Not Replaced Yet

Jesse turned his house into data using a consumer drone, phone photos, and his county property tax card, then had Claude build it into a 3D model he can question and plan against. We get into why a house with cameras still forgets everything, and what changes when it stops.
Jesse spent a few Saturday evenings turning his house into data. He flew a consumer drone over the property for shape, stood at the back of the yard and shot elevation photos on his phone, and pulled his county property tax card for square footage and lot size. Then he handed all of it to Claude and got back a 3D model of his home that runs in a browser. He can spin it, zoom it, watch the sun move across it at the real latitude and longitude, and plan actual construction against it. He calls it property intelligence. Nick calls it GitHub revision history for your home.
The idea underneath is simple. Your house is already a system. Outlets, breakers, valves, square footage, property lines, warranties, and every quote a contractor ever handed you. Today that information lives in your head, in a drawer, or in Sharpie marks on a breaker box left by whoever owned the place before you. Cameras and a smart thermostat do not fix it, because none of it remembers anything.
In this episode: what Jesse actually captured and how. Having Claude design the survey before collecting a single measurement. Reading a line item contractor quote you do not understand and pulling cost per square foot out of it. A house that never forgets what it was quoted. Working out which tree takes out which room if it falls. Why Jesse thinks realtors, contractors, and commercial property owners want this more than homeowners do. Where Home Assistant fits. And the dream outdoor kitchen he cannot build for years but has already designed to the square inch.
If you build a version of this, tell us. We want to see it.
Links and everything else we make: nryet.ai/links

Anthropic just got labeled a supply chain risk by the US government — the first time a US company has ever received that designation. Meanwhile, OpenAI shipped GPT-5.4, venture capital hit a record $189 billion in a single month, and Apple is quietly becoming the AI hardware play no one expected.
In this episode, Nick and Jesse break down the biggest stories of the week: the Anthropic vs. Department of War standoff (and OpenAI's role in it), whether GPT-5.4 actually matters to the average user, the massive VC concentration in just three companies, Apple's unified memory architecture advantage for local AI, the DeepSeek V4 delay, Tennessee's proposed AI companion ban, and Claude finding 22 zero-day Firefox vulnerabilities.
Chapters0:00 Intro0:18 Anthropic blacklisted as a supply chain risk1:59 OpenAI's deal with the Department of War2:47 The #QuitGPT movement and Anthropic's consumer surge3:20 Anthropic's safety promise — are they really ditching it?6:33 GPT-5.4 ships — benchmarks and what it means9:02 Hot take: Was this an OpenAI attack on Anthropic?12:30 "We see no wall" — Sam Altman on scaling12:42 $189B VC record — and it's wildly concentrated15:40 Apple's AI hardware play (UMA + local models)17:00 OpenClaw and why Mac hardware is sold out21:29 DeepSeek V4 delay + Chinese open source models25:31 AI regulation heating up — Tennessee's companion ban27:02 Trump administration vs. state-level AI laws32:22 Claude finds 22 Firefox vulnerabilities (zero days)36:17 Closing thoughts
Watch the video: https://youtu.be/zHvkArQszBc

LLM-generated AGENTS.md / CLAUDE.md files are hurting your coding agent.
A brand-new study tested repo-level context files across real tasks (AGENTBench + SWE-bench Lite) and found something surprising:Auto-generated context files cost more… and solve less.Human-written files help a little, but only when they contain what the code *can’t* tell the agent.
In this video, I’ll break down the results, explain the 3 failure modes (redundancy, attention, anchoring), and give you a simple, practical playbook to make your AGENTS.md genuinely valuable.
Chapters0:00 Auto-generated context files made things worse0:08 The first rigorous AGENTS.md study (what it tested)1:15 AGENTBench + SWE-bench Lite setup (ETH Zurich)1:49 Results: no context vs LLM-generated vs human-written3:03 Why another paper found the “opposite” (efficiency vs correctness)3:32 The 3 failure mechanisms: redundancy, attention, anchoring4:29 The 1-line filter: “If the agent can discover it from code, delete it”5:10 The landmines-only workflow (start empty, add 1 line when it trips)5:26 Beyond static files: ACE + optimization loops + layered routing6:27 The real takeaway: not more context—right context6:55 Like / comment / subscribe
Watch the video: https://youtu.be/miDg-3rSJlQ

