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做了一個 Omarchy bar 的 LINE 面板： @OmarchyLinux
bar 上看未讀數，點開就能回訊息、傳貼圖和圖片，有已讀和通知。全部在本機跑，背後是一個用 linejs 寫的 Deno daemon。
Frank Y retweeted
Day 25/
We are doubling all usage limits for all Pro 100/200/500 tiers and Business Premium, effective today.
We heard your kind feedback and won’t be reducing usage limits for Pro 200, so your Work and Codex usage will stay the same. We’re also going to continue offering extended limits for dots for now, and usage of your dot will still continue to be separate from your default usage.
And we'll of course also keep shipping better models and other efficiency improvements that increase your effective usage over time.
Frank Y retweeted
DeltaDB is a new, Git-compatible version control system for agent context. It keeps every edit linked to the conversation that produced it.
@as__cii takes a deep dive into how we built it with Rust, SQLite, and Durable Objects:
delta.dev/blog/fine-grained-…
Frank Y retweeted
Announcing Artificial Analysis Intelligence Index v5, featuring 2 new benchmarks and 3 upgrades. For the first time, the majority of the Index is built on private datasets
The Artificial Analysis Intelligence Index is our composite measure of model capabilities across tasks reflecting real-world usage today. v5 is our biggest upgrade since v4 launched in January. With v5, 60% of the Index is now based on evaluations with private test sets or held-out answers.
Alongside Intelligence Index v5, we’re launching the Artificial Analysis Progress Index v1, which tracks model capability over time on one continuous scale.
New to the Intelligence Index:
➤ FrontierCode 2.0: Cognition's newly-launched private benchmark of real-world coding tasks, graded on whether a repository maintainer would merge the agent's change
➤ Terminal-Bench-Science: 70 agentic scientific research tasks, run in a terminal environment
Upgraded:
➤ HLE-Diamond (replacing Humanity's Last Exam): a refined 1,000-question version of Humanity's Last Exam, split evenly between reasoning and knowledge questions
➤ AA-Omniscience v1.1: An updated dataset with saturated and public set questions removed, new questions added, and ambiguous or erroneous questions revised
➤ CritPt v2: a private, 40-question hard subset of CritPt v2, revised by the CritPt authors and run with a code-execution tool
Staying in the Index: AA-Briefcase, AutomationBench-AA, GDP.pdf and Terminal-Bench 4.0
Removed from the Index: GDPval-AA v2.1, SciCode, and AA-LCR v1.1
Key results:
➤ Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55): Anthropic and OpenAI hold the top five models. Gemini 4 Argon (high) is next at 50
➤ The open weights gap widens from 3 points to 12: Kimi K3.1 Pro (weights not yet released) is the top open weights model at 46. Among released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) lead at 32 - 26 points behind the leader and below Claude Haiku 5.5 (max, 36)
➤ Cost per task varies by more than 13x among the leading models: of the 10 highest-scoring models shown, GPT-6.1 Terra (max) is the cheapest at $0.96 per Intelligence Index task and Claude Fable 5.1 (max) the most expensive at $13.04
Frank Y retweeted
Lol I am pretty sure someone is runing a deepseek swarm to remotely patch unpatched php boxes. Well good on them.
Frank Y retweeted
Recursive_SI 联合创始人、前 Meta FAIR 研究科学家总监、前 Google 工程师田渊栋宣布，公司发布首个模型 Flint-40B-A2B。
这是一个开源本地编程模型，可在 OpenCode 和 Pi 上免费使用。
今天就试试
Today, we release our first model, Flint-40B-A2B, a fast local coding model built with our automated research system. huggingface.co/RecursiveSI/f…
1. Strong performance. 54.9% on Terminal-Bench 2.1, 89.7% on SWE-bench Pro v2, and 74.2% on SWE-bench Verified.
2. It is trained from scratch (tokenizer->Pretrain->Midtrain->SFT->RL).
3. Fast in your local laptop. Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark.
4. We build the complete model training pipeline in 4 month!
Frank Y retweeted
Aloha!🌺 Meet Ornith-1.5-122B: No Tradeoff Between Strong and Fast
Stronger. Faster. Leaner. Ornith-1.5-122B delivers frontier-level capabilities with exceptional inference efficiency. With superior instruction following, stronger task decomposition, and deeper understanding of real-world workflows, it achieves state-of-the-art performance among open-source models of comparable size, while running on a single NVIDIA DGX card!!
Open weights under the MIT license.
Coding
✅SWE-bench Verified: 0.845
✅TB2.1: 0.813
✅CyberGym: 0.800
✅NL2Repo: 0.512
Agentic
✅HLE: 0.446
✅BrowseComp: 0.785
✅MCP-Atlas: 0.746
✅Toolathlon: 0.708
🤗Hugging Face: huggingface.co/ornith-ai/Orn…
We can’t wait to see what you build with Ornith-1.5-122B.
Good read
Ali Ghodsi of @Databricks on the four conditions that would have to hold for AI to truly improve itself:
"The next model requires less resources, less GPUs to train... The second condition: the next model takes less time to train as well. Third, accuracy of the model, the intelligence is increasing. And fourth, we can do the former three again and again and again... All at the same time, not just any of them."
