This story was originally published on HackerNoon at: https://hackernoon.com/using-zmanim-wp-shortcodes-for-shabbat.
This installment in the free WordPress plugin tutorial covers Shabbat shortcodes: candle lighting, Torah portion, and holiday names.
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This story was written by: @leonadato. Learn more about this writer by checking @leonadato's about page, and for more stories, please visit hackernoon.com.

This time I'm covering the free WordPress plugin Zmanim-WP's Shabbat specific shortcodes, including candle lighting time, weekly Torah portion, and holiday names.

This story was originally published on HackerNoon at: https://hackernoon.com/hellocv-earns-a-63-proof-of-usefulness-score-by-building-ai-powered-profiles-that-network-autonomously.
hello.cv earned a 63 Proof of Usefulness score for combining AI-powered profiles, autonomous networking, and search-optimized professional identity.
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This story was written by: @ope_1p3aiql. Learn more about this writer by checking @ope_1p3aiql's about page, and for more stories, please visit hackernoon.com.

hello.cv earned a 63 Proof of Usefulness score for building an AI-first professional networking platform that gives users search-optimized personal sites, custom domains, and autonomous networking assistants.

This story was originally published on HackerNoon at: https://hackernoon.com/ellas-ai-agent-earns-a-4606-proof-of-usefulness-score-by-building-an-autonomous-e-commerce-ai-support-agent.
Ella’s AI Agent earned a 46.06 Proof of Usefulness score for automating e-commerce support, order lookups, ticket triage, and human escalation.
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This story was written by: @its.ella.codes_0jr3tfqo. Learn more about this writer by checking @its.ella.codes_0jr3tfqo's about page, and for more stories, please visit hackernoon.com.

Ella’s AI Agent earned a 46.06 Proof of Usefulness score for building a real-time e-commerce support prototype that handles order inquiries, ticket triage, sentiment analysis, and escalation to human support. Built with n8n, Groq, Supabase, Bright Data, and Gmail integrations, the project is still pre-launch. Its biggest limitation is that the strongest performance claims, including 70–80% automated resolution and sub-two-second response times, are projections rather than live-user results.

This story was originally published on HackerNoon at: https://hackernoon.com/pmb-personal-memory-brain-earns-a-115-proof-of-usefulness-score-by-building-local-first-persistent.
PMB earned a 70 Proof of Usefulness score with a local-first memory layer that gives AI coding agents persistent project context across sessions.
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This story was written by: @oleksiijko. Learn more about this writer by checking @oleksiijko's about page, and for more stories, please visit hackernoon.com.

PMB earned a 70 Proof of Usefulness score for an open-source, local-first memory layer designed to give AI coding agents durable project context across sessions. Built around MCP, SQLite, LanceDB, MiniLM embeddings, BM25, and graph retrieval, PMB lets tools such as Claude Code, Cursor, and Codex share decisions, facts, corrections, and goals without sending that memory to the cloud. The project is still pre-traction, with its 1.0 release arriving in June 2026, but distribution is already live through PyPI and the MCP registry.

This story was originally published on HackerNoon at: https://hackernoon.com/continuous-aggregate-refresh-demystified-invalidation-lookback-and-late-arriving-data.
Learn why late-arriving time-series data can leave aggregates stale and how TimescaleDB continuous aggregate refresh windows determine reconciling corrections.
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This story was written by: @tigerdata. Learn more about this writer by checking @tigerdata's about page, and for more stories, please visit hackernoon.com.

Late-arriving and corrected time-series data can make pre-computed dashboards report plausible but stale numbers. This article compares four aggregation strategies, from scheduled materialized views and insert-triggered views to streaming dataflows and TimescaleDB continuous aggregates. It explains how invalidation tracking works and why the start_offset and end_offset of a refresh policy determine whether late data is ever reconciled.

This story was originally published on HackerNoon at: https://hackernoon.com/the-telegram-session-architecture-nobody-documents.
Learn how server-side sessions connect Telegram chats, Mini Apps, OAuth browsers, authentication, callbacks, and real-time updates.
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This story was written by: @gooslee. Learn more about this writer by checking @gooslee's about page, and for more stories, please visit hackernoon.com.

Learn how server-side sessions connect Telegram chats, Mini Apps, OAuth browsers, authentication, callbacks, and real-time updates.

This story was originally published on HackerNoon at: https://hackernoon.com/the-hidden-infrastructure-tax-of-running-autonomous-agents-247.
Learn how to run autonomous AI agents reliably 24/7 by comparing laptops, unmanaged VPSs, and managed runtimes, with a practical look at agent infrastructure.
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This story was written by: @cloudways. Learn more about this writer by checking @cloudways's about page, and for more stories, please visit hackernoon.com.

Running autonomous AI agents 24/7 requires more than a working model and framework—it requires reliable infrastructure. This guide breaks down the four layers of the AgentOps stack and compares laptops, unmanaged VPSs, and managed runtimes for continuous agent hosting.

This story was originally published on HackerNoon at: https://hackernoon.com/robotaxis-are-coming-for-the-worlds-most-skilled-drivers.
Robotaxis are expanding across major cities, raising new questions about driver displacement, safety, labor shortages, and the economics of autonomous transport
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This story was written by: @thetechpanda. Learn more about this writer by checking @thetechpanda's about page, and for more stories, please visit hackernoon.com.

The article argues that robotaxi expansion is turning autonomous driving from a technology story into a global labor question, with professional drivers facing different pressures depending on the city. It compares markets including London, Wuhan, Phoenix, San Francisco, Tokyo, New Delhi, and Mumbai, and argues that adoption will depend on a mix of economics, road complexity, labor shortages, and public trust.