Minimal plant downtime thanks to an optimally executed retrofit at AB InBev in Belgium
AB InBev is the largest beer producer in the world. After 20 years of service, one of the palletizing lines – processing 500,000 liters of beer a day – was due for replacement. Siemens Digital Enterprise Services managed to complete a retrofit in seven days rather than the planned ten thanks to efficient planning and […]
Could not retrieve the full article text.
Read on blog.siemens.com →blog.siemens.com
https://blog.siemens.com/2026/03/minimal-plant-downtime-thanks-to-an-optimally-executed-retrofit-at-ab-inbev-in-belgium/Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
service
My YouTube Automation Uploaded 29 Videos in One Afternoon — Here is What Broke
My YouTube Automation Uploaded 29 Videos in One Afternoon. Here's What Broke. I run 57 projects autonomously on two servers in my basement. One of them is a YouTube Shorts pipeline that generates, reviews, and uploads videos every day without me touching it. Yesterday it uploaded 29 videos in a single afternoon. That was not the plan. Here's the postmortem — what broke, why, and the 5-minute fix that stopped it. The Architecture The pipeline works like this: Cron job fires — triggers a pipeline (market scorecard, daily tip, promo, etc.) AI generates a script — based on market data, tips, or trending topics FFmpeg renders the video — text overlays, stock footage, voiceover Review panel scores it — if it scores above 6/10, it proceeds Uploader publishes — uploads to YouTube, posts to Twitter
I Tested Every 'Memory' Solution for AI Coding Assistants - Here's What Actually Works
Every AI coding session starts from scratch. You open Claude Code or Codex, and it has no idea that your team uses JWT with 15-minute expiry, that you migrated from REST to GraphQL last month, or that the payments service is the one thing you never touch on Fridays. You re-explain the same architecture decisions, the same conventions, the same constraints. Every single time. This is not a minor annoyance. It is compounding time loss. The first 5-10 minutes of every session is wasted on context that the assistant already learned yesterday. Over weeks, that adds up to hours. I went looking for solutions and tested everything I could find. What I Tried a) Obsidian Mind (574 stars on GitHub) Obsidian Mind is an Obsidian vault template that gives Claude Code persistent memory. It works by loadi
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products
Intelligent cloud-based RAS management: integration of DDPG reinforcement learning with AWS IoT for optimized aquaculture production | Scientific Reports - Nature
Intelligent cloud-based RAS management: integration of DDPG reinforcement learning with AWS IoT for optimized aquaculture production | Scientific Reports Nature
Claude Code slash commands: the complete reference with custom examples
Claude Code slash commands: the complete reference with custom examples If you've been using Claude Code for more than a week, you've probably typed /help and seen a list of slash commands. But most developers only use /clear and /exit . Here's everything else — and how to build your own. Built-in slash commands Command What it does /help Show all commands /clear Clear conversation context /compact Summarize and compress context /memory Show what Claude remembers /review Request code review /init Initialize CLAUDE.md in current dir /exit or /quit Exit Claude Code /model Switch between Claude models /cost Show token usage and cost /doctor Check your setup The ones you're probably not using /compact vs /clear Most people use /clear when the context gets long. But /compact is usually better:
Coding Agents Have Hands But No Eyes
Sebastian Raschka just published a clean taxonomy of coding agent components . Six categories: live repo context, prompt caching, structured tools, context reduction, memory, and resumption. It's solid engineering work. But read it carefully and you'll notice something: every component serves task completion . Not a single one serves perception . The Hidden Assumption Most agent frameworks start here: given a goal, decompose it into steps, execute. This is goal-driven architecture. You tell the agent to fix a bug, write a test, refactor a function. It doesn't need to perceive its environment — you are its eyes. This works great for coding agents. The problem is when people assume this is what all agents look like. What If the Agent Looks Before It Leaps? Imagine a different starting point:
My YouTube Automation Uploaded 29 Videos in One Afternoon — Here is What Broke
My YouTube Automation Uploaded 29 Videos in One Afternoon. Here's What Broke. I run 57 projects autonomously on two servers in my basement. One of them is a YouTube Shorts pipeline that generates, reviews, and uploads videos every day without me touching it. Yesterday it uploaded 29 videos in a single afternoon. That was not the plan. Here's the postmortem — what broke, why, and the 5-minute fix that stopped it. The Architecture The pipeline works like this: Cron job fires — triggers a pipeline (market scorecard, daily tip, promo, etc.) AI generates a script — based on market data, tips, or trending topics FFmpeg renders the video — text overlays, stock footage, voiceover Review panel scores it — if it scores above 6/10, it proceeds Uploader publishes — uploads to YouTube, posts to Twitter
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!