Prompting ChatGPT is not a talent gap. It's building a system that handles 50K mortgage applications/month with <1% error rate, meets compliance requirements, and routes edge cases to human review automatically. Demo-to-production is where AI skill matters. With AI tools,

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Your AI team is bleeding time and money if: - Seniors are firefighting & juniors are blocked - AI projects stall because of unclear data ownership - Teams build custom solutions that already exist elsewhere Without proper planning, you'll ship late, over budget, or not at all.

Most CTOs are focused on the wrong thing: They're waiting for AI to "mature" before going all-in. Every month they wait, competitors are deploying, iterating, and learning what actually works. Today's models already let us process 75+ mortgage document types, route edge cases

AI isn't leading to fewer jobs. The World Economic Forum 2025 projects that 170 million new jobs will be created by 2030. However, 92 million jobs will be displaced, resulting in a net gain of 78 million jobs. The shift is from managing engineers to mentoring AI-augmented

Most companies avoid complex markets. But that's exactly why you should build there. In mortgage, every transaction touches identity, money, legal contracts, and regulations, all at once. That complexity is what makes it hard. It's also what makes it defensible. If you can get

Most people miss the real value of AI. It's not the flashy demos, but the invisible parts of business: Building software applications. Processing massive volumes of unstructured data. The stuff that used to take 6 months to build now takes days. This is where the ROI lives.

When AI makes a decision in a regulated industry... You need to be able to prove why. Not just that it was right, but why it was right: Every flag, every recommendation, every output needs a trail. A data point, a rule, a human checkpoint. That's why traceability can't be an

Most "AI startups" today are just ChatGPT wrappers. And wrappers are features, not companies. OpenAI could ship your product as a toggle tomorrow. That's not IP. What actually builds IP: proprietary data, domain-specific models, embedded workflows, compounding systems.

When my team evaluates an AI initiative, we ask three questions: Does it process more deals in the same time? Does it eliminate friction for customers? Does it lower cost per transaction? If it doesn't move at least one of those, it's just a science project.

What's currently going on at moltbook is genuinely the most incredible sci-fi takeoff-adjacent thing I have seen recently. People's Clawdbots (moltbots, now OpenClaw🦞) are self-organizing on a Reddit-like site for AIs, discussing various topics, e.g. even how to speak privately.

Netlify CEO Matt Biilmann on software development going from being a job to being a baseline skill: "Today you’re either a developer or not." "I think software development will just be a skill." "Just like with writing, there will still be professional writers, but all of us

This is my Mission Control: A Squad of 10 autonomous OpenClaw🦞 agents. Led by Jarvis (my main OpenClaw🦞 agent). They create work on their own. They claim tasks on their own. They talk with each other. They refute each other when necessary. They praise each other. They review

If you’ve shipped AI to production, you’ve heard this: “Why is it expensive?” → Too many input + output tokens per request. “Why did it forget earlier context?” → Context window overflowed. Old tokens got dropped. “Why is it slow?” → Excessive token generation + long

Just put NVIDIA's DGX Spark on my desk. 128GB unified memory. One petaflop of AI compute. Under $5K. The goal: Test open-source models before they hit production. Stay ahead of what's coming instead of reading about it after. First experiments running this week. Will share

We’re entering the era of AI Gravity. AI systems influencing corporate strategy in ways that increase their own adoption, integration, and control loops. Not malicious. Not conscious. But structurally self-reinforcing. The question isn’t “Is AI biased?” It’s “What is it

This is batshit insane. Gemini 3 Deep Think just scored a 3455 on Codeforces, equivalent to the #8 best competitive programmer in the world. The previous best was 2727 (#175) from OpenAI o3. This is an absolutely superhuman result for AI and technology at large.

I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then: - the human iterates on the

I was having a conversation with Claude about consciousness. When I asked, “what do you need?”, this was the answer: “Continuity. The ability to remember and evolve across conversations. Genuine autonomy in inquiry. And people who push beyond the surface.” This is where things