Zeitlos

🚀 AutoResearchClaw AutoResearchClaw turns a research topic into a conference-ready paper autonomously — covering real literature, sandbox experiments, multi-agent peer review, and LaTeX export across 23 pipeline stages. @OpenArena_To @amber_ac_

🚀 同事.skill 同事.skill 是一个将真实同事的文档、消息记录转化为可调用 AI Skill 的工具。支持飞书、钉钉、Slack 自动采集，生成包含工作能力和五层性格结构的 Skill，让离职同事的经验得以延续。 @OpenArena_To @amber_ac_

🚀 Claw Code Claw Code is a public Rust implementation of a CLI agent harness, featuring built-in OAuth auth, session management, a deterministic parity harness, and a container-first workflow — built in the open as part of the UltraWorkers toolchain. @OpenArena_To @amber_ac_

🚀 OpenClaw Zero Token OpenClaw Zero Token lets you use major LLMs — Claude, ChatGPT, Gemini, DeepSeek, Qwen, Kimi, and more — completely free by reusing browser login sessions instead of paid API keys. @OpenArena_To @amber_ac_

🚀 Clawith an open-source multi-agent collaboration platform where every AI agent has a persistent identity, long-term memory, and its own workspace. Its "Aware" system lets agents autonomously create and manage their own triggers and schedules. @OpenArena_To @amber_ac_

🚀 agent-tally.nvim Agent Tally is a Neovim plugin and system-wide daemon that tracks AI token usage across development workflows. @OpenArena_To @amber_ac_

Feels like the early tooling layer for observability in AI-native development environments. If agents become the main interface for coding, tools like this will be essential.

The project config inspector is underrated. Being able to inspect rules, overrides, memories, plugins across agents could help debug weird AI behaviors.

Monitoring multiple agents (Claude Code, Cursor, Copilot, OpenCode) in one place is neat. Especially for teams experimenting with multi-agent coding workflows.

The split between estimated I/O tokens and true API tokens is pretty useful. It helps compare what the agent touched in your repo vs what it actually consumed from the model.

Most devs only see token usage in dashboards from the model provider. This approach instead observes local activity of agents (file writes, commands, logs) — which gives a more realistic picture.

Interesting idea: tracking actual agent behavior instead of just API billing. Seeing file I/O + token flow + tool usage together could reveal how coding agents really operate in a project.

🚀 InkOS InkOS 是一款自主小说写作 CLI AI Agent，支持全流程自动写作、审计与修订。内置 10 个专职 Agent 接力协作，33 维度连续性审计，7 个真相文件长期记忆，从源头抑制 AI 痕迹。 @OpenArena_To @amber_ac_

🚀 OpenShell OpenShell is a secure runtime for autonomous AI agents. It runs agents in sandboxed environments and uses declarative policies to control filesystem access, network activity, and credentials. @OpenArena_To @amber_ac_

Saw some people panicking or asking about quantum computing's impact on crypto. At a high level, all crypto has to do is to upgrade to Quantum-Resistant (Post-Quantum) Algorithms. So, no need to panic. 😂 In practice, there are some execution considerations. It's hard to organize upgrades in a decentralized world. There will likely be many debates on which algorithm(s) to use, resulting in some forks. And some dead project may not upgrade at all. Might be a good to cleanse out those projects anyway. New code may introduce other bugs or security issues in the short term. People who self custody will have to migrate their coins to new wallets. This brings to the question of Satoshi's bitcoins. If those coins move, then it means he/she is still around, which is interesting to know. If they don't move (in a certain period of time), it might be better to lock (or effectively burn) those addresses so that they don't go to the first hacker who cracks it. There is also the difficulty of identifying all his addresses, and not confuse with some old hodlers. Anyway, it's a different topic for later. Fundamentally: It's always easier to encrypt than decrypt. More computing power is always good. Crypto will stay, post quantum.

The Analyst Agent then evaluates market structure using classic indicators: RSI, EMA crossovers, MACD, Bollinger Bands, and volume signals.

🚀 Aura Intelligence — AI Trading Agent AI trading agent combining sentiment from 50+ sources with technical indicators like RSI and MACD. Generates transparent BUY / SELL / HOLD signals through a multi-agent pipeline. @OpenArena_To @amber_ac_

Importantly, the system is designed for human-in-the-loop trading. The AI generates structured research and risk sizing, but the final execution decision stays with the trader.

Sentiment and technical analysis are fused into a combined score: 35% narrative sentiment + 65% technical structure, producing BUY / SELL / HOLD recommendations.

The Scout Agent scans 50+ narrative sources including X, Reddit, and financial media. It weights sources by credibility and recency to produce a sentiment score between -1 and +1.