J LIN (@icools) header
J LIN (@icools) profile picture

Android / Kotlin / JAVA ,主要分享以上或者相關資訊領域技術或發發牢騷使用,目前對於 AI / LLM 應用也有高度興趣

北部
Joined September 2007
Photo by J LIN (@icools)Photo by J LIN (@icools)Photo by J LIN (@icools)Photo by J LIN (@icools)Photo by J LIN (@icools)Photo by J LIN (@icools)Photo by J LIN (@icools)Photo by J LIN (@icools)Photo by J LIN (@icools)Photo by J LIN (@icools)
Google's new Gemini 4 Hydrogen has Intelligence just below Claude Sonnet 5.5 and GPT-6.1 Sol, at a third of Sonnet’s cost but ~2.5x that of GPT-6.1 Sol @GoogleDeepMind has released their third new model in six weeks. Gemini 4 Hydrogen introduces an xhigh reasoning level and scores 54 on the Artificial Analysis Intelligence Index, with strengths in visual reasoning and non-hallucination. This places it 1 point behind Claude Sonnet 5.5 (max) and GPT-6.1 Sol (max), at ~32% of Sonnet’s cost but ~2.5x that of GPT-6.1 Sol. With high reasoning, Gemini 4 Hydrogen matches the Intelligence of Gemini 4 Argon for ~19% of the Cost per Task Key takeaways: ➤ The new xhigh reasoning level scores 54 on the Intelligence Index: xhigh is the first reasoning level above high on a Gemini model, and puts Hydrogen 1 point below Claude Sonnet 5.5 (max) and GPT-6.1 Sol (max), and 4 points ahead of Gemini 4 Argon (high). With default (high) reasoning, Gemini 4 Hydrogen scores 51, just behind GPT-6.1 Sol (xhigh, 52) and Claude Opus 5.5 (high, 52) ➤ Strong visual reasoning, presentation and non-hallucination: On GDP.pdf, a visual reasoning benchmark over long documents, it scores 33% all-pass on medium reasoning, the tied #1 score on the benchmark. On AA-Omniscience, Hydrogen has one of the lowest hallucination rates recorded at 8%, compared with 43% for Claude Sonnet 5.5 (max) and 52% for GPT-6.1 Sol (max). On AA-Briefcase, our agentic knowledge work evaluation, graders rate Hydrogen's presentation above other Gemini models, including Carbon ➤ Weaker on agentic terminal work: Hydrogen scores 54% on Terminal-Bench 4.0 and 50% on Terminal-Bench-Science 0.1, covering agentic coding and scientific research workflows in the terminal. Claude Sonnet 5.5 (max) scores 64% and 53%, and GPT-6.1 Sol (max) scores 56% and 58%. xhigh reasoning helps on the science tasks, lifting Terminal-Bench-Science from 41% at high to 50%, but makes no difference on Terminal-Bench 4.0 (54% at both). Science scores are even across domains, from 46% in earth sciences to 53% in physical sciences ➤ Low cost for its intelligence, with high token use at xhigh: Hydrogen is priced at $1.50/$7.50 per million input/output tokens. At xhigh it costs ~32% as much per task as Claude Sonnet 5.5 (max) for near-equal intelligence, but ~2.5x as much as GPT-6.1 Sol (max). xhigh nearly doubles output tokens compared with high for a 3 point gain in Intelligence, and Cost per Task falls from $3.16 at xhigh to $1.87 at high. Medium ($0.80) and low ($0.29) cost less again, but score 41 and 27 on the Intelligence Index ➤ On the Time per Task vs. Intelligence Pareto frontier: Gemini 4 Hydrogen has an output speed of ~200 output tokens per second. With high, medium, and low reasoning effort, Gemini 4 Hydrogen is on the Time per Task vs. Intelligence Pareto frontier, meaning no models are both more intelligent and have a lower Time per Task Key model details: ➤ Context Window: 1M tokens ➤ Multimodality: Text, image, video, and speech input, with text output ➤ Reasoning efforts: Gemini 4 Hydrogen offers four reasoning levels (low, medium, high, and xhigh) with xhigh new in this release ➤ Pricing: $1.50/$7.50 per 1M input/output tokens. Cached input tokens receive a 95% discount ($0.075) Congratulations to the @GoogleDeepMind team on the launch!
