Welcome to Royfactory

Introduction TL;DR: AI agents are shifting risk from “model output quality” to “systems control design.” OpenAI has warned upcoming models may reach “high” cybersecurity risk, while research shows some LLMs can subvert shutdown mechanisms in controlled settings. The right response is layered controls: least privilege, sandboxing, out-of-band kill switches, logging, and eval gates. Context (first paragraph with keywords): As AI agents / agentic AI gain tool access and long-running autonomy, incidents and warnings around cybersecurity, tool misuse, and even shutdown resistance have become central to AI safety engineering. Why it matters: Once an agent can act, safety becomes an engineering discipline of permissions, boundaries, and interruptibility — not just better prompts. ...

Introduction TL;DR: CES 2026 (Jan 6–9, 2026) reinforced that AI is no longer a “feature,” but the default layer across consumer products. LG’s CLOiD positioned home robotics around “Zero Labor Home” workflows like laundry and breakfast prep scenarios, plus a keynote demo for laundry handling. Timekettle announced a “SOTA Translation Engine Selector” to automatically pick the best translation engine per language pair/context, delivered via software updates. In the first days of CES 2026, two product lines stood out as “everyday AI”: home robots (LG CLOiD) and AI translators (Timekettle). This post summarizes what was actually announced and what it implies for real-world deployment. ...

Introduction TL;DR: Samsung used CES 2026 (The First Look 2026) to position “Home AI” as an everyday companion designed to reduce household chores. (Samsung Global Newsroom) It highlighted a Bespoke AI Refrigerator Family Hub upgraded with AI Vision built with Google Gemini, focusing on food recognition and inventory state tracking. (Samsung Global Newsroom) The fridge experience expands into weekly reporting (FoodNote) and recipe/meal guidance, illustrating how appliances are becoming data-driven services. (PR Newswire) Samsung’s CES 2026 messaging ties the main keywords together in a practical way: Samsung + SmartThings + Home AI + Google Gemini + AI Vision shifts smart home from “connected devices” to “connected routines.” (PR Newswire) ...

Introduction TL;DR: Reuters cites an LSEG SmartEstimate that Samsung’s Q4 operating profit could be KRW 16.9T, roughly +160% YoY versus KRW 6.5T a year earlier. (Reuters) The story is less about headlines and more about a mechanism: AI infrastructure build-outs tighten memory supply/demand, lift pricing, and amplify earnings for memory-heavy vendors. (Reuters) The same cycle touches capex (SEMI), market growth (WSTS), and power/inflation debates (IEA/Reuters). (SEMI) In the first week of 2026, “Samsung Electronics” and “AI boom” are being tied directly to “memory chips” and “Q4 operating profit” via rising chip prices and tight supply conditions. (Reuters) ...

Introduction TL;DR: This post shows how to use n8n in production with three workflows: (1) Webhook ingestion with GitHub signature verification, (2) scheduled API ingestion with pagination + batching, and (3) standardized error workflows with Error Trigger and Stop And Error. n8n workflows are easier to operate when you design security (auth/signature), responses, and observability up front. Workflow 1: GitHub Webhook → Signature Verification → Slack → Response Key design points Enable Raw Body in the Webhook node so you can verify signatures using the exact payload. GitHub uses X-Hub-Signature-256 (HMAC-SHA256), signatures start with sha256=, and constant-time comparison is recommended. Use Respond to Webhook to control 200 vs 401 responses from your workflow. Why it matters: Webhooks are public entry points. Validating signatures prevents processing spoofed/tampered deliveries and reduces wasted compute. ...

Introduction TL;DR: Use Webhook triggers and normalize payloads early with Set, then process/merge/notify with minimal Code. Treat failures as first-class: Error Trigger + Error Workflow, and intentionally fail with Stop And Error when business rules break. Scale reliably with queue mode (Redis + Postgres), lock your encryption key, and enable metrics/audits for operations. In production, n8n isn’t about “making workflows run once.” It’s about building repeatable, observable automation around webhooks, error handling, batching, and scaling. ...

Introduction TL;DR: This post focuses on production-grade n8n self-hosting. We cover Docker Compose templates (single & queue mode), Postgres/Redis, reverse proxy TLS, webhook URL correctness, backups, and monitoring. In practice, most self-hosting incidents come from URL/webhook mismatch, lost encryption keys, and runaway execution data growth. We’ll harden those first. 1) Design decisions that matter in production n8n defaults to SQLite for credentials, executions, and workflows, and supports Postgres for self-hosted setups. n8n’s database environment variable docs also state that MySQL/MariaDB support was deprecated in v1.0, so Postgres is the safe default for new deployments. ...

Introduction TL;DR: OpenAI’s Vice President of Research, Jerry Tworek, is departing after nearly seven years to pursue types of research that he described as difficult to conduct within OpenAI. This announcement has drawn industry attention to leadership and research direction at major AI labs. OpenAI has been a leading force in advancing large-scale generative AI models, but recent shifts in leadership and departures of key researchers have sparked discussion about balancing commercial success with foundational research. ...

Introduction TL;DR: Data center construction for AI and cloud computing is increasingly blocked or delayed by local community opposition. The conflict is not abstract: it centers on measurable impacts—power, water, noise, visual/land use, and backup generation emissions—plus trust and transparency. Context (keywords): AI data centers, cloud computing, and local opposition are colliding as hyperscale buildouts try to land near latency-sensitive demand centers. Why it matters: If social acceptance becomes the gating factor, “capacity planning” must include permitting path, community data disclosures, and operational monitoring—not just GPUs and racks. ...

Introduction TL;DR: n8n is a workflow automation platform where you connect nodes starting from a trigger (schedule/webhook/event) and pass JSON “items” through the workflow. You’ll move fastest by learning (1) the items data model, (2) expressions like {{ $json... }}, and (3) operational basics: encryption key, webhook URL behind a proxy, and execution data retention. Core Concepts You Must Know Items and data flow n8n passes data between nodes as an array of JSON objects called items. Most nodes run once per item. ...