Tools I Use to Build Predict & Profit
Predict & Profit is built on a lean stack of tools I actually use: VPS infrastructure, automation scripts, dashboards, databases, AI-assisted development, and content tools. This page is not a random affiliate list. It is the practical stack behind the project.
Operating principle
The goal is reliability, transparency, low operating cost, and control. Some links on this page may be affiliate or referral links, and those links are disclosed near the call to action.
Not every tool on this page has an affiliate or referral link. Some are listed simply because they are part of the real stack I use to build, run, test, document, and market Predict & Profit.
Featured tools
The primary stack
These are the two tools most directly tied to how Predict & Profit is hosted, operated, and connected to real prediction market activity.
Featured infrastructure
RackNerd VPS Hosting
The VPS infrastructure I use to run Predict & Profit.
I use RackNerd as part of the lean VPS stack behind Predict & Profit. It gives me direct Linux control, predictable hosting cost, SSH access, and enough flexibility to run bots, dashboards, logs, deployment scripts, and supporting services without building everything on expensive cloud infrastructure.
Disclosure
Disclosure: This is an affiliate link. If you sign up through it, I may earn a commission at no extra cost to you. I use RackNerd for my own infrastructure.
Best for
- ›Builders who want direct Linux VPS control
- ›Small products, bots, dashboards, and automation projects
- ›Developers who are comfortable with SSH, Linux, PM2, cron, systemd, Python, Node, and Postgres
- ›Projects that need predictable hosting cost
Not best for
- ›People who want fully managed enterprise cloud services
- ›Teams that need hands-off infrastructure management
- ›Users who are not comfortable maintaining a Linux server
Next featured tool
Practical engineering
Why a lean VPS stack?
Predict & Profit is a real product with bots, logs, dashboards, and deployment workflows. A VPS keeps the system understandable and affordable while the operating surface is still small enough to reason about directly.
The point is practical engineering, not infrastructure theater. A lean stack avoids unnecessary cloud complexity before the business actually needs it, while still giving enough control to run automation, inspect failures, and improve the system over time.
Runtime surface
What runs on the stack?
Bot services
Trading and prediction market services that can run continuously, restart cleanly, and leave useful logs behind.
Postgres logs
Postgres-backed trade logs, scan history, results tables, and operational records that can be queried directly.
Dashboards
Streamlit or dashboard-style reporting for live status, settled trades, open positions, and system health.
Deploy workflows
Website deployment workflows and scripts that keep the public site and supporting services easy to update.
Monitoring scripts
Operational scripts for health checks, scheduled jobs, bot restarts, log review, and simple alerting.
Docs workflow
Content and documentation workflows that turn real implementation notes into pages, posts, and support material.
Featured trading platform
Kalshi
The prediction market platform used by Predict & Profit.
Kalshi is the regulated prediction market platform I use for Predict & Profit research, market analysis, and bot workflows. The project tracks weather and economic event markets, records results, and uses Kalshi market data as part of the broader trading automation stack.
How I use it
- ›Researching prediction market contracts
- ›Monitoring weather and economic event markets
- ›Testing bot workflows against real market structure
- ›Reviewing fills, positions, results, and trade history
- ›Building dashboards around actual market activity
Disclosure
Disclosure: This is a referral link. If you sign up through it and meet Kalshi's referral requirements, we may each receive a reward. Referral terms, rewards, and eligibility can change.
Risk note
Prediction markets involve risk. Nothing on this page is financial advice, trading advice, or a guarantee of profit.
Best for
- ›People who want to understand regulated prediction markets
- ›Builders studying event contracts, order books, and market probabilities
- ›Developers interested in market automation and analytics
- ›Users who understand that trading involves risk
Not best for
- ›Anyone expecting a guaranteed outcome
- ›Anyone who does not understand trading risk
- ›Users outside Kalshi's supported eligibility rules
- ›People expecting returns without doing the work
Referral details
Kalshi shows the current referral reward and requirements inside the app.
Builder workspace
Where the product gets built
Stack tool
VS Code
My main IDE for Python, Next.js, SQL, markdown, scripts, and repo work.
Stack tool
Tabby
My preferred terminal client for Linux VPS work, SSH sessions, and day-to-day server operations.
Stack tool
iTerm2
My Mac terminal when working from macOS.
Stack tool
Notion
My notebook, planning system, product notes, launch notes, checklists, and project documentation hub.
Stack tool
Postman
Used for API exploration, auth testing, request validation, and debugging endpoints before turning them into production scripts or connectors.
Data and automation stack
The core implementation layer
Stack tool
Python
The main language behind the bots, automation scripts, data processing, API integrations, and operational tooling.
Stack tool
Postgres
Used for bot logs, results storage, dashboards, trade history, and operational reporting.
Stack tool
DuckDB
Used for fast local validation, lightweight data checks, and testing outputs before pushing changes deeper into the stack.
Stack tool
Git
The backbone of source control, change tracking, branching, and safe project iteration.
Stack tool
GitHub
Used for repo hosting, version history, deployment flow, and project organization.
Operations stack
Keeping services inspectable
Stack tool
PM2
Used to keep Node and service processes running reliably on the VPS.
Stack tool
systemd
Used for long-running Linux services where a native service manager makes sense.
Stack tool
cron
Used for simple scheduled jobs, scripts, health checks, and automation tasks.
Stack tool
curl
Used constantly for checking endpoints, headers, redirects, uptime, and deployment verification.
Stack tool
psql
Used for direct Postgres inspection, bot logs, results, and debugging.
Stack tool
journalctl
Used for reading Linux service logs and troubleshooting services.
Stack tool
nginx
Used as the web server and reverse proxy layer where applicable.
AI and content stack
Tools for implementation and publishing
AI tools help speed up implementation, but the architecture, testing, product decisions, and deployment responsibility still stay with me.
Stack tool
ChatGPT
Used for architecture thinking, debugging, prompt design, copy review, and product decisions.
Stack tool
Claude Code
Used for repo-level implementation, larger code changes, refactors, and guided development tasks.
Stack tool
Codex
Used for implementation work, code edits, validation, and build-focused changes.
Stack tool
Canva
Used for simple design assets, social posts, thumbnails, and visual polish.
Stack tool
InVideo AI
Used for short-form video creation and marketing content experiments.
Stack tool
CapCut
Used for video editing, captions, music, and short-form publishing workflows.
More tools coming soon
The stack will keep getting documented
More tools will be added here as I document the stack.
Planned
Analytics and monitoring tools
Planned
Additional data vendors
Planned
More reporting workflows
Planned