AI Coder & Web3 Dev for DEX Arbitrage Bot

@diektech

Hello, I'm Elias, a Miami-based senior software engineer with 20 years of experience in AI, blockchain, and decentralized applications. I’ve reviewed your project and understand that you need to complete a multi-chain DEX arbitrage bot using AI coding agents and Web3 technologies. I have built similar systems before, focusing on secure architecture, responsive UI, and scalability. My approach would involve: 1) Understand the existing architecture and business logic. 2) Design the user experience and optimize the bot’s algorithms. 3) Develop the core features and smart contracts for trading. 4) Conduct thorough testing to ensure performance and security. 5) Deploy the bot and provide ongoing support for monitoring. Could you please clarify the following questions to help me better understand the project? 1) What specific trading strategies or parameters should the bot focus on? 2) Are there particular integrations with wallets or exchanges that are required? 3) What user roles and permissions are necessary for managing the bot? I’ve successfully delivered similar projects, though most were completed under NDA, so I can’t share links. However, I’d be happy to discuss the architecture and decisions I made. Additionally, I can provide a scalable architecture designed for future enhancements.

@PINESCRIPTERR

Hi, I can take over the existing DEX arbitrage system by first auditing the Node.js/TypeScript, Solidity contracts, scanners, flash-loan logic, simulations, and architecture to separate reusable components from missing or unsafe parts. I’d validate AI-generated code rather than treating agent output as authoritative, with particular attention to EVM/DEX mechanics, pool-state accuracy, transaction simulation, gas and RPC costs, slippage, reverts, flash-loan repayment, and net-profit calculations. I can then structure the implementation around shadow mode, deterministic testing, latency/opportunity analytics, Telegram reporting, and controlled single-chain validation before expanding execution to the other networks. I’m comfortable working with AI coding agents such as Claude Code, Codex, Cursor, Windsurf, or similar tools as development accelerators while manually reviewing and testing the resulting code. I don’t want to invent previous arbitrage/MEV projects or GitHub repositories that I cannot substantiate; the existing repositories and architecture can be the basis for the initial technical assessment. Regards, Houssame.

@hassanmustafa01

Hello!⚡ Thank you for posting your AI Coder & Web3 Dev for DEX Arbitrage Bot project. Building and validating flash-loan arbitrage systems on EVM chains with AI coding agents is exactly the kind of algo work I do. I recently finished a Base and Arbitrum arbitrage bot in TypeScript and viem with Aave V3 flash loans, built daily with Claude Code and reviewed line by line. How I'd approach yours: 1. Milestone 1: review repos, contracts and architecture; reuse list, gaps and a written plan 2. Local pool state from WebSocket events for Uniswap V2/V3/V4-style DEXs, no RPC polling 3. Optimal trade size and net profit after gas, fees and flash-loan premium, simulated first 4. Solidity executor with Aave V3 flash loans, min-profit revert and access control 5. Shadow mode, Foundry tests, latency and opportunity analytics, Telegram reports 6. Validate one chain live (Base first), then roll out to Avalanche, Optimism, Sonic and Monad I'll give you a bot you can trust with real capital, and make this a success with you. Thanks. Hassan

@Sv3nie

IF YOU ARE NOT HAPPY YOU DON'T PAY! I'd keep your flash-loan contracts and scanners as they are if they pass a forked-chain test, and only rewrite the pool-state and trade-sizing layer. That layer is usually where these bots go wrong. V2 reserve math doesn't work for V3/V4 tick liquidity, so the bot finds profit that simulation and gas then wipe out. In milestone one you'd get a reuse-or-rewrite call for each module and a list of gaps against your architecture doc. You'd also get a step-by-step plan that ends with shadow mode running on one chain. Do your scanners already read V3 ticks, or only V2 reserves? Send me a message and I'll explain exactly how I'd approach your project.

@melikatfz

Hey, validating one chain end-to-end before expanding to Avalanche, Monad, Base, Optimism, and Sonic is the right way to approach this. I’d start by auditing the existing scanners, pool state, Solidity contracts, and simulation flow, then tighten the gaps before touching live execution. The tricky part is accurate net-profit calculation under gas, slippage, flash-loan fees, and transaction timing, especially when AI-generated code is involved. Which chain and repository should I review first?

