Cursor AI for Thinking

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Hi Tobi here. Eight months ago, I lost my software developer job and decided that rather than getting another role, I would spend most of my time learning how to build with AI. I earned just enough to survive and spent the rest of my time building AI products. Over those months, I tried my hand at multiple projects and came up with two somewhat contradictory conclusions: 1. *AI is a springboard for the autodidact.* I was able to learn much faster than I ever had before, taking on projects I'd never considered—from simple summarization tools to complex software requiring IMAP integration. 2. *AI made me intellectually lazier.* I had become too dependent on it for solving problems and brainstorming ideas. My workflow was disorganized—files scattered across different models and providers. I couldn't tell if my thought process was genuinely mine or if I was being nudged toward biased conclusions. I realized this was a big problem. The cause wasn't AI itself, but the environment we interact with AI in. So I decided to build and launch *Irulan*—a thinking space designed for an ideal symbiotic relationship between humans and AI. ## What Irulan Does Irulan puts you in control by giving you space to write down your thoughts uninterrupted, while AI can edit your documents or review your work. You can also record voice sessions, which we process to extract notes, insights, and other valuable nuggets from your voice notes. Our AI chat features a *Socratic mode* where we guide users through their learning topics using the Socratic method. Our AI agent can also generate artifacts like quizzes with progressive difficulty based on your conversations. We want to create a symbiotic space for humans and AI that puts control in human hands—allowing us to enjoy AI's benefits without experiencing cognitive decline. Users can choose between different flagship models like ChatGPT, Gemini, and Claude. Please check it out and tell me what you think. All feedback is welcome.

Visit product View on Hacker NewsJun 19, 2025

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

97%97% 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

92%92% 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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% 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

20%20% 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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