scheduling

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.

The author explains how they built a compute platform capable of launching millions of sandboxes per second in constant time, focusing on decoupled scheduling and capacity aggregation using Cassandra and S3.

EvalStop is a scheduling primitive for multi-tenant RLHF platforms that detects and corrects reward overoptimization by monitoring downstream evaluation scores and terminating jobs on consecutive declines, achieving 98% precision and 99% recall while improving job completion time by 9% and cutting wasted compute by 22%.

TimeTuna.com is a meeting scheduling product similar to Calendly but featuring gorgeous video backgrounds as a differentiating feature.

Testing of LuMay Voice Agent in a healthcare clinic for 30 days showed improved response availability, patient willingness to use AI for routine scheduling, and reduced staff workload.

This paper analyzes the trade-off between mixed batching and exclusive batching for LLM inference, showing that the optimal choice depends on GPU memory bandwidth. It proposes a threshold-based hybrid scheduler that dynamically switches between the two methods, achieving up to 41.9% higher throughput on bandwidth-constrained GPUs.

This article advocates for using systemd timers over traditional cron jobs for scheduled tasks on Linux, highlighting their better integration, logging, and clearer syntax.

Tendlet is a product that helps users know, plan, and share care for pets and plants.

This paper presents a comprehensive survey and taxonomy of federated learning over human-body communication for on-body edge intelligence, including a scheduling vignette called BODYFED-HBC.

This paper presents the aircraft disassembly scheduling problem, a large-scale combinatorial optimization task involving thousands of tasks, precedence relations, balance constraints, and limited space. It proposes a Constraint Programming model and a MIP model tested on real operational instances with up to 1450 tasks.

This paper presents a hybrid approach combining dynamic programming and constraint programming to solve the Partial Shop Scheduling Problem, demonstrating the viability of integrating both paradigms despite not outperforming pure CP solvers.