Rafter – an MCP server that shares one team's memory, skills and agents

Hacker News

Supercharge your team with Rafter at https://heyrafter.xyz ! :rocket: Your team members is spending too much time and tokens in their AI tools just to solve a trivial problem. They copy data from drive, jira, onedrive and they have some prompts they paste in. Every time they go back and forth explaining how the problem should be solved. For some this is the reason not to use AI. It's complicated. Rafter allows you team to share agents, skills and memory Connect your tools. The knowledge graph gets built allowing your team to stay in flow. Works in ChatGPT, Claude, Gemini, Codex, Claude Code, Cursor, Opencode etc Go to https://heyrafter.xyz/#get-started to setup Rafter in your favorite AI tool.

Visit product View on Hacker NewsAug 28, 2026

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

92%92% 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: started, gemini · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

37%37% 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

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

14%14% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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