Running Amazon Bedrock across many accounts shows you the bill but not what happened inside each call. This post builds a centralized observability data lake that aggregates Bedrock invocation logs from every linked account into one queryable table, so you can see prompts, token counts, model choice, and per-invocation cost across your organization.
Discover microservices best practices to build resilient systems. Learn what the best patterns look like and how workflow automation fits into the process.
TL;DR We serve Meta’s Muse Spark Open, an 816B-parameter mixture-of-experts model, on a single TPU v7 host of four chips, with SGLang running on the TorchTPU PyTorch backend. At the... The post Optimizing Muse Spark Open on TPU v7 with SGLang-TorchTPU appeared first on PyTorch.
As AI writes more code, the bottleneck has become human-driven code reviews. Code Lens serves to aid that task by augmenting the review outside of line-level AI feedback. Check it out and let me know what you think. Thanks.Try it on a public PR: npx @blong8334/codelens pr https://github.com/expressjs/express/pull/7574 Comments URL: https://news.ycombinator.com/item?id=50446651 Points: 1 # Comments: 0
AI agents spend most of their time idle, waiting on a human prompt, a tool result or a model response. Kubernetes wasn’t built for that. Running one Pod per...
For several years, the conversation about enterprise artificial intelligence has focused on capability: which model is smartest and which GPU is fastest. That made sense when technology was the constraint. It isn’t anymore. AI can now do far more than most organizations will allow, and the gap is trust. That’s the core finding of “Permission […] The post Cisco research: Trust, not technology, is now the speed limit on enterprise AI appeared first on SiliconANGLE.
DuckDB is fast among other reasons because it stores data by column, so a query reads only the columns it names. This post covers DuckDB features that take advantage of that design choice: querying files in place, column-level SQL, aggregation shortcuts, window functions, ASOF joins, and writing well-organized Parquet.
CrossnoKaye brought physical AI to heavy industry with Amazon Bedrock AgentCore. Its ATLAS platform and AURA agentic system ground AI reasoning in live plant data and run verifiable Python in isolated AgentCore sandboxes, giving plant engineers checkable, math-backed answers instead of model guesses.
Learn how Stripe built Kai, a company-wide AI agent on LangChain, LangGraph, and Deep Agents, reaching 5,000 users in roughly 4 weeks.
Article URL: https://www.sandromaglione.com/articles/indie-game-development-with-ai Comments URL: https://news.ycombinator.com/item?id=50432523 Points: 22 # Comments: 16
