Challenge Overview
Presented by Antler
Antler is one of the most active early-stage AI investors globally, constantly seeking to
identify emerging founders before they incorporate a company or enter the traditional VC
funnel. Currently, much of the early-stage founder discovery is reliant on platforms like
LinkedIn — but this excludes a wide swath of promising builders. They’re publishing code,
sharing ideas, launching tools, and engaging with communities across GitHub, Twitter,
Discord, Reddit, Substack, and beyond.
Your task is to design and prototype a system that helps automatically surface high-
potential, early-stage AI founders in Australia — without relying on LinkedIn. This solution
should identify signals of founder intent, technical or commercial AI activity, and early-stage
momentum, using non-traditional, digital-first approaches.
Challenge Overview
Presented by Antler
Antler is one of the most active early-stage AI investors globally, constantly seeking to
identify emerging founders before they incorporate a company or enter the traditional VC
funnel. Currently, much of the early-stage founder discovery is reliant on platforms like
LinkedIn — but this excludes a wide swath of promising builders. They’re publishing code,
sharing ideas, launching tools, and engaging with communities across GitHub, Twitter,
Discord, Reddit, Substack, and beyond.
Your task is to design and prototype a system that helps automatically surface high-
potential, early-stage AI founders in Australia — without relying on LinkedIn. This solution
should identify signals of founder intent, technical or commercial AI activity, and early-stage
momentum, using non-traditional, digital-first approaches.
Challenge Scope
Your solution should help scout and filter early-stage AI talent who are:
Technical Founders
• Engineers, data scientists, ML/AI researchers
• OSS contributors, LLM tinkerers, side project builders
Commercial Founders
• Product managers, operators, content-led growth marketers
• Individuals building MVPs or launching GTM experiments
These individuals may not yet have a company but are already building in public, experimenting with AI technologies, or sharing their learnings. Your system should identify these builders using publicly available signals from alternative platforms.
To focus your discovery, Antler is specifically interested in:
• Australia-based individuals, or those with clear AU ties (e.g., .edu.au email, affiliation with AU orgs or events)
• Clear signals of founder intent — actively shipping, launching, speaking, or writing about their work
• Involvement in AI — whether on the infrastructure, model, or applied use-case level
Your solution should help scout and filter early-stage AI talent who are:
Technical Founders
• Engineers, data scientists, ML/AI researchers
• OSS contributors, LLM tinkerers, side project builders
Commercial Founders
• Product managers, operators, content-led growth marketers
• Individuals building MVPs or launching GTM experiments
These individuals may not yet have a company but are already building in public, experimenting with AI technologies, or sharing their learnings. Your system should identify these builders using publicly available signals from alternative platforms.
To focus your discovery, Antler is specifically interested in:
• Australia-based individuals, or those with clear AU ties (e.g., .edu.au email, affiliation with AU orgs or events)
• Clear signals of founder intent — actively shipping, launching, speaking, or writing about their work
• Involvement in AI — whether on the infrastructure, model, or applied use-case level
Solution Requirements
Note: This is a builder’s challenge. We are evaluating working systems — not slide decks.
1. Working Prototype/Demo
A functional system, tool, or workflow that identifies early-stage AI founders from non-LinkedIn sources. This could be a dashboard, scraping engine, alert system, LLM-powered classifier, discovery UI, a combination of the above, or more creative forms.
2. Live Demo or Demo Video (10-20 minutes)
Prepare a short, clear walkthrough (10-20 minutes) of your system to showcase during the Hackathon Demo Day. This should explain what it does and how it works.
3. Technical Brief (maximum 5 pages)
Your written brief should include:
A. Technical Details
• Data sources and signals used
• Filtering and ranking criteria
• How AU-based individuals are identified
• Technical stack overview
B. Value Proposition
• Why your solution meaningfully addresses Antler’s challenge
• Strengths of your signal detection or approach
C. Limitations & Next Steps
• Key assumptions and limitations
• Potential improvements or scale-up paths
Note: This is a builder’s challenge. We are evaluating working systems — not slide decks.
1. Working Prototype/Demo
A functional system, tool, or workflow that identifies early-stage AI founders from non-LinkedIn sources. This could be a dashboard, scraping engine, alert system, LLM-powered classifier, discovery UI, a combination of the above, or more creative forms.
2. Live Demo or Demo Video (10-20 minutes)
Prepare a short, clear walkthrough (10-20 minutes) of your system to showcase during the Hackathon Demo Day. This should explain what it does and how it works.
3. Technical Brief (maximum 5 pages)
Your written brief should include:
A. Technical Details
• Data sources and signals used
• Filtering and ranking criteria
• How AU-based individuals are identified
• Technical stack overview
B. Value Proposition
• Why your solution meaningfully addresses Antler’s challenge
• Strengths of your signal detection or approach
C. Limitations & Next Steps
• Key assumptions and limitations
• Potential improvements or scale-up paths
Inspiration and Starting Points
NOTE: Use these suggestions as jumping-off points — not constraints.
Antler recommends sourcing from platforms where early builders are active:
• GitHub: Frequent or solo authors on AI repos, filtered by AU metadata
• Twitter/X: Users posting about “finetuning”, “GPT”, “RAG”, etc. with AU-based bios or activity
• Product Hunt: Launches of AI tools referencing .com.au domains or AU founders
• Reddit / Hacker News: “Show HN” posts, r/MachineLearning threads from AU contributors
• Discord: Users sharing demos, side tools, or co-founder searches in AI groups
• Substack / Blogs: Authors discussing AI MVPs, GTM experiments, or tooling
• University and Hackathon Communities: Students or researchers building applied AI projects
NOTE: Use these suggestions as jumping-off points — not constraints.
Antler recommends sourcing from platforms where early builders are active:
• GitHub: Frequent or solo authors on AI repos, filtered by AU metadata
• Twitter/X: Users posting about “finetuning”, “GPT”, “RAG”, etc. with AU-based bios or activity
• Product Hunt: Launches of AI tools referencing .com.au domains or AU founders
• Reddit / Hacker News: “Show HN” posts, r/MachineLearning threads from AU contributors
• Discord: Users sharing demos, side tools, or co-founder searches in AI groups
• Substack / Blogs: Authors discussing AI MVPs, GTM experiments, or tooling
• University and Hackathon Communities: Students or researchers building applied AI projects
Prize
• 1;1 coach session
• Residency Opportunities