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Publish API endpoints in seconds (PoC)

Hacker News

Publish API endpoints in seconds (PoC)

Hi HN! I'm Elias, here to present Camarere (PROOF OF CONCEPT): A proxy server + python package combo to publish API endpoints in very few lines of code: (CLI) $ pip install camarere (python) # Import the camarere client from camarere import Cmrr # Instantiate the client to authenticate cmrr = Cmrr('https://cmrr.es') #> Using temporary user_id="pPh4nVeAu", token="zs5j9rKJR6bLT4bD6PU" #> Head over to https://cmrr.es to create a permanent user. # Create a new endpoint (your computer acts as the server) cmrr.serve(lambda name='World': 'Hello ' + name, 'hello') #>serving <lambda> at https://cmrr.es/call/pPh4nVeAu/hello?name=World print('\nCall this from another notebook (anywhere in the world):\n', "cmrr.call('hello', kwargs={'name': 'stranger'}, user_id='"+cmrr.user_id+"')") ---- I'm building this to scratch my own itch in two areas: - Prototype web app backends faster - Quickly distribute compute in cases with complicated dependencies If this interests you, please visit https://github.com/e-neuman/camarere or register your email at https://cmrr.es and I’ll select the first group for a closed beta. As always, all feedback is welcome!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, computer, new · Missing: mac, agents, macos
82%82% 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
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
60%60% 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: way · Missing: mobile apps, ios, personal
45%45% 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: mrr · Missing: arr, revenue, profit
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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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