AI 写简历容易,但写完总会遇到模板难看、排版溢出、页面留白、改一处全局变形等问题。dsh-resume 专注解决“内容生成后的视觉复核”:让 AI 和用户一起把简历调到真正适合投递的刚好一页。AI can write a resume, but the result often looks unbalanced, overflows the page, leaves large blank areas, or breaks after a small edit. dsh-resume focuses on visual review after generation, helping AI and users refine the resume into a polished.
Codex-style attachment formats for the DeepSeek Harness Web GUI: PDF text-layer extraction, Office text extraction, scanned-PDF OCR, long-document spill + index cards, image-to-PNG.
Kimi (Moonshot AI) official Formula API tools for DeepSeek Harness — web_search via kimi-official provider + 10 kimi_* tools, no DeepSeek/Exa/Perplexity key needed
API-key-free aggregate search (16 domestic+overseas engines with result-layer failure filtering) plus direct target-URL fetch with SSRF protection, as providers for the DeepSeek Harness web seam (ctx.web).
DeepSeek Harness all-in-one: no model switching — regular DeepSeek auto-routes to vision & image gen. Multi-backend: Gemini + any OpenAI-compatible (GPT-4o, Qwen-VL, GLM-4V, gpt-image, DALL-E, Flux, OpenRouter). gemini_vision/gemini_generate_image/gemini_optimize_image with vision self-check. Better than modlens.