I Talked to 500GB of Retail Data With Zero Domain Knowledge. AI Designed a Strategy I never expected
I Talked to 500GB of Retail Data With Zero Domain Knowledge. The AI Designed a Strategy I Didn’t Know to Ask For. The end of a single investigation: the AI’s planogram optimisation strategy with projected business impact — +10% total sales, +£86,424 annual revenue. 134.9 seconds. $1.66 in compute. The agent is already suggesting what to investigate next. Here is what happened. A retail chain had 500GB of transactional data stored in Apache Iceberg. Sales records, promotions, product catalogues, store layouts, inventory movements — the full picture of a multi-store retail operation spread across multiple tables with complex relationships. I connected this data source to an agentic data platform I had built. One connection. No schema mapping, no ETL pipeline, no data dictionary. The system a
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Two-Pass LLM Processing: When Single-Pass Classification Isn't Enough
Here's a pattern I keep running into: you have a batch of items (messages, tickets, documents, transactions) and you need to classify each one. The obvious approach is one LLM call per item. It works fine until it doesn't. The failure mode is subtle. Each item gets classified correctly in isolation. But the relationships between items -- escalation patterns, contradictions, duplicate reports of the same issue -- are invisible to a single-pass classifier because it never sees the full picture. The problem Say you're triaging a CEO's morning messages. Three Slack messages from the same person: 9:15 AM : "API migration 60% done, no blockers" 10:30 AM : "Found an issue with payment endpoints, investigating" 11:45 AM : "3% of live payments failing, need rollback/hotfix decision within an hour"
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:
Interpreting Gradient Routing’s Scalable Oversight Experiment
%TLDR. We discuss the setting that Gradient Routing (GR) paper uses to model Scalable Oversight (SO) . The first part suggests an improved naive baseline using early stopping which performs on-par with GR. In the second part, we compare GR’s setting to SO and Weak-to-Strong generalization (W2SG) , discuss how it might be useful in combination, say that it’s closer to semi-supervised reinforcement learning (SSRL) , and point to some other possible baselines. We think this post would be useful for interpreting Gradient Routing’s SO experiment and for readers who are trying to build intuition about what modern Scalable Oversight work does and does not assume. This post is mainly about two things. First , it’s about the importance of simple baselines. Second , it's about different ways of mode
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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
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