Agent Otto – Fully costed BOMs of electronic components

Hacker News

Hi HN, I built agentotto.com as a response to a problem in the electronic manufacturing industry. From large companies like Foxconn to engineering departments to hobbyists, it is extremely time-consuming and labor intensive to find in-stock components in a comprehensive manner. Services like Octopart (YCombinator alum) work hard to make this easy, they don't take into account things like overages, tape and reeling needs, and cross referencing. Agent Otto combines big data and good old fashioned smart-people to provide fully costed BOMs in a matter of days (typical turnaround of RFQ BOMs at CMs is usually measured in weeks). Would love feedback if anyone is experiencing the same problem of getting BOMs populated with price and delivery info in a timely manner.

Visit product View on Hacker NewsJan 29, 2018

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4points

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

53%53% 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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: agent · Missing: mac, agents, macos

44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

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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