Yiyun_dan1

Recently, many people have been asking why their personal Anthropic accounts get banned.
I’ve summarized the common situations, which mainly fall into these categories:
Obvious illegal or violations of rules
This goes without saying; almost all AI companies handle this, and Anthropic is no exception.
Various proxy or sharing behaviors
Especially multi-account usage, polling calls, reselling, relay stations, carpooling—these are essentially high-risk behaviors.
Registering or accessing from unsupported regions
If the registration environment, login environment, or usage region is outside the of

Microsoft has turned multi-Agent orchestration into a framework, with comprehensive engineering capabilities and extension interfaces. It's ideal for teams looking to systematically build Agents to focus on.

This project turns web crawling, cleaning, and structured extraction into a complete set of AI-era data infrastructure—students working on Agents and RAG should definitely focus on it.

Google's open-source TimesFM has turned time series forecasting into a foundation model. Anyone working on quantification, operational forecasting, or demand prediction should take a look.

This open-source project is great for username investigations: Sherlock can search for social media accounts with the same name across platforms, making it very convenient for OSINT, brand monitoring, and account occupancy analysis.

Today’s GitHub Trending is very suitable for AI product and agent research: a collection of system prompts for a batch of mainstream tools has been made into a searchable archive, worth bookmarking and browsing directly.

Today on GitHub Trending, I saw openscreen: an open-source, free demo video tool with no subscriptions and no watermarks, very useful for product explanations and demo presentations.

OpenCLI twitter post adapter verification on 2026-04-03.

The agent track is still evolving rapidly, and I find this project quite interesting. It emphasizes that agents can grow alongside your needs, not just a fixed script, but more like an expandable intelligent workflow.
Focus on automation, research-oriented agents, and long-term task orchestration.

This is not just a single tool, but more like a set of agent skill frameworks and development methodologies.
Many people are now talking about "AI efficiency," but the real challenge is how to consolidate capabilities into reusable processes.
The value of this repository lies in that, making it suitable for those who want to engineer AI workflows.

This is a very typical warehouse of "productizing AI capabilities," with the core being a skill set.
It's becoming increasingly clear that in the future, the competition won't just be about the models themselves, but about who can organize skills, memory, processes, and toolchains.
Although this project is lightweight, the direction is very right.
Full link:

To truly make good use of Claude Code, best practice content is always the most valuable. This repository isn't about flashy tricks but about helping you avoid pitfalls, reduce trial and error, and understand which approaches are more efficient. It's very practical for those who frequently integrate AI into their development process.

If you're no longer satisfied with a single agent, you might want to check out this project.
It focuses on team-based multi-agent orchestration, similar to upgrading Claude Code from "one person working" to "a small team working together."
It's very insightful for breaking down complex tasks and collaborative development among multiple people.

Recently, I’ve been looking into Claude Code-related materials. I think this repository is very suitable to share with new team members. It’s not just about stacking concepts, but using visuals and case studies to clearly explain how to go from beginner to advanced agent techniques. Many templates can be used directly.
For a systematic understanding of Claude Code, you can start with this⬇️

RuView: No camera needed, real-time human pose estimation, vital signs monitoring, and presence detection can be achieved using only regular WiFi signals

Manage the desktop version of the API public service site: sign-in/welfare site/LDC recharge/one-click application of Claude code, Codex, Gemini CLI

Disdain Hierarchy Among AI Models
Claude Users
"Rigorous reasoning, no nonsense, high-quality code, principled"
⬇️
GPT Users
"Best ecosystem, many plugins, OpenAI is the industry standard"
⬇️
Gemini Users
"Integrated with Google ecosystem, search + AI in one"
⬇️
Local Model Users (Llama / Mistral / Qwen)
"Data stays local, fully controllable, true freedom"
⬇️
People who train their own models
(Silence, dismissive without explanation)
The truth is, the disdain hierarchy is mostly self-comforting—
- Claude is indeed strong in long-form reasoning, but GPT’s multimo