Seedream 5.0 Pro — 多模型 AI 图片工作台，支持文字生图、图片生图、复杂布局与多语言文字编辑

@spectragai · X

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～

davidtsong · HN

Benchmarklist: track AI benchmarks (2.4k+), models, and capabilities

pampas · HN

Redactle LLM Leaderboard

ahmed_hassan7 · HN

CostPerPrompt – Live AI API pricing and real-workload cost calculators

@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

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

zizheruan · HN

XTokenChecker – Verifies model identities of your AI gateway

cocodot LLM 降智检测 — 免费的 LLM API「降智/偷换模型」在线检测：填入任意 OpenAI 兼容端点的 base_url 和临时 API Key，跑 6 项探针（模型声明、动态题、能力完整性等）生成分项报告；Key 仅用于当次检测、不落库不留存，检测方法[开源](https://github.com/cocodot2026/cocodot-llmprobe)

u/12qwww · Reddit

I built a live benchmark to see which AI actually writes the best frontend code Hey everyone! I built OpenVibeEval because I was tired of "vibe-checking" AI-generated frontend code. I wanted to know which model actually produces the most accessible and clean React/Tailwind output. What I built: •A leaderboard of 24 models (Claude, GPT, DeepSeek, etc.) ranked by axe-core accessibility scores. •A Harness Comparator to show how different system prompts change the same model's output. •

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.