Still love to read about it, even though I’m all in TypeScript land now. So much better tooling, infra and ecosystem.
SOFTWARE
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Swift Concurrency Isolation Violations: Progressive Discovery Challenges
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“There is no way to progressively discover and deal with such isolation violations, just enabling 6 mode and praying.” https://
mjtsai.com/blog/2024/10/0
1/swift-concurrency-and-objective-c/
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SambaNova Cloud Success: Developer Feedback and CEO Insights
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Since we launched SambaNova Cloud, #devs have been giving us some positive feedback! 🎉
— SambaNova (@SambaNovaAI) 2 octobre 2024
Our CEO & Co-Founder, @RodrigoLiang, shares some of the reasons why it's been such a success.#AI
Start developing ⤵️Since we launched SambaNova Cloud, #devs have been giving us some positive feedback! Our CEO & Co-Founder, @RodrigoLiang
, shares some of the reasons why it's been such a success. #AI Start developing -
Scheduler Complexity and Infrastructure Provisioning Strategies
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the scheduler side of things is a whole another level of complexity. we dont reprovision from scratch though.
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Data Engineers Critical for GenAI Success and Code Development
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The best #GenAI models will not succeed without good data. That’s why data engineers are even more critical today. This guide shares practical guidance on using AI tools for faster code dev, implementing data governance & more. Read here
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Building FastHTML Apps Without Writing JavaScript Code
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Lots of my fasthtml apps involve me writing no js fyi
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Training Models at Scale: 10K+ H100 Infrastructure Guide
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quick mini-post I wrote for @francoisfleuret broadly summarizing the things one needs to do to train models on 10k+ H100s.
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Scaling AI Training Across Thousands of H100 GPUs
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There's three parts. 1. Fitting as large of a network and as large of a batch-size as possible onto the 10k/100k/1m H100s — parallelizing and using memory-saving tricks.
2. Communicating state between these GPUs as quickly as possible
3. Recovering from failures (hardware, -
MongoDB Schema Modeling: Beyond Schemaless Myth
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Challenge accepted! I think my point would be that you can model your data in MongoDB as you would in PG. there are good reasons why documents could get you farther, depending on the workload, but this idea of "schemaless" MongoDB has always been wrong. There's always schema.
