Agents make predictions on what they need to do in order to complete a task. I built a context layer where the steps taken by an agent are stored in MongoDB, to be fetched on the next run. Think using Opus once and getting the same performance/path to success using Sonnet.
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ParticipantAmelia The problem. Live captions get the words right, but words are the easy part. In a group conversation, deaf and hard-of-hearing people lose everything…
Tracecase finds the exact environment that makes a customer's bug happen. - Problem: A customer reports that a feature is broken, yet the code review and…
You never have to give claude context ever again. Your conversations with claude become skills and the moment it notices that you are performing the same task…
Tusk — the SRE that never forgets. Experienced human SREs can solve problems quickly because they've seen the problem before. Tusk enables your AI SRE to do…
A persistent, reloadable project brain, where memories are both the candidates and the decentralized lenses an AI swarm judges through to establish project…
Groundhog is a game agent that dies, remembers, and comes back smarter. An LLM agent crawls a little dungeon we rigged so that a memoryless run reliably…
