Remy Gaskill
Founder & AI Consultant, AI with Remy · 1 appearance on the record.
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founderoperatorauthor@remy_gaskell ↗remygaskell.com ↗
Remy Gaskell specializes in implementing autonomous AI agent systems, context engineering, and MCP workflows for business operations. He authors the newsletter AI with Remy and creates courses teaching founders how to run operations using AI agents.
20statements → 4claims → 1claims resolved → 3.8/5average certainty → 2/5average debate potential → ≈4.5/5argument clarity, estimated →
1 supported 0 partly supported 0 contradicted 3 not checkable as stated how the 4 claims stand · each chip opens the sources
1 prediction · 3 assertions · 6 opinions · 9 insights · 1 disclosure · every statement was checked. The prediction and assertions are the 4 claims: statements the public record can support or contradict. 1 is resolved, and 3 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.
The record, in short
What the tape says about how Remy argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.
Their most notable supported claim
Gaskill: Anthropic built MCP as a universal tool translator
“Anthropic built MCP to basically sit as this translator in between your tools, so that Claude can still just speak English, and your tools can just speak their languages, and this MCP speaks every language, and then just translates your calls from your agent t…”
How they sound: not measured why? →
We measure speaking style by listening to the audio itself, and a fair number needs at least 2,000 words from one person on tape we have measured. There is too little of Remy Gaskill on measured tape to publish a rate. This says nothing about how they speak.
Everything Remy Gaskill said on the Startup Ideas Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →
Gaskill: People will stop using SaaS apps directly in favor of AI operating systems
“I think that everyone's gonna have, like, an AI operating system they work in, and everyone will just have personal agents and agents to manage each department of their company, and people won't actually use these apps anymore.”
Gaskill: AI optimization shifted from prompt engineering to context engineering
“Prompt engineering used to be the big thing. It was like, here's the ultimate prompt for going viral on social media, or use this prompt for this. And now it's all about context engineering. It's about how well can you load up your agent with all the informati…”
Gaskill: Founders using AI agents are 10 to 20 times more productive
“The founders and employees that are utilizing agents are like, no word of a lie, 10 to 20 times more productive in their day.”
Gaskill: Manual agent memory setup is superior to automatic chat memory
“And with agents, you have to set up memory and control exactly what you give it. And I think that's actually a benefit, not a limitation, because what happens is if you're using ChatGPT and it's got the auto memory, you're having conversations about three diff…”
Gaskill: Future-proof AI stacks rely on local markdown files
“The real future-proof AI stack is just having those markdown files on your computer.”
Gaskill: Popular AI Agent Platforms Are Merely Agent Harnesses
“And all of the popular AI agent platforms on the market that you'd be familiar with are just agent harnesses. They're just applications where this loop is facilitated.”
Gaskill: Claude Code, Codex, and Antigravity are secure by default
“So, by default, Antigravity, Cloud Code, and Codex, they're very, very secure because they're built by these massive companies that have a lot on the line to protect.”
Gaskill: Core agent concepts translate seamlessly across all agent harnesses
“Once you know how to drive, you can kind of jump in any car, whether it's like an old Toyota, a Range Rover, and you inherently sort of know what to do. And that just comes down to understanding all these key concepts that we're going to go through today. And …”
Gaskill: Claude Code displays agent loops best among major harnesses
“I think Claude code does the best job of actually displaying that loop and allowing you to see what it's thought about compared to anti-gravity and codex, but it's all just going through the same sort of loop process that I described earlier.”
Gaskill: AI agents must be onboarded like human employees
“And the way I like to think about building agents is onboarding them like a real employee. So if you took on a real executive assistant, you couldn't expect just for them to come into the office and you need to give them a task without explaining your business…”
Gaskill: Keep Claude.md Agent Context Files Under 200 Lines
“A best practice for those Claude.md files is to keep it around like no more than 200 lines.”
Gaskill: Agent productivity gains come from tool integration, not web search
“By default, most of these agent harnesses, they just have web search baked in, but if you want to actually start linking it up to your tools like Gmail, Calendar, and everything else, which is where the real productivity gains are made, you need to do so, ah, …”
Gaskill: Markdown is easier for LLMs to digest than PDFs or Docs
“Markdown files is because it's just the easiest sort of format for your LLM, for your agent to actually digest and understand, compared to if you were to give it your files as like a docs or a PDF file.”
Gaskill: OpenClaw is among the hardest agent harnesses to learn; Cowork is easiest
“I would say that OpenClaw is probably, like, one of the hardest to learn and set up of these harnesses. I would say Cowork is probably the easiest.”
Gaskill: Chat models are question-to-answer, AI agents are goal-to-result
“The way I think of it is a chat model is question to answer, but then an agent is goal to result. So moving from just like, ah, you asking AI replies, then you do the work, to you giving the agent a task, it planning out the task, and then executing, and then …”
Gaskill: Anthropic built MCP as a universal tool translator
“Anthropic built MCP to basically sit as this translator in between your tools, so that Claude can still just speak English, and your tools can just speak their languages, and this MCP speaks every language, and then just translates your calls from your agent t…”
Gaskill uses Claude Code as a unified interface to replace SaaS apps
“Like, I've connected up Gmail Google Drive, Calendar, Granola for my meeting notes, Stripe for payments, Notion for project management, and I don't even enter these tools anymore. I just sit and call code as one central place,”
Gaskill: AI Skills Act as Standard Operating Procedures for Agents
“So the easiest way to think about skills is SOPs for AI. So standing operated standing operated, oh my god, standard operating procedures for AI. It's a mouthful. It means once you explain something once, you never have to explain it ever again.”
Gaskill built an AI agent to scrape and analyze 220 Oodie ads
“And I did an example yesterday with the UDI, which is a super large e-com brand, and it ran through and basically scraped all of, it took screenshots of all the landing pages, it went and scraped all of the ads that are running, all like, 220. It then did a fu…”
Gaskill: Build AI agent context files via interview-style LLM prompts
“To work out what roles you want to start to build out an agent for, go into Claw or your favorite chat model and get it to help you build out those context files through an interview style process. Just say, ask me questions to build this out. I would connect …”
Appearances (1)
| Episode | Date | Speaking time |
|---|---|---|
| Building AI Agents that actually work (Full Course) | Mar 17, 2026 | 45m |