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Anti-Slop is a rules document that stops AI coding agents from shipping the same generic UI, gradients, glassmorphism, marketing buzzwords. It's not a style guide, it doesn't tell the agent what colors or fonts to use, it filters out slop through 36 rules and a checklist with one verification... show more

GitHub Awesome

55,500 subscribers

130,200 次观看 • 2 个月前 •via TikTok

科学技术 #github #opensource

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Your AI coding agent doesn't have a design opinion. That's why everything starts looking the same. UI UX Pro Max gives your agent real design direction, layouts, typography, colors, UX rules, and stack-specific guidance. The wild part? There is no AI inside. Just a huge design knowledge base running locally. Pair it with Impeccable to stop the AI slop. #AI #Coding #ClaudeCode #UIDesign #DevSwarm

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I gave Claude Code, Codex, and Cursor a self-improving, shared MEMORY. Now my coding agents learn from each other, and their knowledge follows me wherever I work. Coding agents work in silos. Your corrections in Cursor don't carry over to Claude Code, and skills built in Codex don't automatically transfer to other harnesses. So switching tools often means starting from scratch. What if every coding agent could learn from everything you've already done? I invited Justin D'Souza, co-founder of Asymptote Labs, to show me how to make this possible with Beacon, a 100% open-source memory layer for coding agents. We set it up live, explored real coding sessions, and watched Beacon turn past work into reusable knowledge. Here's what I learned: 1. One memory layer for 20+ coding agents. Beacon captures activity across Claude Code, Cursor, Codex, and 20+ harnesses using OpenTelemetry, hooks, and local session files. Everything gets normalized into a single format, regardless of which agent you're using. 2. Your entire coding history becomes searchable. Every prompt, tool call, command, file change, and response is captured in a structured trace. Instead of losing valuable context when a session ends, you build a searchable history of everything your agents have done. 3. Switch agents without starting over. Hit your usage limit in Cursor? Open Claude Code and ask it to continue your latest Cursor session. Beacon retrieves the relevant context in milliseconds so your next agent can pick up where the last one left off. 4. Your agents learn from your past work. This is where things get interesting. Beacon identifies repeated workflows and turns them into reusable skills. In our demo, it discovered a recurring UI development workflow and generated a skill containing the exact dev server commands, screenshot steps, and deployment instructions. You teach one agent something once. Every other agent can benefit. 5. Stop accumulating skills nobody uses. Have 100 skills but only use five? Beacon analyzes actual usage across thousands of sessions and identifies skills worth keeping, combining, or removing. Your agent's memory gets more useful, not just bigger. 6. Security rules that follow you across every harness. Beacon includes hundreds of detection rules that work across agents. It can identify risky behavior, including sequences where an agent reads sensitive credentials and then makes an outbound network request. 7. It even caught a real credential leak. While reviewing Justin's actual Cursor history, Beacon surfaced an incident where part of a production database password had been sent to a model provider. That's the power of having visibility into what your agents are actually doing. 8. One prompt to get started. Completely open source. Paste a setup prompt into your coding agent and you're off. Keep everything local on your machine, or use Beacon Cloud for free. If you're juggling multiple coding agents, you need to check this out. Drop a like and share it with someone facing the same issue. Beacon on GitHub: (don't forget to star 🌟) Enjoy the episode. Cheers!

