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Ask the Software Architect

Hacker News

Ask the Software Architect

Have you ever wondered what adding that missing feature to your favorite open-source software would take? I want to explore how effective RAG-focused LLMs can be in accurately answering complex or nuanced questions about improving sophisticated systems. With over a decade of experience as a software architect, I have formed some opinions about what constitutes a good signal when analyzing large amounts of code for this purpose. So, I wrote this web application, an MVP that allows you to ask a question (once a day) about the software architecture of a popular open-source project. You can ask about the current architecture or about how to enhance that architecture to accommodate new capabilities or to pay down specific forms of tech debt. Here is the list of open-source projects that have already been indexed: Cassandra, Debezium, Druid, Elastic Search, Lucene, Kafka, Neo4j, and Spark. Are there other open-source projects you would instead ask software architecture questions about? Feel free to post a comment about them or upvote another comment that already lists them. The pipeline behind this app is compute-intensive. This project is self-funded and on a considerably lean budget. Waiting for the answer to your question would not be a good experience. There is no signup, but I do require an email address to verify that the question comes from you and to receive the answer. Outside of that, I have no interest in your email, so feel free to use a temporary email service such as Mailinator if you feel uncomfortable with providing your real email address. This is a learning adventure for me, so I look forward to your feedback, especially regarding any hallucinations in the answer to your question. The app also has a rating and feedback collection experience, which you can use if you would prefer a little more privacy.

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Actual performance

2points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, email, code · Missing: mac, agents, macos
78%78% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% 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
11%11% 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
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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