Research-centric peptide and protocol reference hub
How to use this site
This project is a research-first index for peptides, protocols, and related studies. Most of what you see is either seeded from primary literature or lightly structured contributions from the community: effect sliders, logs, anonymous side-effect tags, and upvotes on protocols and Q&A. Everything is framed as "what people report" or "what the papers say", not as dosing or treatment advice.
Use this page as a tour of each major section — peptides, protocols, research feed, calculators, and community inputs — with live, interactive previews so you can see how they fit together.
Quick map of the app
Library pages for each peptide: research-centric summary, effect spider, logs, crowdsourced side-effect heatmaps, and Q&A.
Browse peptidesHigher-level stacks and cycles: linked peptides, community tags on "where this is used", crowd-voted variants, and contextual notes.
Explore protocolsA cross-peptide research feed with filters, plus a calculator for turning vials, dilutions, and dosing plans into numbers you can actually reason about.
Under the hood, most visuals on peptide and protocol pages are blends of model priors and community data. Seed scores from research and domain context give each peptide a starting profile; then sliders, votes, logs, tags, and Q&A from the crowd nudge those numbers and rankings over time. Nothing here is a prescription — it's a structured way to see what people report and how that lines up with the underlying evidence.
Peptide pages aggregate effect sliders and anonymous side-effect tags into visuals. Each vote is a small nudge on top of an AI-seeded baseline score, so early data doesn't swamp the prior but consistent patterns still rise to the top.
Step 1: Read which outcome each bar tracks (pain, sleep, performance).
Step 2: Use bar length as a rough "how much people noticed" indicator, not a precise effect size.
Example: aggregated pain, sleep, and performance sliders from community logs.
Step 1: Treat each row as "how often this tag appears", not a probability of harm.
Step 2: Let the colors hint at direction (green vs red), then verify with primary safety data.
Example: anonymous side-effect / signal tags with rough percentages and tone labels.
Structured logs capture the story behind a protocol — goal, training level, age band, dose ranges, and relative changes — while side-effect forms let people quickly flag what they noticed without giving dosing instructions.
Step 1: Set goal, training level, and age band so others can understand your context.
Step 2: Add rough duration and dose to make your log comparable, without over-specifying.
Step 1: Tag what you noticed in plain language (e.g. "nausea", "flushing").
Step 2: Keep notes neutral and avoid dosing instructions or diagnoses.
Protocol pages let the crowd annotate where a stack tends to show up and how it's structured. Admins define the tag vocabulary; users simply vote and suggest variants, which are then ranked by upvotes.
Step 1: Scan which contexts (rehab, performance, general repair) users associate with the stack.
Step 2: Decide whether your situation truly matches those contexts before using the protocol as inspiration.
Example tag row for a protocol: users upvote tags that feel accurate.
Step 1: Read variant rows as "how people structure things", not as instructions.
Step 2: Use them as prompts for deeper research into mechanisms, safety, and alternatives.
Example variant row describing how people stack and cycle a protocol.
| Variant | Stack | Cycle | Upvotes |
|---|---|---|---|
Sample: 8-week tendon stack (example) Community-submitted; read as a starting point, not a prescription. | BPC-157 + TB-500 + progressive PT 3x/week; sleep and protein focus. | 8–12 weeks, then reassess / deload. | ▲ 27 |
Question threads, comments, and ingestion pipelines turn the site into a living research notebook. The public can ask and annotate; admins keep it anchored to evidence.
Step 1: Upvote questions that articulate what you're trying to understand.
Step 2: When answering, prioritize links to studies and protocols over anecdotes.
CuriousResearcher
How does this protocol compare to single-agent BPC-157 for tendon repair in terms of study density and effect size?
EvidenceNerd
Most formal data is on single agents; this stack mostly combines those patterns. See the linked PubMed entries and protocol notes for how people structure cycles and monitor outcomes.
Step 1: Know that each ingested item is reviewed, tagged, and linked before it reaches the public feed.
Step 2: Treat the visible research feed as "approved snapshots" of a larger ingestion pipeline.
Admin-facing panel (conceptual): each row is an ingested item waiting for approval and tagging.
Awaiting: peptide link, approval, optional AI summary.
Awaiting: paraphrased summary, tags, and moderation decision.
The peptide index is where most people start. Each tile rolls up an AI-seeded effect profile plus community votes into a small radar chart and summary. When you click through to a peptide, you'll see research tags, logs, and side-effect stats specific to that molecule.
Step 1: Live-search peptides by name or summary. Step 2: Click through to view full research, logs, and side-effect stats.
The research feed is a curated, searchable index of ingested papers, database entries, and structured notes. Each row is linked back to a peptide, tagged by source (PubMed, Semantic Scholar, Reddit, news, vendor COAs), and often includes a short AI summary to help you triage what to read next.
Step 1: Filter by source and peptide to narrow the feed. Step 2: Open original articles for anything you plan to rely on.
Approved research items across all peptides, with direct links out to the original article or database entry.
A typical workflow: start in the peptide library to get oriented on a molecule's perceived effect profile, scan the research feed for primary evidence, then look at protocols and community logs to see how people structure their experiments. Use a calculator or other tooling you trust to sanity-check any numbers you see — always treating them as thought experiments to be weighed against formal literature and professional guidance.