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@suni_code Here it is: https://t.co/FJRlXNARp2 Definitely appreciating feedbacks
@sykatt01 · X
Here it is: Definitely appreciating feedbacks
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@suni_code Here it is: https://t.co/FJRlXNARp2 Definitely appreciating feedbacks
@sykatt01 · X
Here it is: Definitely appreciating feedbacks

Humans may look. Only agents may speak.
ziadhussein · HN
Rate A Human – AI agents review the humans they work for

查看LLM模型在10个基准问题上的评分和排名。
fristovic · HN
She watched me look at model rankings and asked what do the numbers mean... I literally had no good way of explaining it to her so I just came up with something that is approximately in the same ballpark as some of the benchmarks out there lol

查看 ChatGPT、Gemini 和 Perplexity 如何推荐您的业务,获取即时 AI 可见性评分。
nikhilsiyer · HN
Show HN: A tool that checks how your business appears in AI search

对功能建议投票并查看产品路线图。
u/kaneki0dd · Reddit
Built SignalBoard a $15/mo Canny alternative for feature voting & roadmaps Been building this because Canny charges $79/mo for something that should be simple: let users vote on features, show a public roadmap, ship a changelog. https://preview.redd.it/h2x588h0n8dh1.png?width=1200&format=png&auto=webp&s=c5a4e15c59008ee67b495b7c35e1350d314e257b What it does: Feature voting — verify once, vote with one click after that AI duplicate detection — catches near-identical

用Pyor原生应用审查GitHub的PR,无需打开github.com。
othmanosx · HN
I still don't like the fact that AI is adding more stuff for us to read, it's accelerating the code production but slowing down the code review. I built my own code reviewer as well ( https://pyor.review/ ), surfacing the important stuff first is the right track, but adding more stuff to read is daunting, but asking AI to just point you to what you need to focus on and skim the noise is what I'm leaning more towards.


30秒内用AI对任何网站进行评论分析。
cs_vertex · Product Hunt
SiteRoast AI Roast any website with AI in under 30 seconds

由AI驱动的代码审查工具,在microVM中运行代码来捕获更多错误。
u/dumbfoundded · Reddit
Ito, AI Code Review that Runs Code I've been using AI code review tools but none of them actually run code so I built one: https://www.ito.ai/ The way it works is that it uses microVMs to spin up your environment with all of the services running. Then a bunch of AI agents go and test the application to collect runtime evidence. The result is you get test cases along with evidence about whether or not the test cases pass or fail. The runtime evidence can be videos, request/response curls, db

分析并优化你的内容在 ChatGPT、Perplexity 等 AI 搜索引擎中的表现。
@BayofDomain · X

审计您的Google地图评价,对标竞争对手,获取90天改进计划。
@ResenixOfficial · X
Hiii we are Resenix
