Reinhardt – Django-style Rust framework; WASM+SSR from one DSL

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Reinhardt is a Rust web framework where one component DSL compiles to both WASM (client) and server-rendered HTML — a single file describes both sides of a page, with no separate frontend codebase, no JS build toolchain, and no duplicated types across the client/server boundary. It also bundles what Django/DRF users expect: an ORM with auto-generated migrations from #[model] macros, DI, auth, admin, REST, background tasks, and i18n. Feature flags let you pull in just what you need (minimal / standard / full), or import individual crates directly. I built it after moving from Django/DRF to Rust and repeatedly re-assembling the same Axum + ORM + migrations + auth stack for every project. Quickstart: https://reinhardt-web.dev/quickstart/ v0.1.0-rc.18 release: < https://github.com/kent8192/reinhardt-web/releases/tag/reinh... > Crates.io (published as reinhardt-web; the shorter name was taken): https://crates.io/crates/reinhardt-web BSD 3-Clause.

Visit product View on Hacker NewsApr 22, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, user · Missing: agents, macos, agent

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users, para · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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