Launch Intel
CL

CLI for Hacker News

Hacker News

CLI for Hacker News

Hi HN! I've built hnterminal, a pip package that lets you browse, upvote, and comment on HN from terminal: $ pip install hnterminal $ hnterminal hnterminal is an interactive REPL environment with commands like "get_front_page", "get_tree", "upvote", "reply_to", etc, each of which supports rich arguments and comes with full help documentation. On the read side, hnterminal uses the HN API to construct front page and comment trees. On the write side, it reads the webpage and constructs an API response from parsed information. Unfortunately, a write API does not exist for HN (that I know of). Login is accomplished with a login cookie that is only stored in memory as it runs. You don't need to login to browse HN, its only for upvotes and commenting.

Visit productView on Hacker NewsMar 26, 2023
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8points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, hacker news, ide · Missing: https docs, excited, just released
72%72% 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 HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
63%63% 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
29%29% 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
17%17% 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.

Correct prediction on native model

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