Supplement Truth
Supplement analyzer. Stop buying hype, start buying results
What it does
The supplement industry is worth $177 billion built on marketing, not science. SupplementTruth analyzes any supplement's ingredient list against real clinical research and tells you exactly what works, what's underdosed, and what's pure hype. → Evidence ratings per ingredient (Strong / Moderate / Limited / None) → Hype vs. Science meter → Dose analysis — catch "pixie-dusting" → PDF export reports to share with your doctor or coach → Analysis history to track your stack over time Free to start
Does the same job
all alternatives →- IVI've been using AI to analyze every supplement on the marketJan 2026 · pillser.com · ▲94
Hey HN! This has been my project for a few years now. I recently brought it back to life after taking a pause to focus on my studies. My goal with this project is to separate fluff from science when shopping for supplements. I am doing this in 3 steps: 1.) I index every supplement on the market (extract each ingredient, normalize by quantity) 2.) I index every research paper on supplementation (rank every claim by effect type and effect size) 3.) I link data between supplements and research papers Earlier last year, I took pause on a project because I've ran into a few issues: Legal: Shady…
- IWI want to change how people buy health supplements2023 · backoflabel.com · ▲70
I made a table where you can find out the source/location of factory for where health supplements are made. Then, I spent a year reading product labels so you can save time and money when buying supplements. This is that update. This is still a work in progress but it functions fine. My previous post was a simple database of company data showing ingredient sourcing/location. That took 10 days, this has taken me close to 9 months. BackOfLabel is an extension of that initial interest with dosage information at the product & ingredient level. This update allows sorting by many more…
SupplementsAIJun 2026 · supplements-ai.vercel.app · ▲3Evidence-based supplement advice from real PubMed studies
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
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AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com