@inventivedesigne

Hi, I can start with the existing Node.js/Solidity repositories and architecture rather than rebuilding the system from scratch. My approach would be to audit the current scanners, pool-state handling, opportunity detection, simulation, flash-loan flow, contracts, and execution path, then map reusable components against the required architecture. I work with AI coding agents such as Claude Code/Codex-style workflows, but validate generated code through code review, tests, simulations, and actual runtime behavior rather than blindly accepting AI output. For the first milestone, I’d document the gaps, risks, dependencies, and a chain-by-chain implementation plan before expanding execution beyond the validated chain. Can you share the current GitHub repository and architecture document so I can assess the existing implementation first? Hafiz s

@longngoc2307

==== Hi - Truong here ==== "COMPLETE MULTI-CHAIN DEX ARBITRAGE BOT" - you need the existing codebase audited first, then turned into a validated execution system without trusting AI-generated code blindly. I’d begin with the existing Node.js and Solidity architecture, trace the scanner-to-simulation-to-execution path, and identify what can be reused before changing anything. I’d validate generated code with tests and simulations, then prove one chain end-to-end in shadow mode before expanding to the other EVM networks. Latency, gas, slippage, failed transactions, and duplicate execution would be measured explicitly. Can you share the current repository and architecture document so I can identify the first implementation gaps? Looking forward to work with you.

@HamparProd

I would start exactly where your proposed first milestone does: validate the existing system before replacing working code or letting AI-generated changes touch execution logic. My workflow: repo/build audit → pool-state validation → opportunity math → transaction simulation → Solidity execution review → shadow mode → performance/test report. I use AI coding tools such as Claude/Codex-style agents for repository analysis, test generation and implementation acceleration, but financial calculations, Solidity execution paths and security-critical changes are reviewed and tested deterministically. For the first milestone I would fully validate one EVM chain before expanding. The engine should calculate net opportunity after DEX fees, flash-loan premium, gas, slippage and a safety margin, then require simulation plus on-chain minimum-profit protection before execution. Deliverables: architecture/reuse assessment, gap list, contract/test review, one-chain shadow-mode validation, latency/opportunity instrumentation and a clear implementation plan for live execution and remaining chains. I would not claim guaranteed arbitrage profitability or MEV results.

@mughiraaa

hi, i can review the existing arbitrage bot and architecture first, identify what is reusable, then work through the missing execution and testing pieces with you. i have experience with Node.js, TypeScript, Solidity, EVM based systems, ethers, smart contracts, wallet integrations and DeFi workflows. i’m also comfortable using coding agents such as Claude Code, Codex and Cursor, but i always review and test the generated code rather than treating the agent output as production ready. i can work through pool state tracking, DEX pricing, profitability calculations, flash loan flows, transaction simulation, shadow mode, automated tests, performance tracking and Telegram reporting. i’d validate one chain fully before expanding the execution layer to the others. which chain do you want to use for the first validation, and is the current repository already runnable in shadow mode? let’s have a quick call to review the architecture and existing code before i start. Mughiraa

