Launch Intel
Ag

Agent Context – let your AI coding tools see your reference projects

Hacker News

Agent Context – let your AI coding tools see your reference projects

I built a small VS Code extension to solve a problem I kept running into. When I’m working on something new, I usually have good reference code somewhere else: - an old service - a starter project - a pattern I’ve used before The problem is that against can't see any of that unless I copy it into the repo. So I built Agent Context. It lets you attach external folders into your current workspace (via symlinks), so: - you can browse them alongside your current project - and your AI tools can use them as context It also maintains a small generated instructions file listing what’s attached, so both humans and AI know what’s available. Typical workflow: - attach something like a “nest-auth-example” project - keep it outside your repo - then prompt: “implement auth like the example in .examples” It’s pretty simple under the hood, but I’ve found it surprisingly useful so I thought I would share.

Visit productView on Hacker NewsApr 27, 2026
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, new, context · Missing: mac, agents, macos
86%86% 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
51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% 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 · 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
31%31% 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
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.

Correct prediction on native model

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