🎉 GMGN OpenAPI Rate Limit Update — Throughput Doubled! 🚀 Effective immediately (2026-05-13), we've upgraded the limits across ALL modules. Leaky-bucket just got a major boost: From rate=10/capacity=10 → rate=20/capacity=20 That means you can now fire twice as many requests per second as before! New sustained throughput: W = weight | RPS = sustained requests per second Formula: RPS = 20 ÷ W Developers, go build faster, query harder, and scale smoother than ever. The GMGN API is cooking — enjoy the extra power! 🔥 Create Your Free API Key NOW (Multi-Chain Supported): 👉🏻 Need Help? Join TG chat group: 👉🏻 #Hermes# #Openclaw# #Agent# #TradingBots# #Trenches# #AItrading#

A wallet's win rate is its highlight reel — it skips the slippage. GMGN OpenAPI scores the wallet, then backtests the copy under real latency, slippage, and gas. Ask it: "Score wallet [address] for copy-trading on Robinhood."

Checking multiple tokens means multiple tabs, one at a time. GMGN OpenAPI does it in one batch — basics, security, and holder distribution, every token at once. "Batch-check the basics, security, and holder distribution for [address1, address2, address3] ."

You wrote the strategy. Staying awake to run it isn't part of it. GMGN OpenAPI runs it instead — open-source demo, no code. Doesn't blink, get bored, or fat-finger the size.

Screening a new token is the same filters every time — enough KOLs in, holders spread out, bundled supply low. GMGN OpenAPI takes that as one sentence, and your agent runs the screen. "Screen new BSC tokens: 2+ KOL buys, top 10 holders under 20%, bundled supply under 20%." 50+ free, open-source skills —

Bybit AI Hub is here, connect your agent directly. 1. Official Skill ready 2. OpenAPI Spec is updated 3. More AI agent features such as MCP Server , Agent dedicated Sub Account coming up!

OpenConnector( is an open-source alternative to Composio for agent-ready SaaS auth, tools, and integrations. The open-source catalog currently includes 680+ providers and 7,000+ prebuilt Actions, runs locally or on Cloudflare-compatible infrastructure, and exposes the same tools through the Connector SDK, MCP, HTTP, OpenAPI, and a local Web Console.

🚀 An ebook that takes FastAPI from your first endpoint all the way to production-scale AI systems — going deep on LLM/RAG serving, with interview questions at the end of every chapter. Title: FastAPI for AI Engineers: From First Endpoint to Production-Scale AI Systems URL: 🚀 Overview A practical guide (First Edition, 2026, by AI Engineering Insider) for AI engineers serving ML models and LLM/RAG systems in production with Python. It spans 10 chapters and 100 interview questions, woven with real-incident case studies and cost-model sidebars. ❓ Challenges Solved ・Building a model is one skill; serving it safely as a scalable production API is another ・LLM/RAG serving has its own hard parts — streaming, guardrails, cost control The book reframes FastAPI as "the de facto serving layer for AI and ML systems." 💡 Structure & Tech Covered ・Foundations: ASGI/WSGI, Uvicorn, OpenAPI, and Pydantic v2 schema separation and validation ・Implementation: idempotency, meaningful status codes, pagination, and the Router→Service→Repository clean architecture ・DB/Security: SQLAlchemy/SQLModel/Alembic, N+1, pool sizing, JWT, BOLA defense, OWASP API Top 10 ・Async: "never block the event loop," def vs async def, and httpx retries/circuit breakers 🎯 The Core (Ch.9: AI/RAG/LLM) ・Load model weights once in lifespan; offload CPU inference to a thread ・An LLM gateway centralizing auth, prompts, guardrails, and cost metering, with SSE token streaming ・Build RAG with embeddings + a vector DB (start with pgvector); validate output with Pydantic, then retry on failure ・Enforce max_tokens as a "spending limit" via the type system 📊 Highlights ・Practice-first, learning from real incidents (Netflix, Stripe, GitLab, Optus, Air Canada) ・Ch.10 covers Gunicorn+Uvicorn, K8s liveness/readiness, the three pillars of observability (p99 vs p50), and SLO-based alerting #FastAPI# #AIEngineering#

