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

The Lens

This turns Sahil Lavingia's Minimalist Entrepreneur methodology into Claude Code skills. Ten of them install through the plugin marketplace: find a community, validate an idea, scope an MVP, processize, find first customers, set pricing, plan marketing, grow sustainably, define company values, and run a minimalist review. Instead of reading the book and trying to apply it, you invoke a skill and the agent, prompted to act as an advisor channeling the book's philosophy, walks your specific situation through the framework. It asks questions, works through your answers and hands back structured output. The skills are free; you need Claude Code to run them. The catch is that this is one founder's business framework, not a technical tool. Output is only as good as your inputs, and if you disagree with the minimalist approach you will disagree with the advice. The repo has not been updated since April and ships no LICENSE file, only an MIT declaration in its plugin manifest, so be careful about reusing the prompts.

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.