Arkloop – Open-source, local-first Agent client

Hacker News

Hi HN, I built Arkloop – an open-source, local-first Agent client. You can think of it as Claude Desktop, but open source with its own taste. Not a wrapper around any Agent SDK, not based on Claude Code – I built this from scratch, solo, over 3 months. One thing I focused on: reducing cognitive load. For example – if you only use one model for chatting, why does every message show which model was used? That's noise. Another example: developers love showing full tool use details in the UI, but what users actually need to know is that the agent is working and which direction it's going. We don't need all that detail. Design philosophy: cognitive load, information density, value-driven, aesthetics. I'd love your feedback – on anything from a 1px spacing issue to a major architectural direction. The README has a section about this that I'm particularly proud of: "I welcome all forms of contribution. Even if you're not a developer – if something feels off during use, even just a spacing issue or a color, open an issue. If you're a developer, Arkloop's agent core, memory system, and hook mechanisms are all open." It also supports importing configs from OpenClaw/Hermes. GitHub: https://github.com/qqqqqf-q/arkloop Website & Download: https://arkloop.cn

Visit product View on Hacker NewsApr 30, 2026

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

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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