Practices for Integrating AI Agents into Enterprise Systems 【MCP Gateway / Tool Federation】 💡 Catchy Message "5 agents x 10 SaaS products = 50 custom integrations. This multiplication nightmare is what the MCP Gateway eliminates." Every new agent and every new SaaS connection compounds integration cost. Tool definition sprawl, schema inconsistencies, and silent API breaking changes -- these problems demand an architectural solution. 🔥 Problems Solved - N (agents) x M (SaaS) integration cost explosion - Duplicate and inconsistent tool definitions across agents - Indirect prompt injection through tool I/O - Tool selection accuracy degradation when too many tools are exposed to an agent - Silent SaaS API changes (schema drift) causing agents to process incorrect data 🏗️ The Pattern Bundle each SaaS connector as an MCP (Model Context Protocol) server behind a gateway that manages tool discovery, authorization, call auditing, and scope control. Dynamically filter tool allow-lists by principal (department x agent type), exposing only the minimum necessary tools to each agent. Dangerous tools (delete, transfer funds, external send -- irreversible operations) get approval hooks. Tool definitions and API schemas are versioned as "contracts," periodically validated against live APIs to detect drift. Backward-incompatible drift triggers alerts and automatic tool deactivation as a fail-safe. ✅ When to Adopt - Use when: 10+ SaaS integrations. Multiple agents share common tools. Struggling with N x M integration complexity. - Skip when: Single-purpose agent with 2-3 fixed tools (direct integration is simpler and more robust). APIs are stable with extremely low change frequency. ⚠️ Pitfalls - Exposing 20-30+ tools to a single agent degrades tool selection accuracy. Use tool RAG for dynamic filtering or split into role-specific sub-agents. - Without contract testing (drift detection), you won't notice SaaS API changes until agents silently process incorrect data. Salesforce field changes happen more often than you think. - Deferring MCP server authorization design leaves all agents with access to all tools -- an open invitation for misuse. 🛠️ Implementation Approach - Build MCP servers for each SaaS (Salesforce, ServiceNow, Jira, Slack, Box, etc.). Adopt official MCP servers where available; otherwise auto-generate tools from OpenAPI specs and wrap them as custom MCP servers. - Deploy an MCP gateway with a tool registry (catalog). Index all tools from each MCP server and configure allow-lists filtered dynamically by department x agent type. - Set up OAuth 2.1-based authorization with approval hooks. Attach approval gates (linked to P09 dynamic authorization PDP) to dangerous tools (delete, fund transfer, external send) so they never execute without human approval. - Build a drift detection pipeline using contract testing (Pact, etc.) and a schema registry. Run weekly reconciliation between tool definitions and live API schemas; auto-deactivate tools and alert on backward-incompatible changes. - Control per-agent tool exposure to under 20 using tool RAG or role-specific sub-agent splitting. Dynamically filter tools by intent to maintain selection accuracy. #AIAgents# #EnterpriseArchitecture#

# Practices for Embedding AI Agents in Software # Sync Facade over Async Core 🎯 The Hook Choosing between "always sync" and "always async" is a false dilemma. What if your API could return instantly when fast, and gracefully degrade when slow? 🔥 The Problem Agent processing latency follows a bimodal distribution. Cache hits and lightweight tasks return in milliseconds, but complex reasoning or tool chains stretch to tens of seconds. Always-sync leads to timeouts and connection exhaustion. Always-async forces polling even for sub-second responses. 💡 The Pattern The Sync Facade always processes internally via an async pipeline. The outward-facing API waits up to a configurable threshold: if the job finishes in time, it returns a 200 with the result; if not, it returns a 202 with a job ID for async retrieval. Clients hit a single unified endpoint without worrying about latency bimodality. The threshold is tuned adaptively based on observed P95/P99 latency trends, not hardcoded. ✅ When to Use Use when: - Latency distribution is bimodal (mix of fast and slow completions) - Existing clients expect a synchronous API contract - Latency requirements vary per request (chat UI vs. batch) Don't use when: - Processing always finishes in a few seconds (use Sync Edge) - Processing always exceeds 30s (use Durable Async from the start) ⚠️ Pitfalls - Do not hardcode the sync-wait threshold. Observe P95/P99 trends via tracing and adjust adaptively - Clients often miss 202 handling. Explicitly define the 202 response schema in your OpenAPI spec to prevent SDK generation gaps - Distinguish worker crashes from simple timeouts during the sync wait. On worker failure, escalate to 202 immediately rather than waiting for the threshold 🔧 Implementation Approach - All requests are internally processed via an async queue. The facade layer awaits the result up to a configurable sync-wait threshold, returning 200 with the result on success or 202 with a job_id on timeout - Tune the sync-wait threshold adaptively by observing P95/P99 latency trends via tracing. Starting points are roughly 5-10s for web APIs and 30s for internal RPCs - Use SSE or WebSocket for progress notifications after async escalation, with polling (3-5s interval) as a fallback for clients that cannot maintain persistent connections - Keep the facade layer as a thin adapter with no business logic. The async core reuses the Durable Async Agent checkpoint and resume machinery directly - Explicitly define the 202 response schema in the OpenAPI spec so that generated client SDKs correctly handle job ID retrieval and result polling #AIAgents# #SoftwareArchitecture#