Tzap – Simple contextual code generation

Hacker News

One of the notable limitations of GPT when generating code is its generic answers; to remedy this we've built Tzap. We index your entire repository using embeddings which enables GPT to give contextual code suggestions. Just ask it a very specific question, and it'll generate a solution that fits your codebase seamlessly. For instance, you can ask Tzap, "How do I implement a new endpoint that enables customers to pay and add a Stripe subscription?". Regardless of your backend setup—be it GraphQL, Express, or Java Spring — Tzap will search your existing code and endpoints and propose a contextualised Stripe subscription solution. Or, you might be looking to refactor some dependencies; just query Tzap with "In 'file_name.a' I have 'function x' that is depended on by 'file_name.b' and 'file_name.c', refactor away this dependency", and watch it do the magic. Tzap functions: * tzap prompt: Indexes your entire repository * tzap commit: Applies semantic Git commit messages based on a Git diff * tzap search: Search in natural language and find things like where you defined or used a particular variable or function, e.g., "Where did I define 'User'?" or "Where did I use 'User'?". We'd love to hear about how you're using GPT and if we can help you out!

Visit product View on Hacker NewsMay 28, 2023

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Actual performance

4points

2comments

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, stripe, new · 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

71%71% 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, existing, io · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

11%11% 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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