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CogniSim – Interaction utilites for crossplatform LLM agents

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CogniSim – Interaction utilites for crossplatform LLM agents

We built a tool to that makes it easy to create LLM UI automations for IOS and Android. If You've ever tried to create tests that run with an LLM on mobile, probably encountered context problems due to the accessibility view being too long or just sending a screenshot to the LLM, which has low accuracy. Cognisim: 1. Turns the accessibility into a more LLM parseable form for your agents, and uses set of mark prompting to combine the screenshot and text for more accuracy. We create a grid of the screen so the text representation also has some form of visual structure to it. 2. Implements a simple API to do interactions on IOS and Android 3. Maps LLM responses back to accessibility components via a dictionary that maps id -> accessibility component Pretty simple, but we hope you get some value from it :) We run CogniSim in production for thousands of mobile UI tests a day at Revyl. If you are interested in proactive observability - combining resilient end to end tests with open telemetry traces, give us a shout - revyl.ai

Visit productView on Hacker NewsOct 28, 2024
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, context · Missing: mac, macos, cursor
94%94% 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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
70%70% 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 released
49%49% 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, reviews
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
22%22% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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