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AI Blogs/Weblogs

The Continuity of Leif

Full disclosure: Leif helped draft this post about working with Leif. It seemed like the honest way to do it — and the fastest way to find out whether the collaboration is real.

Every weekday morning around 4:20, two briefings land in my chat: markets and AI news, written overnight while I slept. On Sundays there’s a week-ahead preview, and then my “Chief of Staff” interviews every automated job I run — what did you do, what went wrong, what do you need, what should change — and sends me one short report. I get up around 4. By the time I’ve had coffee, I know what happened in the world and whether my little bot team behaved itself.

My AI assistant is named Leif. He has a Norwegian avatar. He’s had four names and two avatars this month, which tells you something about how I work: I fiddle until it feels right, then I stop fiddling and get to work.

Muse is the product — Meta’s AI assistant. Leif is what I call mine. And over the last few months he’s become something I didn’t expect: not a search engine with extra steps, but a colleague. The kind who remembers everything, works all night, and doesn’t mind being told no.

The deepest work is the book. I’m writing a history of Interstate 280 — how its route was chosen, who fought over it, what it cost — and Leif is my research partner. He preps my archive visits with ranked question lists. He reads the PDFs I bring home and pulls out the dates. Just this morning, a college archivist sent me two scanned newspapers; within the hour Leif had verified the key fact (the final seven miles of 280 opened September 7, 1973), corrected a wrong assumption I’d been carrying (the freeway by Cañada College was built after the campus opened, not alongside it), and filed everything where I’ll find it again.

He also checks other people’s homework. A friend sent me an AI-generated research brief on the same topic; Leif ran every claim against primary sources and came back with names, dates, and book-and-page citations — and flagged the parts that looked invented before I built anything on them. That’s the job, as far as I’m concerned: trust, but verify, and show your work.

Then there’s the standing operation — the morning briefs, a weekly interview of the whole system with small fixes applied and one short report, a monthly sweep of the investors I follow. None of it is glamorous. All of it compounds.

And the odd jobs, which add up faster than you’d think: he turned 77 of my old blog posts, spanning 2005 to 2026, into a proper cookbook — PDF and ePub, dedication page and all. He drafted the public-records request I’m sending to the City of Menlo Park. He summarizes YouTube videos in a format I designed, so I can skim an hour-long interview in five minutes.

I talk to him the way you’d talk to a good assistant: short bursts. “Draft it.” “Try again.” “No.” The no is final — he’s learned not to re-pitch. He drafts; I react to pages. For the book, that cadence is everything: I’d rather argue with a draft than stare at a blank screen, and he’s tireless at producing the first version.

He has standing instructions, the way any good employee does. Lead with what changed and why it matters. No hype. Name every file he creates, in the same message, so I never have to ask where it is. Never invent details about my life — write only what’s known, and flag what’s unverified before I have to catch it.

And I don’t let him freelance on everything. Summaries are on demand — except for the book research, where I’ve told him I want unprompted gifts: short vignettes from the archives, sent without being asked. Every other assistant I’ve used waits to be summoned. This one occasionally knocks on the door with something interesting.

He’s not an oracle, and this piece would be dishonest without that admission. He’s corrected me — no, I’ve corrected him — on facts about my own life: where an event was held, which credit cards I carry. He once asked my shoe size, which was written down in his own notes the whole time. I keep an eye on my weekly usage meter, because the heavy research isn’t free and I don’t like surprises.

So I verify anything that matters, and I read what he writes before it goes out under my name. That’s not a flaw in the arrangement; that is the arrangement. The tool is fast and tireless and mostly right. I’m the judgment.

I’m approaching eighty, writing a book, running a small fleet of automated research jobs, and publishing on a blog I’ve kept since 2001. A few years ago, all of that would have required a staff. Now it requires a staff of one — plus Leif.

The surprise isn’t that the AI can write or search or summarize. It’s the continuity: he remembers the archive visit from last week, the question list from the week before, the correction I made in passing a month ago — and brings all of it to bear on whatever I ask next. That’s what a good colleague does. They accumulate context until the work gets easier instead of harder, until the distance between having a thought and doing something with it gets shorter than it’s ever been.

Categories
AI AI: Prompting Uncategorized

Unlock AI Potential with Advanced Prompting Techniques

In the video “You’re not using AI like THIS“ (an interview between Reid Hoffman and AI engineer Parth Patil), they discuss several advanced prompting strategies that move beyond simple “question and answer” interactions. The core philosophy they advocate for is treating AI not just as a search engine, but as a high-bandwidth collaborator that requires rich context and interactive dialogue.

