Build a Token Burn Dashboard to Track What Your AI Actually Does

The AI Harness Audit: Clean Your Setup Before You Upgrade

Every time an AI missed something, I added another rule. Eventually, the accumulated skills, memories, system prompts, checks, and permissions became a hidden system of their own—and that system was getting in the models' way. In this episode, I audit the harness around my AI and compare what happens when Fable 5 and ChatGPT 5.6 meet compact versus overloaded instruction systems. The audit found 66 skill routes, 172 instruction assets, repeated governance rules, and a discovery layer far beyond Codex's stated budget. The lesson is not simply to shorten every prompt. It is to give each surviving instruction one owner and one reason, load specialist context when the work needs it, and enforce deterministic requirements with hard checks. Privacy Policy: https://www.acast.com/privacy ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.

Pick an AI Model That Fits How You Actually Work

For deeper playbooks and analysis: https://natesnewsletter.substack.com/ [https://natesnewsletter.substack.com/] What's really happening as the model race expands into GPT-5.6, Fable 5, Grok 4.5, GLM 5.2, and increasingly complicated model mixes? The common story is that you should pick whichever model tops the latest benchmark — but the reality is that the best model depends on how you think, how you prompt, and what your hardest work requires. In this episode, Nate shares the inside scoop on choosing a model by work pattern rather than hype. * Why “dumber” does not mean dumb * How model families develop different working styles * Why benchmarks are evidence, not the selection heuristic * How Ringer pairs a strong architect with cheaper workers * What knowledge-work AI still needs beyond coding harnesses For builders, operators, researchers, and team leaders, understanding your own work is becoming more durable than memorizing every model leaderboard. Subscribe for daily AI strategy and news. Hosted on Acast. See acast.com/privacy for more information. ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.

AI-Native Companies Run on Code: 15 Rules for Operators

AI made it cheap to build almost anything. Most companies still ship at the old pace, and it isn't because their AI is worse than Anthropic's or OpenAI's. The real difference is what they've moved out of meetings and documents and into working code. Full post: https://natesnewsletter.substack.com/p/ai-native-company-rules?r=1z4sm5&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true [https://natesnewsletter.substack.com/p/ai-native-company-rules?r=1z4sm5&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true] My Links 🔗 👉🏻 Newsletter: https://natesnewsletter.substack.com/ [https://natesnewsletter.substack.com/] 👉🏻 X: https://x.com/natebjones [https://x.com/natebjones] 👉🏻 TikTok: https://www.tiktok.com/@nate.b.jones [https://www.tiktok.com/@nate.b.jones] 👉🏻 Instagram: https://www.instagram.com/nate.b.jones [https://www.instagram.com/nate.b.jones] What's really happening inside the companies that ship AI features every week? The common story is that they just have better AI. The real question is what they rebuilt underneath the model to make that speed possible. In this video, I share the inside scoop on the 15 rules I use to move a company from tool adoption to real AI-native speed: * Why moving repeatable coordination into code is now an operator's job * How 15 commandments work together as one operating system * What changes first: roadmaps, meetings, documentation, and design * Where partial adoption fails, and why every rule has to move together Moving this fast is real, but only if you treat these as one system instead of picking off the rule that feels easiest to adopt. ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.

Agent-Shaped Work: When to Use AI Agents (and When Not To)

Most people bought AI agents and never figured out what to point them at. This is the one-minute test that tells you whether a task belongs in a chat, a single agent, a team of agents, or nowhere near AI. My Links 🔗 👉🏻 Newsletter: https://natesnewsletter.substack.com/ 👉🏻 X: https://x.com/natebjones 👉🏻 TikTok: https://www.tiktok.com/@nate.b.jones 👉🏻 Instagram: https://www.instagram.com/nate.b.jones What's really happening inside the AI agent economy? The common story is that you need more agents. The real question is which tasks are worth any agents at all. In this video, I share the inside scoop on how to spot agent-shaped work: - Why buying more thinking now beats hiring or waiting - How four estimates sort any task in about a minute - What a 40-tool audit surfaced and what it cost to run - Where human judgment still beats every frontier model The agents already work. The scarce skill now is knowing which tasks to hand them and which to keep for yourself. ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.

How to Trust AI Agents: Verify the Work, Not the Model

Multi-agent AI systems just went from research project to recipe. I ran 20+ AI agents across 4 model families to rebuild a website in one afternoon for about $8 — and the system caught every hallucination, every shortcut, and even the boss model's own bug without me lifting a finger. Full post: https://natesnewsletter.substack.com/p/trust-ai-agents?r=1z4sm5&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true My Links 🔗 👉🏻 Newsletter: https://natesnewsletter.substack.com/ 👉🏻 X: https://x.com/natebjones 👉🏻 TikTok: https://www.tiktok.com/@nate.b.jones 👉🏻 Instagram: https://www.instagram.com/nate.b.jones What's really happening inside multi-agent AI systems? The common story is that hallucinations make AI agents too untrustworthy for real work — but the real question is whether trusting the agent was ever the right design in the first place. In this episode, I share the inside scoop on running a verified agent swarm:  - Why one frontier boss plus cheap workers beats frontier-only pricing  - How executed checks caught a hallucination, a cheat, and the boss's bug  - How to audition new models before trusting them with real work  - What a written constitution does that task-by-task prompting can't Hallucinations aren't solved — but with verification built into the structure, delegating big work to AI agents becomes a design question instead of a trust question. ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.