SYeomans · HN

TokenMaxxer – track every AI token you spend across your coding tools

fcten · V2EX

做了一个大模型 leaderboard 网站 最近一个月 CodeX 疯狂送重置，token 根本用不完，顺手做点东西。 地址：[知行录]( https://leaderboard.cn/) 排行依据主要为模型官方基准测试成绩。非主观排名。 数据会持续更新。如果有点用，欢迎各位 v 友收藏～

u/Rabus · Reddit

I got TestingModels too overcomplicated over the month it is running: looking for some feedback how to make it more useful and simpler I run a benchmark like arena.ai , but with pre-generated prompts. So far, nearly 6k people came in and like 30k comparisons has been made - which means the thing is genuinely useful for people to compare the models. The problem is the more features i started adding the more overblown and complicated UI became - like old internet explorer tab bars Old: ht

@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.

barretlee · GitHub

agent-pulse Evidence-backed AI industry intelligence — trends, source updates, daily data refreshes, and weekly decision briefs.

u/TasteMysterious5285 · Reddit

I built AI Census, a live field bulletin for how people are actually talking about AI models I’ve been building AI Census, a public “field bulletin” for how people are talking about current AI models. I kept running into the same problem: benchmark tables tell me how a model performs on a test, but not whether people are actually finding it useful, frustrating, reliable, etc. So I built a rolling view from public technical conversations across Reddit, Hacker News, Bluesky, GitHub, and Huggi

AI 今日热榜 — 汇集全球顶尖来源的最新 AI 新闻、研究和趋势，为您节省宝贵时间

baraklaniado · HN

I audited my AI leaderboard scale – every score dropped 6-15 points

@ChenglongW98225 · X

做了一个AI新闻日报，感兴趣的可以点下方链接看一下 有什么需要改进的也可以直接评论我，我都会认真回复

@MixRoute_ai · X

@apicorafrica · X

Yes please, Apicora helps people understand plant suitability, ecological risk, and site-level decisions using structured African plant intelligence.

uxff · V2EX

做了一个 AI 职业风险地图，直观看看各个国家各个行业的工作受到 AI 冲击的程度 前段时间自己频繁搜索 AI 会如何影响我从事的职业，我周边的人从事的职业，然后又开始搜索哪些职业很难被 ai 代替。搜到的信息很零散，几乎没有数据完整、可视化好的网站。 所以我做了一个可视化网站： ## AI Job Risk Map https://aijobriskmap.com 网站使用 Treemap 展示不同职业的 AI 风险。每个方块代表一个职业： - 方块大小表示职业规模 - 颜色表示 AI 风险等级 - 可以按国家查看职业分布 - 可以比较不同职业和职业类别 - 支持查看具体职业的风险数据 目前主要覆盖美国、英国、加拿大、澳大利亚、新西兰及欧洲、亚洲的多个国家。风险评分关注的是职业中的任务有多少可能被 AI 自动化或显著改变，并不等同于预测某个职业会彻底消失。 这个项目仍在完善中，尤其希望听听大家对下面几个问题的意见： - 风险地图是否容易理解？ - 你更关心“职业被替代”，还是“职业会如何被 AI 改变”？ - 除了风险、薪资和就业规模，