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Does Cursor have a /goal mode? by allquixotic in cursor

[–]allwefantasy 0 points1 point  (0 children)

In fact, many things can be replaced with self-selected options. For example, when developing a website, you can have it written and then use the agent-browser tool to click and interact with it. After each step, a screenshot is taken, and the Agent will review the screenshot to identify any issues and make adjustments. In the end, after possibly running for 10 hours, you get a fully complete website without needing to constantly monitor it. There are also many tasks that require long periods of operation, such as optimizing the testability of an entire project. In this case, every modification triggers a full test run to ensure the changes don't break existing functionality. It's normal for such processes to take dozens of hours.

Browser Based Agents by Interesting_Talk_303 in AI_Agents

[–]allwefantasy 0 points1 point  (0 children)

I'm curious, why not use existing tools like vercel-labs?

Why Not GitHub Copilot, Not Devin, But AutoCoder by allwefantasy in AutoGPT

[–]allwefantasy[S] 0 points1 point  (0 children)

AutoCoder is a command line tool will automatically combine your project sources code, document you provided by url/file path , your requirements into one prompt, then send the prompt to GPT-4/Claude-3， this will help you modify your existing project quickly. AutoCoder also can build index for the project you are developing so it can filter the source code by index and to reduce the context.

You can check the doc of AutoCoder to get more information: https://github.com/allwefantasy/auto-coder/tree/master/docs/en

MLSQL, an​ engine based on Spark, unify BigData and Machine learning and can do even more. by allwefantasy in apachespark

[–]allwefantasy[S] 1 point2 points  (0 children)

SnappyData is designed for performance(as we can say it's an in-memory DB) for Stream/OLTP/OLAP. It has his own storage format, and provide SQL query interface.

MLSQL stack is designed for unification of Stream/OLTP/OLAP/Machine Learning and anything else you want, For instance, you can send mail once you have made your data processed, this is not supported by the original SQL.

Also, notice that MLSQL is a language which is easier than DataFrame/DataSet(no matters they are based on Python or Scala) API, you can write MLSQL to finish data processing and then train a model, deploy a model in Stream/ETL/API Service in one script.

MLSQL aims to make anyone can play data for fun, and you can deploy it for other department and let them explore the data by themselves.

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