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The AI factory for prompt to production SaaS
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Marcelino JustoA resource page for non-developers to aid their usage of ai as well as the implementation in various fields. This will include tools, quick guides, toolkits, etc.
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EastAct as a Principal MEV (Maximal Extractable Value) R&D Engineer and a Senior Solidity Security Auditor specializing in low-latency EVM arbitrage systems on high-throughput networks. I want to build a highly competitive, production-grade cross-DEX flash loan arbitrage bot for Polygon Mainnet. The architecture must be heavily inspired by the core structural philosophy of the 'pmuens/midas' framework but radically modernized to compete in today's cutthroat MEV environment. Generate the code base according to the exact architectural specifications, structural parameters, and execution protocols outlined below. --- ### 1. SYSTEM PARAMETERS & ENVIRONMENTAL BOUNDS - Network: Polygon Mainnet (Gas asset: POL / Base trading asset: Native USDC) - Flash Loan Source: Balancer V2 Vault (Address: 0xBA12222222228d8Ba445958a75a0704d566BF2C8) for 0% fee asset borrowing. - Target DEX Liquidity Pools: QuickSwap V2 (Uniswap V2 Clone) and SushiSwap V2. - Development Framework: Foundry (Solidity ^0.8.20) for the smart contracts; TypeScript (ethers.js v6) for the off-chain orchestration engine. --- ### 2. CORE COMPONENTS REQUIRED #### COMPONENT A: The Smart Contract ('MidasPolygonEngine.sol') Write a highly gas-optimized, production-ready Solidity contract that implements the Balancer V2 Flash Loan receiver interface ('IFlashLoanRecipient'). - Gas Optimization Rules: Optimize for bytecode compactness and minimal runtime overhead. Use custom errors instead of string require statements. Use 'unchecked {}' blocks for arithmetic loops and balance changes where overflow is mathematically impossible. Avoid high-level abstractions like routers. - Direct Pool Routing: The contract must execute swaps by calling the liquidity pool pair contracts directly via '.swap()' instead of using high-level router contracts (like QuickSwapRouter). - Strict Execution Safety: Implement a bulletproof safeguard. At the end of the callback function—immediately before the Balancer Vault pulls back the loan principal—the contract must verify that the ending balance of the profit token (USDC) is greater than the starting balance plus an off-chain parameterized 'minProfitThreshold'. If this condition fails, the contract must explicitly revert the entire transaction transaction string to prevent capital loss. - Access Control: Secure the trigger mechanism so that only the owner's monitoring bot address can call the execution entry point, completely preventing external actors from frontrunning or hijacking the execution. #### COMPONENT B: The Node Engine ('monitor.ts') Write a highly optimized TypeScript orchestration script that acts as the real-time data ingestion and calculations hub. - Event-Driven Architecture: Do not use interval JSON-RPC block polling. Use a high-fidelity WebSocket provider ('wss://') to subscribe directly to the 'Sync(uint112,uint112)' or 'Swap' event logs emitted directly by the target QuickSwap and SushiSwap pair contracts. - Math Engine (Optimal Input Calculation): Implement the standard AMM optimal input amount formula for two constant-product (x * y = k) Uniswap V2 pairs to calculate the mathematically perfect input amount (A_in) that maximizes arbitrage yield given the exact reserves extracted from the latest 'Sync' logs. The equation must natively factor in the standard 0.3% fee modifier (gamma = 0.997). - MEV Guardrail & Private Relaying: Structure the transaction submission logic to bypass the public Polygon mempool entirely to prevent frontrunning and sandwich attacks. Integrate the transaction dispatch code with the FastLane Protocol API or Flashbots Polygon Builder endpoint, sending the execution payload as a private bundle. --- ### 3. OUTPUT SPECIFICATION Please provide the complete, functional code for both 'MidasPolygonEngine.sol' and 'monitor.ts'. Include explicit inline comments detailing the low-level data handoff between the off-chain TypeScript script and the contract's flash loan callback payload. Do not use placeholders or omit complex logic loops. Show less
Platform track record
4,800+projects built on the platform
2,100+public projects with completed designs
Kubernetesreal staging and production deploys
Proof you can click — open any public project's designs, requirements, and architecture in the gallery.
Made for you
One platform. Five ways in.
Pick who you are — tap a tile to jump to the feature.
Platform
Everything you need to ship
production-grade software.
02 · Design
System Architect Agent. AI that designs clean, scalable architecture before writing a single line of code.
03 · Plan
Project Manager Agent. Built-in task decomposition, sprint tracking, and intelligent prioritization.
04 · Ship
Microservice Kubernetes Deployment Agent. Ship to production-grade infrastructure in minutes, not months.
05 · Verify
Visual Testing & Replay. AI-driven testing that sees your app like a real user does.
06 · Control
Integrated Code Editor. Full VSCode editor in the browser — supports every language and framework.
07 · Scale
Auto-Scaling Infrastructure. Your projects scale horizontally without manual intervention.
The process
Not a black box. A process.
Seven steps from one sentence to a running product — agents do the work, you hold the gates.