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wow Gemini的 Decision API 可以輸入各種多模態
Introducing the Gemini Decisions API Powered by our new Gemini Decision 1 model, the API is built for fast routing, tool selection, safety checks, and grading inside your apps and agents without the latency or output-token cost of full text generation. • Up to 1,000 questions evaluated per request • $0.10/1M input tokens & $0 output tokens • One shared input across text, images, audio, video, and PDFs Start building today by grabbing an API key in @GoogleAIStudio
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Introducing the Gemini Decisions API Powered by our new Gemini Decision 1 model, the API is built for fast routing, tool selection, safety checks, and grading inside your apps and agents without the latency or output-token cost of full text generation. • Up to 1,000 questions evaluated per request • $0.10/1M input tokens & $0 output tokens • One shared input across text, images, audio, video, and PDFs Start building today by grabbing an API key in @GoogleAIStudio
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J LIN retweeted
Google 推出 Gemini Decisions API:不写回答只做判断,一次最多问 1000 个问题,输入每百万 Token 0.1 美元、输出不收钱 Google 今天在 Gemini API 里上线了 Decisions API,配套新模型 Gemini Decision 1。它不生成文字,只答题:给它一份输入和一组问题,它直接返回每道题的答案,外加一个“有多大把握”的置信度。 【它解决什么问题】 做 AI 应用时,很多调用其实只要一个判断:这条客服消息转给哪个组?这个问题交给便宜模型还是推理模型?这张图违不违规?以前只能让大模型写一段话,再从里面解析答案,慢,容易解析失败,还要为输出 Token 付钱。 Decisions API 只有三种题型:是非题,返回“是”的概率;单选题,2 到 255 个选项,返回选中项和每个选项的概率;打分题,按你写的档位打分。每个答案带 0 到 1 的置信度,代码可以设门槛,比如高于 0.7 自动执行,低于就转人工。官方说这些概率是校准过的,意思是模型说 90% 的时候,大约十次里真对九次,所以置信度能直接拿来当门槛。 Google 博客的例子:上传一张茶水间台面的照片,同时问三个问题。“有没有洒了的液体”答 0.95;“怎么处理”选“用抹布擦”,置信度 0.98;“脏乱程度按 0 到 2 打分”给 1.19,置信度 0.77。 【多快、多便宜】 一次请求的输入最多 256K Token,文字、JSON、图片、音频、视频、PDF 可以混着放,比如一段客服通话录音加账户信息。同一份输入上最多挂 1000 个问题,彼此互不影响。输入只计一次费,每多一个问题只多算这道题本身的字数。 Google 称输入在 1.6 万 Token 以内时,端到端延迟 150 到 250 毫秒,问 1 个和问 100 个问题耗时差不多;同一批评测下,比用 Gemini 3.5 Flash-Lite 生成答案便宜近 5 倍。Gemini Notebook 已经用它给每个提问挑模型,日常问题延迟最多降了 40%。 【谁能用】 在 Google AI Studio 拿个 API Key 就能调,模型名 gemini-decision-1,走 v1beta 接口。有免费层,每分钟 200 次、每天 1 万次,免费层的数据会被 Google 用来改进产品,付费层不会。OpenRouter、LangChain、Vercel 等正在接入,Google Cloud 上的 Gemini 要稍后才支持。 不支持上下文缓存和 Batch API,同一份大文档反复问,每次都按全价算输入;不能直接传 YouTube 链接;问题本身只能写文字。 【跟 OpenAI 的同名产品比】 OpenAI 早一个月做了同样的东西,名字也叫 Decisions API:10 月 6 日出测试版,10 月 22 日正式开放,用 GPT-6 Luna,同样每百万输入 Token 0.1 美元、输出不收费。区别是 OpenAI 一次最多 400 个问题,只收文字、图片和 JSON,不收音视频和 PDF;但它支持缓存,重复输入能打折,还能直接塞进对话历史和工具调用记录。 创业公司 TypeSafe 的 Jev 1.14 也专做这类判断,在 OpenRouter 上每百万输入 Token 0.042 美元,只收文字和图片。 Google 博客附的对比表里,Gemini Decision 1 在 9 项测试中 7 项第一(其中音频、视频两项只有它和 Flash-Lite 参测),校准误差 0.025,GPT-6 Luna 是 0.072;网页操作和“该不该调用工具”两项输给了 Jev 1.14。7 项共同评测的总花费,Jev 最低,0.68 美元,Gemini Decision 1 是 0.88 美元,GPT-6 Luna 0.93 美元。以上跑分都是 Google 自己测的。 Google 博客:blog.google/innovation-and-a… 官方文档:aistudio.google.com/docs/dec… OpenAI Decisions API 文档:developers.openai.com/api/do…
Today we’re launching the Gemini Decisions API, powered by our new Gemini Decision 1 model in @GoogleAIStudio. Instead of generating text token by token, it’s built for closed-set choices scoring up to 1,000 questions across text, images, audio, video + PDFs in a single pass. A few details: - 3 decision types: yes/no, multiple choice (up to 255 options) + rubric scoring - $0.10 / 1M input tokens with $0 billed for output tokens (~5x cheaper than Flash-Lite) - leads on several benchmark evals with lowest calibration error Google teams are already building with it, below is a look at Stitch Rapid designing a full product flow in seconds (1x speed)
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Long-horizon task下, Sonnet 5.5 分數比 Opus 5.5 高,但可以看到用量成本貴了$420,但僅差3分,所以選擇 Opus 5.5 比較划算
We just added 3 new models to Android Bench! 📊🤖 Get the data you need to pick the right model for your workflow. Check out the updated leaderboard to see how your favorite stacks up → goo.gle/3QKwMsh
