VS

VSCode Extension to Speed Up Using ChatGPT for Coding

Hacker News

VSCode Extension to Speed Up Using ChatGPT for Coding

I built this because I realized I was wasting a huge amount of time doing it manually. When I'm first starting on a coding project and there are only a handful of code files (and none of them are particularly long), I like to give the full project code to the LLM in order to check it over or to ask it to modify something or to add a new feature. So I was constantly writing a short preamble to explain that the following was Python code or whatever, and then for each code file, I would include the relative path to the file, followed by a markdown code block (enclosing the code file contents in 2 pairs of ``` characters) with the file contents, with a separator between code files of ---. I ended up just keeping that prompt in a separate text editor window and whenever I would update any part of the code, I'd manually modify that separate prompt file. It worked, but was still annoyingly manual. With this new extension, you can just open the VSCode command palette, type in "prepare" to find it (short for "Prepare for LLM"), and get a convenient interface for selecting the code files you want to include in your prompt. It remembers your file selections for the next time, and also includes a handy token counting feature that shows how many tokens each code file would use. It then keeps track in the status bar at the bottom of how many remaining tokens you could fit in your prompt (you can update this Max Tokens parameter easily depending on whether you are using regular ChatGPT4 with ~4000 token context window, or another model with smaller or larger context window). It tries to be smart about caching the token counts and only updating them when the files are changed, and also filtering out non-code files and files that are excluded in your .gitignore file. Anyway, I hope you find it useful, and please let me know if you have any feedback. Now that I know how to make extensions, I'm planning to make another one next for debugging a single code file with an LLM that would take the code file and then include each line with a "problem" in it (from a linter or something like Rust Analyzer) and the problem description-- another task that I waste tons of time on doing manually. I use this literally every day, sometimes dozens of times a day. Give it a try!

Share card

Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, context · Missing: mac, agents, macos
97%97% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting
87%87% 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, para · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · 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, smart · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

VS
VSCode extension for GitLab24%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

VSCode extension for GitLab

Hacker News2
Tf
Tfsec now has a VSCode Extension (basic for now)26%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tfsec now has a VSCode Extension (basic for now)

Hacker News1
VS
VSCode Extension to toggle between string cases29%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

VSCode Extension to toggle between string cases

Hacker News1
I
I made a GPT4 VSCode extension for detecting and fixing insecure code47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made a GPT4 VSCode extension for detecting and fixing insecure code

Hacker News9
I
I made a GPT4 VSCode extension for detecting and fixing insecure code47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made a GPT4 VSCode extension for detecting and fixing insecure code

Hacker News1
I
I made a GPT4 VSCode extension for detecting and fixing insecure code47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made a GPT4 VSCode extension for detecting and fixing insecure code

Hacker News3
Cl
Claude Debugs for You (VSCode Extension)25%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Claude Debugs for You (VSCode Extension)

Hacker News1
Ne
New VSCode extension: Objectify Params27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

New VSCode extension: Objectify Params

Hacker News4
Py
Python live coding extension for vscode32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Python live coding extension for vscode

Hacker News3
VS
VSCode extension for simulating Boids models29%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

VSCode extension for simulating Boids models

Hacker News1