cd /news/ai-agents/the-slow-and-steady-approach · home › topics › ai-agents › article
[ARTICLE · art-153277] src=dev.to ↗ pub= topic=ai-agents verified=true sentiment=↑ positive

The "Slow and Steady" Approach

A developer built a containerized, on-chain central limit order book (CLOB) trading bot kit for the DreamDEX protocol on Somnia, deploying it via GitHub and Railway for continuous background order execution. The kit includes a shared TypeScript and Python client library, a bar-by-bar backtest engine, five runnable strategies, and sanitized source from the first DreamDEX alpha trading competition. Private keys are managed through environment variables, and the pipeline runs autonomous dry-run tracking and order loops without local resource locks or manual intervention.

by read2 min views2 publishedOct 19, 2026

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady What I Built

A containerized, on-chain CLOB (Central Limit Order Book) trading bot pipeline deployed via GitHub and Railway. It automates repetitive dry-run tracking and order execution in the background, keeping hands off the keyboard so you don't have to manually babysit routine market loops.

Demo

https://dev.to/rcortez056/shipping-web3-automation-from-repository-fork-to-cloud-execution-2bc6 Code

Build automated trading bots on [DreamDEX](https://docs.dreamdex.io) — the on-chain
central-limit order book (CLOB) on [Somnia](https://somnia.network). This kit gives you a

shared client library, five runnable strategies, operations docs for running a bot 24/7, and the sanitized source of the top bots from the first DreamDEX alpha trading competition.

For competent TS / Python devs, zero prior DreamDEX knowledge required. Everything on the DreamDEX protocol itself is documented at docs.dreamdex.io — this repo does not rewrite the docs, it links to them and shows you working code.

Path What it is
packages/core Shared client — auth, REST, WebSocket, order execution, gotcha guards, nonce manager. TypeScript and Python. Every strategy imports it.
packages/backtest Bar-by-bar backtest engine ( SimPool , fill model, metrics). Drive it withnpm run backtest — seedocs/backtesting.md .
strategies/ Start with starter — the simplest bot, where you edit onedecide() function. Then

How I Built It

Leveraged open-source Python libraries, transparent repository forks, and a containerized cloud execution setup on Railway. Using environment variables for secure private key management, the pipeline runs continuous, autonomous background routines with zero local resource locks or manual intervention.

Why Does Open Innovation Matter?

Open infrastructure matters because it ensures total portability and zero vendor lock-in. Running on transparent Python frameworks and open-source foundations keeps sensitive key configurations completely under your direct control, avoiding closed APIs and rigid enterprise constraints.

My Agent Session

Automated dry-run loop and background execution pipeline running continuously via containerized cloud architecture.

The Lazy Payoff

By setting up the containerized pipeline once and letting it manage routine tracking autonomously, it eliminates the manual grind of babysitting terminal scripts—freeing up hours of time and keeping your focus locked entirely on high-level system architecture.

── more in #ai-agents 4 stories · sorted by recency
── more on @dreamdex 3 stories trending now
sponsored brought to you by zahid.host 4,200+ EU-deployed projects
reading about agents? ship yours in a single git push.

Run your AI side-project on zahid.host

EU-based hosting, git-push deploys, automatic HTTPS, no cold starts. Free tier with a custom domain — perfect for shipping the agent you just read about.

$git push zahid main
→ Live at https://your-agent.zahid.host ✓
Get free account → Pricing
from €0/mo · no card required
LIVE [news/the-slow-and-steady-…] indexed:0 read:2min 2026-10-19 · —