免费 / 开源在本地或云端运行和构建开源 AI 模型

Screenify is an AI interview platform that screens and qualifies job applicants with automated, human-like video interviews. Recruiters define the role and scoring criteria once, then Screenify invites candidates by email, runs the interview through Google Meet or Zoom at a time that suits the candidate, and returns structured scores and insights so hiring teams can focus on the strongest people.
| 维度 / 指标 | ![]() | ![]() |
|---|---|---|
| 官方域名 | wandb.ai | screenify.ai |
| 定价模式 | 免费增值,定价请咨询,免费 | 免费试用,付费,定价请咨询 |
| 月访问量 | 31.56万 (315,624) | 4326 (4,326) |
| 月度环比流量变化 | -127.55万 (-80.2%) | -890 (-17.1%) |
| 主要市场与份额 | 🇺🇸 美国 (32.2%) | 🇺🇸 美国 (86.3%) |
| 直接访问 / 月 | 23.09万 | 1694 |
| 用户评分 | 4.7 | 4 |
| 支持的平台 | 网站 | 网站 |

在互联环境中跟踪、评估并交付 AI 模型与智能体。
记录训练运行中的指标、损失和媒体样本,并在交互式图表中并排对比,找出真正提升模型的做法。
运行自动化超参数搜索并可视化参数重要性,更快收敛到更优配置。
对数据集和模型产物进行版本管理、追踪血缘,并通过注册表把模型从训练推进到生产。
构建、记录并评估 LLM 提示词与应用,配合追踪与评估工具,构成 LLMOps 工作流。

Screenify is an AI interview platform that screens and qualifies job applicants with automated, human-like video interviews. Recruiters define the role and scoring criteria once, then Screenify invites candidates by email, runs the interview through Google Meet or Zoom at a time that suits the candidate, and returns structured scores and insights so hiring teams can focus on the strongest people.
Create an interview, define scoring criteria, and send invitations automatically so screening, scheduling, and feedback collection happen without manual coordination.
Every interview is tailored to the role and the candidate's experience, and the AI follows up on responses the way a human interviewer would.
Candidates are scored against your company's criteria using AI evaluation, which reduces interviewer bias and keeps comparisons fair across applicants.
Applicants get an email invite, a custom intro video, and the freedom to interview any time through Google Meet or Zoom, with your own branding throughout.
免费 / 开源在本地或云端运行和构建开源 AI 模型

免费 / 开源提供深入的生产环境 LLM 可观测性与追踪,重点聚焦于生产错误与偏移的调试。

专项更强Features simulation and guardrail tools for testing LLM safety and robustness before production deployment.
专项更强Features simulation and guardrail tools for testing LLM safety and robustness before production deployment.

免费 / 开源提供强固的 LLM 评估与测试套件以及整合式企业网关,并优先考量提示词基准测试。
免费 / 开源提供强固的 LLM 评估与测试套件以及整合式企业网关,并优先考量提示词基准测试。
Humanly is an AI recruiting platform built for high-volume, frontline and hourly hiring. It engages every applicant across chat, SMS, email, voice and video, screens candidates through structured conversations instead of keyword resume scans, runs AI interviews around the clock, and schedules the next step automatically, then hands recruiters a ranked shortlist with transcripts and reasoning behind each recommendation.
Humanly is an AI recruiting platform built for high-volume, frontline and hourly hiring. It engages every applicant across chat, SMS, email, voice and video, screens candidates through structured conversations instead of keyword resume scans, runs AI interviews around the clock, and schedules the next step automatically, then hands recruiters a ranked shortlist with transcripts and reasoning behind each recommendation.

Hyring is an AI-native hiring platform that screens, interviews, and shortlists candidates across one shared workspace. Employers get AI resume screening, phone screening, async and live video interviews, coding interviews, an English proficiency test, plus ATS, HRIS, and a recruiter marketplace, while jobseekers get free AI resume, cover letter, mock interview, and auto-apply tools.
Hyring is an AI-native hiring platform that screens, interviews, and shortlists candidates across one shared workspace. Employers get AI resume screening, phone screening, async and live video interviews, coding interviews, an English proficiency test, plus ATS, HRIS, and a recruiter marketplace, while jobseekers get free AI resume, cover letter, mock interview, and auto-apply tools.
专注于提示词版本控制、协作式 CMS 管理与代理追踪,而非一般 MLOps。

专门从事自动化 LLM 单元测试与资安红队演练,不过对传统 MLOps 实验追踪的关注较少。
专门从事自动化 LLM 单元测试与资安红队演练,不过对传统 MLOps 实验追踪的关注较少。
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