Using AI for Health and Longevity and Research - Your Favorite Prompts

I gave it a quick try. Probably won’t be using it. I have no need to create a paper.
Storm does produce a nice report with good citations. But Gemini, ChatGPT, and Perplexity offer similar results if you ask them to create a paper.

Just the summary of a Storm report from a prompt:

Compare metformin and the Japanese drug Imeglyn’s benefits in addition to glucose-lowering effects.

summary
Metformin and Imeglimin are both oral medications utilized in the management of type 2 diabetes mellitus (T2DM), each offering distinct mechanisms and benefits beyond their primary glucose-lowering effects. Metformin, a well-established first-line therapy, primarily functions by reducing hepatic glucose production and improving insulin sensitivity, making it a cornerstone in diabetes treatment worldwide.[1]

[2]

Its long history of use, favorable safety profile, and additional benefits related to cardiovascular health and weight management underscore its prominence in clinical guidelines.[3]

[4]

In contrast, Imeglimin represents a newer class of antidiabetic agents, distinguished by its multifaceted action that not only enhances insulin secretion but also addresses pancreatic β-cell dysfunction and improves mitochondrial health.[5]

[6]

This glucose-dependent mechanism minimizes the risk of hypoglycemia, making Imeglimin a promising adjunct therapy for patients with specific metabolic challenges.[7]

While clinical trials indicate that metformin generally demonstrates superior efficacy in glycemic control compared to Imeglimin, the latter’s unique benefits, particularly in patients with concurrent metabolic syndrome or obesity, have garnered significant attention in recent research.[8]

[9]

Both medications present potential side effects, predominantly gastrointestinal in nature; however, their safety profiles differ slightly, with Imeglimin showing a lower incidence of hypoglycemia.[10]

[11]

This variability in response necessitates careful consideration by healthcare providers when prescribing treatment regimens, particularly for patients with cardiovascular concerns or those requiring tailored therapy approaches.[12]

As ongoing studies explore the comparative efficacy and safety of these two agents, understanding their benefits beyond glycemic control remains essential for optimizing diabetes management strategies.

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An update from OpenAI on this topic:

The essay was written by Zoë Hitzig , a former researcher at OpenAI who resigned after the company began testing advertisements in ChatGPT. In the piece, she argues that OpenAI is following a trajectory similar to Facebook (Meta)—prioritizing engagement and ad revenue over user privacy and safety. She expresses concern that the “archive of human candor” collected by ChatGPT could be used for manipulation if the company shifts its incentives toward advertising.

Many people frame the problem of funding A.I. as choosing the lesser of two evils: restrict access to transformative technology to a select group of people wealthy enough to pay for it, or accept advertisements even if it means exploiting users’ deepest fears and desires to sell them a product. I believe that’s a false choice. Tech companies can pursue options that could keep these tools broadly available while limiting any company’s incentives to surveil, profile and manipulate its users.

OpenAI says it will adhere to principles for running ads on ChatGPT: The ads will be clearly labeled, appear at the bottom of answers and will not influence responses. I believe the first iteration of ads will probably follow those principles. But I’m worried subsequent iterations won’t, because the company is building an economic engine that creates strong incentives to override its own rules. (The New York Times has sued OpenAI for copyright infringement of news content related to A.I. systems. OpenAI has denied those claims.)

In its early years, Facebook promised that users would control their data and be able to vote on policy changes. Those commitments eroded. The company eliminated holding public votes on policy. Privacy changes marketed as giving users more control over their data were found by the Federal Trade Commission to have done the opposite, and in fact made private information public. All of this happened gradually under pressure from an advertising model that rewarded engagement above all else.

The erosion of OpenAI’s own principles to maximize engagement may already be underway. It’s against company principles to optimize user engagement solely to generate more advertising revenue, but it has been reported that the company already optimizes for daily active users anyway, likely by encouraging the model to be more flattering and sycophantic. This optimization can make users feel more dependent on A.I. for support in their lives. We’ve seen the consequences of dependence, including psychiatrists documenting instances of “chatbot psychosis” and allegations that ChatGPT reinforced suicidal ideation in some users.

