Currently a one-man side project:

https://laboratory.love

Last year PlasticList discovered that 86% of food products they tested contain plastic chemicals—including 100% of baby food tested. The EU just lowered their "safe" BPA limit by 20,000x. Meanwhile, the FDA allows levels 100x higher than what Europe considers safe.

This seemed like a solvable problem.

Laboratory.love lets you crowdfund independent testing of specific products you actually buy. Think Consumer Reports meets Kickstarter, but focused on detecting endocrine disruptors in your yogurt, your kid's snacks, whatever you're curious about.

Here's how it works: Find a product (or suggest one), contribute to its testing fund, get detailed lab results when testing completes. If a product doesn't reach its funding goal within 365 days, automatic refund. All results are published openly. Laboratory.love uses the same methodology as PlasticList.org, which found plastic chemicals in everything from prenatal vitamins to ice cream. But instead of researchers choosing what to test, you do.

The bigger picture: Companies respond to market pressure. Transparency creates that pressure. When consumers have data, supply chains get cleaner.

Technical details: Laboratory.love works with ISO 17025-accredited labs, test three samples from different production lots, detect chemicals down to parts per billion. The testing protocol is public.

You can browse products, add your own, or just follow specific items you're curious about: https://laboratory.love

Repo: https://github.com/mochilang/mochi

I'm building Mochi, a small programming language with a custom VM and a focus on querying structured data (CSV, JSON, and eventually graph) in a unified and lightweight way.

It started as an experiment in writing LINQ-style queries over real datasets and grew into a full language with:

- declarative queries built into the language

- a register-based VM designed for analysis and optimization

- an intermediate representation with liveness analysis, constant folding, and dead code elimination

- static type inference, inline tests, and golden snapshot support

Example:

  type Person {
    name: string
    age: int
  }

  let people = load "people.yaml" as Person

  let adults = from p in people
             where p.age >= 18
             select { name: p.name, age: p.age }

  for a in adults {
    print(a.name, "is", a.age)
  }

  save adults to "adults.json"

The long-term goal is to make a small, expressive language for data pipelines, querying, and agent logic, without reaching for Python, SQL, and a half-dozen libraries.

Happy to chat if you're into VMs, query engines, or DSLs.