Practical decisions for AI and software
Technical guides to choose models, verify software and plan delivery.
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New comparisons, practical guides and engineering lessons.
Business & RegulationAnthropic’s self-replicating AI warning: the local AI takeaway
A security warning with an unexpected local-AI lesson. Separate the three experiments, check the speed arithmetic and make ownership testable.
AI & AgentsGoogle SAM is now Agent Mesh: MCP tools across machines
Connect approved tools across machines. Understand the control plane, signed EU labels and the checks a useful first pilot should pass.
AI & AgentsAnatomy for Claude Code: Interactive SVGs for Documentation
Turn a technical explanation into a figure readers can explore. Install Anatomy, choose HTML or React, and check the model before embedding it in your docs.
AI & AgentsEdgeQuake: PostgreSQL GraphRAG for MCP agent memory
Connect document relationships to your agents. A v0.34.0 review of PostgreSQL deployment, MCP access, local model roles and the LightRAG benchmark.
AI & AgentsBitNet on CPU: 2B4T, real RAM needs and the 100B claim
Which BitNet model can you download, and what fits your CPU? Separate the 100B benchmark, 0.4 GB metric and 1.19 GB file, then test a real task.
Product & MVPEnterprise software: settle the business rules before AI writes the code
A refund looks like one if statement until sales, finance and support disagree. Resolve the rule, its owner and its boundary cases before automating it.
AI & AgentsSuperPenguin: AI Cost per PR, Customer and Feature
What did a merged PR cost, and what does each customer cost to serve? A practical SuperPenguin guide with billing reconciliation, worked examples and an optimization prompt.
AI & AgentsClaude Dashboards + Motion: From KPIs to Video
Turn a reviewed product metric into a short animated update. A practical Claude Dashboards and Motion workflow with a cohort example, two prompts and sharing rules.
AI & AgentsOpenAI Intelligent UI vs OpenUI: Generative UI for Business Apps
ChatGPT’s new interface raises a practical question: how do generated views become reliable business workflows? Compare APIs, actions, persistence and evaluation.
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