Pointer – A general purpose AI agent via command line

Hacker News

Hi HN, Jian and Thomas here (co-founders of Pointer). Today we're sharing something we've been working on for the last two months. We've built a sota General Purpose AI Agent (GPA) that exists completely in the command line as a CLI tool. Our goal was to show that with just minimal scaffolding, we could unlock the full potential of a model's agentic capabilities (Claude Sonnet 3.7) to create a highly effective GPA. What sets Pointer apart is its ability to be more agentic than interactive coding tools like Claude Code, using multiple tools autonomously. Via CLI, we can direct Pointer to perform multi-step tasks autonomously. It aims to assist you in your workflows by allowing an LLM to access: - Web Search - Browser: Using a headful, stateful browser with a browser agent to interact with web pages (tasks like filling forms, navigating, exploring content) - File System Access: Read/write/edit local files - Code: Generate and run code - Bash: Can run commands We've used it to work on itself, perform complete end-to-end software development workflows, reorganize our file systems on our desktop (:D), submit forms, and do research for us. There's a Discord for installation instructions, discussion, and showcases where we'd welcome your feedback and testing! Thanks for your attention. Cheers, The Pointer team ----- Links Installation instructions: https://discord.gg/ZsSwZM7v Website: https://www.trypointer.com/ Demo video 1: https://youtu.be/00typbr9Rg8 Demo video 2: https://youtu.be/9OsMpBrIg7Q -----

Visit product View on Hacker NewsMar 26, 2025

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

98%98% 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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released

50%50% 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 · Strong signals: video, month · Missing: mobile apps, ios, personal

48%48% 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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

18%18% 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.

Incorrect prediction on native model

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