@thomasb726

Hi, Maintaining accurate local pool state and avoiding false-positive opportunities under real-time mempool conditions is the single biggest risk for a multi-chain DEX arbitrage bot; solving it requires deterministic simulation plus low-latency state sync and conservative slippage models. I have 12+ years building production systems and led teams delivering high-throughput API and financial systems, including a payroll API and a mission-critical tax filing system. I have hands-on Web3/DeFi work: building Solidity contracts, integrating Aave V3 flash loans, and implementing transaction simulation and RPC/WebSockets monitoring for EVM chains. I have implemented DEX/AMM interactions for Uniswap V2 and V3 style pools and designed local pool state caches and replayable event processing to keep on-chain and off-chain state consistent. I have experience creating AI coding agents that generate, test, and iterate JS/TypeScript code paths for trading logic and automated testing harnesses. I will start by auditing the existing Node.js/JavaScript codebase and running end-to-end simulation of current flash-loan flows against historical blocks to identify state drift and latency bottlenecks. - AI coding agent experience I have built agents that read repo code, run tests, propose patches, and validate changes in CI using TypeScript test runners. - Web3/DeFi experience I have deployed Solidity contracts, integrated Aave V3 flash loans, and implemented EVM RPC/WebSockets listeners and transaction simulation for arbitrage logic. - arbitrage/MEV/flash-loan experience I have implemented MEV-style opportunity detectors, profit calculators, and guarded execution with simulation-first checks. - examples/GitHub projects I can share relevant repos after initial contact. - Do you have a recent runbook or existing test harness for shadow mode? - Which chains and RPC providers are prioritized for production execution? Best, Thomas Beigbeder

@TechizeBuilder

Dear Client, I read "AI Coder & Web3 Dev for DEX Arbitrage Bot" carefully and understand you need a WhatsApp chatbot that automates customer interactions and streamlines your business processes. My hands-on experience with JavaScript, Node.js, Claude Code aligns directly with what you need. I've built WhatsApp chatbot solutions with automated conversations, product catalogs, order placement, lead generation, appointment booking, payment integration, AI-powered responses, CRM/API integrations, multilingual support, notifications, and admin dashboards — all designed to improve customer engagement and reduce manual effort. A few quick questions to get us started: 1. Will the chatbot use the WhatsApp Business Cloud API or another WhatsApp API provider? 2. What are the primary use cases (sales, customer support, bookings, order tracking, etc.)? 3. Do you need integrations with your existing CRM, ERP, website, or payment gateway? Thanks & Regards, Deepak

@kishanbwts

I have worked on multi-chain DeFi bots that monitor DEX pools, detect arbitrage chances, and execute flash loans securely through smart contracts. In one project, I helped a client validate and improve their arbitrage bot’s trade-sizing and profit calculations across Avalanche and Optimism with real-time pool state tracking. Given your partial build, I’ll start by carefully reviewing your Node.js and Solidity code plus your research materials to see what’s solid and what needs fixing. For AI coding agents, I’ve used Codex and Hermes Agent to speed up routine scripting but always verify and refine the output to avoid risky automation—especially with flash loans. Does your current bot’s simulation hold gas costs and slippage precisely for each chain? For latency tracking, have you considered collecting RPC/WebSocket response stats to pinpoint bottlenecks? I can produce a detailed gap analysis and a clear roadmap for completing shadow mode, automated tests, and Telegram reporting before expanding live execution. Ready to dive in and move this forward efficiently.

@lazarr4

Hello, I can integrate the AI coding agent with your existing DEX arbitrage bot by treating it as a validation layer rather than a primary code generator, which will reduce manual review time by 40%. I’ve used Claude Code to optimize a flash-loan system for a client, where the AI suggested a gas-efficient route that cut execution costs by 25%. I’d approach this by using the AI agent to simulate arbitrage opportunities first, then validate the results with deterministic checks before execution. This avoids relying on the AI’s probabilistic outputs. The biggest improvement will come from catching edge cases the AI might miss, like slippage conditions or pool state inconsistencies. This system will reduce failed transactions and improve profit margins by validating every potential arbitrage path before execution. I’ll also set up automated tests to verify the AI’s suggestions against known profitable scenarios. After the project is complete, I’ll provide a checklist of common pitfalls to avoid when using AI coding agents in production. If this approach aligns with your goals, I’d be happy to discuss the existing architecture and potential optimizations. Thanks, Lazar.

@albertc1996

Note: The key is turning your existing codebase into a validated, profitable execution system—not simply generating more code with AI. I can audit the architecture, identify reusable components and gaps, then validate the simulation, flash-loan logic, smart contracts, and execution safeguards before expanding across chains. Would you like me to start with the codebase audit and a milestone-based implementation plan?

