Join us

ContentUpdates and recent posts about kueue..
Link
@faun shared a link, 5 months, 2 weeks ago
FAUN.dev()

Meta reportedly in talks to invest billions of dollars in Scale AI

Metawants a piece of the$10 billion pieat Scale AI, diving headfirst into the largest private AI funding circus yet.Scale AI'srevenue? Projected to rocket from last year’s $870M to$2 billionthis year, thanks to some beefy partnerships and serious AI model boot camps... read more  

Meta reportedly in talks to invest billions of dollars in Scale AI
Link
@faun shared a link, 5 months, 2 weeks ago
FAUN.dev()

Agentic Coding Recommendations

Claude Codeat $100/month smirks at the spendyOpus. It excels at spinning tasks with the nimbleSonnet model. When it comes to backend projects, lean intoGo. It sidesteps Python's pitfalls—clearer to LLMs, rooted context, and less chaos in its ecosystem. Steer clear of pointless upgrades. Those tempti.. read more  

Link
@faun shared a link, 5 months, 2 weeks ago
FAUN.dev()

How we’re responding to The New York Times’ data demands in order to protect user privacy

OpenAI is challenging a court order stemming from The New York Times' copyright lawsuit, which mandates the indefinite retention of user data from ChatGPT and API services. OpenAI contends this requirement violates user privacy commitments and sets a concerning precedent. While the company complies .. read more  

How we’re responding to The New York Times’ data demands in order to protect user privacy
Link
@faun shared a link, 5 months, 2 weeks ago
FAUN.dev()

God is hungry for Context: First thoughts on o3 pro

OpenAIjust took an axe too3pricing—down 80%. Entero3-prowith its $20/$80 show. They boast a star-studded 64% win rate against o3. Forget Opus;o3-pronails picking the right tools and reading the room, flipping task-specific LLM apps on their heads... read more  

God is hungry for Context: First thoughts on o3 pro
Link
@faun shared a link, 5 months, 2 weeks ago
FAUN.dev()

Automate Models Training: An MLOps Pipeline with Tekton and Buildpacks

Tekton plusBuildpacks: your secret weapon for training GPT-2 without Dockerfile headaches. They wrap your code in containers, ensuring both security and performance.Tekton Pipelineslean on Kubernetes tasks to deliver isolation and reproducibility. Together, they transform CI/CD for ML into something.. read more  

Automate Models Training: An MLOps Pipeline with Tekton and Buildpacks
Link
@faun shared a link, 5 months, 2 weeks ago
FAUN.dev()

GenAI Meets SLMs: A New Era for Edge Computing

SLMspower up edge computing with speed and privacy finesse. They master real-time decisions and steal the spotlight in cramped settings like telemedicine andsmart cities. On personal devices, they outdoLLMs—trimming the fat with model distillation and quantization. Equipped withONNXandMediaPipe, the.. read more  

Link
@faun shared a link, 5 months, 2 weeks ago
FAUN.dev()

The End of Static AI: How Self-Evolving Meta-Agents Will Reshape Work Forever

Meta-agent architectureunleashes AI agents to craft, sharpen, and supercharge other agents—leaving static models in the dust. Amazingly, within a mere 60 seconds, one agent slashes response times by40%and boosts accuracy by23%. The kicker? It keeps learning from real data—no human nudges needed... read more  

The End of Static AI: How Self-Evolving Meta-Agents Will Reshape Work Forever
Link
@faun shared a link, 5 months, 2 weeks ago
FAUN.dev()

The AI 4-Shot Testing Flow

4-Shot Testing Flowfuses AI's lightning-fast knack for spotting issues with the human knack for sniffing out those sneaky, context-heavy bugs. Trim QA time and expenses. While AI tears through broad test execution, human testers sharpen the lens, snagging false positives/negatives before they slip t.. read more  

The AI 4-Shot Testing Flow
Link
@faun shared a link, 5 months, 2 weeks ago
FAUN.dev()

BenchmarkQED: Automated benchmarking of RAG systems

BenchmarkQEDtakes RAG benchmarking to another level. ImagineLazyGraphRAGsmashing through competition—even when wielding a hefty1M-tokencontext. The only hitch? It occasionally stumbles on direct relevance for local queries. But fear not,AutoQis in its corner, crafting a smorgasbord of synthetic quer.. read more  

Link
@faun shared a link, 5 months, 2 weeks ago
FAUN.dev()

What execs want to know about multi-agentic systems with AI

Lack of resources kills agent teamwork in Multi-Agent Systems (MAS); clear roles and protocols rule the roost—plus a dash of rigorous testing and good AI behavior.Ignore bias, and your MAS could accidentally nudge e-commerce into the murky waters of socio-economic unfairness. Cue reputation hits and.. read more  

What execs want to know about multi-agentic systems with AI
Kueue is a Kubernetes-native job queueing and workload management system designed for large-scale, mixed compute environments such as AI/ML training, batch workloads, and HPC workflows. Instead of scheduling individual Pods, Kueue operates at the job level, deciding when a job should run based on resource quotas, fair-sharing policies, cluster availability, and workload priorities.

Kueue integrates tightly with Kubernetes, working alongside the default scheduler rather than replacing it. It provides features such as all-or-nothing (gang) admission, workload preemption, quota-based sharing across teams or tenants, and support for advanced frameworks like JobSet and Ray. Its goal is to help Kubernetes clusters run efficiently under heavy load while ensuring that critical, latency-sensitive, or large training jobs receive the resources they need without starving lower-priority workloads.