SpaceX's valuation has increased by $1 trillion since its shares started trading on Friday.

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Father’s Day is nearly here. Hopefully, you already got a gift for dads you care about, but if not, here’s a quick, easy recommendation for anyone who enjoys a good comic strip. The Complete Calvin and Hobbes contains every one of Bill Watterson’s beloved strips made during the comic’s ten-year run from 1985 through 1995, […]

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Your second brain for product management Discussion | Link

I was frustated by having to draw system design diagrams by hand when discussing it with my team mates or in an interview. So I thought "Wouldn't it be great if someone draws it for me, while I think out loud?". That is when I came up with VoiceDraw. You can just think out loud or discuss your system architecture with a friend/interviewer, the diagrams are automatically drawn along with your reasoning, open questions and tradeoffs beautifully written on to the side. Demo Video: https://youtu.be/

AI Drum Machine MPC in your browser Discussion | Link

By spring 2026, the division of labor between human engineers and AI had become precise enough to describe. Not speculate about. Describe. Delegate these 7 immediately: Boilerplate generation: CRUD scaffolding, config files, standard patterns. Near-human accuracy. Review required is a naming scan, not a logic audit. Test generation: 40-60% faster test development with no measurable decline in coverage quality, provided the tests are reviewed by someone who understands the domain. Documentation: 67% of companies rely on AI-assisted doc generation in 2026. The first draft is a solved problem. Your job is verifying and contextualizing. Code translation: Python to TypeScript. React to Vue. Framework migrations that once consumed sprint cycles now take hours. Routine bug fixing: Claude Code, Devin, BugBot can resolve 60% of reported bugs autonomously. Resolution time down 30-50%. Automated code review: First-pass filter before human review. Misses context issues. Doesn't replace human review. Eliminates noise so you focus on signal. Commit hygiene: Messages, PR summaries, changelog entries. Fully automatable. No meaningful error rate. Never delegate these 6: Architecture and system design: AI proposes. You decide. The tradeoffs require organizational context, team capability assessment, and long-horizon thinking no model possesses. Business context translation: The spec says "export to CSV." You ask: which users, under what conditions, with what compliance implications? AI cannot know the specification is wrong. You can. Security architecture: AI generates vulnerabilities as readily as it detects them. Adversarial thinking is not statistical. It is human. Long-horizon product thinking: What to build and why. Not how. Multi-stakeholder navigation: The politics, the relationships, the conversation with the PM that keeps the sprint on track. No model has stakes in the outcome. Agent orchestration: Designing, managing, and correcting the AI systems themselves. This is the ne

AI for Preparation. Humans for Judgment. Most AI projects today are one of these: A chatbot A customer support bot A voice assistant A Q&A system But I wanted to explore something bigger: What if businesses could build an AI Workforce? Instead of one AI assistant, imagine: Customer ↓ AI Workforce ├── Discovery Agent ├── Research Agent ├── Policy Comparison Agent ├── Recommendation Agent ├── CRM Agent └── Follow-up Agent ↓ Human Advisor ↓ Customer This article explains the architecture and design decisions behind such a system. Why Insurance? Insurance is an interesting industry for AI. Because: Research is repetitive. Recommendations are data-driven. Follow-ups are expensive. Trust is critical. Human judgment is still necessary. This makes Insurance a perfect Human-in-the-Loop AI use case. Human In The Loop This is the core philosophy. I don't want AI to automatically sell insurance. I don't want AI replacing advisors. I want: AI prepares. Humans decide. The workflow becomes: Customer ↓ AI Workforce ↓ Human Advisor Review ↓ Customer This creates: Faster recommendations Better customer experience Safer AI adoption Human accountability AI Workforce Architecture Customer ↓ WhatsApp Phone Call Website Chat Email ↓ AI Workforce ├── Discovery Agent ├── Research Agent ├── Comparison Agent ├── Recommendation Agent ├── CRM Agent └── Follow-up Agent ↓ Human Advisor ↓ Customer Discovery Agent The Discovery Agent understands the customer. Responsibilities: Collect customer profile Understand goals Assess risk Understand existing insurance Identify gaps Example Output: { "risk_level" : "medium" , "family_type" : "married_with_children" , "insurance_goal" : "health_and_term" , "recommended_health_cover" : "20L" , "recommended_term_cover" : "3Cr" } Research Agent The Research Agent acts like an insurance analyst. Responsibilities: Analyze policies Compare waiting periods Review exclusions Evaluate premiums Generate recommendations Example: { "customer_profile_summary" : "..." , "t