Anthony (@OipsAnthony) profile picture

🧑‍💻 Full-Stack Dev | 🔍 GenAI explorer | 📦 OSS lover

Joined October 2023
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Yab 真好呀,用的是 WebKit 内核还支持 Chrome Extensions,Arc 的 Instant links 也都有,🐮 yetanotherbrowser.com/
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@ElliotChen 老湿,快来玩这个!这个好!
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🤔deepseek-flash 的意图识别能力比 grok-4.7 要强?
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终于有点好东西了,Codex 订阅无限缓存时间
Day 16/ You can now cache for 12h in the API and for logged in users across Codex & Work we keep cache forever and you don’t have any penalty for coming back to an old conversation or resuming after a few hours and are now guaranteed to hit the cache.
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多 Agent 用户的记忆痛点,Wei佳老师这篇 EverOS Cloud 实测讲得非常到位! 欢迎各位彦祖亦菲来试用 EverOS Cloud 呀~ 加推限时活动:新老用户都有机会领取上海霍建华 @ElliotChen 限定写真(x 不是,这行删掉 🤫
Agent Memory 我已经用了好久了。手头同时跑好几个 Agent 和 Bot,最烦的就是换个工具或者团队交接时,之前排障踩过的坑又要从头交代一遍。所以在公司我一直想把记忆做成云端共享,让大家做过的工作变成下一次任务所有人都能直接调用的经验。 这次体验 EverOS Cloud,刚好把团队云端记忆这块补齐了。不管你是主力用 Antigravity、Codex 这类开发 Agent,还是自己搭的 Grok Bot、Muse、Hermes 等 Personal Bot,都能通过支持的 API 或 MCP 等方式接入。对于手头跑着好几个 Bot 的玩家来说,只要是已写入或导入云端、且允许访问的内容,它都能在不同 Agent 之间把上下文串联起来,不用换个窗口或换个 Bot 就从头交代背景。 它的能力主要分成三块。第一块是 User Memory,用来记录用户偏好与长期上下文;第二块是文档知识库,把文档、PDF、表格等业务资料转成可检索的记忆;第三块是 Agent Memory,专门把每次任务过程记录成 Case,从重复经验里提炼出可复用的 Skill,结合团队 SOP 给后续任务提供参考,正式流程调整依然由团队审核。 录屏里我专门拿 User Memory 做了一个演示,直接从控制台复制官方的 Agent Prompt 丢给 Antigravity 自动配好 SDK,把一次接口排障的上下文写入 User Memory,新开任务前再调 API 把这条记忆检索出来用。 进后台的 Memory Bank 就能直接翻看存下来的每条记忆,记偏了随时能调 API 纠正、删除或评价,而且 Cloud 版和自托管版共享核心 API,后续做核心功能迁移时也很省心。 现在订阅 Pro,每月额度变 3 倍,连享 6 个月。 Cloud 注册体验 everos.evermind.ai #EverMind #EverOS
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「Agent 不应在每次对话结束就失忆」 EverOS Cloud 来了,开发 Agent 的朋友可以关注一下 👇
Meet EverOS Cloud. 💛 Your agent shouldn’t have to start from scratch every time a conversation ends. The preferences a user shared. The decisions made during a project. The approach that worked on a previous task. That context should be available when it matters again. EverOS Cloud gives your agents persistent memory through an API: • Remember users and retrieve relevant context across sessions • Turn task experience into Cases and reusable Skill memories • Bring conversations, images, and documents into your memory workflow You build the agent experience. We handle the memory infrastructure, so you can spend less time maintaining the stack and more time making your product useful. Subscribe to EverOS Cloud Pro and get 3× the standard Pro usage allowance for 6 months. Start here: everos.evermind.ai
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👀 个人用户也可以关注下我们的 EverMe,更轻量、更简单、更好用,底层同样是 EverOS! everme.evermind.ai/
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Honored to be part of the EverMind Open-Source Ambassador community 💛
Some guide. Some build. Some create. Some spread the word. Together, you keep our open-source projects moving forward. To our advisors, builders, creators, and evangelists. Thank you for being part of the EverMind Open-Source Ambassador community. Your time, ideas, and contributions mean a lot to us. 💛 @li9292 @Tz_2022 @xiqingongzi @hylarucoder @OipsAnthony @yinmin1987 @t20000622yy @Av1dlive
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用 ChatGPT Desktop 增加了个定时任务:每天早上 6 点更新本机 brew/mise 依赖 开工的时候就会很少看到更新提醒了,也不用盯着更新进度了
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在推油的提醒下,改了全局 mise.toml 原有 minimum_release_age = "24h",额外加了 tool_update.global_auto = "6h"、pi 和 oh-my-pi 单独配置 minimum_release_age = "0" 舒服了😊 nitter.cf/darry_darr/status/2110…
Replying to @OipsAnthony
mise 新版本加入 daemon 自动更新了
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Jev 支持图像了,还支持 box 元素定位,Computer/Browser Use 又要卷起来了
we announced Jev 1.14 on 10.14 at 1:14pm PT 🔥 a new and improved model, along with experimental features for our dreamers: • images • a new box type • longer context surprise, Jev was already able to see you! 👁️ BUT our bar for production-grade intelligence is quite high, so builders beware, these are preview features. enter the ~*~ danger zone ~*~ at your own risk. 🚨
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Prisma??? 🤡
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Anthony retweeted
很多人想让我分享一下工作流,我一直没想好怎么分享,因为其实我没有任何的魔法,甚至还特别简陋,总觉得分享出来毫无意义。其实我就是用 Claude Code,然后用官方的 Opus 5.5 模型执行 loop 就可以了。 我会并行执行图里的 3 个 loop,然后每晚抽一点时间铲铲屎(主要是 UI/UX 的问题),然后就没活儿了。
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Nice!
