Free AI Code Completion for Xcode with model choice/codebase context

Hacker News

Download link: https://www.cgft.io/xcode Here are a few reasons to give this a shot, compared to others (e.g. Apple’s Swift prediction): Model Choice Use any local model you prefer through Ollama, or opt for our cloud-hosted model for longer context windows if RAM is tight (no code is retained on our servers). Local Code Context Your codebase is indexed locally and relevant snippets are fed into model prompts for more relevant code suggestions. In-line Suggestions Suggestions show up nicely in-line with your code, not in a separate modal. Give it a try—hope it’s helpful!

Visit product View on Hacker NewsOct 21, 2024

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

89%89% 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: llama, io · Missing: https docs, excited, just released

51%51% 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 HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting

37%37% 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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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