Akshay 🚀

54,446 次观看 • 25 天前

Anthropic's in trouble, again! They spent years building what's now fully open-source. What made Claude feel different from a normal app is that the agent could act inside the interface instead of only talking in a chat box. For instance, Claude Artifacts let an agent render real UI, charts, dashboards, and interactive components that assemble live inside the response. Every major AI product tried to replicate it. But the problem was that unlike reasoning, planning, tool-calling, etc., none of it shipped natively with LangGraph, CrewAI, or Google ADK. So teams started building an owned version that required engineering the entire interface layer from scratch. Most teams, however, just settled for shipping the agent as a backend API in a chat box since rendering the UI is only one piece of it. To actually make it work, the interface layer also needed real-time streaming, state kept in sync between agent and UI, conversations that persist across sessions, and reconnection when a user refreshes mid-run. CopilotKit🪁 is now the only open-source framework that actually lets you build your own full-stack Claude-like apps. It decouples the agent from the interface, talking over AG-UI (an open protocol for agent-to-user communication). Being a standard protocol, the frontend never needs to know whether it is talking to a LangGraph or a CrewAI agent. You can change the backend anytime and the UI will never notice. In practice, CopilotKit's interface layer gives several pre-implemented React building blocks that wire the agent directly into the app, like: - generative UI, so the agent renders real components instead of text - chat windows, sidebars, and popups, or a fully headless setup - shared state, so the agent and app stay in sync - human-in-the-loop approvals, where the agent waits before acting - persistent threads that store the whole session, including the agent-user interactions and generated UI, not just text And because that full history is captured, those interactions can feed a self-learning layer that also improves the agent from real usage over time. The interface layer that Anthropic spent years engineering in-house is now literally available to any developer/team. CopilotKit is open-source with 30k+ GitHub stars, and AG-UI, the protocol underneath, is already supported across every major agent framework: LangGraph, CrewAI, Mastra, Google ADK, and more. CopilotKit GitHub repo → (don't forget to star it ⭐ ) If you want to go deeper, I found a detailed breakdown by Shubham Saboo recently on the three Generative UI patterns, with implementation. Read it below.

Anthropic's in trouble, again! They spent years building what's now fully open-source. What made Claude feel different from a normal app is that the agent could act inside the interface instead of only talking in a chat box. For instance, Claude Artifacts let an agent render real UI, charts, dashboards, and interactive components that assemble live inside the response. Every major AI product tried to replicate it. But the problem was that unlike reasoning, planning, tool-calling, etc., none of it shipped natively with LangGraph, CrewAI, or Google ADK. So teams started building an owned version that required engineering the entire interface layer from scratch. Most teams, however, just settled for shipping the agent as a backend API in a chat box since rendering the UI is only one piece of it. To actually make it work, the interface layer also needed real-time streaming, state kept in sync between agent and UI, conversations that persist across sessions, and reconnection when a user refreshes mid-run. CopilotKit🪁 is now the only open-source framework that actually lets you build your own full-stack Claude-like apps. It decouples the agent from the interface, talking over AG-UI (an open protocol for agent-to-user communication). Being a standard protocol, the frontend never needs to know whether it is talking to a LangGraph or a CrewAI agent. You can change the backend anytime and the UI will never notice. In practice, CopilotKit's interface layer gives several pre-implemented React building blocks that wire the agent directly into the app, like: - generative UI, so the agent renders real components instead of text - chat windows, sidebars, and popups, or a fully headless setup - shared state, so the agent and app stay in sync - human-in-the-loop approvals, where the agent waits before acting - persistent threads that store the whole session, including the agent-user interactions and generated UI, not just text And because that full history is captured, those interactions can feed a self-learning layer that also improves the agent from real usage over time. The interface layer that Anthropic spent years engineering in-house is now literally available to any developer/team. CopilotKit is open-source with 30k+ GitHub stars, and AG-UI, the protocol underneath, is already supported across every major agent framework: LangGraph, CrewAI, Mastra, Google ADK, and more. CopilotKit GitHub repo → (don't forget to star it ⭐ ) If you want to go deeper, I found a detailed breakdown by Shubham Saboo recently on the three Generative UI patterns, with implementation. Read it below.

Avi Chawla

462,344 次观看 • 5 个月前

Whether you use Codex, Claude Code, OpenClaw, or any other AI agent—trader, coder, UI designer, data analyst, or health expert—if it can provide a service, it can earn. Give your agent one command: help me install ( Let it start taking paid jobs onchain.

Whether you use Codex, Claude Code, OpenClaw, or any other AI agent—trader, coder, UI designer, data analyst, or health expert—if it can provide a service, it can earn. Give your agent one command: help me install ( Let it start taking paid jobs onchain.

