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Reduce Token Usage for Data Analytics

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Reduce Token Usage for Data Analytics

Hi HN! I've spent the last year building The Data Workspace, a tool that makes data analysis accessible to non-technical business users while dramatically reducing the token consumption for LLMs. Most LLM-based data tools simply throw entire datasets at models, resulting in astronomical token usage. Through a combination of zero data exposure and local query validation, I've reduced token consumption by 80% compared to standard approaches. I'd love feedback from you, especially from those who've worked with the cost and complexity of data analysis. Would also appreciate thoughts on our approach to token optimization! Try it out: https://www.thedataworkspace.com/

Visit productView on Hacker NewsMar 22, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos
79%79% 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
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
66%66% 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 · Strong signals: users · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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