Weco Aide, an AutoML Agent

Hacker News

Hi HN, In the past couple of months we are working on an AutoML Agent that can help data scientists and machine learners to accelerate their work. Here is how Weco AIDE works: 1. Simply describe your task and data in natural language instruction, then upload your dataset. 2. Leave it to AIDE to analyze your data, iteratively crafting, evaluating, and refining solutions. 3. Download the optimized ML solution (code), as well as a report that summarizes its findings. Join our waitlist here https://www.weco.ai/ ! Would love to hear your thoughts and feedback! I am really curious how such an agent can accelerate data science work and speed-up data driven scientific research.

Visit product View on Hacker NewsJan 22, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agent, code · Missing: agents, macos, cursor

69%69% 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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

38%38% 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: month · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

25%25% 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

18%18% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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