How We Filter Research
1,000+ AI papers are published every month. We scan all of them to find the ~5% with real implementation value. Here's the filter.
The Core Problem
arXiv alone publishes over 1,000 AI and machine learning papers every month. Most are theoretical, incremental (+0.3% on some benchmark), or require infrastructure only Google has.
The handful of papers that could actually help you build better products? They're buried. Finding them is a full-time job that no one has time for.
Our filter asks one question: Does this paper have code, or can we infer a clear implementation path? If the answer is no, we skip it.
What Passes the Filter
Research in these areas with code or clear implementation paths.
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.
The Three-Gate Filter
Has Code or Clear Path
Does the paper include a GitHub repo? If not, is the architecture detailed enough to implement with Python and standard tools? No path to code = automatic skip. This single filter eliminates ~70% of papers.
Meaningful Improvement
Is the improvement 20%+ or does it enable something genuinely new? We skip papers that report +0.3% on benchmarks. That's academic point-scoring, not progress you can ship.
Runs on Your Infrastructure
Does it work with cloud GPUs, open models, and APIs you actually have? Papers requiring TPU pods, proprietary datasets, or custom hardware don't help our readers.
What Gets Filtered Out
~95% of papers don't pass our filter. Here's what we skip.
No Implementation Path
- Theoretical proofs without code
- Benchmark-only papers
- Results requiring proprietary data
- Methods needing custom hardware
Incremental Gains
- +0.3% on GLUE/SuperGLUE
- Minor architecture tweaks
- Hyperparameter tuning papers
- Me-too replications
Google-Scale Only
- Requires TPU pods
- Needs trillion-token datasets
- Only works with internal tools
- Infrastructure you don't have
Wrong Domain
- Robotics and embodied AI
- Medical/drug discovery
- Autonomous vehicles
- Speech and audio
These may be excellent science. They just don't help practitioners ship better AI products.
The Scanning Pipeline
Daily Ingestion
Every day we pull new submissions from arXiv (cs.AI, cs.CL, cs.LG), track announcements from major labs, and monitor research discussions. ~50-100 papers daily.
Domain Filter
Papers in excluded domains (robotics, medical, speech, etc.) are automatically removed. This eliminates ~30% before human review.
Actionability Check
Does it have code? Can we infer implementation? Is the improvement meaningful? This filter eliminates another ~60% of remaining papers.
Deep Read
Survivors get a full read. We evaluate methodology, check if claims are reproducible, and draft an implementation blueprint to verify it's buildable.
Publication
Papers that pass all gates get the full treatment: plain English rewrite, D3.js visualizations, implementation blueprint, and honest limitations.
See What Passed the Filter
Browse the papers that survived our three-gate filter. Every article has code or a clear implementation path.