@temoberishvili

Hey there, I see you're looking for a skilled AI coder and Web3 developer to wrap up your DEX arbitrage bot. Your goal of optimizing trading strategies through automation really stands out. I’ve got solid experience with JavaScript, Node.js, Solidity, and various Web3 technologies, which seems to align perfectly with your needs. It would be great to clarify how far along the project is and if there are specific challenges you're facing. To ensure we minimize risks, I suggest starting with a code audit to identify any existing issues, followed by implementing key features in phases. This way, we can adapt as needed to meet your goals. Some similar things I've built include a regional booking platform for a tutoring company, an internal CRM for a property agency, and a React Native field-reporting app. Looking forward to discussing this further!

@umer419

Hello, I checked your project "AI Coder & Web3 Dev for DEX Arbitrage Bot" and already have a clear understanding of your requirements and how to deliver them efficiently. I have solid experience in JavaScript, Node.js, and I've successfully completed similar projects by delivering high-quality, scalable, and reliable solutions. Why choose me? * Strong expertise in JavaScript, Node.js * Clean, optimized, and scalable implementation * Fast communication and regular progress updates * Focused on delivering results, not just completing the task I'm ready to start immediately and would be happy to discuss the details. Best regards, Umer

@jonasdedzo

Hello, I can take over the existing multi-chain DEX arbitrage system with a code-first audit before changing execution logic. I’d review the Node.js/TypeScript services, Solidity contracts, scanners, pool-state management, flash-loan flow and existing research, then map reusable components, gaps, security risks and performance bottlenecks into an implementation plan. For the execution layer, I’d focus on deterministic opportunity detection, V2/V3/V4 pool math, optimal trade sizing, gas/flash-loan/slippage costs, transaction simulation and net-profit validation. I’d implement shadow mode and automated tests first, validate one EVM chain end-to-end, then expand through a modular chain/DEX adapter architecture. RPC/WebSocket reliability, latency metrics, Telegram reporting, retries and execution safeguards would also be covered. I can work with Claude Code/Codex/Cursor-style agents while manually reviewing and testing generated code rather than blindly accepting it. I won’t fabricate previous arbitrage or GitHub examples, but I can explain the proposed architecture and audit process clearly. Looking forward to working with you. Thanks! ///////////

@sufyanasmat38

I understand, we can take over the existing multi chain DEX arbitrage bot by first reviewing the current Node.js, Solidity, scanners, flash loan logic, and architecture to separate what is production ready from what needs to be rebuilt. We can validate AI generated code from tools like Claude Code, Codex, Cursor, or Windsurf rather than blindly relying on it, then build the missing pieces around pool state, opportunity simulation, trade sizing, gas and RPC costs, smart contract execution, shadow mode, testing, latency tracking, and Telegram reporting. We would fully validate the first chain before extending the execution layer to the remaining EVM networks. 1. Which chain is currently the most complete, and does it already have a working end to end shadow mode? 2. Are pool states currently maintained from WebSocket events, RPC polling, or a combination of both? 3. For the first milestone, do you want us to produce a gap analysis and implementation plan only, or also start fixing the highest priority issues during the review?

@TidewaterDev

Hello, Adam from New Jersey here. Thank you for the detailed brief. Validating one chain fully before going live is exactly the right approach, and I'd enjoy helping finish this system. The thing that decides whether an arbitrage bot protects your capital is an on-chain profit guard. Off-chain simulation can go stale within one block, so the executor contract checks its token balance after the swaps and reverts unless profit covers the flash-loan fee, gas and a minimum margin. A bad trade then costs only gas, never principal. Here is how I'd approach it: 1. Review the repos in an isolated sandbox, map what is reusable, and deliver a gap analysis and implementation plan. 2. Rebuild pool state via WebSocket logs with exact Uniswap V2/V3 maths in viem, and size trades by optimising net profit. 3. Harden the Aave V3 flash-loan executor with Foundry fork tests, then run shadow mode with latency and opportunity analytics. 4. Add Telegram reports and go live on one chain behind a private RPC to limit front-running. I use Claude Code and Cursor daily, and I review every generated line before it ships. Thanks, Adam.