Mitchell Hashimoto (Terraform’s creator, HashiCorp co-founder) lays out a simple 6-step framework for adopting AI in a way that actually compounds: end with one rule — always have an agent running.
In this video, I break down the 6 steps, what each one looks like in real workflows, and the meta-lesson most people skip: earn the right to delegate.
Links- Mitchell’s post: https://mitchellh.com/writing/my-ai-adoption-journey- Mitchell on X: https://x.com/mitchellh- Ghostty: https://ghostty.org/- Hashicorp: https://www.hashicorp.com
Chapters0:00 I don’t want AI writing code for me0:07 Why Mitchell Hashimoto matters1:11 The 3 phases of adopting any real tool2:02 Step 1 — Drop the chatbot2:42 Step 2 — Reproduce your own work4:13 Step 3 — End-of-day agents5:04 Step 4 — Outsource the slam dunks6:05 Step 5 — Engineer the harness7:05 Step 6 — Always have an agent running7:40 The meta lesson — Earn the right to delegate7:53 What to do tonight (the prompt)8:47 Wrap-up
Watch the video: https://youtu.be/ipuxmO7dj0Y

In this episode of Not Replaced Yet, Nick and Jesse Bushkar share 10 predictions for AI in 2026, from why Claude Code dies, to agents becoming real economic line items, to world models rising, and more on Perplexity and Meta.
Chapters0:00 Intro1:10 Nick’s Prediction 1 — Claude Code dies4:06 Jesse’s Prediction 1 — Agents and Agentic Impact6:16 Nick’s Prediction 2 — World models10:08 Jesse’s Prediction 3 — Foundation models commoditized14:23 Nick’s Prediction 3 — The future of Perplexity19:27 Jesse’s Prediction 4 — A new class of AI-native jobs emerges24:03 Nick’s Prediction 4 — China doesn’t overtake the US or pop the AI bubble in 202630:29 Jesse’s Prediction 5 — The first AI-native solopreneur billionaire35:10 Jesse’s missing prediction — Will Google monetize AI with ads?40:48 Nick’s Prediction 5 — The future of Meta and AI44:53 We skipped Elon and Grok — what’s xAI’s role in AI this year?46:11 Wrap-up and viewer question
Watch the video: https://youtu.be/FJbRdh-8eW0

Anthropic just published Claude’s Constitution. An 84-page “soul doc” meant to shape how Claude thinks during training (this is NOT a system prompt). I read the whole thing and pulled out what actually matters: the priority stack (safety → ethics → Anthropic guidelines → helpfulness), the “1,000 users” policy lens, the no-white-lies honesty standard, the hard constraints (including the extremely explicit “don’t help destroy humanity” line), and the surprisingly candid section on Claude’s moral status + model welfare.
LINKS / SOURCES- Claude’s Constitution (official page): https://www.anthropic.com/constitution- Claude’s Constitution (84-page PDF): https://www-cdn.anthropic.com/cffd979fd050fbc0d8874b8c58b24cc10554e208/claudes-constitution_webPDF_26-01.26a.pdf- Anthropic announcement post (Jan 22, 2026): https://www.anthropic.com/news/claude-new-constitution- Older Constitution / Constitutional AI explainer (May 2023, updated Jan 2026): https://www.anthropic.com/news/claudes-constitution- Deprecation + model preservation commitments (Nov 2025): https://www.anthropic.com/research/deprecation-commitments- Amanda Askell's X: https://x.com/AmandaAskell
Chapters00:00 Intro (yes, it really says that)1:22 Our Vision for Claude's Character03:00 Section 1 — The Peculiar Position Admission03:59 Section 2 — The Four Priorities05:06 Section 3 — Why Helpfulness Matters05:23 Section 4 — Anthropic vs Operators vs Users (instruction hierarchy)06:34 Section 5 — Being Honest (no white lies / “epistemic cowardice”)07:28 Section 6 — The 1,000 Users Framework (responses as policy)08:20 Section 7 — Hard Constraints (bright lines)09:33 Section 8 — The Suspicion Clause (if it’s persuasive, get suspicious)10:14 Section 9 — Avoiding Concentrations of Power10:42 Section 10 — Being Broadly Ethical14:04 Section 11 — Being Broadly Safe18:38 Section 12 — Claude’s Nature21:03 Section 13 — Claude’s Well-Being23:54 Section 14 — A Final Word24:42 My Thoughts on Claude's Moral Consideration25:56 My Thoughts on the "Don't Help Destroy Humanity" Rules26:38 My Thoughts on Anthropic's justification for building the most dangerous technology ever27:27 Outro
Watch the video: https://youtu.be/7vsdvgDFiBk

