skills is gaining serious momentum, plus 3 more tools worth watching

The Lens

This turns Sahil Lavingia's entire Minimalist Entrepreneur methodology into executable Claude Code skills. Nine skills you install in your terminal: validate your idea, scope an MVP, find first customers, set pricing, and more. Instead of reading a book and trying to apply it, you invoke a skill and your AI walks through Sahil's exact framework applied to your specific situation. It's interactive: it asks you questions, processes your answers, and gives you structured output. The skills are well-structured and the methodology is proven (Gumroad was built on it). The catch: this is a business methodology, not a technical tool. The quality of the output depends entirely on how good your inputs are. And it's one person's framework; if you disagree with the minimalist approach, you'll disagree with the advice. No pricing page for the skills themselves: they're free, but the book is $17 on Amazon if you want the full context.

The Lens

A server without a GPU. llama.cpp makes it possible. It runs quantized versions of open models (Llama, Mistral, Phi, Qwen, and dozens more) in pure C/C++ with optional GPU acceleration. No Python, no PyTorch, no CUDA dependency hell. Everything is free under MIT. No paid tier, no cloud, no account. Download a model file (GGUF format), point llama.cpp at it, and you're running inference. It includes a built-in HTTP server that exposes an OpenAI-compatible API, so your existing code that talks to GPT can talk to a local model with one URL change. The catch: you need hardware. A 7B parameter model needs ~4GB RAM (quantized). A 70B model needs ~40GB. Quality depends entirely on the model and quantization level; a heavily quantized model on a laptop won't match GPT-4. But for privacy-sensitive workloads, offline use, or just not wanting to pay per token, nothing else comes close.

The Lens

This is the 'awesome list' for AI. Models, tools, infrastructure, datasets, organized by category with brief descriptions and links to the actual projects. Awesome lists live or die by curation quality. This one focuses on 'truly open source,' not source-available, not 'open weights with a restrictive license.' That distinction matters when you're building on top of these tools. The list is maintained on GitHub and follows the awesome-re standards. The catch: awesome lists are snapshots. They go stale unless someone actively maintains them, and the growth spike suggests this was recently featured somewhere. The real question is whether it'll be maintained in 6 months. Also, 'curated' means one person's opinion of what's worth including; your needs might differ. Use it as a starting point, not a definitive source.

The Lens

Superset is a full business intelligence platform you can self-host for free, with interactive dashboards, charts, and reports without writing code. Think Tableau or Looker, but open source. Connect it to Postgres, MySQL, ClickHouse, BigQuery, or dozens of other databases, then build interactive dashboards with drag-and-drop. Fully free under Apache 2.0. No feature gating, no user limits, no enterprise unlock. You get the complete BI platform: SQL editor, chart builder, dashboard designer, role-based access, and scheduling. At, this is one of the most popular open source projects period. The catch: Superset is resource-hungry and complex to operate. The Docker Compose setup works for testing, but production deployment requires Redis, a metadata database, Celery workers for async queries, and careful memory tuning. Expect 4-8GB RAM minimum. And the learning curve is real. Building useful dashboards takes time, and the UI can feel overwhelming. If you just need a few charts, Metabase is much simpler to get running.