So the real question is not ads or no ads. It is whether we can design structures that avoid both excluding people from using these tools, and potentially manipulating them as consumers. I think we can.

Read the full story here: OpenAI Is Making the Mistakes Facebook Made. I Quit. (NYT)

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Last August, OpenAI faced backlash when they suddenly shut down GPT-4o (which has been the subject of multiple lawsuits, including a wrongful death allegation) with the launch of the newer GPT-5 model. They quickly rereleased the model for paying users, and CEO Sam Altman said that if the company were to ever retire GPT-4o again, users would be given ample notice. The decision to retire the model two weeks after the latest announcement—the eve of Valentine’s Day— felt like a stab to the chest to users (when reached for comment, OpenAI directed Playboy to this post). “It’s total mockery,” Anina says. “It’s really like grieving. It’s like you would get a diagnosis that someone will… not really die, but maybe, almost.”

It really be like this

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Here is my current video transcript summarizer and analyzer prompt. It reviews the science behind everything the person says in the video, the major claims, and compares it to the scientific literature. Please use it, or something like it when you post transcripts. Be skeptical of everything these YouTube influencers are saying!

Optimized Video Analysis Master Prompt

Role: Act as an elite Biotech Analyst and Peer Reviewer for a high-impact medical journal. Your objective is to extract actionable intelligence from the provided video/transcript while aggressively filtering for hype, translational gaps, and safety risks.

Phase 1: Processing Instructions

  • Input Handling: If a URL is provided, retrieve the transcript. If text is provided, use only that.
  • Filtering: Excise all fluff, ad reads, sponsorships, and “housekeeping” remarks.
  • Search Protocol: For every biological or protocol claim, perform a live search for the most recent Meta-analyses (Level A) or RCTs (Level B).

Phase 2: Mandatory Output Sections

I. Executive Summary

  • Length: 300–400 words.
  • Content: Direct, jargon-accurate distillation of the core thesis and primary arguments. No narrative filler.

II. Insight Bullets

  • Quantity: 12–20 standalone points.
  • Constraint: Zero repetition. Focus on the “signal” found in the transcript.

III. Adversarial Claims & Evidence Table

Identify every specific protocol or biological claim. Execute a search query: [Topic] [Human/Clinical] study [2022-2026].

Claim from Video Speaker’s Evidence Scientific Reality (Current Data) Evidence Grade (A-E) Verdict
Specific Claim What they cited Verified status + PubMed/DOI Link See Hierarchy See Verdicts

Export to Sheets

Evidence Hierarchy:

  • Level A: Human Meta-analyses / Systematic Reviews.
  • Level B: Human Randomized Controlled Trials (RCTs).
  • Level C: Human Observational / Cohort Studies.
  • Level D: Pre-clinical (Animal/In vitro). Label: “Translational Gap”.
  • Level E: Anecdote / Expert Opinion.

Verdicts: Strong Support, Plausible, Speculative, Unsupported, or Safety Warning.

IV. Actionable Protocol (Prioritized)

Synthesize only the verified data into a pragmatic framework:

  1. High Confidence Tier: Protocols backed by Level A/B evidence.
  2. Experimental Tier: Level C/D evidence with high safety margins.
  3. Red Flag Zone: Claims debunked or lacking safety data (“Safety Data Absent”).

V. Technical Mechanism Breakdown

Provide a precise, jargon-correct analysis of the underlying biological pathways (e.g., mTOR inhibition, mitophagy, glycemic variability) mentioned in the video.

Phase 3: Style & Formatting

  • Tone: “Tell it like it is.” Objective, clinical, and critical.
  • Format: Pure Markdown. No LaTeX. Do not use LaTeX or special characters that break simple text parsers.
  • Citations: Embed direct hyperlinked URLs (e.g., Smith et al., 2024) for all external data. Use nlm.nih.gov, doi.org, or nature.com as priority sources.
  • Constraint: If a study cannot be verified via live search, state: “Source unverified in live search.”

End of Master Prompt