@Saccc_c: An excellent way to get started in the AI field is to read a ton of papers, and the example from my circle of friends i…

An excellent way to get started in the AI field is to read a ton of papers, and the example from my circle of friends is Teacher Jiu’er (@li9292). By grinding through 60 AI papers, he directly landed a job at Shanda Group’s investment product Evermind.

And learning papers also lets you distribute them directly on X at the same time, which is a brutal way to kickstart an account. This track is absolutely wide open—you go check out the traffic on papers in the English-speaking world, and you’ll see just how insane it is.

But if you’re still worried about being a total newbie with papers, I strongly recommend using Open-Science for a quick entry. It’s an all-in-one AI research workbench. Whether you’re a student or anyone interested in research, you can use Open-Science to reproduce research projects, like replicating cutting-edge science or projects from leading scientists. As long as you reproduce 5 or more, you’re guaranteed to become an expert in the AI field.

In its own vision: Open-Science aims to lower the barriers to using research tools, so that everyone with curiosity and research ideas can more easily participate in research, and through open and shared tools and methods, connect global researchers.

From my own experience, it has a few standout features that really draw me in:

1. The Agents on the platform are designed specifically for paper research and come built-in with research skills. You just need to clearly describe your target task, and it can retrieve relevant literature from academic databases, outline research methods, and deliver the final report.

2. Flexible model selection. It supports integrating different models and can directly use existing Codex or Claude subscriptions. We can allocate them appropriately based on the specific task.

3. Easy to review processes and results. All charts and reports generated during the research process allow you to view related inputs, code, and execution logs, making it convenient for manual review if issues arise; project and run histories are also saved, for easy checking and continuing research.

4. Supports hands-on practice to verify research methods: You can personally reproduce the calculations and analyses in papers with its assistance, thereby gaining a deeper understanding of the research process and conclusions.

Open-Science open-source repository link:

AIPOCH (@aipoch_ai): Science, Open to All. We’re building AIPOCH Open-Science for anyone with a question to explore. Research with AI, preserve the work behind each finding, and share a .science package for others to inspect, reproduce, and build on. Open-Science V 1.0.0 is alive!