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Smol Gardens began as a concept presented by the Femmecubator team at BetaNYC's UnSchool of Data 2026 conference on March 2026, emerging from the Open Civic Tech initiative. The second workshop was presented at Makeshift 2026: Accountable Tech by Design event during NYC Design Week.

Current Challenges: Human-AI Collaborative Workflows

Designers, builders, and civic technologists are facing a core problem: how to develop civic tech responsibly in the age of AI without creating redundant, unmaintainable, or harmful solutions.

Is it possible to create meaningful work using LLM models? When a new wave of vibecoders (builders with no programming skills) are shipping websites in less than a day, there's a clear gap in accountability that needs to be addressed.

This creates several cascading challenges:

  1. Lack of frameworks for responsible AI-enabled design - there's no clear, accessible methodology that allows practitioners to balance speed with responsibility while building civic tech. Read more: Impact Assessment
  2. Lack of transparency and unaccountability - current AI development practices often lack transparency about tradeoffs, making it difficult for communities to understand how AI systems will impact them or to offer meaningful critique. Read more: Transparency Reporting
  3. Scale mismatch - large language models (LLMs) are presented as the default solution, but they may be overkill for many applications and come with disproportionate environmental and resource costs. Read more: Model Right-Sizing
  4. Weakened community agency - without continuous accountability mechanisms and openness to critique, affected communities lose the ability to shape and govern the AI systems being built for them. Read more: Community Governance
  5. No clear alternative path - practitioners who want to build differently lack both a practical toolkit and evidence that smaller, more efficient AI models can deliver meaningful civic tech solutions. Read more: Open-Source Tooling

Who Is This For?

The Smol Gardens Project is a guide created for technologists, designers, researchers or educators who are already using AI tools in their workflow.

You can use the Seedscore Impact Assessment Tool as a framework or guide for your workflow, Smol gardens aims to educate and push for alternatives to using LLM models, with an end goal to incorporate Accountable tech principles in a builder's workflow.

In the workshops, participants will be invited to document and audit their work using an impact assessment framework.

Check out our research docs
Sticky notes on a wall mapping out challenges around AI accountability

Our Approach

Design Workshops

Invite participants to document actual AI usage through a "diary mission," using the Impact assessment tool as guide.

Read more: Transparency Reporting

Impact Framework

Create the impact assessment tracking tool. We're developing a framework that measures human, systems, and environmental impact on AI-enabled web-development.

Read more: Impact Assessment

Small Language Models

Use small language models (SLMs) to build a curated repository of civic tech tools hosted by the Accountable Tech working group and will be made available open source for future builders.

Read more: Open-Source Tooling