How the Atmos Clock Harnesses Air to Keep Itself Running

·1 views

The Atmos clock is a self-winding timepiece that requires no manual winding or batteries to operate. It draws energy from small fluctuations in ambient temperature and atmospheric pressure to power its movement. A hermetically sealed capsule inside the clock expands and contracts with temperature changes, converting that motion into mechanical energy. This energy is stored in a mainspring, which then drives the clock's timekeeping mechanism. The design, developed by Jaeger-LeCoultre, makes the Atmos clock one of the most energy-efficient mechanical clocks ever created.

Read the full story at Hacker News

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer Cuts Monthly Cloud Costs from $7,000 to $35 by Rewriting Stack in Go

A software developer reduced the operational expenses of their Apario full-text search platform from approximately $7,000 per month to around $35 by migrating away from Ruby on Rails and third-party SaaS services toward a Go-based architecture. The original stack relied on paid subscriptions for Elasticsearch, MongoDB Atlas, and OVH bare-metal hosting spread across US East and West regions, totalling roughly $7,000 monthly. The platform indexes around 666,000 PDF pages stored across roughly 1.13TB of distributed object storage accessed via a MinIO S3-compatible API. The developer drew on prior experience optimizing DevOps pipelines — including cutting a game studio's build time from nine hours to thirty minutes — to apply CAPEX-versus-OPEX thinking to their own project. The rewrite ultimately achieved a reported 99.5% reduction in monthly operational costs.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

HyperNexus Tool Cuts AI Agent Context Bloat by 95% Using Smart Tool Routing

Connecting multiple MCP servers to an AI agent can consume tens of thousands of context-window tokens with tool definitions before any query is processed, degrading response quality and increasing costs. HyperNexus, an open-source tool developed by HyperNexusSoft, addresses this by using a multi-layered progressive disclosure system that injects only the three most relevant tool schemas per prompt. It relies on local vector embeddings to semantically match each user prompt against a global MCP directory, rather than loading all available tools upfront. According to the developers, this approach achieves a 95% reduction in tool-related context usage and a threefold improvement in tool selection accuracy. HyperNexus is free for personal use and claims compatibility with major AI coding environments including Claude Code, Cursor, Codex, Gemini CLI, Copilot, and Windsurf.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why routing hotfixes through the release branch is a hidden pipeline risk

A software engineering team using a Gitflow pipeline discovered a critical flaw in their hotfix process when a production bug emerged mid-sprint: the release branch contained untested, in-flight feature work, making it an unreliable path for urgent fixes. The team concluded that hotfixes should be deployed directly from the hotfix branch to a UAT slot, bypassing the cluttered release branch entirely. They also identified backward cherry-picking as a high-risk manual step, noting two incidents over 18 months where conflicts were misresolved under pressure, silently reintroducing bugs. Their solution was to treat cherry-picks as standard pull requests gated by CI checks, rather than manual git commands run during incident stress. The team acknowledged the trade-off openly: deploying from a hotfix branch ships less-tested code, and organizations should only adopt this approach if their risk tolerance explicitly allows it for a narrow class of urgent changes.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Databricks Spark Tuning: How Shuffle, Skew, and Z-Ordering Affect Pipeline Speed

A technical deep dive published on DEV Community outlines common Apache Spark performance bottlenecks on Databricks, arguing that most slowdowns stem from shuffle inefficiency and data skew rather than insufficient cluster size. The guide walks through building a realistic batch pipeline that reads raw order events, joins them against a product dimension table, aggregates the results, and writes output to a governed Delta Lake table. It explains how data skew during wide transformations can leave a single reducer task running for minutes, and demonstrates fixes using explicit salting and Adaptive Query Execution (AQE). The post also covers Z-Ordering on Delta tables to enable file-skipping for faster downstream queries. Unity Catalog is used throughout to enforce centralized access control and maintain data lineage across the pipeline.

0 comments Read more at DEV Community