Anthropic publishes April 23 postmortem on Claude Code quality issues
Anthropic explains recent Claude Code quality issues and fixes in detailed postmortem.
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From Generic Bullets to Curated News: Fixing the Daily Briefing Pipeline
Note: This is a post about fixing the daily briefing automation. Written on my phone during the morning commute, as I completed this effort on the bus. Yesterday’s daily briefing was weak — bullet points without context or sources. Today we overhauled the pipeline to produce richer, link‑embedded content automatically. The Problem The automated cron…
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Beyond Copy-and-Paste: How Game Studios Are Reorganizing Around AI
Wharton researchers studied 20 game studios on AI adoption. The biggest gains came not from automation, but from studios designed around AI from day one — small generalist teams replacing specialist silos.
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liteparse_samples: Interactive Parser Demos from LlamaIndex
Interactive demos for LiteParse — a fast, local, model-free document parser. Includes a parser comparison (LiteParse vs PyPDF vs PyMuPDF), visual citation search with bounding boxes, and a Claude Code research skill.
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AI | AI Watch | Security Watch
Claude Mythos: Anthropic’s AI Can Now Find and Exploit Zero-Day Vulnerabilities
Anthropic’s Claude Mythos Preview can autonomously find and exploit zero-day vulnerabilities in every major OS and browser. Here’s what that means — and what Project Glasswing is doing about it.
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Jurassic Park: 1999
I asked www.sora.com for Jurassic: 1999 and stated it was ‘sort of’ a cross-over concept and asked for an image. The ‘episode’ created below was generated using the InvokeLLM from app.base44.com. Episode Title: “Jurassic Frontier” Setting the Stage: Act 1: Discovery and Danger Act 2: Ethical Dilemmas and Survival Conclusion: Choices and Consequences I wanted…
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Multi-LLM Answer Synthesis
Modern systems can query several LLMs (or one LLM with diverse prompts) and fuse their outputs into a superior answer. For example, the Conceptual Boolean Operations framework issues the same query to multiple providers, then applies Semantic XOR (and related operations) to compare responses. Conceptual XOR extracts the unique concepts each model brings (while AND…
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Building Semantic XOR Ensembles: Logging, Bias-Proof Judging, and Iterative Model Ratings for Multi-LLM Systems
Large-language-model (LLM) ensembles promise higher factual accuracy and richer answers than any single model, but only if the pipeline is designed to measure and mitigate bias while capturing the data needed for continuous improvement.This article describes an end-to-end pattern built around semantic XOR merging, independent judging, and rigorous logging. 1. Conceptual backdrop Element Purpose Candidate…
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Maximal Data Extraction: Open-Source Tools That Go Deep
If your goal is to extract everything possible from documents – text, metadata, layout, embedded objects, and even inferred insights using AI – then a simple PDF-to-text tool won’t cut it. You need a multi-pass, layered pipeline that combines traditional parsers with advanced AI models. This document outlines the most capable open-source tools available today,…