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MoraCode – AI assistance on large/multi-repo codebases

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MoraCode – AI assistance on large/multi-repo codebases

We built MoraCode, a JetBrains IDE plugin that helps navigate large and messy codebases—including multi-repo workspaces—using an index-first approach. Instead of heavy file-system crawling, we rely on an AI code index for context, which often reduces the number of agent turns needed to answer questions. It’s multi-repo aware, so you can combine several repositories in a single conversation. Running multiple tools concurrently helps reduce turnaround. It’s privacy-first/BYOK: your code goes directly to the LLM you choose, and indexes plus API keys stay local. It works with OpenAI, Anthropic, OpenRouter, and others. We’d love feedback on how well it ranks relevance in monorepos and multi-repo workspaces, how it behaves on very large codebases (and any comparisons you’ve tried with other tools), which latency and embedding/provider setups worked or didn’t, and which features you consider must-haves. Links: JetBrains plugin — https://plugins.jetbrains.com/plugin/28318-moracode • Website — https://moracode.ai

Visit productView on Hacker NewsOct 20, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, context, openai · Missing: mac, agents, macos
81%81% 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: including · Missing: supports, reddit linkedin, podcasting
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
53%53% 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
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% 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.

Correct prediction on native model

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