Chatpdb – GPT for your Python debugger

Hacker News

chatpdb - gpt for your python debugger ChatGPT can be an awesome tool for quickly diagnosing issues from error output, but pasting snippets into the chat window often misses important context and forces you to switch focus and lose your flow. That’s why we built `chatpdb` - a python debugger with ChatGPT! Simply use chatpdb like you would use pdb or ipdb; it’s a drop in replacement with the exact same functionality. The only addition is the new `y` keyword, which will trigger a response from ChatGPT. chatpdb automatically passes relevant context about your program, including the source code, stack trace, and error information if available to ChatGPT. > /Programming/test-chatpdb/lib.py(2)echo_platform() 1 def echo_platform(platform: str): ----> 2 print("You are running on:" + platform) 3 ipdb> y The exception occurred because the function `echo_platform` tries to concatenate the string "You are running on:" with the `platform` variable, which is `None`. [...] If you have a more specific question you can also supply a prompt to `y`, with the syntax `y "prompt"` It’s easy to install and set up: # bash pip install chatpdb export OPENAI_API_KEY=.... # python import chatpdb chatpdb.set_trace() chatpdb meets you where you are – AI tooling that’s only invoked when you need it. We hope you give it a try! We’d love any feedback or suggestions. docs - https://github.com/Never-Over/chatpdb

Visit product View on Hacker NewsApr 18, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, context, chatgpt · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · 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: io, including · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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.

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