jimmy
8.7 KiB
20260928-044404 session 20260927_124641_b32c61ba
user: Ok what do you need from me
assistant: At http://127.0.0.1:8188, please check:
- Settings → User: signed in or signed out?
- Settings → Credits: what balance is shown?
Just send those two values—no password, code, or payment details. The one test clip is estimated at $0.3432; I’ll queue it only if the account is signed in and the balance covers it.
20260928-044747 session 20260927_124641_b32c61ba
user: Can I do this from my phone?
assistant: Not through a normal phone browser. ComfyUI is bound to 127.0.0.1:8188 on your PC, so localhost on your phone means the phone itself.
If you have remote desktop set up, connect to the PC and check Settings → User and Settings → Credits there. Don’t expose ComfyUI to your LAN; Partner Nodes require localhost access.
20260928-150222 session 20260927_124641_b32c61ba
user: ok I see the comfy ui screen
assistant: Great. In ComfyUI, open Settings → User and tell me whether it shows you as signed in. Then open Settings → Credits and send the displayed balance. No password or codes—and don’t buy credits yet. Once I have both, I’ll queue the one 6-second test.
20260928-150436 session 20260927_124641_b32c61ba
user: logged in
assistant: Sign-in is confirmed. I still need the balance shown under Settings → Credits; I won’t submit the test until I have it.
20260928-150558 session 20260927_124641_b32c61ba
user: 0 balance
assistant: Recorded in G:\garage-lectures\SETUP.md: signed in, $0 credits. No clip was queued. When you’ve topped up through ComfyUI’s Credits settings, send me the new balance; I’ll run the single 6-second test, then wait for your approval before any remaining shots.
20260928-150748 session 20260927_124641_b32c61ba
user: do we need to get the dialogue first? Maybe a subject?
assistant: Yes—lock the subject and dialogue before spending on video. We already have both:
- Subject: use a small classifier/router for simple decisions; save the large model for writing.
- Dialogue: the six lines from your original skeleton are in
01-scripts\ep001.md.
I corrected its still-model note to Z-Image-Turbo. No dialogue changes needed unless you want a different lesson. The 6-second test remains paused until credits are above $0.
20260928-152922 session 20260927_124641_b32c61ba
user: give me a list of subjects to cover. I like the Jev idea. its a new addition to how we think about ai. give me some other interesting ideas that people need to learn. Naybe a hermes agent series?
assistant: Assuming “Jev” means Jevons paradox: yes—that’s a strong episode. It reframes AI as economics, not just technology: when each use gets cheaper, people often use it so much more that total consumption rises.
The dialogue should be locked before any paid clip. Ep001 already has a subject and six dialogue lines: use a classifier/router for simple decisions; save the large model for writing.
Strong subjects for the main series
- Jevons paradox — “AI got cheaper. Why did our bill go up?” Lower cost per call can create far more calls.
- Cost per completed task — Model price is only part of the bill: retries, long context, tools, and human review count too.
- Small model, big model — “Don’t hire a novelist to sort the mail.” Route classification and scoring to small systems; reserve LLMs for language work.
- Chatbot vs agent — A chatbot answers. An agent takes actions, observes results, and decides what to do next.
- The verification gap — A confident answer isn’t a checked answer. Show why agents need read-backs and tests.
- Context isn’t memory — A long prompt is temporary working space; durable memory needs selection and retrieval.
- The approval boundary — Let software handle reversible work; put a human gate before money, publishing, or destructive actions.
- Prompt injection — The web page or document being analyzed is data, not the boss.
- Why agents fail halfway — Tool errors, expired sessions, and missing state
20260928-153433 session 20260927_124641_b32c61ba
user: the original rick and morty was this: https://x.com/nischalsharma_/status/2102869384310247701?s=20
assistant: Got it. The post is a Jev AI explainer: use probability-based decisions for agent tool choices instead of spending a large language model on every small fork; Jev complements ChatGPT rather than replacing it. It uses the Einstein-sorting-mail gag and closes with an “87% chance” beat.
ep001.md has that core arc, but doesn’t name Jev or include the 87% tag. I’ll keep Dr. Kade, Jules, and the garage original—not copy the Rick-and-Morty characters or set. Do you want Jev named in the dialogue, and should I add the 87% closing beat?
20260928-153732 session 20260927_124641_b32c61ba
user: Im simply giving you an example of what subject was used
assistant: Understood—the link is a reference for the kind of subject, not a request to revise Ep001 or name Jev in the dialogue. I’ll keep the current script unchanged and use that example to guide future topic ideas.
20260928-153832 session 20260927_124641_b32c61ba
user: so based on this what other subjects should I cover?
assistant: Right—Jev AI, not Jevons paradox. I misread that.
The appeal of the reference is a new AI idea explained through one clear problem: a general LLM is wasteful for tiny decisions; a specialist can return probabilities to help an agent choose what to do.
Other subjects in that vein
- Models that decide vs. models that write — classification, routing, scoring.
- Small model as the agent’s dispatcher — choose the tool or model before spending on a large one.
- Memory vs. context window — why stuffing everything into a prompt isn’t durable memory.
- Search rerankers — a second model sorts candidate results instead of generating an answer.
- Confidence and abstention — when an AI should say “not sure” or hand off.
- Evaluation beats demos — a convincing clip isn’t evidence a system works across real tasks.
- The verifier pattern — one model produces; a separate check or deterministic code catches errors.
- Tool calls are contracts — structured inputs and outputs make agents more reliable than clever prompting.
- Prompt injection — why a web page or document shouldn’t be allowed to take control of an agent.
- The real unit cost is a completed task — include retries, context, tool calls, latency, and review.
- Local vs. cloud models — privacy, control, speed, maintenance, and cost trade-offs.
- Multi-agent delegation — when parallel agents help, and when coordinating them costs more than the work.
- **Human a
20260928-164501 session 20260927_124641_b32c61ba
user: My point is to explain concepts that are just now arising. JEV is a new idea in AI. The vast majority of people still don’t know what it means or what it is. So I would like to continue in that vein. I would like to surface terminology or ideas that weren’t even heard of six months ago.
assistant: You’re right—I was giving you evergreen AI topics. You want concepts just entering the conversation. I also misread Jev earlier: you mean Jev AI, not Jevons paradox.
I filtered for ideas announced, published, or newly coined since March 28, 2026. The strongest subjects:
- System One models: What if an AI model returned structured choices and probabilities instead of writing text? TypeSafe introduced this category with Jev in September. It’s their framing, not yet a settled industry term. [1][2]
- RLCD: “Reinforcement Learning for Calibrated Decisions”—training for useful probability estimates rather than preferred-sounding answers. TypeSafe’s new term; explain it as their approach, not an established replacement for RLHF. [1][4]
- Noul: A probabilistic yes/no question. The model doesn’t just say “yes”; it returns how likely “yes” is. A very teachable new primitive in Jev’s interface. [2]
- Decision seams: A newly coined name for a bounded fork inside an agent—known options, a judgment needed, and code takes the next action. The phrase appeared in a practitioner guide days after Jev launched; it isn’t standard terminology yet. [5]
- Speculative fan-out: Ask several potentially relevant questions in parallel, then let code use the answers that matter. [3]
- Jev Engineering: The emerging “LLM writes, decision model decides, code acts” framing. The term spread in guides within days of Jev’s launch; present it as a fresh community label,