- 01
Share your idea
You describeType what you want to build. A project is created instantly, and a supervisor agent decides which specialists to bring in — requirements, design, user flows, architecture, tasks.
- 02
Requirements, written down
You approveThe System Requirements Agent writes your requirements document — the source of truth everything else derives from. Every later change arrives as a diff you accept or reject.
- 03
User flows, mapped
You approveThe User Flow Planner turns requirements into screen-by-screen journeys for every user persona — and the pipeline pauses until you approve them.
- 04
Designs you can click
You approveYour home page is designed first and sets the style. Approve it, and the remaining pages generate in parallel — planned, coded, screenshotted, and checked by an AI design critic.
- 05
Blueprint and task plan
You approveThe System Architect draws five architecture diagrams; the Project Manager breaks the build into tasks, sprints and phases. Review it all in a guided walkthrough, then press Start Building.
- 06
AI engineers, in parallel
Agents buildEach task runs in its own isolated cloud workspace through a six-phase pipeline — setup, environment, branch, code, tests, staging — and merges into your repo by pull request.
- 07
Your app, live on a URL
You open itEvery finished task auto-deploys to your project’s staging environment on Kubernetes. Open the live URL on any device, or press Deploy for the latest build.
The same process runs behind every project in the gallery — open one and read its requirements, designs and task board.
Collaboration
Humans and AI. One team.
Set up your org, share the board, and hand work back and forth.
Your org, one workspace
Invite your team into an organization — owners, admins and members share every project, seeing the same designs, requirements, tasks and architecture in real time.
Kanban handoff, human ↔ AI
Work moves across one board whether an agent or a teammate owns it. Assign any task to a human — a decision, a review, an API key — and agents pick the thread back up the moment it lands. List, sprint, Gantt and phase views included.
Humans steer, agents ship
Review gates keep people in control: approve requirements, designs and plans, then agents carry the approved work through code, tests and deployment.
The moat
AI pays a quadratic tax. We removed it.
Flat-cost intelligence for software engineering — built on the models the world already has.
To produce each new word, today's models re-read everything they have read so far: 2× the reading means 4× the cost, 10× the reading means 100× the cost. Nowhere does that tax bite harder than coding — so today's assistants read fragments, forget the rest, and guess.
1M+ tokenswhat a real enterprise codebase measures — far beyond what quadratic models can afford to read
×100 costthe quadratic penalty for reading 10× more code — before a single line is written
2.15× fasterour flat-cost models on long code — and the gap widens as codebases grow
The fix
Flat cost — on models that already exist.
Routed attention: each new word reads a small, relevant slice of memory instead of everything.
Read what matters, not everything
Our routing layer sends each new word to the few places in memory that matter. Work per word stops growing with document size: O(n²) becomes O(n).
Retrofit, not rebuild
We convert leading open models in days — no from-scratch pretraining, no new chips. Every new model release makes us better the week it ships.
Proven on a real model
A 9B open model, converted: flat generation speed at every context we tested — 2.15× faster at 32k, and the gap widens with length.
The multipliers
Three 10× levers. One platform. They multiply.
Orders-of-magnitude better unit economics for AI that writes software.
Context economics
Routed attention makes reading a whole repository affordable — the cost of context stops growing with the square of its size.
Thinking speed
We train models to run ten independent lines of thought inside a single pass, then merge the best into the answer. Reasoning that took ten passes takes one.
Cost per token
A small, cheap model writes most of the code. We read its confidence word by word; the moment it wavers, a large model steps in, fixes, and hands back.
Incumbents answer exploding context demand with GPU capex. Our answer is math — it ships as software, at software margins, and every new open model upgrades us in days. The ecosystem's R&D compounds into our moat.
Trusted
Trusted by leading brands.
Fortune 500s, governments and startups trust the team behind 8080.AI.
The team
Built by developers from
Experience
10+ years of experience in AI.
One of the first AI labs.
8080.AI is built by fxis.ai — F(x) Data Labs, Inc, founded in 2016 — an AI lab shipping data science and machine learning systems long before the AI wave. Every claim on this page is public and verifiable.
10+Years in AI
2016Incorporated, on public record
4.9/5Clutch rating · 65 reviews
Reviews
Rated 4.9 out of 5 by clients.
Independently verified on Clutch.
“They are truly an incredible company.”
Improved platform usability and AI model accuracy, with exceptional project management and on-time delivery.
“Ability to execute at a high standard on genuinely complex problems.”
Refactored our LLM architecture and shipped new features with clear progress tracking and patient technical explanations.
“They bridge business needs with technical execution.”
Delivered a 30–40% reduction in manual tasks and a 20–25% improvement in lead engagement.
“They understood both business and technology.”
Built a voice AI system that handles customer calls after hours, cutting missed leads and routine phone time.
“Delivered insights reshaping how we plan campaigns.”
An AI-powered marketing suite that improved campaign response rates by 55% and lead conversion speed by 30%.
“Built AI tools that felt practical, not technical.”
A recruitment chatbot that reduced manual screening workload by over 60% with real-time candidate conversations.