Agents Never Sleep vs Container Diet: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Agents Never Sleep and Container Diet — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Agents Never Sleep
Agents Never Sleep
A tiny $4.99 Mac menu bar app that keeps long-running AI agents alive with the laptop lid closed, using one three-level slider.
Key features
- Closed-Lid Operation: Keeps agents and background jobs running with the MacBook lid shut, which the built-in caffeinate command cannot do.
- Three-Level Slider: One control with Sleepy, Awake, and Never sleeps settings, so changing sleep behavior takes a single drag rather than a terminal command.
- Menu Bar Only: Lives entirely in the menu bar with no window, dock icon, or configuration screens to manage.
- Native and Tiny: A small native macOS binary rather than a wrapped web app, minimizing overhead on a machine already running agents.
- Zero Data Collection: No telemetry, analytics, or tracking; what runs on the machine stays on the machine.
- Safe Revert: Toggling back down restores normal sleep behavior, avoiding the common mistake of leaving sudo pmset -a disablesleep 1 permanently enabled.
- No-Questions Refund: A 14-day money-back guarantee with no form to fill in and no reason required.
Best for
- Overnight Agent Runs: Letting a coding agent work through a long task while the laptop is closed and put away.
- Long Builds and Tests: Preventing a multi-hour build, test suite, or data job from being interrupted mid-run by sleep.
- Mobile Working: Carrying a MacBook between rooms or offices without propping the lid open to keep a job alive.
- Model Downloads and Training: Keeping large model downloads or local fine-tuning runs going unattended.
- Avoiding pmset Mistakes: Replacing manual sudo pmset toggles that are easy to enable and easy to forget to undo.
- Remote Sessions: Keeping a Mac reachable and working for a remote session or background service while it sits closed on a desk.
Container Diet
k1lgor
AI-powered CLI that analyzes Docker images and Dockerfiles to provide context-aware, actionable optimization advice to slim images.
Key features
- CLI Analysis: Runs as a command-line tool to inspect Docker images and Dockerfiles and produce readable reports for developers.
- Context-Aware Recommendations: Uses AI to generate optimization advice tailored to the specific Dockerfile and image contents rather than generic tips.
- Dockerfile Evaluation: Identifies inefficiencies in Dockerfile instructions (for example build dependencies or unnecessary layers) and suggests concrete edits.
- Size Reduction Guidance: Highlights packages, files, and layers that contribute most to image size and recommends removal or substitution strategies.
- Open-Source Distribution: Provided as a lightweight, community-accessible project that can be run locally and integrated into development workflows.
- Analyzes Docker images and Dockerfiles to identify optimization opportunities
- Provides actionable, context-aware optimization advice
- Targets Docker image size reduction and efficiency improvements
- Presented as a web-hosted project (GitHub Pages) — user interface exposed via website
- No API, CLI, or integration details are specified on the provided page
Best for
- Pre-deployment Image Slimming: Analyze production container images to reduce registry storage and lower network transfer times for deployments.
- CI Integration for Preventing Bloat: Integrate into CI pipelines to detect regressions in image size and enforce optimization guidance before merging.
- Dockerfile Hardening and Cleanup: Review Dockerfiles to find leftover build dependencies, redundant steps, or opportunities for multi-stage builds.
- Cost Reduction for Cloud Deployments: Reduce container size to lower bandwidth and storage costs when distributing images across environments.
- Audit Third-Party Images: Inspect base or third-party images to identify unnecessary components and decide whether to replace or trim them.
- Reduce Docker image sizes for faster pull and deployment times
- Optimize CI/CD pipelines by producing smaller build artifacts
- Minimize container attack surface by removing unnecessary packages and layers
- Educate developers on Dockerfile best practices and layer optimization