🧠 What I Built

I built an autonomous AI Research Digest Agent using Runner H to automate the process of:

Fetching trending AI research papers from arXiv

Summarizing each paper into digestible insights

Selecting the most impactful paper

Generating a polished LinkedIn post

This workflow eliminates the need to manually browse research feeds, summarize academic content, or spend time crafting posts — perfect for students, researchers, and professionals who want to stay up-to-date and build their thought leadership.

🎥 Demo

📄 Click here to download the full PDF summary

If you'd like to see a short walkthrough video (coming soon!), stay tuned or comment below and I’ll update.

📄 Top 3 AI Papers Summarized
Control Tax: The Price of Keeping AI in Check

Introduces the idea of a “Control Tax” for AI oversight.

Builds a model to quantify trade-offs between safety and cost.

Empirical results show feasible safety mechanisms without high expenses.

Just Enough Thinking: Adaptive Length Penalties in RL

Introduces Adaptive Length Penalty (ALP) to improve token efficiency.

Allocates computational power based on task complexity.

Results show reduced waste and better model scaling.

LLM-First Search: Self-Guided Exploration

Proposes LLM-First Search (LFS), a self-guided reasoning approach.

Uses internal evaluation over static rules.

Demonstrates superior flexibility and efficiency in problem-solving.

📢 LinkedIn Post (Runner H Drafted)

"Just explored the latest breakthroughs in AI research: From optimizing control measures to enhancing reasoning efficiency! One standout is our ability to autonomously navigate complex problems with LLM-First Search, reducing the need for predefined strategies.
Imagine what this means for AI's adaptability across diverse domains! The future is exciting, and these innovations pave the way forward."

🔗 #AI #LLM #MachineLearning #AIResearch #RunnerH

Bonus: 20 AI Research Prompt Ideas
These prompts were also generated to inspire follow-up investigations and projects:

• Explore the impact of Control Tax on AI system design.
• Investigate adaptive reasoning mechanisms to optimize efficiency.
• Design self-guided AI algorithms for unknown data spaces.
• Evaluate the cost-benefit of oversight mechanisms in enterprise AI.
• Tailor reinforcement strategies for LLMs based on task feedback.
• Scale LLM-First techniques to multi-agent environments.
• Apply self-guided logic to autonomous robotics.
• Analyze penalty adaptation methods across AI domains.
• Embed control-tax-aware policies into real-time AI systems.
• Build models to optimize AI under strict cost constraints.
• Study adaptive length methods in multi-agent cooperation.
• Benchmark LLM-First Search versus classical planning.
• Assess startup viability under safety compliance rules.
• Develop resilient AI prediction frameworks for uncertainty.
• Propose affordable pipelines for critical AI services (e.g., health).
• Train AI via unsupervised, self-guided exploration methods.
• Measure training benefits from adaptive reasoning penalties.
• Use LLM-First Search to tackle environmental modeling.
• Simulate economic outcomes of AI Control Tax policies.
• Explore industrial-scale rollout of self-guided AI systems. ##🌍 Use Case & Impact Who It’s For:

AI Researchers who need daily summaries.

Tech Enthusiasts building a personal brand via research content.

Students who want simplified summaries of new work.

Product Managers who need to stay aware of emerging tech trends.

Why It Matters:

This automation turns a complex, multi-step intellectual task into a single-prompt solution — saving time, increasing reach, and bridging the gap between deep research and public knowledge.

❤️ Social Love

Shared on LinkedIn!
👉 Connect with me on LinkedIn

(Once shared, you can paste the LinkedIn or Twitter embed here for consideration in the Community Champion prize category.)

🙏 Credits

Special thanks to H Company for creating Runner H, a powerful no-code AI automation platform that enables anyone to build intelligent, multi-step agents with just a single prompt.

Big shoutout to the DEV community for organizing the Runner H "AI Agent Prompting" Challenge and giving creators and builders a platform to showcase their ideas.

This challenge highlights the incredible potential of prompt-driven AI workflows — and I'm excited to see what others create too!

🙌 Special Thanks

Huge thanks to the Runner H team for making prompt-based workflows so accessible. Looking forward to building more advanced autonomous agents!