Diffyn VS Blazeway.app

Diffyn's answer

Addresses workflow and change management on LLM prompts, provide teams with traceability and visualization of tests across multiple models, provide deeper understading into efficiency of these prompts.

Blazeway.app's answer:

The A/B testing market has spent ten years optimizing the wrong half of the problem. Bigger statistics engines, finer-grained dashboards, more event volume. Every tool assumes that more data leads to better decisions. None of them help a founder remember what they actually learned six months ago.

Blazeway is an experiment journal. Every test starts with a four-part hypothesis (Observation, Mechanism, Prediction, Success Threshold) and ends with a written insight that joins a permanent decision timeline. A founder with twenty documented insights makes better product decisions than one sitting on two million events and no record of what they tried.

The cookieless A/B test runner is built in so the journal stays the source of truth. The category is compounding knowledge: each test makes the next one smarter. PostHog cannot sell "less data, more learning". Optimizely cannot retire its statistics engine. The journal-first stance is structurally ours.

Diffyn's answer

Diffyn is the platform that specializes on both change management and multi-model analysis.

Blazeway.app's answer:

Most founders already document their experiments somewhere. A Notion page, a Google Sheet, a Linear ticket. The page works for the first ten tests, then collapses under its own weight by test fifteen. Meanwhile the actual A/B testing platforms (Optimizely, VWO, Convert, GrowthBook, PostHog) are priced for growth teams and treat the test result as the deliverable. The documented learning is left as homework.

Blazeway closes both gaps in one place:

• Hypothesis wizard forces clarity before the test starts
• Insight log captures the learning when it ends
• Decision timeline keeps every past test searchable years later
• One-click LLM export turns your full history into a single optimized prompt
• Cookieless A/B runner included so EU founders skip the consent banner entirely

Diffyn's answer

React, Next.js, POSTGRESQL

Blazeway.app's answer:

App (app.blazeway.app) React, TypeScript, Vite, Tailwind CSS, shadcn/ui, Bun

Backend Supabase: Postgres, Auth, Edge Functions for cookieless server-side variant assignment

Landing page (www.blazeway.app) Astro with prerendered static HTML, hosted on Netlify

Public profiles (www.blazeway.app/@handle) Astro SSR on Netlify with React 19 islands. Shared Supabase session via subdomain cookie. Markdown rendered through marked + rehype-sanitize. Editorial Fraunces theme distinct from the app.

Tracking snippet Vanilla TypeScript. Under 2KB. No third-party dependencies, no cookies, no localStorage.

Diffyn's answer

Professionals incorporating LLMs or AI tools in their workflow and wants to keep track of changes and test their prompts.

Blazeway.app's answer:

Solo founders, indie hackers, and bootstrapped small teams shipping their own product. People running two to ten experiments a year who want every test to teach them something they can find again next quarter.

The audience skews EU and privacy-conscious. The cookieless architecture removes the GDPR cost: no consent banner, no legal review, no cookie modal eating 40% of visitors before the test even starts.

Not the right fit: growth teams at large companies, agencies running tests for clients, anyone who needs feature flags as a primary product. Those use cases are well served by Statsig, GrowthBook, or PostHog.

Diffyn's answer

I started working on Diffyn when I notice that prompting has become an essential part of work across many industries. While there are version control platofrms like github, they are not designed for just prompt management are can be overkill such applications, it is also not integrated natively with various LLMs and relevant tools for users to validate ideas and visualise results properly.

Blazeway.app's answer:

Blazeway started as a tool the founder needed for his own work. After running A/B tests across several side projects and watching every documented insight disappear into stale Notion pages, the question became: what if the test runner and the decision journal were the same product?

The first version shipped in early 2026, built by a single solo founder, in public, on weekends and evenings, under the handle @deliverhonestly. Every weekly build log, every closed experiment, every learning is shared openly. The product is built the way it teaches its users to build: hypothesis, test, insight, repeat.