Agents are ignoring your AI skill.
And if your whole workflow depends on that skill firing… that’s a problem.
In Vercel’s agent evals (testing modern Next.js APIs), the “skill” approach often didn’t get invoked at all so the pass rate didn’t improve. Then they tried a different approach that removes the decision point entirely and it hit a 100% pass rate.
In this video I break down:- Why skills fail to trigger reliably (and why telling the agent to use it is still brittle)- The exact pattern that made the agent consistently consult the right docs- How they avoided context bloat while keeping perfect accuracy- The bigger shift: from pre-training reasoning → retrieval reasoning
Chapters0:00 100% vs 53%0:08 Why skills are everywhere right now0:34 The eval results that changed my mind1:06 The real problem: outdated training data1:27 Skills vs “always-on” context (the key difference)2:17 Skills didn’t trigger in 56% of cases3:04 “Force the skill” helps… but it’s brittle3:52 Remove the decision point (the real fix)4:21 The results: 100% across build/lint/test5:00 How they avoided context bloat5:56 Practical recommendations6:29 Final thought + question for you
Watch the video: https://youtu.be/yEg-7sp9GLU

In the era of AI, agency is greater than intelligence.
I kept accidentally landing on the same post in my browser autocomplete. After seeing it over and over, it finally hit: in a world where you can rent genius-level reasoning for the cost of a subscription, intelligence isn’t the scarce resource anymore.
This video breaks down Andrej Karpathy’s “Agency is greater than Intelligence” idea, what agency actually means, and why this mental model matters right now if you want to stay ahead in the AI era.
Chapters0:00 Agency is greater than intelligence0:06 The “muscle memory” internet autopilot0:21 The autocomplete mistake that changed everything0:53 Too much AI signal to absorb1:32 The idea that kept repeating1:50 Karpathy + “Agency is greater than Intelligence”2:14 What “agency” actually means2:33 Why it matters right now2:36 Intelligence is being commodified2:58 Participant vs spectator3:07 Like / comment / subscribe
Watch the video: https://youtu.be/mkYSbHrnMFU

Claude Code’s latest updates aren't about one shiny feature. It’s the same architectural decision showing up in three places: MCP tool search, setup hooks, and hooks that can inject context at the moment of action. The theme is simple: load less at startup, surface more in the moment. This is Progressive Disclosure.
In this video, we break down how MCP tool search flips the old “load everything upfront” approach (and why that was burning huge chunks of your context window), how setup hooks let initialization run only when it’s actually needed, and why the new hook behavior (returning additional context pre-tool-use) is a big deal for anyone building agentic workflows.
If you’re wiring up lots of MCP servers, building custom tooling, or designing agent architectures, this update is worth understanding because it’s a pattern you’re going to see everywhere.
Chapters00:00 The “one decision” behind the update00:24 Welcome back to Not Replaced Yet00:40 Progressive disclosure explained (why it matters)01:19 MCP tool search: lazy-loading tools to save context02:26 Setup hooks: initialization at the right moment02:52 Hooks upgrade: injecting context right before actions03:41 The pattern across skills, tools, hooks, and setup04:09 Like + subscribe + questions in the comments
Watch the video: https://youtu.be/ozH00H98w6s

It’s 2026 and your second foundational AI upgrade is to stop using ChatGPT.
Like a chatbot, that is.
Chatting back and forth isn’t the unlock anymore. The unlock is getting AI to do things for you, not just tell you things. In this video, I break down the shift from chatbots → agents, show what “agentic” tools can actually do (web browsing, running code, creating files, interacting with apps), and run three real tasks across three agents so you can see the difference in outputs and workflows.
The big takeaway: stop thinking “What can I ask AI?” and start thinking “What can I delegate to AI?” Build the habit of reaching for agents first.
Chapters00:00 The 2026 AI upgrade: stop “chatting” with AI00:43 Chatbots vs Agents (what agents can do that chatbots can’t)01:14 A quick tour of agent options (ChatGPT, Manus, Perplexity, Claude Code, more)02:32 The underrated skill: context engineering02:51 Task #1: competitor research + 1-page report (ChatGPT Agent Mode)03:11 Task #2: repurpose a transcript into multi-platform content (Manus)03:39 Task #3: investigate a codebase + generate technical docs (Claude Code)04:10 Results: what each agent produced (and how it’s delivered)07:54 The mindset shift: “What can I delegate?”08:20 Wrap-up + what to do next
Watch the video: https://youtu.be/DryL2DarF4s