Build Internal Tools in Minutes Using Plain English Requirements

Hacker News

We built a way to go from requirements to a fully working admin panel/internal tool -type application within minutes. As a user you provide the various entities and their relationship plus any reports or dashboard widgets and it will generate it for you. The problem with existing no code tools is that you still need to spend a lot of time building after figuring out what is needed (a problem of its own). Being able to focus on what is needed means you can build faster than designing, dragging, dropping, configuring. What can you describe? Everything from tables, field types, relationships between tables and columns/fields, role-based access restrictions (RBAC), reports, dashboard metrics, bar/line/pie charts, lists. This is made possible by fine-tuning Llama 3.1 to convert requirements into specs and code. Applications evolve over time and this means that the model has to be predictable between versions to ensure that the data/schema migrations can be generated cleanly, as well as provide any errors and warnings related to the requirements or data loss. For non-developers this can be a huge time-saver since you don't have to use any additional developer resources. For developers it can be a great way to get 80+% of the way for complex apps (it's like super-scaffolding). Generated apps can be fully evaluated before purchase. A full git repo containing the back-end, front-end, migrations, etc. is the output of the entire process. Currently the generated code base is TypeScript, JavaScript, React, Next.js but the capability is there to generate different stacks because of the approach taken to not go directly to code when fine-tuning. Brought to you by the team at Red Axle ( https://redaxle.com ) Check out the video at https://youtu.be/rPppwB4jPlQ for a smaller app. We look forward to your thoughts. Does building apps this way make sense for the super savvy HN crowd?

Visit product View on Hacker NewsDec 17, 2024

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

95%95% 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 · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, lua, existing · Missing: https docs, excited, just released

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: apps, video, widgets · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% 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

15%15% 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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