Claude Code Source Leak: What Anthropic’s Hidden Features Actually Reveal
512,000 lines of unintentional honesty — here’s what the leaked code actually reveals about Anthropic’s product roadmap Continue reading on All About Claude »
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You test your code. Why aren’t you testing your AI instructions?
You test your code. Why aren't you testing your AI instructions? Why instruction quality matters more than model choice, and a tool to measure it. Every team using AI coding tools writes instruction files. CLAUDE.md for Claude Code, AGENTS.md for Codex, copilot-instructions.md for GitHub Copilot, .cursorrules for Cursor. You spend time crafting these files, change a paragraph, push it, and hope for the best. Your codebase has tests. Your APIs have contracts. Your AI instructions have hope. I built agenteval to fix that. The variable nobody is testing A recent study tested three agent frameworks running the same model on 731 coding problems. Same model. Same tasks. The only difference was the instruction scaffolding. The spread was 17 points. We obsess over which model to use. Sonnet vs Opu
Explainable Causal Reinforcement Learning for circular manufacturing supply chains during mission-critical recovery windows
Explainable Causal Reinforcement Learning for circular manufacturing supply chains during mission-critical recovery windows Introduction: A Learning Journey Through Broken Supply Chains My journey into this specialized intersection of AI began during a particularly challenging consulting project in early 2023. I was working with an automotive manufacturer whose just-in-time supply chain had collapsed when a critical semiconductor supplier experienced a factory fire. The recovery window was measured in days, not weeks, and traditional optimization algorithms kept suggesting solutions that looked perfect mathematically but failed catastrophically in practice. They would recommend rerouting through suppliers that appeared available in the database but were actually allocation-constrained, or
viable/strict/1775253422: Update third_party/kineto submodule to 628e1d0 (#179244)
Includes the following commits: Add host_name to OSS Kineto trace metadata via gethostname() ( pytorch/kineto#1323 ) 628e1d0 Revert D97166802 ( pytorch/kineto#1326 ) 9d7373b Fix Lingering INT32 Overflow ( pytorch/kineto#1324 ) 3a61657 Re-enabled some hardcoded tests ( pytorch/kineto#1321 ) 50a0085 Expose occupany limiting factors ( pytorch/kineto#1322 ) e19dd92 Authored with Claude. Pull Request resolved: #179244 Approved by: https://github.com/malfet
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Explainable Causal Reinforcement Learning for circular manufacturing supply chains during mission-critical recovery windows
Explainable Causal Reinforcement Learning for circular manufacturing supply chains during mission-critical recovery windows Introduction: A Learning Journey Through Broken Supply Chains My journey into this specialized intersection of AI began during a particularly challenging consulting project in early 2023. I was working with an automotive manufacturer whose just-in-time supply chain had collapsed when a critical semiconductor supplier experienced a factory fire. The recovery window was measured in days, not weeks, and traditional optimization algorithms kept suggesting solutions that looked perfect mathematically but failed catastrophically in practice. They would recommend rerouting through suppliers that appeared available in the database but were actually allocation-constrained, or
FinancialClaw: making OpenClaw useful for personal finance
We often talk about AI agents as if their greatest value lies in understanding natural language. But understanding isn't enough. An agent starts becoming truly useful when it can help with concrete tasks, reduce friction, and do so consistently. FinancialClaw was born from exactly that idea. I wanted OpenClaw to do more than just chat about personal finance — I wanted it to help me manage it: log expenses, record income, handle recurring payments, and query summaries without relying on memory, scattered notes, or repetitive manual steps. From the start, the project took a clear direction: a personal tool with local persistence, designed for daily use, and with multi-currency support. What's interesting is that this usefulness didn't come simply from adding more features. It emerged from co
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