Drawbar – boring linear based workflows for Claude Code

Hacker News

I had been using Beads and it was really buggy and tailored to a workflow with much higher throughput than I found desirable. I work more slowly than that. I saw some other cool projects (lavra and linear-beads, credieted in my repo readme) which added an accumulating knowledge base and linear to the beads concept, so I just stole both ideas and put them into a plugin for myself. I have been dogfooding it for a couple of days. It works pretty well but I'm the only user and it's opinionated around how I like to do things. It also uses the linear MCP which is way slower and less token-efficient than the linear CLI on homebrew. I might update it in the future. If this appeals to you, check it out.

Visit product View on Hacker NewsJun 24, 2026

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2points

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

93%93% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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: efficient · Missing: plus, platform, intuitive

25%25% 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

16%16% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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