"If you just mean that the software is writing itself, we're already there today. Like 90-some percent of the software in Databricks is written by AI. Does it matter if the last few percent is also written by AI? No."
"If you're getting these four conditions, then you might get a speed-up where the next model, let's say, takes half amount of time and half the resources, and it is more intelligent... By the way, I don't even know if that necessarily leads you to superintelligence."
"A lot of people, their definition is just, 'Hey, if I'm not even coding anymore and it's coding itself'... I think they're conflating, 'Hey, what's my value and is it scary for me?' Versus, 'Hey, that then means we'll get that superintelligence that in 2014 was hypothesized by Bostrom.'"
@alighodsi @martin_casado @sarah_wang
Frank Y retweeted
day 3, cwd file browser and git viewer. inline comments that get back to the agent are in progress. then integration of switching execution env, so agent can jump from local to remote machine, while its brain still works on my phone.
decided to not publish this. it's a slop design study for where pi coding agent may go next. that will take some time.
well, this works better than Claude for Android or ChatGPT for Android has ever worked. It runs on my phone directly, no cloud vm. I can live edit it. Its multiplayer. And I can pick any provider/model. Just added artifacts.
There is no moat. 24h of slop, developed on the phone, on top of Pi Durable :)
Frank Y retweeted
Good analysis and similar to what I saw with VulcanBench.
Overall, Muse is very promising, just seems like there’s an opportunity to fix token use/overthinking issues.
Frank Y retweeted
New NVIDIA checkpoint for GLM-5.3 Flash focusing specifically local AI.
Fits on 2 x NVIDIA DGX Spark 128 GB with 2M tokens of FP8 KV Cache
huggingface.co/nvidia/GLM-5.…
Frank Y retweeted
🚨Anthropic正式确认，第三方应用可以继续使用Claude 订阅额度！
10月7日， @AnthropicAI 更新了Claude Agent SDK的官方政策，明确确认，第三方应用仍然可以通过Claude Agent SDK消耗用户现有的 @claudeai 订阅额度。
这事其实挺有意思的。
今年5月，Anthropic曾宣布准备将Claude Agent SDK、claude -p以及第三方应用的使用量从订阅额度中剥离，改成独立的Credits计费。
结果6月份临时暂停了这项调整。
现在到了10月份，Anthropic又明确确认，开发者仍然可以沿用原来的订阅额度调用方式。
Max和Team用户甚至还额外获得了每月API Credits。
而且已经有产品已经接入了。
10月10日，Amp宣布正式支持用户接入自己的Claude Pro / Max 、订阅，底层直接使用Claude Agent SDK，同时保留自己的云端运行环境、线程共享、多Agent编排等能力。
这意味着，第三方Agent产品可以把Anthropic官方的 Agent SDK当作底层Runtime，在上面继续构建自己的产品和工作流。
我觉得这对整个Agent生态其实挺重要的。
当模型能力和Agent Runtime都可以作为基础设施被复用，第三方开发者就有机会把更多精力投入到应用、工作流和用户体验上。
而且用户已经为Claude付过一次订阅费了，能直接带着订阅额度使用其他产品，确实会降低尝试新Agent的成本。
接下来就看Anthropic愿意把这条路开放到什么程度，以及会有多少第三方Agent跟进了？
@lobehub 快接一下
Frank Y retweeted
Well what do we have here. Interesting new NVFP4 technique out of NVIDIA.
Here’s one for Qwen
huggingface.co/nvidia/Qwen3.…
Frank Y retweeted
A retired NASA engineer who had worked on the Space Shuttle program for 25 years was hired by SpaceX at age 62. He described the first week as the most disorienting experience of his professional life.
At NASA, a design change required 3 meetings, 2 committees, a review board, and 6 to 18 months of documentation. At SpaceX, his new supervisor said "if it works, build it. If it doesn't, change it by Friday."
He sat at his desk after the first day and called his wife. He told her he felt like a man who had been swimming with ankle weights for 25 years and someone just cut them off. He could move. He could decide. He could build something in the morning and test it in the afternoon. The constraint that he had accepted as the nature of aerospace engineering turned out to be the nature of bureaucracy.
Within 6 months he had contributed to more design iterations than he had completed in his last 5 years at NASA. Not because he was working harder. Because the time between having an idea and testing the idea collapsed from months to hours. The same brain. The same knowledge. A different operating system.
He described a specific moment where he proposed a modification to a Raptor engine component. At NASA, the proposal would have entered a queue. At SpaceX, the modification was machined by the end of the day and tested by the following morning. He watched his idea become a physical object in 18 hours. At NASA, 18 hours would not have been enough to schedule the first meeting to discuss whether to schedule a meeting.
He told an interviewer: "I didn't get smarter when I went to SpaceX. I got faster. The intelligence was always there. The system was in the way."
The difference between SpaceX and NASA is not talent. NASA has extraordinary talent. The difference is the time between a thought and a test. SpaceX compresses it. NASA elongates it. The same engineer produces 10x more at SpaceX than at NASA because the bottleneck was never the engineer. It was the meetings about the meetings about the work.
