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Grammarly for Prompts Built with Go, Htmx, Templ

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Grammarly for Prompts Built with Go, Htmx, Templ

Hey HN, I recently built a plan-validate-solve agent and struggled to write high-quality prompts so I decided to build a Grammarly for prompts. I used Go, HTMX, a-h/templ, Tailwind as well as the new OpenAI assistants - wanted to share a few learnings with you here: - The overall “weight” of the solution. Basically, no external dependencies needed. I haven’t even touched web frameworks like Gin so far - The packaging size when putting everything into an alpine-based image - Super fast hot reload with cosmtrek/air in comparison to TS & React - Basically no learning curve moving from React- to Templ-Tailwind templating - Almost no JS is needed (except for interacting Web APIs like Permissions and Clipboard API) For now, handling state on the server side is no big deal when using HTMX So far I only faced some minor issues where, in some cases, Templ’s wrappers didn’t always transpile correctly or where hx-vals isn’t working due to Templ’s lack of support for single-quotes (used URL Query Params instead for now). Re the OpenAI assistants - They worked quite well for my use case. But going forward I don’t see myself using them for very long since they are “just” isolated model instances that you can instruct and connect to your internal documents in a no-code fashion. I’ll probably drop them for a self-hosted Mistral 7B in the coming days, especially since fine-tuning becomes drastically easier these days. Excited to see how this scales! An interesting direction I’ve been thinking about for the solution is to build something like this as a proxy that sits in front of LLMs and optimizes user prompts on the fly. Would appreciate any feedback on this approach.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, user · Missing: mac, agents, macos
92%92% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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.

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