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@faun shared a link, 2 months, 2 weeks ago

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The Raku Programming Language: There's More Than One Way To Do It

Raku throws togethermulti-paradigm support,gradual typing,first-class regex grammars, andmetaprogrammingthat actually earns the name. It comes with built-in concurrency,multiple dispatch, and fresh tools likeRakuASTfor syntax-aware code wrangling... read more

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@faun shared a link, 2 months, 2 weeks ago

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Developer Experience at Pinterest: The Journey to PinConsole

Pinterest rolled outPinConsole, a custom-built Internal Developer Platform powered byBackstage. Years of scattered tools had piled on complexity. This is their clean slate. PinConsole pulls developer workflows into one place, plugging intoPinCompute (Kubernetes),GitHub,Jira, andPagerDuty. It also b.. read more

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@laura_garcia shared a post, 2 months, 2 weeks ago

Software Developer, RELIANOID

The Importance of Reliability in Airport Systems

✈️ In aviation, reliability isn’t optional—it’s mission-critical. From flight scheduling to baggage handling, every system must perform flawlessly to keep passengers moving. Our latest blog explores how Navitaire by Collins Aerospace is transforming airport operations and how RELIANOID helps ensure ..

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@laura_garcia shared a post, 2 months, 2 weeks ago

Software Developer, RELIANOID

The Importance of Reliability in Airport Systems

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@faun shared a link, 2 months, 2 weeks ago

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OpenAI eats jobs, then offers to help you find a new one

OpenAI just fired a shot across LinkedIn’s bow. Its new jobs platform—part ofOpenAI Academy—aims to certify AI skills, then plug users directly into hiring pipelines. Walmart's already on board. Market signal:OpenAI’s not just training people anymore. It's moving in on talent placement, pulling the .. read more

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@faun shared a link, 2 months, 2 weeks ago

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In a first, Google has released data on how much energy an AI prompt uses

Google dropped detailed stats on energy, water, and carbon use per query for its Gemini models. Median energy:0.24 Wh, with TPUs eating58%of that. They’re claiming a33× efficiency boostin the last year—credit goes to model and software tuning. System shift:A public hyperscaler posting this means th.. read more

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@faun shared a link, 2 months, 2 weeks ago

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AI Models Need a Virtual Machine

Microsoft and academic researchers want to give AI models a new kind of home: theAI Model Virtual Machine (MVM). Think of it like theJVM, but for LLMs—an interface layer that standardizes how models plug into host software. The MVM enforcessecurity,isolation, andtool-calling rules, while also unloc.. read more

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@faun shared a link, 2 months, 2 weeks ago

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Building Etsy Buyer Profiles with LLMs

Every day, nearly 90M buyers look for unique items out of over 100 million listings on the Etsy. The platform uses large language models to create detailed buyer profiles anonymously capturing their interests. Adjustments in data retrieval and processing have reduced the time and cost of generating .. read more

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@faun shared a link, 2 months, 2 weeks ago

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Writing effective tools for AI agents—using AI agents

Anthropic’s sharpening the blueprint for building tools that play nice withLLM agents. TheirModel Context Protocol (MCP)leans hard into three pillars: test in loops, design for humans, format like context matters—because it does. They co-develop tools with agents like Claude Code. That means protot.. read more

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OpenAI reorganizes research team behind ChatGPT's personality

OpenAI just folded itsModel Behavior team—the crew behind AI personality design and anti-sycophant training—into thePost Training group. Behavior tuning now lives inside the same house as model refinement. Joanne Jang, who led Model Behavior, now runsOAI Labs, a fresh research unit digging intopost.. read more

Slurm Workload Manager is an open-source, fault-tolerant, and highly scalable cluster management and scheduling system widely used in high-performance computing (HPC). Designed to operate without kernel modifications, Slurm coordinates thousands of compute nodes by allocating resources, launching and monitoring jobs, and managing contention through its flexible scheduling queue.

At its core, Slurm uses a centralized controller (slurmctld) to track cluster state and assign work, while lightweight daemons (slurmd) on each node execute tasks and communicate hierarchically for fault tolerance. Optional components like slurmdbd and slurmrestd extend Slurm with accounting and REST APIs. A rich set of commands—such as srun, squeue, scancel, and sinfo—gives users and administrators full visibility and control.

Slurm’s modular plugin architecture supports nearly every aspect of cluster operation, including authentication, MPI integration, container runtimes, resource limits, energy accounting, topology-aware scheduling, preemption, and GPU management via Generic Resources (GRES). Nodes are organized into partitions, enabling sophisticated policies for job size, priority, fairness, oversubscription, reservation, and resource exclusivity.

Widely adopted across academia, research labs, and enterprise HPC environments, Slurm serves as the backbone for many of the world’s top supercomputers, offering a battle-tested, flexible, and highly configurable framework for large-scale distributed computing.