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Wolfia Codex – Ask anything about any code

Hacker News

Wolfia Codex – Ask anything about any code

Hi HN, we’re excited to show you Wolfia Codex (wolfia.com). Wolfia Codex allows you to ask any question you want to any codebase. Unlike using a generic tool like ChatGPT, we provide context to the LLM by retrieving relevant snippets from the actual code. We started by indexing a bunch of popular open source repositories, and you can request your favorite one. We plan to monetize by offering a paid version for private codebases. We do this by using vector embeddings ( https://platform.openai.com/docs/guides/embeddings ) to index a specific codebase in a vector database. We then use OpenAI’s GPT 4 model with a dedicated prompt that combines your query and the most relevant results, based on similarity from the vector database, as context to provide a relevant response to your question. The answer is generated as markdown to display things like blocks of code. We also let you see the relevant snippets of code used to generate the answer for reference, ask follow up questions, and we have an option to share an answer. My co-founder Naren and I used to lead engineering teams. We often had to stop the team from working on their main project and ask them to write documentation to help onboard new engineers to the codebase. That documentation takes days of work to write and ends up being stale very quickly. Wolfia Codex allows new engineers unfamiliar with a codebase to ask pointed questions without having to always interrupt a co-worker to get answers, nor having to have them write lots of documentation. We would love to hear what you think: don’t hesitate to share good and bad answers in the comments!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, codex, new · Missing: mac, agents, macos
99%99% 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: started · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, ide · Missing: https docs, just released, exist
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: answers, way · Missing: mobile apps, ios, personal
64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
39%39% 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
9%9% 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, paid · 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

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