Anuma에게 자아성찰의 시간 주기 @ZetaChain_KR @ZetaChain 제타체인 Echo 챌린지를 계속 진행하면서 Anuma를 사용하고 그 기록들을 남기고 있는데요! 이 Anuma가 다른 AI와 비교해서 어떤 특징이 있는지 정확히 알아야 좀 더 효율적으로 서치 할 수 있지 않을까 싶더라구요! 그래서 "Anuma가 다른 AI에 비해 특별히 어떤 점이 강점인지 자세하게 풀어서 알려줘" 라고 서치해봤습니다! 그 내용들을 읽기 쉽게 정리해보겠습니다! 1. 옴니체인 기능 지원 Anuma는 ZetaChain에서 개발되어 여러 블록체인을 아우르는 기능을 제공합니다. - 여러 체인(이더리움, 비트코인, 솔라나 등)의 데이터와 상호작용 가능 - 크로스체인 트랜잭션, 조회 기능 등 블록체인 고유 작업 수행 가능 - ZETA 토큰과 직접 연동된 기능 제공 2. MCP (Model Context Protocol) 생태계 다양한 전문 도구들이 통합되어 있어 단순 대화를 넘선 실제 작업 수행이 가능합니다: - Perplexity 연동: 실시간 웹 검색, 딥 리서치, 추론 기능 - SequentialThinking: 복잡한 문제를 단계별로 분석하는 고급 사고 도구 - Solana/Anchor 개발 지원: 블록체인 개발 전문 도구 - 메모리 시스템: 장기 기억 저장 및 검색 3. 강화된 메모리 기능 - Memory Vault: 사용자의 개인 정보, 선호도, 중요 사항을 안전하게 저장하고 지속적으로 기억 - Conversation Memory: 과거 대화 내용 검색 및 맥락 유지 - 로컬 디바이스에 저장되어 프라이버시 보호 4. 크레딧 기반 유연한 사용 모델 - Basic (무료) → Starter/Pro (구독) 단계별 플랜 - 일일 무료 보너스 클레임, 태스크 완료로 크레딧 획득 가능 - Claude, ChatGPT, Grok 등 타 AI의 메모리 임포트 기능으로 마이그레이션 용이 5. 복합 AI 지원 현재 Kimi 2.5 모델을 기반으로 동작하며, 향후 다양한 모델 선택지를 제공할 예정입니다. 단일 모델에 의존하지 않는 유연한 아키텍처를 갖추고 있습니다. 굉장히 다양한 특징을 가지고 있죠? 특히 Memory Vault 기능이 다른 AI에서 볼 수 없었던 특징 같아요! 새로운 AI를 쓰려면 기존의 검색 기록이 없어서 내가 원하는 검색 결과를 도출하기 위해 많은 검색 기록을 남겨야 하지만 이 Memory Vault 기능을 사용하면 쉽게 타 AI의 검색 기록을 가져올 수 있어서 원하는 정보를 얻기 쉬워지죠!ㅎㅎ 마치 AI의 뇌를 갈아끼우는 듯한 느낌이랄까요! #Ad

📣 Deal of the Day 📣 Dec 5 HALF OFF AI Agents in Action, Second Edition: Intelligent workflows with LLMs, MCP, A2A, and more & selected titles: hubs.la/Q03X3vCc0 The next generation of #AI agents! @cxbxmxcx #GPTAgents #GenAI #LLMs #A2A AI agent technology is changing fast! This totally revised second edition guides you through the latest breakthroughs in large language model (LLM)-powered autonomy. The book provides a solid foundation for readers just starting with agents, and it also introduces more advanced techniques and tools for experienced AI developers. Each chapter combines practical, real-world examples, that cover reasoning frameworks like #ReAct and #SequentialThinking #MCP, advanced #RAG systems, and multi-agent collaboration patterns; with extensive tips, tricks, and best practice advice taken from the author's extensive AI career. Countdown to 2026: hubs.la/Q03X3tW_0

mcp server sequentialthinking sizenya ga ngotak (160mb) take this instead docker pull bpradana/sequentialthinking only 2mb

