack flows in a stack-like manner, allowing you to perform actions downstream then filter and manipulate the response upstream. Only methods that are common to nearly all HTTP servers are integrated directly into Koa's small 570 SLOC codebase. This includes things like content negotiation, normalization of node inconsistencies, redirection, and a few others. Koa is not bundled with any middleware. Installation Koa requires node v18.0.0 or higher for ES2015 and async function support. Hello Koa Getting started Kick-Off-Koa - An intro to Koa via a set of self-guided wo…
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DeepSeek-V4.1-Flash on A100 (sm80) This repository contains a custom inference runtime for DeepSeek-V4.1-Flash on NVIDIA A100 GPUs. It keeps FP8 dense weights and packed FP4 MoE experts on GPU, uses Triton/CUDA kernels with BF16 tensor cores, and keeps the large Engram tables in host RAM. Updated 2026-09-16. The numbers below are from the live five-GPU server logs in results/prefix-audit-20260916/. Current status backbone layers and 384 experts are distributed across GPUs 2,0,1,3,4 with EP shards 77,77,77,77,76. ,000,000-Token Prefill Demonstrated: Full 1M context cold prefill is empirically verified and benchmarked on 4$\times$ A100 80GB (cuda:0,1,2,3) using 4-stage Chunked Pipeline Parall…
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