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kala
@kala shared an update, 2 weeks, 6 days ago
FAUN.dev()

DeepSeekMath-V2 Launches with 685B Parameters - Dominates Math Contests

#DeepSee...  #AI mode...  #Machine...  #Mathema... 
DeepSeekMath-V2

DeepSeekMath-V2, an AI model with 685 billion parameters, excels in mathematical reasoning and achieves top scores in major competitions, now available open source for research and commercial use.

DeepSeekMath-V2 Launches with 685B Parameters - Dominates Math Contests
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anjali
@anjali shared a link, 2 weeks, 6 days ago
Customer Marketing Manager, Last9

9 Monitoring Tools That Deliver AI-Native Anomaly Detection

A technical guide comparing nine observability platforms built to detect anomalies and support modern AI-driven workflows.

anamoly_detection
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kala
@kala added a new tool DeepSeekMath-V2 , 2 weeks, 6 days ago.
News FAUN.dev() Team Trending
kala
@kala shared an update, 2 weeks, 6 days ago
FAUN.dev()

A New Challenger: INTELLECT-3's 100B Parameters Punch Above Their Weight

#LLMs  #INTELLE...  #Mixture...  #PRIME-R...  #Reinfor... 
Ansible Slurm Lustre INTELLECT-3

INTELLECT-3, a 100B+ parameter model, sets new benchmarks in AI, with open-sourced training components to foster research in reinforcement learning.

A New Challenger: INTELLECT-3's 100B Parameters Punch Above Their Weight
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kala
@kala added a new tool INTELLECT-3 , 2 weeks, 6 days ago.
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devopslinks
@devopslinks added a new tool Lustre , 2 weeks, 6 days ago.
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varbear
@varbear added a new tool Slurm , 2 weeks, 6 days ago.
Course
eon01
@eon01 published a course, 3 weeks ago
Founder, FAUN.dev

Cloud Native CI/CD with GitLab

#Cloud N...  #DevOps  #GitLab ...  #docker  #kuberne... 
GitLab GitLab CI/CD Helm Prometheus Docker GNU/Linux Kubernetes

From Commit to Production Ready

Cloud Native CI/CD with GitLab
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Course
eon01
@eon01 published a course, 3 weeks, 2 days ago
Founder, FAUN.dev

Observability with Prometheus and Grafana

#Grafana  #metrics  #monitor...  #observa...  #prometh... 
Prometheus Docker k3s Grafana GNU/Linux Kubernetes

A Complete Hands-On Guide to Operational Clarity in Cloud-Native Systems

Observability with Prometheus and Grafana
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Course
eon01
@eon01 published a course, 3 weeks, 2 days ago
Founder, FAUN.dev

Cloud-Native Microservices With Kubernetes - 2nd Edition

#Cloud N...  #Istio  #docker  #kuberne...  #prometh... 
Helm Jaeger OpenTelemetry Prometheus Docker Grafana Loki Grafana Kubernetes Kubectl

A Comprehensive Guide to Building, Scaling, Deploying, Observing, and Managing Highly-Available Microservices in Kubernetes

Cloud-Native Microservices With Kubernetes - 2nd Edition
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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.