nagato-ai
coffee
| nagato-ai | coffee | |
|---|---|---|
| 1 | 4 | |
| 19 | 1,363 | |
| - | 1.8% | |
| 7.6 | 8.8 | |
| 11 days ago | 21 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nagato-ai
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Ask HN: Is anybody getting value from AI Agents? How so?
I get the same feeling. AI Agents sounds very cool but reliability is a huge issue right now.
The fact that you can get vastly different outcomes for similar runs (even while using Claude 3 Opus with tool/function calling) can drive you insane. I read somewhere down in this thread that one way to mitigate these problems is my implementing a robust state machine. I reckon this can help, but I also believe that somehow leveraging memory from previous runs could be useful too. It's not fully clear in my mind how to go about doing this.
I'm still very excited about the space though. It's a great place to be and I love the energy but also measured enthusiasm from everyone who is trying to push the boundaries of what is possible with agents.
I'm currently also tinkering with my own Python AI Agent library to further my understanding of how they work: https://github.com/kenshiro-o/nagato-ai . I don't expect it to become the standard but it's good fun and a great learning opportunity for me :).
coffee
What are some alternatives?
specs-blit - 🤹 2D sprite rendering extension for the specs ECS system
java-iceberg-toolkit - Java implementation for performing operations on Apache Iceberg and Hive tables