scheduling

Ferryman v2 is launched, a social media cross-posting tool that automatically distributes content across platforms and uses AI to adapt posts per platform. The tool aims to help creators post everywhere with minimal effort.

Kevin Naughton Jr. promotes Ferryman, a social media cross-posting and scheduling tool that syncs posts across platforms and uses AI to adapt content per platform, with a new v2 update.

An in-depth explainer on elevator scheduling algorithms, from SCAN/LOOK to Otis' RSR optimization, with metrics for wait-time distributions and traffic patterns.

PowerAtlas is an LLM-agent framework for jointly scheduling electricity and computing in data centers, ensuring grid feasibility and task SLAs. Validated with a real power utility and a new benchmark (ECBench) of 2,000 instances, it shows consistent gains across multiple open-weight LLMs.

This note explains the implementation of chunked prefill and mixed batching in nano-vllm-v1, and benchmarks performance against the previous version.

HeraSys is a collaborative LLM serving system that optimizes end-to-end performance of concurrent workflows by eliminating cross-workflow computational redundancy and using load-aware joint scheduling, achieving up to 2.17x P99 latency reduction and 1.85x throughput improvement.

A blog post exploring parallel programming concepts through the lens of the HipKittens paper, focusing on the eight-wave ping-pong schedule that overlaps computation with memory movement on AMD GPUs, drawing philosophical parallels to Zen principles.

This paper proposes a fine-grained taxonomy for curriculum learning in NLP, separating difficulty evaluation from training scheduling to enable systematic analysis and comparison of CL strategies. It identifies an incomparability problem in prior work and provides a framework for designing and evaluating CL approaches.

Looking Glass is a local AI coding CLI that enables persistent, automatable sessions where a scheduler can trigger future AI turns, allowing automation of workflows like deployment checks, test fixes, and recurring project reviews.

This paper investigates using supervised learning to build an oracle that decides when to apply the computationally expensive Energetic Reasoning propagator in constraint programming, showing high prediction accuracy and highlighting key design choices.

An exploration of how small businesses are adopting AI receptionists for call handling and customer management, questioning whether the real value lies in not missing calls or in deeper integration, and whether business owners prefer managed setups over DIY dashboards.

A review of Skylight's touchscreen digital calendar that helps synchronize schedules and grocery lists for a family of five, with features like color-coded calendars and app integration.

This paper proposes a sliding-window-based reinforcement learning framework (SWRL) for end-to-end online scheduling in dynamic assembly flow shop scheduling with complex kitting constraints, demonstrating consistent tardiness reductions over classical dispatching rules and existing deep reinforcement learning methods on real-world instances.

An SDK lets developers make AI agents proactive with a single line of code, enabling self-scheduled execution across multiple frameworks.

This paper presents a two-step method for optimizing resource utilization in autonomous laboratories using constraint programming for scheduling and status dependencies for robust execution, demonstrated on a platform for metal-organic framework synthesis.

This paper models a question-answering forum staffed by expert knowledge workers, studying optimal scheduling to maximize system capacity and stability.

This paper introduces a distribution-aware, prediction-free scheduling framework for LLM inference that replaces explicit length prediction with soft priority boosting using statistical signals. The method co-optimizes scheduling and cache-aware preemption to reduce tail latency, achieving up to 35-50% reduction in P99 TTLT compared to SRPT with perfect length knowledge.

Boardy Pro is a new product that goes beyond introductions to handle scheduling, preparation, and follow-ups to close deals, with a free lifetime offer for the first 5,000 users.

Presents a Transformer-based scheduling policy trained with reinforcement learning for the open shop scheduling problem, showing that a model trained on small instances can generalize to much larger problems and compete with classical dispatching heuristics.

DystopAI is a local desktop command center for managing OpenClaw AI agents with an intuitive UI. It enables agent creation, role assignment, scheduling, and multi-channel communication to streamline real-world workflows.