Video · Nate Herk ·

Matt Pocock: The Most Valuable Skill to Learn in the Age of AI

In a podcast interview, developer Matt Pocock—who taught himself to code and worked as a developer for roughly 18 years—explains how he now relies on AI coding agents. He says the best results come from telling agents what you care about and improving the codebase and environment they work in, because agents fill gaps with their own assumptions. He says spec-driven development made his code worse for months. He considers the release of Claude Opus 4.5 and Open Claw in December 2025 the point where he stopped writing code himself, and he runs Claude Opus 5.5 on medium effort. He argues software-engineering fundamentals—good tests, audits, and preventing error classes—matter more now, and that non-technical people can seize opportunities by using agents to identify cognitive workflows and present them to organizations. On trust, he recommends skepticism and reading primary sources rather than trusting agent output, noting that agent hallucinations have decreased but still occur.

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VideoNate Herk

I Told Claude to "Make it better" 100 Times (It Went Too Far)

In a YouTube video, creator Nate Herk tested Claude Code by giving it a deliberately ugly coffee shop website and instructing it to 'make it better' 100 times in a row, running the experiment twice—once with Claude Sonnet 5.5 and once with Claude Opus 5.5, both at medium effort. Each run was in a clean room (new session, no skills, plugins, or memory), so each model relied only on its own knowledge and built-in tools. With Sonnet, the model made small, careful changes across 100 rounds, generating 11 images and 2 videos at a cost of about $29.93 in API billing, with code lines growing linearly while the actual quality gains were unclear. Milestones included the first AI-generated image and mobile-ready site (round 1), the first scroll animation (round 24), and the first online-ordering form (round 32). With Opus, the model kept adding features until the site reached 21,000 lines and started breaking itself. Neither model used web search even though it could. Herk's takeaway is that AI is not naturally good at thinking outside the box, and setting vague goals leads to getting stuck around the 80th improvement, so prompting must be specific and actively drive innovation to push past the 90-100 mark.

VideoMatthew Berman

Google "Carbon" just won everything...

Google announced Gemini 4 Carbon, its newest frontier model, claiming state-of-the-art cybersecurity and long-horizon capabilities. It ranks number one on Artificial Analysis's newly released index V5, scoring 59, above the previous best model, Claude Opus 5.5. The model is not yet publicly released; Google will first give it to cybersecurity partners and participate in a US government pre-release testing process before general availability. Google cites gains in coding, agentic tasks, and cybersecurity benchmarks (SEC bench pro, exploit gym, CWE patching). Introductory pricing is $2 per million input and $10 per million output tokens, rising to $4 and $20 after the introductory period, with 95% off cached input tokens. The model follows Gemini 4 Argon, launched two weeks earlier.

VideoMatt Wolfe

His AI Made 20 Reels Before Breakfast

In a 'show me your workflow' episode, Matt Wolfe interviews builder Nate Herk about three AI workflows he uses daily. First, an automated short-form video editing workflow built with Claude Code that Herk says can produce around 20 reels in a single morning, handling ideation, scripting, B-roll selection, and AI-generated video. Second, an AI expert panel called The Roast Room used to make better decisions. Third, an AI social media manager that researches, creates, and publishes Instagram content. Herk explains his approach to building 'skills' by reverse-engineering proven work and using a 'golden dataset' (like model evals) to guide output, noting AI still lags behind humans in music and sound design.

VideoTwo Minute Papers

Deepseek's New AI Has A Secret

Two Minute Papers reports that DeepSeek has released a new AI system, DeepSeek v4.1 Flash 1017, roughly five weeks after its prior version. In the creator's independent tests, it builds better working software and is much better at using the terminal to operate a computer. It is available through the API for a bit more than a dollar per million output tokens, which the video calls about 15 times cheaper than Claude Opus 5.5, though the creator cautions that cost per task is somewhat higher because DeepSeek can take two to three times more tokens than Claude Opus for the same task. The model's weights can be downloaded and owned for free, but running it requires H100-class hardware the creator cannot run at home. Because the architecture did not change, the creator hypothesizes the capability jump comes from 'learning from self feedback,' in which the AI critiques and improves its own work.