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@faun shared a link, 9 months, 2 weeks ago
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Are OpenAI and Anthropic Really Losing Money on Inference?

DeepSeek R1 running on H100s puts input-token costs near$0.003 per million—while output tokens still punch in north of$3. That’s a 1,000x spread. So if a job leans heavy on input—think code linting or parsing big docs—those margins stay fat, even with cautious compute. System shift:This lop-sided .. read more  

Are OpenAI and Anthropic Really Losing Money on Inference?
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37 Things I Learned About Information Retrieval in Two Years at a Vector Database Company

A Weaviate engineer pulls back the curtain on two years of hard-earned lessons in vector search—breaking downBM25,embedding models,ANN algorithms, andRAG pipelines. The real story? Retrieval workflows keep moving—from keyword-heavy (sparse) toward embedding-driven (dense). Across IR use cases, the .. read more  

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Combining GenAI & Agentic AI to build scalable, autonomous systems

Agentic AI doesn’t just crank out content—it takes the wheel. Where GenAI reacts, Agentic AI plans, perceives, and acts. Think less autocomplete, more autonomous ops. Hook them together, and you get a full-stack brain: content creation, real-time decisions, adaptive workflows, all learning as they .. read more  

Combining GenAI & Agentic AI to build scalable, autonomous systems
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How to prepare for the Bitnami Changes coming soon

The Bitnami team has delayed the deletion of the Bitnami public catalog until September 29th. They will conduct a series of brownouts to prepare users for the upcoming changes, with the affected applications list being published on the day of each brownout. Users are advised to switch to Bitnami Sec.. read more  

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Observability in Go: What Real Engineers Are Saying in 2025

Go observability still feels like pulling teeth. Manual instrumentation? Tedious. Span coverage? Spotty. Telemetry volume? Totally out of hand. Even with OpenTelemetry gaining traction, Go lags behind Java and Python when it comes to auto-instrumentation and clean context propagation. Devs are hunt.. read more  

Observability in Go: What Real Engineers Are Saying in 2025
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Availability Models: Because “Highly Available” Isn’t Saying Much

Antithesis and Jepsen want to kill hand-wavy "high availability" talk. Instead, they push for clearavailability models—majority,total,sticky, etc.—that spell out when an operationactuallyworks during failures. It's about precision, not platitudes. Why it matters:This reframes availability from a va.. read more  

Availability Models: Because “Highly Available” Isn’t Saying Much
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ECScape: Understanding IAM Privilege Boundaries in Amazon ECS

A new ECS security mess—ECScape—lets low-privileged tasks on EC2 act like the ECS agent. That’s bad. Real bad. Why? Because it opens the door to stealing IAM credentials from other ECS tasks sharing the same host. Here’s the trick: The attacker hits the instance metadata service (IMDS) and fakes a .. read more  

ECScape: Understanding IAM Privilege Boundaries in Amazon ECS
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Google Develops KFuzzTest For Fuzzing Internal Linux Kernel Functions

Google droppedKFuzzTest, a lean fuzzing tool built to hit Linux kernel internals—way past just syscalls. It brings a clean API, docs, and sample targets to get fuzzing fast. Why it matters:KFuzzTest marks a shift. Kernel fuzzing’s no longer just about hammering syscalls—it’s going deeper into the g.. read more  

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v1.34: User preferences (kuberc) are available for testing in kubectl 1.34

Kubernetes v1.34 pusheskubectlinto the future with a betauser preferencessystem. Drop a.kubercfile in place, and you can bake in default flags, toggle features likeinteractive deleteorServer-Side Apply, and wire up custom aliases—including pre- and post-args... read more  

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CNCF Incubates OpenYurt for Kubernetes at the Edge

OpenYurt just leveled up—now officially an incubating project under the CNCF. It pushes Kubernetes out past the data center, into the messy edges of the network, without breaking upstream compatibility. No forks, no duct tape. The maintainer roster’s growing too. Folks fromVMware,Microsoft, andInte.. read more  

CNCF Incubates OpenYurt for Kubernetes at the Edge
Grafana Tempo is a distributed tracing backend built for massive scale and low operational overhead. Unlike traditional tracing systems that depend on complex databases, Tempo uses object storage—such as S3, GCS, or Azure Blob Storage—to store trace data, making it highly cost-effective and resilient. Tempo is part of the Grafana observability stack and integrates natively with Grafana, Prometheus, and Loki, enabling unified visualization and correlation across metrics, logs, and traces.

Technically, Tempo supports ingestion from major tracing protocols including Jaeger, Zipkin, OpenCensus, and OpenTelemetry, ensuring easy interoperability. It features TraceQL, a domain-specific query language for traces inspired by PromQL and LogQL, allowing developers to perform targeted searches and complex trace-based analytics. The newer TraceQL Metrics capability even lets users derive metrics directly from trace data, bridging the gap between tracing and performance analysis.

Tempo’s Traces Drilldown UI further enhances usability by providing intuitive, queryless analysis of latency, errors, and performance bottlenecks. Combined with the tempo-cli and tempo-vulture tools, it delivers a full suite for trace collection, verification, and debugging.

Built in Go and following OpenTelemetry standards, Grafana Tempo is ideal for organizations seeking scalable, vendor-neutral distributed tracing to power observability at cloud scale.