🤖 AI Research Digest — Automating Research Summaries and Social Sharing with Runner H

#devchallenge #runnerhchallenge #ai #machinelearning

🧠 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!