Max Wolffe

Anyscale 🤝GKE Through our continued collaboration with the Google Kubernetes Engine (GKE) team, we’re advancing the experience to scale Ray and Kubernetes in the cloud. This work includes: Label Selectors for improved scheduling: https://lnkd.in/gUZ8iVUW New Features for AI Scaling: https://lnkd.in/gAGSMiir Enhanced TPU Support: https://lnkd.in/gsKDvwpv

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Later this month, I'll be challenging the thought - "Just use Postgres" A pattern that I see is that people suggest PostgreSQL for solutions that, while it is possible to use PG, it may be an early decision that doesn't scale well or isn't the best solution for the task. We'll look at if PG is the tool you should use based on complexity, learning curve, and support for many areas such as - Event Streaming, Caching, Analytics, Ephemeral Data and more. Register for this online presentation with myself and Maja Schermuly. https://lnkd.in/es5BWWD7

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Exciting news for Amazon MSK users! AWS Lambda now natively supports Avro and Protobuf formatted events from Apache Kafka, including Amazon MSK, Confluent Cloud, and self-managed clusters—all in Provisioned Mode. This advancement marks a significant leap forward for event-driven architectures, as it eliminates the need for custom deserialization code and manual schema registry management. By integrating directly with AWS Glue Schema Registry, Confluent Schema Registry, or self-managed registries, Lambda can now validate and process Avro or Protobuf events seamlessly, allowing teams to focus more on delivering business value and less on infrastructure plumbing. The benefits extend well beyond simplified integration. Avro and Protobuf are renowned for their compact message sizes and rapid (de)serialization, offering notable performance improvements over JSON. With native support, Lambda users can choose to receive events in their original binary format for direct object mapping or as clean, validated JSON for easy integration across various programming languages. Tools like Powertools for AWS Lambda further streamline this process, making it effortless to work with business objects. Event filtering at the source is another standout feature. Teams can now set up rules to filter out irrelevant events before they reach Lambda functions, reducing costs and improving pipeline efficiency. When schemas evolve, Lambda automatically retrieves and applies the latest versions, ensuring functions always process up-to-date, validated data—with no code changes required. Robust error handling is also enhanced, as problematic events can be routed to #SQS, #SNS, or #S3 for debugging and analysis, keeping workflows reliable and resilient. #AWS #lambda #MSK #ApacheKafka #avro #protobuf https://lnkd.in/gutjCjkD

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And just like that, v2.0 is live. It now implements SSH natively, by default, using Paramiko instead of wrapping SCP, with a failback to SCP. It also fixes a few issues in complex GitIgnore style parsing.

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POV: It’s 2026 and your manual infrastructure is a ticking time bomb. 💣 If you’re still running terraform apply from your local terminal, we need to talk. Production-grade IaC isn't just about "making it work"—it’s about making it unbreakable. The 2026 Playbook for Terraform Elites: 🔒 State Isolation is King: Local state is a myth. Use S3/GCS with DynamoDB locking. Separate your Dev/Prod states or prepare for a $100k "oopsie." 📦 Modular or Bust: Stop copy-pasting code. Build versioned modules. Pin everything. If a provider update can break your stack, your stack is already broken. 🛡️ Zero-Trust Secrets: Hardcoding passwords in .tfvars? Straight to jail. 🚩 Fetch them dynamically from HashiCorp Vault or AWS Secrets Manager. 🤖 Policy-as-Code (PaC): Don't just plan. Use Open Policy Agent (OPA) to auto-kill any PR that tries to open Port 22 to the world. Stop clicking buttons. Start scaling logic. 🚀 What’s your #1 Terraform "lesson learned" the hard way? Drop it below. 👇 #DevOps #Terraform #CloudNative #SRE #InfrastructureAsCode

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As LLMs continue to show up across the federal space, one thing matters more than model size or hype: control. AWS quietly made an important move by adding response streaming support to Amazon API Gateway, and it directly addresses a challenge many of us hit when deploying AI in the federal arena. Streaming responses mean APIs no longer have to wait for a full LLM output before responding. Users can see results as they are generated, which improves perceived performance and reduces impatience waiting for a response. With streaming, teams gain: - More control over how quickly responses are delivered, including intentionally slower or paced outputs when overuse is detected. - Better user experience without bypassing security, logging, or gateway controls that come from workarounds. - Support teams that want to stay serveless and keep their architecture simple. The ability to shape how an AI responds is just as important as what it responds with. Serveless infrastructure is finally catching up to how LLMs are actually being used in production. https://lnkd.in/gxe_tkrp

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