How do we automate business analytics with Claude?
New blog post covering our best practices for skills, data foundations, and evaluations when building agents to perform data analysis:
https://t.co/mfEJMAQFBU
Transformer architecture can be difficult to conceptualize. This clip makes it considerably more accessible.
At the International Semiconductor Industry Group (ISIG) Executive Summit, Marvell President of the Data Center Group Sandeep Bharathi walks through how large language models process information using a restaurant analogy, from the moment an order is taken through to the kitchen, the prep work, and the pantry. The parallel to encoder/decoder stages, KV caches, and memory fetches is intuitive and clearly drawn.
The analogy also surfaces a fundamental challenge: modern AI inference has become an energy-intensive data movement problem, and memory access sits at the center of it.
Marvell today introduced Teralynx T100, the industry’s first 102.4 Tbps switch silicon purpose-built for the AI era.
Unlike legacy switching platforms designed for traditional enterprise and cloud data centers, the Teralynx T100 was architected from the ground up for AI—enabling the industry’s lowest power consumption and lowest latency at this bandwidth tier to address critical bottlenecks in today’s large AI clusters. The T100 will start sampling to customers beginning this quarter.
Learn more: https://t.co/Y7rVW5V2BU
Every day, 100+ people ask me, "How can I learn AI evals?"
I copy-paste these 11 links (every time):
1. AI evals & observability (series): https://t.co/erSJcqpAV7
2. Using LLM-as-a-judge: https://t.co/xMBt9j4JRc
3. Demystifying evals for AI agents: https://t.co/HBbCe5PnXJ
4. There are only 6 RAG Evals: https://t.co/gwfyhIozqK
5. Evaluation-driven development: https://t.co/GMtp6bewol
6. Binary evals vs. Likert scales: https://t.co/WyMw1hHTfm
7. The mirage of generic AI metrics: https://t.co/ugryF5zfKO
8. Error analysis: https://t.co/OXgPZd8IXi
9. Carrying out error analysis: https://t.co/OXgPZd8IXi
10. Evaluating the effectiveness of LLM-evaluators: https://t.co/NuaXhr19TV
11. LLM judges aren't the shortcut you think: https://t.co/fDep2HFjCq
Binge these to skyrocket your skills.