
Klority — Replace Jira, Confluence & TestRail with One Affordable Tool
项目管理、文档和测试管理的一体化工作空间。
@klorityx · X
One workspace for project management, documentation, test management & CRM. Enterprise features without enterprise pricing.
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项目管理、文档和测试管理的一体化工作空间。
@klorityx · X
One workspace for project management, documentation, test management & CRM. Enterprise features without enterprise pricing.

在共享层中记录产品决策、背景和推理,团队不会遗忘决策背后的原因。
@TimEngng · X
Stop losing the "why" behind product decisions -

用自然语言或 YACQL 查询 NFL 统计数据,生成可共享的表和图表。
u/Fun-Calendar8486 · Reddit
I am building yacdb.fyi, letting users ask natural-language questions about NFL data and turning them into queryable results. Yacdb.fyi goal is to allow users to construct questions about NFL data, think "Best 1st down conversion rate in 2025", and exposing a custom query layer on top (think SQL) allowing users to define their own queries to build data sets. They can chart in the app, using built in tooling, but can export the data as well if they want to use their own tooling. I am look

托管 JSON 数据库,用于存储 agent 内存,具有 REST 和 MCP 连接能力
@StuSim · X
hey Adam, I run , lightweight agent memory

检测API中转站输出是否与官方100%一致
@nodeloc_cc · X
🌈 7月,你好,MODELOC上线算力池。 MODELOC自上线以来,已检测2000余次,覆盖600+中转站,为众多AI用户提供的使用参考。 MODELOC近期进行了改版,上线了算力池及市场。 加入算力池 查看帖子: 用 MODELOC 便宜地调各家大模型:一次讲清它的价格体系

通过真实的Wikidata关系连接任意两个主题的知识图谱游戏。
u/hyperschlauer · Reddit
I built a free knowledge-graph game where every move follows a real Wikidata relationship I’m building Webwoven for OpenAI Build Week, and the beta is now live: https://www.webwoven.org It’s inspired by Wikipedia hopping: you start with one topic and try to reach a target in as few moves as possible. Every move follows a real relationship from a reviewed Wikidata graph. The game therefore shows why two topics are connected instead of treating ordinary page links as paths. Webwoven curr

为现代团队自动化负载测试,无需复杂设置或手动脚本编写。
@GorodkovVi85373 · X
- load testing made easy even without enginnering team. Faster, cheaper, distributional

让AI模型通过3D动画展现香蕉植物的完整生命周期来比较性能。
fran-mora · HN
I gave 5 AI coding agents one prompt: grow a banana plant through its whole life in three.js: sprout, leaves, flower, fruit, rot, then pups that restart the loop. It's deceptively simple and yet very hard to get right from procedural code: you have to write working three.js and understand how the plant is actually built; how it hangs, ages and decays. Get the biology wrong and the code renders something weird. These are agents, not bare models (Claude Code and Codex for now). They can use tools, including playwright to check their work and improve it.