Qa

Qalam - a CLI that remembers commands

Hacker News

Qalam - a CLI that remembers commands

I kept running into the same problem as a developer: I forget commands I’ve already figured out. Docker cleanup sequences. Deployments with 15 flags. Test commands that finally worked. Every time, I ended up digging through bash history or Googling. It was wasting mental energy. So I built Qalam, a CLI that actually remembers commands. What it does: - Ask in natural language: “How do I kill the process on port 3000?” - Save commands with meaningful names: “deploy” instead of cryptic abbreviations - Automate workflows: my 5-command morning setup is now one command - Keep everything local: no cloud, no privacy worries - Zero configuration: works immediately I’ve been using it for weeks. Deployments are foolproof. Morning setup is one command. When something breaks, I ask my terminal instead of Googling. https://docs.qalam.dev I’d be curious what others do to remember complex commands or automate repeated workflows.

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Product HuntOn track for Day 1 leaderboard · Strong signals: dock, using · Missing: mac, agents, macos
89%89% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
45%45% 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
34%34% 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
28%28% 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 · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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