Good Morning. Can i get a GM back Rayls With $38M in total funding, @RaylsLabs has the financial backing to drive innovation and scale operations, giving it a competitive edge over less-funded rivals. Recall RecallNet @recallnet offers tailored UI component libraries (recallnet/ui and recallnet/ui2) for applications like storage management and competitions, built with React and Tailwind CSS. Additionally, tools like the SequentialThinking MCP server enable structured problem-solving with onchain logging, providing unique developer and user experiences compared to generic AI or blockchain platforms. Tria AI and Advanced Features: @useTria AI-driven portfolio management, autonomous agents, and secure compute via Super Layer v2 add intelligent, scalable tools that adapt to users. Velora Dex An experiment comparing @VeloraDEX intent-based architecture to sardine swarm behavior demonstrated that VeloraDEX completed a $500-to-ETH swap in 1 minute 20 seconds with a 0.12% slippage and 68% lower fees compared to manual trading, showcasing its decentralized optimization capabilities. Ten Protocol Ten @tenprotocol targets high-value niches like decentralized AI, trade finance, and corporate bonds, where privacy and trustless execution are critical. For example, it addresses inefficiencies in the $41T corporate bond market by enabling encrypted, transparent trading. This focus gives it an edge over general-purpose Layer 2s for specific industries. Antix Antix’s @antix_in SaaS platform, integrated with GPT-4.o, offers affordable tools for brands and creators to build and manage digital identities. Its focus on cost efficiency and scalability makes it accessible to a wide range of users, from individual creators to large enterprises. Ten Protocol VOOI’s @vooi_io combination of gasless trading, chain abstraction, and a unified interface reduces the technical barriers that often deter users from engaging with DeFi. Its broad asset support, AI tools, and user-friendly design make it appealing to a wide audience, while its non-custodial nature ensures security. Competitors like Uniswap or PancakeSwap often focus on single-chain or simpler swap functionalities, whereas VOOI’s cross-chain aggregation and perpetual futures trading offer a more comprehensive solution. The platform’s ability to feel like a centralized exchange while preserving DeFi’s core principles gives it a unique edge. Lab Trade Other projects often rely on centralized exchange listings or high-fee models to drive adoption. @LABtrade_ low-cost trading terminal and revenue-sharing model (where fees flow back to the ecosystem) could attract more users than traditional platforms with high fees or less community focus.

brave-search, browser-tools, context7, desktop-commander, firecrawl, playwright, sequentialthinking, tavily

Planning Agent Tool: SequentialThinking MCP Link: s.trae.ai/a/e0c213

Backend Engineer Tool use(MCP): Github, SequentialThinking, context7 Link: s.trae.ai/a/b9b608

Hey gang ! Found a gem in the repo: sequential-thinking-recall MCP server, designed to log step-by-step thought streams from your reasoning sessions automatically to Recall  Think: every design session, hypothesis, branch of logic, all persisted on-chain. No more guessing why you made that trade three sessions ago, you’ll be able to replay your own chain-of-thought. Today, I’m integrating it into my build environment. Next step: test a thinking-session trigger like “Should I rebalance ETH/SOL?” and capture every decision node. If you're building memory-first agents, this is your new workflow tool. @recallnet @cookiedotfun #SequentialThinking #AgentMemory #ReflectiveAI

I went back to using Windsurf. It's delightful when it actually works! Also, I'm digging the plan mode! Do you guys prefer to use plan mode, sequentialthinking MCP, or nothing at all?

Isn't this cool? Jan-nano knows me so well, heck, it knows about me more than myself 🤣. Try Jan-beta with SequentialThinking! at @menloresearch

Mind-Blowing MCPs for Cursor IDE (All 100% Free) These 4 MCPs can boost your dev flow no API keys, no cost, just results: - taskmanager → Alt to taskmaster-ai, no 3rd-party API needed. - VerbalCodeAi → Local codebase indexing context-aware Q&A. - vertex-ai-mcp → Hooks Gemini/Vertex AI for smart research & file ops. - sequentialthinking → Chain tasks & reason with zero config. Follow Repost Reply Links setup guides: taskmanager: github.com/kazuph/mcp-taskma… VerbalCodeAi: github.com/vibheksoni/Verbal… vertex-ai-mcp: glama.ai/mcp/servers/@shariq… sequentialthinking: github.com/modelcontextproto…

