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Codex can track external events with respect to internal data
Hacker NewsCodex can track external events with respect to internal data
Most analytics tools track your data, but completely ignore relevant external events like world news, platform shifts, pandemics, OpenAI announcements, etc. so you may be completely blind to the signals that impact growth most. I built a tool that tracks our internal data with respect to external events. Plug in a Posthog API key, and a model searches for relevant news daily. This helps us 'close the loop' with Codex managing our human automations with enough data to make decisions that iterate growth in the real world.
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, codex, new · Missing: mac, agents, macos87%87% 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, podcasting60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released46%46% 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: platform · Missing: plus, intuitive, reviews38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real world · Missing: web3, chat, crypto6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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