AI 智能体2 months ago
Olapply — We match you with jobs and apply automatically
Olapply用AI代理自动投递职位，职业人士审核您的应聘。
@getOlapply · X
Startup, I built an AI agent that applies to jobs on your behalf with a career professional in the loop, check out
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Olapply用AI代理自动投递职位，职业人士审核您的应聘。
@getOlapply · X
Startup, I built an AI agent that applies to jobs on your behalf with a career professional in the loop, check out
发送真实AI代理测试网站，找出它们卡住的地方并获得修复方案。
@louiswharmby · X
It's funny how that happens! My dog vibe coded and managed to achieve 500 users in the first month! 🤣
60秒内创建任何合同，签署前获得AI风险分析。
@Gaurav_dev01 · X
Claim #1 FREE every 3 hours for 7 days. 3-hour leaderboard, UTC swaps, auto-moderation, 3h duplicate guard.
@Outbidim · X
lets startups claim the #1 spot for up to 3 hours at a time for $0 — completely free for 7 days.
使用AI生成商业创意并获得成功评分，创建投资融资方案。
@pitchspark_ai · X
AI销售代表，自动为小企业捕获、鉴定和预订潜在客户。
agentforgeai_cmd · Product Hunt
AgentForgeAI Your AI Sales Rep That Never Sleeps
为 ChatGPT 和 Claude 提供 LinkedIn、YouTube 等实时研究背景。
garrytan · Product Hunt
Click Live research context for ChatGPT and Claude
部署自动进行销售的AI代理，通过电话、邮件和短信全天候工作。
@shubhdholakiya_ · X
building an autonomous ai agent sales team. tired of standalone apps, so i built a multi-layered sales brain that actually executes the pipeline and closes the deals.
Talentpluto是一个AI语音代理，10分钟内了解你的故事，让你被合适的人和代理发现。
garrytan · Product Hunt
Pluto Your professional profile is now an AI agent
追踪目标和失败的LLM智能体，跨会话保存状态并展示每步推理。
u/OGMYT · Reddit
I built LOLM, a lower-cost LLM agent that shows what it actually did — looking for blunt feedback I’m one of the founders/builders behind LOLM. Most AI products show an answer but hide whether the system retrieved anything useful, verified the result, switched models, hit a limit, or simply stopped. LOLM exposes those parts through controller events and run receipts. It includes: - Live agent - CLI - Coding and small app-building workflows - Memory and self-hosting options - Control decis
在平台上管理和同步你的AI Agent技能配置，避免供应商锁定。
@matebenyovszky · X
I use multiple providers Claude Code, Cursor, Codex, Hermes, Antigravity etc. and multiple machines. Over time, each one ended up with its own slightly different skills and tools 😰 So I started where a playbook is a portable bundle containing my MCP/OpenAPI servers, skills, prompts, personas and memory, which I can take with me into whichever environment I’m using. I’m still trying to figure out whether this is useful beyond my own workflow. Ideas? 🤔 🍎 Source / open source on Github: matebenyovszky/agentplaybooks I also built an npm CLI that audits a project and syncs the same skills and MCP definitions across harnesses.