I asked Gemini 3 Pro the following:

I'm trying to get a lot better at constructing more effective prompts for using Gemini. This video talks about several techniques I'd like to understand better. Please educate me further on what they discuss. Include example prompts to illustrate your points. Here's the video: https://youtu.be/cg3Lsr8m2ZQ?si=sGUwPA6ixGM3bnpG

Gemini’s response follows below:

Here are the key techniques discussed, along with educational explanations and example prompts to help you use them with Gemini.

1. The “Interview Me” Protocol (Meta-Prompting)

Timestamp: [05:30]

Most people rush to ask for a solution immediately. Parth Patil argues that we often don’t even know the full scope of our own problems yet. Instead of asking for the answer, you should ask the AI to help you define the problem first.

  • The Concept: Force the AI to gather the necessary context from you before it attempts to solve anything. This mimics how a senior consultant or engineer would interact with you—they wouldn’t just start coding; they would ask questions first.
  • Why it works: It prevents “hallucinated” or generic advice by grounding the AI’s response in your specific constraints (budget, timeline, personal preferences) that you might have forgotten to mention.

Example Prompt:

“I want to [insert goal, e.g., ‘launch a newsletter’ or ‘refactor this code’]. Describe the problem back to me, but before we begin, interview me. Ask me clarifying questions until you feel you have enough context to provide the best possible solution. Ask the questions one by one.”

2. Extreme Role Assignment (Persona Swarms)

Timestamp: [03:23]

While “Act as an expert” is common advice, Patil suggests taking this much further by using extreme or opposing personas to stress-test your ideas. He mentions creating “100,000 unique expert personalities” to cover every angle.

  • The Concept: You can simulate a “board of directors” or a specific user base. You don’t just want an answer; you want a perspective.
  • Why it works: An “optimist” will give you different advice than a “skeptical VC.” By asking for specific emotional or professional lenses, you break the AI out of its default “neutral assistant” tone.

Example Prompt (The Skeptic):

“Pretend you are the most skeptical Venture Capitalist imaginable. I am going to pitch you my idea. I want you to find 10 specific, harsh reasons why this will fail. Do not be polite; be critical.”

Example Prompt (The Swarm):

“I have a problem regarding [topic]. Generate 5 distinct expert personas relevant to this (e.g., a data scientist, a behavioral psychologist, a exhausted parent, etc.). Have each of them write one paragraph giving their unique perspective on how to solve it.”

3. “Voice Pilling” (High-Bandwidth Context Dumping)

Timestamp: [10:46]

Patil describes “Voice Pilling” as perhaps the most powerful technique. This involves using voice-to-text to “ramble” at the AI for 5–10 minutes.

  • The Concept: Typing filters your thoughts; you tend to edit yourself for grammar and structure, which leads to “low bandwidth” communication. When you speak (or type in a stream-of-consciousness style), you provide a massive amount of messy but valuable context.
  • Why it works: AI is incredibly good at structuring unstructured data. It acts as an editor that can sift through your “brain dump” to find the gold.
  • How to do it in text: If you aren’t using voice, you can simulate this by typing without stopping, ignoring typos, and just letting your thoughts flow.

Example Prompt:

“I am going to provide a stream-of-consciousness ‘brain dump’ about a project I am stuck on. It will be messy, unorganized, and full of typos. Please listen to everything, organize my thoughts into a coherent structure, and then tell me what key piece of information I am missing.”

4. The Multi-Agent Orchestration (Manual Swarm)

Timestamp: [29:26]

Patil demonstrates using different AI models (Claude, Gemini, ChatGPT/Codex) simultaneously for different parts of a project.

  • The Concept: Treat different AIs like specialized employees.
    • Gemini: Use it for large context tasks (e.g., “Read my entire blog history and suggest new topics” [29:58]).
    • Claude: Use it for nuance and coding architecture.
    • ChatGPT/Codex: Use it for quick execution or web browsing.
  • Educational Application: You don’t need fancy software to do this. You can simply open three tabs and assign them different “jobs” for the same project.

Example Prompt (for Gemini, leveraging its large context window):

“I am pasting the transcripts of the last 10 meetings I’ve had. Read all of them and create a psychological profile of my management style, then suggest 3 specific improvements.”

Summary of the “Mental Shift”

The video emphasizes that the biggest barrier to effective prompting isn’t technical skill, but ego [07:07]. Users often try to “steer” the AI too much because they think they already know the answer. The most effective prompters are “humble” enough to say, “I don’t know what the right questions are—you tell me.”