Research Selection
With thousands of AI papers published every month, we focus on the ones that matter for practitioners. Here's how we decide what to cover.
The Selection Challenge
arXiv alone publishes over 1,000 AI and machine learning papers every month. Most are written for academic audiences: theoretical contributions, incremental improvements, or research in highly specialized domains.
Only a small fraction contains insights that practitioners can actually use. Our job is to find those papers and make them accessible.
We ask one question: Could a developer at a startup ship something based on this paper within a month?
What We Focus On
We prioritize research that helps you build better AI-powered products.
RAG and Retrieval
Retrieval-augmented generation, vector search improvements, chunking strategies, reranking methods, and long-context handling.
LLM Techniques
Prompt engineering, fine-tuning methods, inference optimization, reasoning improvements, and cost reduction strategies.
Agent Architectures
Tool use, planning, memory systems, multi-agent coordination, and autonomous task execution.
Code Generation
Repository-level understanding, code completion, refactoring assistance, and developer tool improvements.
Safety and Guardrails
Content filtering, output validation, jailbreak prevention, and reliability improvements developers can implement.
Efficiency
Quantization, inference speedups, cost optimization, and techniques that work on standard cloud GPUs.
Our Selection Criteria
Actionable
Can developers implement this with Python, APIs, and cloud GPUs? Does it provide methods and architectures, not just benchmark results? If there's no clear path to implementation, we skip it.
Relevant
Does it help build chatbots, agents, RAG systems, or code tools? Would a startup building AI features care about this? Research for research's sake doesn't make the cut.
Transferable
Do the methods work across different tasks and domains? Single-dataset improvements or language-specific solutions have limited value for most readers.
What We Don't Cover
Some research is excellent science but not useful for practitioners building AI products.
Specialized Domains
- Robotics and embodied AI
- Medical imaging and drug discovery
- Autonomous vehicles
- Climate and weather modeling
- Satellite imagery and geospatial
Hardware-Specific
- Neuromorphic computing
- Quantum machine learning
- Specialized chip architectures
- Edge-specific optimizations
Academic Focus
- Purely theoretical frameworks
- Survey and review papers
- Benchmark suites without methods
- Incremental improvements (+1-3%)
Niche Applications
- Game playing and game design
- Speech and audio processing
- Financial trading algorithms
- Domain-specific datasets
These are often valuable contributions to their fields. They just don't help our audience build better AI products.
Our Discovery Process
Daily Monitoring
We scan new submissions from arXiv, track announcements from major labs (DeepMind, OpenAI, Anthropic, Meta AI), and follow discussions in the research community.
Automated Filtering
Papers in excluded domains (robotics, medical, etc.) are automatically filtered. This removes about 20-30% of submissions before human review.
Relevance Ranking
Remaining papers are ranked by our criteria: actionability, relevance to practitioners, and transferability of methods. Top candidates get a deeper read.
Deep Evaluation
We read the full paper, evaluate the methodology, check if claims are supported, and assess whether we can write a practical implementation blueprint.
Translation
Selected papers are translated into plain English with interactive visualizations, honest assessments, and practical guidance for implementation.
See Our Selection in Action
Browse our research library to see the papers that passed our selection process and how we translate them for practitioners.