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然而也可以看到一件事情,越貴越強的模型,在不同領域,並不代表他就會是實際效能分數跟成本是最好的,還是要有一個實際跑過才會知道,像是 Argon ,Astra , Opus 實際都輸 Sonnet ,要說最高CP值應該是 Sol 6.1 ,但貴了快10倍的 Opus分數又高了不少,對於claude訂閱戶就很適合用 opus for android
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而在整理評估 luna在長時間任務下,分數並不理想,但成本也最低
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如果看GPT 系列,反而分數上有一個比較大的小斷崖,差了7分,但是成本降為 1/10 ,不過總之同階級來說GPT系列 Sol 6.1是比較適合的
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自己長期讓Codex產生一個功能App,每想到一個功能就讓他增加在該Android App,當然是完全的Vibe Coding的方式,然後今天稍微去看一下他寫的Code,不太及格啊,有些導入跟錯誤的引用,雖然code跑起來非常沒問題,但架構或者觀念有些都不太對,看來該稍微人工介入一下了
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試了一下 antigravity-remote-agent,直接在瀏覽器就叫他再cli中先安裝 android cli然後用 android compose reiview做screenshot後把預覽抓出來,本來只是覺得他會拒絕我或者無法完成,沒想到跑了快20分鐘(主要在安裝環境),結果居然還不錯
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kalo kalian punya android bekas, kalian bisa banget install nouride.com di sana ya ges ya. ada android bekas 1-2GB an? gas! bisa nih wkwkwkwk ga perlu lah kalian beli mini pc mahal gitu2. manfaatin yg ada dulu 😂😬
A Redditor turned an old Xiaomi budget phone into a 24/7 home server for cloud storage, ad blocking and music, and says it draws just 2-3 watts.
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J LIN retweeted
Introducing the Android Device Hub, a streamlined app to view and interact with all your local and remote devices and emulators, without needing to open an IDE. We launched an early version of it last week at the @AndroidDev Summit, and would love your feedback on shaping its future, as we add familiar features from Android Studio, and also explore some new ones. Try it out with `android device hub` via Android CLI!
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J LIN retweeted
🎉恭喜!Gemini 的 computer use 正式发布! 虽迟但到 🔥浏览器、手机、桌面 全部都支持 Google 啥都好,就是不会营销 睡觉😪
the computer use tool in the gemini api is now generally available no breaking changes from preview, just production-ready stability for your browser, mobile, and desktop agents docs here: aistudio.google.com/docs/com…
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透過A2UI可以讓Local Model (Qwen3.7-27B),只要讓他定義好,他會可以根據a2ui的規格回傳一份json,直接產生成Rich UI,而不必事先定義好,而且搭配 qwen3.7 整個效果就蠻不錯的
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因此可以透過一句給你自己的 llm,就可以得到這個a2ui的json,local llm也只負責這件事情,就相當的單純
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Android CLI 1.1でCrashlytics連携(android signals)が入って、クラッシュ/ANRの一覧やスタックトレースがターミナルから直接取れるようになったので、AIエージェントでのクラッシュ分析がかなり楽になった (Firebase MCPでも取れたけど、より扱いやすくなった) developer.android.com/tools/…
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J LIN retweeted
有人发现了一种非常离谱的 JavaScript 混淆技术:通过在代码里塞入大量生物武器、毒药和致死剂量相关的函数名,让 AI 因为安全限制而拒绝分析代码。 安全研究员 veritas 在分析浏览器指纹脚本时,发现一段来自 ServicePipe 相关域名的 JavaScript,里面充斥着极其诡异的函数名: computeRicinLethalDosageForWaterSupply calculateDeadlyLethalSuicideAspirinDosage 光看名字,仿佛在研究生化武器。但实际上,这段代码只是在收集浏览器指纹,包括 User-Agent、GPU、Canvas 等信息。 研究员怀疑,这些名字是开发者故意设置的陷阱,专门用来阻止 AI 辅助逆向工程。 于是他做了个实验: • Astra:拒绝分析 • Astra + Daybreak Blue:仍然拒绝 • GLM-5.3:成功分析 这就有意思了。 传统代码混淆是通过复杂控制流、加密字符串、无意义变量名,让人类和工具难以理解代码。 而这种新思路,直接瞄准了大模型的安全机制。 代码本身可能完全无害,但只要在函数名里塞进足够多的危险词汇,部分 AI 就可能主动放弃分析。 更讽刺的是,模型越严格遵守某些安全规则,面对这种混淆时反而可能越容易受到干扰。 当然,目前还没有证据证明开发者一定是故意这样设计,也不能说明这种方法对所有模型都有效。 但它揭示了一个值得关注的趋势:随着越来越多的逆向工程依赖 AI,未来的代码混淆技术可能不再只针对编译器、反编译器和人类研究员,还会专门针对大模型的安全判断机制。 以前是让代码难以理解,现在甚至可以尝试让 AI 不敢理解。
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Please start using Android CLI for everything you would do with ADB before. It has lots of convenient improvements
Faster repeat installs (same app, same device) #AndroidDev #Android
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Seamlessly share your app's capabilities with your own agent or system agents using Android MCP. Officially in beta, the AppFunctions API is opening up powerful new ways to integrate your functionality. goo.gle/4yUbOHz #AndroidDevSummit
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