lechmazur/nyt-connections

Description and use

**Summary (2‑3 sentences)**
lechmazur/nyt-connections is an open‑source benchmark that tests large language models on 759 New York Times “Connections” puzzles, enriched with additional “trick” words to stress‑test reasoning and word‑association capabilities. Scoring 59 / 100, the suite provides a quick, reproducible way to gauge how well a model can handle complex, multi‑step linguistic puzzles—useful for early‑stage AI prototyping and RAG/agent workflow validation.

**Value proposition**

• **Fast capability signal** – By running a single benchmark you get an immediate, comparable score that reflects a model’s ability to perform abstract reasoning, pattern detection, and semantic linking, without having to design custom test sets.
• **Plug‑and‑play** – The repository is pure Python, requires only a few common dependencies, and can be run against any LLM that offers a text‑completion API, making it easy to bolt onto existing model‑evaluation pipelines.
• **Iterative improvement** – Because the puzzles are deterministic and publicly available, you can track progress across model versions, prompting strategies, or retrieval‑augmented pipelines, helping teams decide when a model is ready for downstream product use.

**Practical adoption path**

1. **Proof‑of‑concept** – Clone the repo, install the requirements, and run the benchmark against a small “sandbox” model (e.g., an open‑source LLaMA or a hosted API with a low‑cost tier). Verify that the output format matches your logging/monitoring standards.
2. **Integration** – Wrap the benchmark call in a CI step or a scheduled job that feeds your preferred LLM endpoint (OpenAI, Anthropic, Cohere, etc.). Store the resulting score alongside other metrics (latency, cost) to inform model selection.
3. **Customization** – If you need domain‑specific evaluation, extend the puzzle set with your own “trick” words or replace a subset of puzzles with internal use‑case examples, keeping the same scoring script.
4. **Scale‑up** – Once the CI integration is stable, run the benchmark on larger model variants or on models that include Retrieval‑Augmented Generation (RAG) components to compare raw LLM performance versus augmented pipelines.

**Production readiness**

• **Maturity** – Medium. The project has 228 ★, recent updates (June 2026), and a clear Python implementation, indicating reasonable community interest and maintenance.
• **Dependencies** – Minimal (standard HTTP client, JSON handling); however, verify that any third‑party API libraries used are pinned to secure versions.
• **Operational risk** – No obvious licensing or data‑privacy issues in the benchmark itself, but you should still review the repository’s license (MIT/Apache‑style) and perform a security audit of any external API keys you expose.
• **Suitability** – Ideal for internal prototyping, model selection, and continuous evaluation loops. For production‑critical systems, treat the benchmark as an *early‑stage* signal and complement it with domain‑specific validation, load testing, and monitoring before full deployment.

In short, lechmazur/nyt-connections offers a low‑effort, high‑value way to benchmark LLM reasoning ability, can be adopted incrementally from a sandbox test to an integrated CI metric, and is ready for internal or prototype use after standard dependency and security checks.

Use in a workflow

lechmazur/nyt-connections helps add AI capability without starting from a blank model stack.

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.