We are very excited to share one of the big projects that we have been working on at @Tabul_AI Metal TreeSHAP. Shapley Values are one of the most reliable and most widely used tools in Machine Learning interpretability. 1/11

Introducing metal-graph 0.1.0, a new Python library for fast graph analytics on Apple Silicon Macs. metal-graph combines a familiar Python interface with optimized native CPU code and GPU acceleration through Apple’s Metal framework. Its goal is simple: make demanding graph workloads faster while keeping computation local and fitting naturally into existing Python workflows. The first public release includes immutable graph structures, external node ID support, optimized CPU and Metal execution paths, and packaging built specifically for modern arm64 Macs. Getting started takes one command: pip install metal-graph This first release includes full-vector PageRank; batched personalized PageRank with top-k retrieval; multi-source, direction-optimizing breadth-first search with dense or sparse output; bounded k-hop neighborhood and ego-subgraph extraction; and experimental weakly connected components. The early results are exciting. On an M4 Max, metal-graph ran equivalent dense-output BFS on Orkut’s 117-million-edge graph in 8.29 milliseconds, compared with 41.2 seconds for rustworkx - a 4,965× speedup. In the included 1.18-million-edge quickstart benchmark, GPU PageRank was 109× faster than NetworkX, while the CPU path was nearly 60× faster. PageRank reached 614× versus igraph on Orkut, and weakly connected components reached 75.6× versus igraph on a 268-million-edge graph. Batched PPR with top-k achieved a contextual 1,059× speedup over a sequential igraph query loop. These are workload-specific, warm, end-to-end measurements from one M4 Max - not universal guarantees. The repository publishes the full methodology and clearly identifies comparisons where solver behavior or output representation differs. metal-graph supports CPython 3.10–3.14 on Apple Silicon Macs running macOS 14 or newer. Install it from PyPI or explore the benchmarks and notebooks in the GitHub repo (link below)

If work becomes optional in the age of abundance, what happens to cities? Cities are a magnet for people that want/need to be productive to earn a living. But if you no longer need to earn a living, why would you CHOOSE to live on top of other people? On top of this - if the age of abundance makes it far easier to build out cities and towns with autonomous labor, there will be a much greater forcing function for people to live OUTSIDE of cities vs inside them. Where would you rather live - in a town/city that is optimized for self-driving cars and humanoid robots, or on top of other people in cities that are FAR less safe and FAR more crowded? It makes a lot of sense that the age of abundance will bring A TON of new cities and towns that are optimized for the newest technology and zoning practices. For example - why would you need parking decks or parking lots in a city that's 100% autonomous? Why not tons of parks, recreational areas, gardens, etc.? The Unites States is so insanely well positioned for this btw. The amount of undeveloped natural beauty in the US is STUNNING. These new cities would be optimized to blend in the country's natural beauty into the next generation of cities. I'm EXTREMELY EXCITED for this!!!!