AI 智能体6 days ago
TODO for AI
TODO for AI - 自动化邮件、文档检索和发票等日常公司任务的AI代理。
@cviklihamar · X
here 🙌 let's connect! - an ai agent that clears your team's todos (ships code, runs your tools). let's connect!
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TODO for AI - 自动化邮件、文档检索和发票等日常公司任务的AI代理。
@cviklihamar · X
here 🙌 let's connect! - an ai agent that clears your team's todos (ships code, runs your tools). let's connect!
通过统一 API 层将网站与多个 AI 系统集成。
@rofarkas · X
社交化AI数学平台，用于解决和协作数学问题。
lemma1729 · HN
Hi HN, This is ProofTree, and in TLDR: it is a platform where you can chat with an AI to do math, the way you already do, with context-awareness and knows how you personally do math, connected to the fora of other live human mathematicians. So, when you are talking to an AI about a proof at 3 am, and you need a human expert looking at it, or a discussion around it, like you do on StackExchange, this is it! You need an approved email account to use it, so please write here if you would live to try it out.
对比 AI 模型在编码任务上的表现,支持成本追踪和 ELO 排名。
@intheworldofai · X
On the World of AI Bench (vibe-coding composite): Claude Fable 5 → 85.2 GPT-5.6-sol → 82.4 kimi-k3 → 81.5 Moonshot’s K3 just walked in and claimed bronze on one of the toughest coding-focused leaderboards out there.
将英文转换为Excel/Google Sheets公式，分析数据并导出报告。
@ExpressSheet · X
Upload a spreadsheet. Ask questions in plain English. Get insights, charts, and a polished PDF report back. ExpressSheet is like having a data analyst on call, 24/7. Start free:
为AI团队提供的协作内存平台，将团队背景信息组织成知识图。
ximihoque · HN
Collaborative context-sharing memory platform for agents and teams
AI工具，通过分析会议记录检测范围蔓延，为代理商和自由职业者标记风险。
@theshaunak_twit · X
AI Todo 用智能方式将目标分解为可行步骤，优化任务优先级。
AI Todo — 像很多程序员一样，我日常要兼顾工作任务、生活琐事、项目计划，时常感到事情多但漏掉的也不少。想用智能方式管理这些待办，但市面上的工具要么设计臃肿，要么 AI 能力很弱。于是我花了几周开发了 AI Todo，目标是:
AI 学习你的风格，自动撰写并发布 X 帖文。
@Shahzebsolodev · X
grow you X on autopilot (Almost done building. Giving the finishing touches)
Flint是为AI代理设计的可视化语言,用于创建交互式数据可视化。
chenglong-hn · HN
Data visualizations are the bridge between user and data. But building AI agents that can generate visualizations reliably can be very tricky: - simple chart specs can be reliable, but generated charts are often of low quality due to reliance on system defaults; - complex chart specs with explicit details can produce good-looking charts, but they are verbose and agents can struggle with reliability We figured out it is a limitation on the language issue (not just AI capability thing) -- current visualization languages are a bit too low-level for AI agents, requiring them to explicitly make visual decisions that are supposed to be handled by a good compiler. Flint is a visualization intermediate language to address this issue, allow AI agents to solve this last-mile human-agent interaction problem. It provides a simple semantic-type based specification, and contains a layout optimization engine that can produce good-looking charts (filled with derived low-level details) from simple
