Hsiang (Alex Xiang)

Zodex gives your Linear superpower (beta) - Create your own agent - Assign to any issue - Let it cook!

@wangyuanzju 祝贺新版发布，里程碑+1 🎉

昨晚我们发布 remio 3.0 Agent版本，这是remio发布以来最重大的一次升级，甚至可以说比remio 1.0的发布更为重要。 之前我们花了约1年半的时间把remio打造成无感数字记忆的标杆，数亿资料借助remio实现天然留存，很多人因为remio改变了信息管理的习惯，有任何资料搜寻的需求时，remio总是成为他们最信得过的帮手。 今天，因为remio 3.0的发布，我相信remio将进一步成为很多人工作中最得力的干将。如果你希望有一个既能干活又不用折腾的好帮手，欢迎访问remio官网 remio.ai 下载体验。

Spent 4 months and built Omi for Desktop, your life architect It sees your screen, hears your conversations and tells you what to do next It’s like having a second brain that actually pays attention Open source, local, link below

Sub-agents in (latent) space! We’ve been working on a side project. As far as I know, this is the first massively multiplayer, completely LLM-driven game. Come play Gradient Bang with us. See if you can catch me on the leaderboard. This whole thing started because I wanted to explore a bunch of things I’m currently obsessed with, in an application of non-trivial size, that felt both new and old at the same time. So … a retro-style space trading game built entirely around interacting with and managing multiple LLMs. Factorio, but instead of clicking, you cajole your ship AI into tasking other AIs to do things for you. Some of the things we’ve been thinking about as we hack on Gradient Bang: - Sub-agent orchestration - Partial context sharing between multiple LLM inference loops - Managing very long contexts, and episodic memory across user sessions - World events and large volumes of structured data input as part of human/agent conversations - Dynamic user interfaces, driven/created on the fly by LLMs - And, of course, voice as primary input If you’ve been building coding harnesses, or writing Open Claw agents, or doing pretty much anything that pushes the boundaries of AI-native development these days, you’re probably thinking about these things too! This is all built with @pipecat_ai, the back end is @supabase, the React front end is deployed to @vercel, and all the code is open source.

@massuhora @intuitiveml You can simply specify the exact locations of the other project materials at the beginning of the CLAUDE.md file in the sub-repo's root directory. In my opinion, adopting a Monorepo structure is not strictly necessary.

@intuitiveml On the other hand, how do you prevent the agent from impacting the other original feature?

互联网历史上最成功的个人知识收集器，其实是微信的“文件传输助手”和 Telegram 的“Saved Messages”。因为它的输入阻力（Friction）是零。

Harness, Memory, Context Fragments, & the Bitter Lesson this is a work in progress mental dump on interesting intersections between how we use and design a harness, implications for memory being accumulated over long timescales, and the search bitter lesson we can’t escape this is v30+, HTML diagrams help me iteratively refine + chat to roughly “see” and alter the mental model Harnesses & Context Fragments: a very important job of the harness is to efficiently & correctly route data within its boundaries into the context window boundary for computation to happen the context window is a precious artifact. Harnesses make decisions on how to populate, manage, edit, and organize it so agents can do work. Each loaded object can be thought of as a Context Fragment and represents an explicit decision by the user and harness designer of what needs a model needs to do work at any given time. many ideas on externalizing objects + loading into the context window are pioneered and very well described by @a1zhang with RLMs Experiential Memory: we’re in the very early days of deploying agents and agents produce massive amounts of data in every interaction they have. this is akin to humans doing things and remembering things they did. however agent memory has a massive advantage as it can be accumulated across all agents which are easily forked and duplicated (unlike humans). @dwarkesh_sp does a good talking about this massive benefit of artificial systems memory can be treated as an externalized object. the harness is tasked with doing good contextualized retrieval which means pulling in the right data from accumulated memories across all agent interactions Search & The Bitter Lesson: As we deploy agents in our world over year timescales, there is going to be a hyper-exponential in the amount of data produced by those agents. We should want to: 1. Own that data for ourselves. Open ecosystems are important here 2. Use that data This means that we’ll have to search over, distill, and organize massive amounts of data. Our brain is exceptional at doing this. Both contextually using prior experience and mostly committing the right stuff to memory with enough intentional practice. Our current infrastructure systems and algorithms will be put to the test and often break as we get used to this new data regime some open questions: - how do we efficiently distill experiences (Traces) into higher level memory primitives that capture the important parts? How do we do this over ultra long time horizons? - How much of the future is Search just-in-time vs Search that gets integrated into model weights? - How do we make models much better at self-managing their context window? How do we reduce error rates in recursively allowing agents to operate over external objects? i’ll be expanding on, altering, and adjusting these mental models but these feel like an important subset to me on the future of designing agents practically

The UI era is ending. 🪦 For 70 years we designed computer interfaces. Mainframe, CLI, GUI, Touch. But with AI, the interface is disappearing. What will come next? My talk from @mastra's conf this week:

If you want your OpenClaw or Hermes Agent to be able to have perfect total recall of all 10,000+ markdown files, GBrain is here to help. It's exactly my OpenClaw/Hermes Agent setup. MIT-licensed open source. Hope it helps you build your mini-AGI. github.com/garrytan/gbrain

看了各种同质化 Agent Coding 项目，这个创意我给满分~

需要什么来什么，真棒~

New on the Engineering Blog: Building Managed Agents—our hosted service for long-running agents—meant solving an old problem in computing: how to design a system for “programs as yet unthought of.” Read more: anthropic.com/engineering/ma…

Introducing Claude Managed Agents: everything you need to build and deploy agents at scale. It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days. Now in public beta on the Claude Platform.

God Mode UX: Why Your Next Interface Will Look More Like StarCraft Than Slack 👁️ It’s time to zoom out and see our AI from above  🛰️ because herding a swarm of digital agents needs more than a single chat window. ✨ Read more here ⬇️ medium.com/sadasant/god-m…

i built a dashboard for my claude code sessions: 254 sessions across 58 projects over 3 months 🤖🧚‍♀️ - 3d terrain map of token usage over time - session cards with first/last prompts, hover to expand - click to resume any past session in-browser - activity heatmaps, project treemaps code available for my x subscribers <3

Karpathy's AutoResearch is changing how campaigns get optimized and most marketers haven´t heard of it yet. Ole Lehmann tested it on landing page copy, 56% → 92% pass rate overnight. here´s how it works for marketing / skills 🧵

Introducing the new /crawl endpoint - one API call and an entire site crawled. No scripts. No browser management. Just the content in HTML, Markdown, or JSON.