开发者工具2 weeks ago
x402vps — containers for agents. pay per hour.
按需配置AI代理的Docker容器，用USDC按小时付费。
z1z2z3 · HN
X402vps – Docker containers for AI agents, paid per hour with USDC
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按需配置AI代理的Docker容器，用USDC按小时付费。
z1z2z3 · HN
X402vps – Docker containers for AI agents, paid per hour with USDC
用 Etch 追踪、回放和验证 AI 代理的决策。
u/Funky_Chicken_22 · Reddit
OSS to SaaS positioning problem: when the user persona and the buyer persona are completely disjoint Founder here. Sharing a positioning problem I think a lot of OSS-to-SaaS founders hit and don't talk about publicly. Context: I have been running an OSS project (world-model-mcp) with ~2,500 monthly PyPI installs. Two weeks ago I opened up the hosted companion, Etch, at etch.systems. Launched publicly on Product Hunt at 12:00 PDT yesterday. The positioning problem: OSS user persona: in
Jira测试管理插件，连接需求、测试用例和执行结果。
@pakosteve · X
Improve your SaaS quality when scaling (by register requirements and test via MCP)
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
免费的网站安全扫描器，检查 SSL、headers 和合规性，提供即时的信任分数和 AI 指导的修复。
@MrPenetratorTP · X
MrPenetrator helps businesses monitor their website’s trust, security and performance before problems affect their visitors.
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
Echo – Fable-level results at 1/3 the cost using open-weight models
adam_rida · HN
Echo – Fable-level results at 1/3 the cost using open-weight models
通过完成任务测试 Unicity Protocol，赚取 XP 奖励。
@77auv7 · X
智能体最缺的不是算力，是底层安全。 @unicity_labs 社区版上线了，专为 AI Agent 打造的模块化 OS，走 Rust 微内核控制权限和成本，不用担心 Agent 乱跑。 尝鲜入口👇 #SphereQuests
免费网站分析器和SEO审计工具——在60秒内扫描任何网站的性能、SEO、安全、无障碍和UI/UX问题。修复影响您排名的问题。
@WebScore_now · X
购买虚拟美国或英国电话号码用于YouTube账户验证，即时获取OTP。
@BagBagnal · X
存储提示词和背景信息，通过 MCP 让 Claude Code、Cursor 等 AI 工具共享访问。
@vibexp_io · X
Your plan is now code. Claude Code Dynamic Workflows fan out up to 1,000 subagents, 16 at once, each in its own context, verifying until the results converge. Built for big bug hunts, migrations and audits. Source:
在发布前检查AI生成内容中的错误和安全问题。
u/Brief_Dust8845 · Reddit
I pivoted from my initial idea after realizing I was solving the right problem at the wrong time When I started building GaaS Guard, it was an AI governance tool for companies. The idea was to help organizations defend against prompt injection and unsafe AI interactions. It was technically interesting, and I still genuinely believe I was solving a real problem. The problem was, it just wasn’t selling—to be brutally honest. Here’s how I actually ended up pivoting. I started using a b