I wrote a cron job that saves me ~2 hours of dead time on Claude Code every day
TL;DR Highlight
This method leverages the 5-hour usage window of the Claude Code Max plan, which starts based on the first message, by automatically sending a 'hi' message every morning to anchor the window to your work hours.
Who Should Read
Developers who heavily use the Claude Code Max plan daily, especially those who have experienced waiting times due to hitting their usage limit in the morning.
Core Mechanics
- The usage window for the Claude Code Max plan is 5 hours and starts based on the time (rounded down to the nearest hour) of the first message sent that day. For example, if the first message is sent at 8:30 AM, the window is from 8 AM to 1 PM.
- By utilizing this timing, sending a short message ('hi') at 6 AM before work starts anchors the window to 6 AM to 11 AM, opening a new window at 11 AM.
- GitHub Actions cron job can be used to automatically send a warm-up message every morning. The related repository is available at https://github.com/vdsmos/claude-warmup.
- A simpler method is to use the native scheduling feature on the Claude Code web interface (https://claude.ai/code/scheduled), which achieves the same effect without separate infrastructure. The author has confirmed that it works correctly.
- This concept can be applied not only to the morning window but also to other times of the day to optimize the timing of multiple windows to match your desired workflow.
- This method does not increase usage or bypass limitations; it only adjusts the starting time of the fixed window. There is no additional usage.
Evidence
- Regarding concerns about whether this method violates the TOS, many opinions stated that 'it's okay because it only adjusts the window start time, not increasing usage.' Some even criticized that it should have been designed with a rolling reset.
- A tip was first suggested in the comments to use the native scheduling feature on the Claude Code web interface (https://claude.ai/code/scheduled) instead of GitHub Actions, and the author tested and confirmed that it 'works perfectly,' which was reflected in the body EDIT.
- There were shared experiences from users who manually type 'hi' every morning for months before showering. Many heavy users were already using the same pattern.
- Real-world experience was shared that using launchd is more stable than cron on Mac environments. Some switched to launchd due to cron-related issues.
- Additional tips related to the Claude Code workflow were shared in the comments: using CLAUDE.md as a project convention repository, focusing on one task per session, and requesting a file summary before making code changes were all found to be effective.
- The issue of the agent stopping at the permission prompt while remotely monitoring the session was mentioned as an unresolved pain point that this method does not solve. The situation where the agent is stopped while you are away is still inconvenient.
How to Apply
- On the Claude Code web interface (https://claude.ai/code/scheduled), schedule a 'hi' message to be sent every morning 2 hours before work starts (e.g., 6 AM) to anchor the window to your desired time without separate infrastructure.
- If you prefer GitHub Actions or Mac launchd, refer to the https://github.com/vdsmos/claude-warmup repository to set up a cron job. Keep in mind that launchd is more stable than cron on Mac based on community experience.
- If you have focused work hours in the afternoon, adjust the start time of the second window in the same way to arrange the entire day's Claude Code usage time to match your work schedule.
Terminology
Related Papers
Jamesob's guide to running SOTA LLMs locally
2천 달러짜리 RTX 3090 한 장부터 4만 달러짜리 RTX PRO 6000 4장 셋업까지, 로컬에서 최신 LLM을 직접 돌리는 방법을 하드웨어 선택·구성·실행 설정까지 통째로 정리한 실전 가이드다.
Faster embeddings: how we rebuilt the ONNX path in Manticore
Manticore Search가 기존 SentenceTransformers/Candle 백엔드를 ONNX Runtime으로 교체해 텍스트 임베딩 생성 속도를 평균 14배 향상시켰다. 별도 모델 서비스 없이 DB 내부에서 직접 임베딩을 처리하는 구조에서 INSERT 속도가 곧 임베딩 속도이기 때문에 이 개선은 실질적인 ingest 처리량 향상으로 직결된다.
Asymmetric Quantization: Near-Lossless Retrieval with 97% Storage Reduction
멀티벡터 검색 모델의 문서 벡터를 1비트 이진값으로 압축하고 쿼리 벡터만 int8로 유지하는 비대칭 양자화 기법으로, 스토리지를 97% 줄이면서 검색 품질 손실을 0.61점(NDCG@10 기준)에 그치게 만든 실제 프로덕션 적용 사례다.
Show HN: Bash4LLM+ – A lightweight, dependency-free Bash wrapper for LLM APIs
Python이나 Node.js 없이 순수 Bash만으로 Groq 등 OpenAI 호환 LLM API를 호출할 수 있는 단일 스크립트 도구로, Termux(Android)를 포함한 모든 Unix 환경에서 동작한다.
Wayfinder Router: deterministic routing of queries between local and hosted LLM
프롬프트의 복잡도를 모델 호출 없이 오프라인으로 점수화해서 간단한 쿼리는 로컬 모델로, 어려운 쿼리는 유료 모델로 자동 라우팅하는 CLI 도구다. LLM 비용을 줄이면서도 응답 품질을 유지하고 싶은 개발자에게 유용하다.
Apple Neural Engine: Architecture, Programming, and Performance
Apple 기기에 내장된 AI 전용 칩인 ANE(Apple Neural Engine)를 리버스 엔지니어링으로 분석한 302페이지짜리 기술 문서로, Core ML 아래 숨겨진 내부 구조와 직접 접근 경로를 처음으로 공개한다.