Affilr VS LoopCode by Heksync

Affilr's answer

Early stage — currently in free trial phase with first users onboarding

LoopCode by Heksync's answer:

Use this until you have customer names approved for publication: - Customer names are not publicly disclosed at this stage.

Affilr's answer

Python, Flask, SQLite, Playwright, Docker, Google Gemini 2.5 Flash, SerpApi, PayPal

LoopCode by Heksync's answer:

LoopCode uses TypeScript and Node.js for its coding-agent engine, React and Vite for its interface, and Electron for the desktop application.

Affilr's answer

The founder spent two days manually finding affiliate programs for 30 products — Googling each one, digging through footers, hitting dead links, and filling the same forms repeatedly. It was tedious, repetitive work that added no value. So he built a bot to do it, and Affilr was born.

LoopCode by Heksync's answer:

LoopCode is a product of Heksync, built around a practical goal: help people move from an idea or development task to working software they can inspect and verify. The product brings together the steps that software development requires—understanding the project, planning changes, editing code, running checks, and reviewing the result. Its desktop, terminal, and VS Code interfaces make that workflow accessible in different development environments. Our direction is to make capable AI-assisted software development more accessible, with transparent workflows, flexible model access, and continued improvements driven by real development tasks.

Affilr's answer

Bloggers, niche site builders, and content creators who promote multiple products and need to find affiliate programs quickly. Also affiliate marketers expanding their portfolio and anyone monetizing a website who doesn't want to spend hours on manual research.

LoopCode by Heksync's answer:

LoopCode is built primarily for developers, independent makers, technical founders, and small engineering teams working on real software projects. Typical uses include understanding unfamiliar repositories, implementing features, debugging failures, refactoring across files, and writing or running tests. Its scope extends beyond website development to backend services, applications, automation, and developer tooling. It is particularly useful for people who want assistance inside their existing workspace and want to review the code and verification results themselves.

Affilr's answer

Because there are no real competitors doing exactly this. The alternative is Googling "product name + affiliate program" for every single product, digging through footers, clicking dead links, and repeating that 30 times. Affilr replaces hours of manual research with a single search and returns the exact signup page — not a blog post about it, the actual page.

LoopCode by Heksync's answer:

Choose LoopCode if you want an AI coding agent that can work through a development task while keeping its actions visible and reviewable. You can inspect a project, implement a feature, investigate failures, and review changes without moving the entire project into a browser-based builder. Desktop, CLI, and VS Code interfaces let you use the workflow that fits your work. LoopCode also offers multiple model options, configurable approvals, MCP integrations, and a free tier for trying the product. Free-tier model availability changes regularly, with expanded free usage offered to early users during early access.

Affilr's answer

Affilr works for any product — not just SaaS tools. Type in a physical product like "yoga mat" or "band aids" and it finds the affiliate program just as easily as it does for software. Most competitors only cover well-known SaaS tools. Affilr also includes a Scout feature that suggests products with affiliate programs based on a keyword or niche, so you don't even need to know what to search for.

LoopCode by Heksync's answer:

LoopCode brings codebase understanding, implementation, verification, and review into one coding workflow across desktop, the terminal, and VS Code. It works with existing projects: inspecting relevant files, using language-server intelligence, making changes across files, running commands and tests, and presenting the results for review. Its agent engine includes configurable approvals, recoverable conversation history, edit checkpoints, and context management designed for token efficiency. LoopCode follows a local-first architecture for workspace operations. When using hosted models, prompts and relevant code context are sent to the configured service.