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Prompt Tower v1 – craft context for large codebases

Hacker News

Prompt Tower v1 – craft context for large codebases

I expanded my VS Code Extension, Prompt Tower, to help with context management issues when using LLMs with large codebases. Tools like gitingest are convenient but I needed more flexibility over token management and context templating. My favorite “vibe coder” workflow: Prompt Tower → Gemini 2.5 → Cursor Agent Prompt Tower helps me rinse and repeat a consistent instruction set for Gemini. Which creates instructions that a cursor Agent can follow to the tee - often around 16 steps - cursor falls on its face working alone and I’m too lazy to just follow Gemini’s guidance directly. Anyway, Prompt Tower features (more to come): Features: - Dynamic context selection from file tree - Directory structure injection (full, dirs-only, or selections) - Robust ignore capabilities (.gitignore, custom project ignores, workspace settings) - Custom templates (XML format default) Tested up to 5M tokens.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, cursor, context · Missing: mac, agents, macos
91%91% 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: gemini · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
22%22% 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
14%14% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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