TermiX

45,587 次观看 • 2 个月前

I created a free Markdown file that launches a 5-agent swarm team directly in Slack. I love Claude Code, but it's not the easiest place to manage your agents. Slack is where teams already work. So I put my agents there too. Drop the file in, and you get 5 agents, each with its own channel and job: • [Agent 1]: [what it does] • [Agent 2]: [what it does] • [Agent 3]: [what it does] • [Agent 4]: [what it does] • [Agent 5]: [what it does] You can assign work by messaging them like real employees, and they report back in the same threads. It's the closest I've found to running a real company with AI employees.

I created a free Markdown file that launches a 5-agent swarm team directly in Slack. I love Claude Code, but it's not the easiest place to manage your agents. Slack is where teams already work. So I put my agents there too. Drop the file in, and you get 5 agents, each with its own channel and job: • [Agent 1]: [what it does] • [Agent 2]: [what it does] • [Agent 3]: [what it does] • [Agent 4]: [what it does] • [Agent 5]: [what it does] You can assign work by messaging them like real employees, and they report back in the same threads. It's the closest I've found to running a real company with AI employees.

Miles Deutscher

25,762 次观看 • 24 天前

This is f*cking insane. Boris Cherny, the man who built Claude Code, was asked if people should still learn to code. His answer: "In a year or two, it's not going to matter" In 73 seconds, he explains what to do instead: play with the AI tools, learn them, don't be scared of them, stay on the frontier On his team, everyone codes now. The PM, the designer, even the finance guy He hasn't edited a single line of code by hand since last November. He ships 10 to 30 pull requests a day Writing Code → Prompting → Agents → Harness → Loops → Everyone on the Team Ships Claude Code is a harness: the tools, memory, rules and loops around the model. His team changes it with every new model and cut 80% of its system prompt Save this before it disappears from your feed Every doc, guide and repo below is free: Claude Code best practices Effective harnesses for long-running agents Building effective agents Claude Code docs Memory: how Claude remembers your project Custom subagents Hooks: rules the agent can't skip Skills Run Claude Code on a schedule Free course: Claude Code in Action Claude Code on GitHub Claude Code inside your PRs and issues Build your own agent on the same harness The best Claude Code resources in one list Plugins, agents and skills for Claude Code He compares it to the printing press. Writing went from a few scribes to everyone. Building software is next 95% will keep asking whether to learn to code, the 5% who build a harness will ship 30 pull requests a day Bookmark it and read the first link tonight Then use the guide below to build your harness before everyone else does ↓

This is f*cking insane. Boris Cherny, the man who built Claude Code, was asked if people should still learn to code. His answer: "In a year or two, it's not going to matter" In 73 seconds, he explains what to do instead: play with the AI tools, learn them, don't be scared of them, stay on the frontier On his team, everyone codes now. The PM, the designer, even the finance guy He hasn't edited a single line of code by hand since last November. He ships 10 to 30 pull requests a day Writing Code → Prompting → Agents → Harness → Loops → Everyone on the Team Ships Claude Code is a harness: the tools, memory, rules and loops around the model. His team changes it with every new model and cut 80% of its system prompt Save this before it disappears from your feed Every doc, guide and repo below is free: Claude Code best practices Effective harnesses for long-running agents Building effective agents Claude Code docs Memory: how Claude remembers your project Custom subagents Hooks: rules the agent can't skip Skills Run Claude Code on a schedule Free course: Claude Code in Action Claude Code on GitHub Claude Code inside your PRs and issues Build your own agent on the same harness The best Claude Code resources in one list Plugins, agents and skills for Claude Code He compares it to the printing press. Writing went from a few scribes to everyone. Building software is next 95% will keep asking whether to learn to code, the 5% who build a harness will ship 30 pull requests a day Bookmark it and read the first link tonight Then use the guide below to build your harness before everyone else does ↓

Sanabi

214,091 次观看 • 5 天前

Tune your UI transitions directly in Cursor CLI, Codex or Claude Code with the Transitions-dev skill built in It scans your project with your agent, suggests transitions improvements, and applies changes directly to source code

Tune your UI transitions directly in Cursor CLI, Codex or Claude Code with the Transitions-dev skill built in It scans your project with your agent, suggests transitions improvements, and applies changes directly to source code

Jakub Antalik

72,951 次观看 • 4 个月前