daniil

4. Create your own skills and commit them to git. Reuse across every project. Tips from the team: - If you do something more than once a day, turn it into a skill or command - Build a /techdebt slash command and run it at the end of every session to find and kill duplicated code - Set up a slash command that syncs 7 days of Slack, GDrive, Asana, and GitHub into one context dump - Build analytics-engineer-style agents that write dbt models, review code, and test changes in dev Learn more: #extend-claude-with-skills" target="_blank" rel="nofollow noopener">code.claude.com/docs/en/skills…

@exolabs @nvidia Exo repo mentions GPUs on Linux not yet supported. Can this be run today?

Full blog post and more details about EXO 1.0: blog.exolabs.net/nvidia-dgx-spa… Thanks @NVIDIA for early access to two DGX Sparks. #SparkSomethingBig

Clustering NVIDIA DGX Spark + M3 Ultra Mac Studio for 4x faster LLM inference. DGX Spark: 128GB @ 273GB/s, 100 TFLOPS (fp16), $3,999 M3 Ultra: 256GB @ 819GB/s, 26 TFLOPS (fp16), $5,599 The DGX Spark has 3x less memory bandwidth than the M3 Ultra but 4x more FLOPS. By running compute-bound prefill on the DGX Spark, memory-bound decode on the M3 Ultra, and streaming the KV cache over 10GbE, we are able to get the best of both hardware with massive speedups. Short explanation in this thread & link to full blog post below.

@hhh0pE 1