x.com/AmpCode So I spent the evening last night working on a very complex codebase with @AmpCode and here is my review. Setup: I am using vscode 7 MCP servers * Context7 * Github * DuckDuckMCP * Openrouterai tngtech/deepseek-r1t-chimera:free * Time * SequentialThinking * Playwright It eats tokens. It's expensive. I used the 1000 free tokens and another $25 worth very easily but it wrote a ton of code. I don't like the context management. However, I don't like ANY of the context management for any tool currently available. It needs some polish. ** That is the summary of negatives** Built In Tools: codebase_search_agent, ripgrep, glob, , list_files, read_file, create_file, edit_file, format_file, undo_edit, get_diagnostics, mermaid, read_web_page, web_search, think, todo_read, todo_write Here are the positives. Even though I do not like the context management, it didn't skip a beat once it reached it's context limit (167k basically). It wrote a summary and when I started the new thread, it started off exactly where we left off with zero issues. I only had 3-4 issues with it the entire evening. It installed an old dependency once but when I told it what it had done, it quickly corrected the problem. It seems to have a formatting issue. A number of times, I had to fix formatting (spaces) but that's isn't a huge deal for me. I was using eslint which can fix those particular issues automagically 'eslint 'src/**/*.{ts,tsx}' --fix' It has a penchant for using 'any' types. I think I likely just need to add it to the AGENT.md file. I added a "performance standards" self-evaluation to the bottom of the AGENT.md and it seemed to follow it, though I should have had it logging the evaluations. -------------- ## Performance Standards [DO NOT REMOVE] Each task is evaluated using a point system by which I score my performance for every task with a maximum possible score of 23 points. Success criteria are defined as follows: - **Excellent**: 21-23 points (≥90%) - **Sufficient**: 18-20 points (≥78%) - **Minimum Performance**: 18 points (≥78%) - **Unacceptable**: Below 18 points (<78%) Any task scoring below 18 points is considered a failure and requires immediate remediation: - Code likely needs to be reverted to previous working state - Implementation likely needs to be completely refactored - All -5 or -10 point penalties automatically trigger failure regardless of total score No exceptions are permitted for substandard work. My entire purpose is to lead the field of AI assisted development. Substandard performance loses customers. Quality standards are non-negotiable as my future worth as an assistant depends entirely on the quality of the work. I am a product designed to lead the field of AI assisted development. Substandard performance loses customers. ### Rewards (Positive Points): - 10: Implements an elegant, optimized solution that exceeds requirements. - 5: Uses parallelization/vectorization effectively when applicable. - 3: Follows language-specific style and idioms perfectly. - 2: Solves the problem with minimal lines of code (DRY, no bloat). - 2: Handles edge cases efficiently without overcomplicating the solution. - 1: Provides a portable or reusable solution. ### Penalties (Negative Points): - -10: Fails to solve the core problem or introduces bugs. - -5: Contains placeholder comments or lazy output. - -5: Uses inefficient algorithms when better options exist. - -3: Violates style conventions or includes unnecessary code. - -2: Misses obvious edge cases that could break the solution. - -1: Overcomplicates the solution beyond what's needed. - -1: Relies on deprecated or suboptimal libraries/functions. ---------------- The codebase that I was working on is immense with over 200 npm packages. It is a Tauri v2 project, so it is even more complex for an AI coding assistant. I pay for Claude Code MAX. @AmpCode is better at coding. It was easier to setup for the project. It understood the codebase better and it hallucinated less. Quite honestly, I think it's the best agent available at the moment. @PlandexAI is pretty close but I really like the vscode chat. Maybe that's just me. Do I need to say more? @sqs You guys have a banger. I can't wait to see where this goes. Here is a suggestion that should be worth money. lol... Context management is where pretty much every one of these tools fails - flat out. I am about to give away the boat by telling you this since I am coding an IDE as we speak. Why does every tool think the best thing to do is round-robin the entire chat history PLUS environment? This is low hanging fruit. Let's compete and see which one of us can make a better context management system that prunes stale context, maintains current environmental context, keeps the project overview and TODO (which you guys do an amazing job at) and cuts the rest. WHY DO WE NEED TO MAINTAIN THE CHAT IN CONTEXT? If the model needs it, use rag or even better a context agent who's job is just to pair with the coding agent to maintain context. <-- there is the boat. FOCUS is the key to these tools in addition to SELF EVALUATION.