Operation Cheepseek Phase 3: $80 of usage for DeepSeek V4.1 Flash
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Anthony retweeted
First Daily. Then Weekly. Now Monthly. 💛 Raven 🐦‍⬛ reached #1 on @HuggingFace @HuggingPapers Daily and Weekly Papers, and now tops September’s Monthly Papers list. We’re building the “harness of harnesses”: coordinating specialist agents, preserving experience through EverOS memory, and turning that experience into reusable skills. Open-source code you can run, research you can examine, and a community helping us move forward. Thank you to everyone who read the paper, tried Raven, and supported the project. This means a lot to our team. github.com/EverMind-AI/raven
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Tibo 的漂亮话:抛开 Astra 不谈,因为 Sol/Luna API 定价减半了,所以我额度减半对用户来说用量没有任何变化
Hi, Tomorrow we are re-opening the Pro $200 subscriptions to new subscribers, but together with it we are also changing how we calculate the usage for it. In effect, if you do the math, it will net out at half the dollar in API spend compared to the old Pro $200 plan. Now that it's said, let me explain why this is happening and why you will still get more work done than if you were on the Pro $200 subscription one month ago. (a) We didn't want to compromise in other ways and are committing to not reintroducing the 5h limit, so that you can fully use the weekly usage when you want. (b) On the subscription, we guarantee that over time you always get more work done and with an increasing level of quality. This means that you will continue to get more value per dollar spent as a result of models getting more efficient and us passing down the improvements in the form of API price reductions. (c) We don't want to put an incentive on ourselves to artificially inflate the API list prices to make it look like you are getting a lot (and workaround it through discounts, etc). Instead we want to continue to both rapidly reduce prices and increase capabilities of models on the API. This week we introduced GPT-6 Sol and GPT-6 Luna at 50% of their previous price. Over time, we see prices go low enough that it makes sense for most to buy usage as needed without there being a significant gap between what you get in a subscription and what you get in the API for a dollar spent. (d) Tomorrow, we are adding more things to the subscription that won't draw on the usage, I won't reveal what that is yet. I wanted to be transparent before all the big announcements tomorrow. Lots of new exciting things are coming to the subscriptions that will make it super compelling, but I wanted to make sure to share this change ahead of time so you can all understand it before we shower you with good news. Codexingly, Tibo
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Raven 0.2.0 — The Harness of Harnesses, built for RSI. 🐦‍⬛ One harness can't be best at everything. Raven combines its own specialist harnesses (Research, Code, Design, Oncall) with the agents you already use (Claude Code, Codex and more) into one team. And it's built for RSI, and not just at the skill level. The whole harness can be rewritten by AI: prompts, policies, strategy code, playbooks. Every sub-harness, including the orchestration layer itself, is its own instance that can be improved. With Raven you can: 1. Orchestrate many agents as one team. Raven's sub-harnesses and external agents work in one task graph with shared memory across sub-agents, powered by leading orchestration (0.963 Node F1 on the Multi-Agent Orchestration Benchmark). 2. Run long, complex tasks. Oncall and proactive execution keep work going for days, from scientific research loops to shipping a full Godot game. 3. Build vertical agents with RSI. Use Raven's RSI to develop and refine an agent for your domain, and we'll optimize it with you. Experimental for now; reach out to the Raven team(Discord:discord.gg/VVSm8MESch). More in the video and slides below. Open source, Apache-2.0. github.com/EverMind-AI/Raven (lots of work made with Raven lives there, and much of this launch's material was made with Raven too)
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