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Text-to-SQL for Blockchain Data Opensourced

Hacker News

Text-to-SQL for Blockchain Data Opensourced

We used text to SQL to build a "Webflow for Web3 Dashboards". We discovered loads of problems in the entire blockchain data space outlined below. Demo: Design your dashboard, type your query in English. code: https://github.com/vatsalaggarwal/blockchain-text-to-sql prompt: https://llmhub.com/2/functions/23/share We're open-sourcing everything. There is A LOT of blockchain data (~1.5Tb on ETH+BTC). While this is "open data", it's basically unusable - there is too much for anyone to understand what's going on without tools that help humans make sense of it. So, in October, we built out something hacky (the video is NOT cherry picked!!). Our aim was to make creating web3 dashboards like https://info.uniswap.org/#/ or https://dune.com/anngel/XDAO-Statistics as “no-code” as possible. But, we found much deeper problems underneath. 3 types of problems: - liveness: time to access data that was last committed - quality: cleanliness/structure of tables - UI for business intelligence NOTHING WORKED. We ignored liveness. On data quality, there were no public APIs we could use to get good tables! (we tried @flipsidecrypto but they took >1 day to return simple queries!). @DuneAnalytics had ridiculously good tables, but doesn't provide public access. On UX/BI: most users either had to use SQL OR use prebuilt dashboards in Dune or Messari. Loads of nontechnical folks need this data, but can't use SQL and pre-made dashboards are insufficient! In summary, the problems are: - Make good data available - Make it possible to run queries that return “current” results (i.e. not outdated) - Reduce the friction for non-technical users to convert their thoughts/questions into computer-executable queries.

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, computer, code · Missing: mac, agents, macos
81%81% 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
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
65%65% 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: video, users · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
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
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: web3, crypto · Missing: chat, cryptocurrency, make money
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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