Squash – your browsing history as context for ChatGPT

Hacker News

We built Squash because every AI tool still feels like talking to a stranger: it forgets what you’ve been researching, writing, or searching the moment you are using these tools. Squash turns your recent browsing history—URLs, titles, and search queries from the last few minutes—into a compact, on‑device “memory pack.” When you open any ChatGPT or any AI tool, one click shares that pack so replies already understand your current task. - Everything processed locally with Gemini Nano—no screenshots, no cloud uploads. - Built with TypeScript + Svelte Would love feedback from HN on: - Where else would you want the context supported (we are starting with ChatGPT and Claude.ai) - Additional context sources you’d find useful Happy to answer questions and iterate in the comments. Thanks!

Visit product View on Hacker NewsJul 24, 2025

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Actual performance

2points

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Launch Intel predictions

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

94%94% 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

68%68% 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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, 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

29%29% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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