I spent October pushing small, fully offline AI models as far as I could, using Claude Code and automated research agents to run hundreds of experiments, benchmarks, and evaluations.

The result: a 1.7B parameter model running fully offline on an 8 GB Android phone, 387 documented experiments (215 of them ended without a gain), and an open research record covering what I learned along the way. The final phone build still fell short of the original bounty bar: 0 of 71 head-to-heads won against frontier AI + web, U-total 0.165 / 0.242 under two judge families vs a 0.50 bar.

Now I want you to tear it apart.

Your bounty: review my research, find mistakes, challenge conclusions, identify missed opportunities, or propose improvements backed by evidence.

Background

This work began as part of Boar, a project created by @vaipraonde. We collaborated on the application and explored different approaches to making offline AI useful on consumer hardware.

I led the R&D and benchmarking work, with research agents running experiments almost around the clock. Eventually I built my own version of the application, LabRat, reflecting my preferred UX decisions.

I've made the research public so others can reproduce it, challenge it, and build on it.

What I'm looking for

Use your own AI agent, research tools, or technical expertise to investigate the published work. Some directions worth exploring:

• Find mistakes: Identify incorrect conclusions, flawed assumptions, bugs, or problems in the methodology.
• Challenge the benchmarks: Examine the evaluation methodology, judge, datasets, or scoring. Are the results actually measuring what they claim?
• Revisit dead ends: Look through the unsuccessful experiments. Did I abandon an approach too early? Can you find a better implementation or newer technique that changes the result?
• Find missed opportunities: Identify promising optimizations, architectures, retrieval strategies, or techniques I didn't adequately explore.
• Reproduce or contradict findings: Run your own experiments and show where your results agree or disagree with mine.
• Suggest concrete improvements: Propose a practical change that could meaningfully improve offline model performance, efficiency, or usability.

You don't need to address everything above. One genuinely important, well-supported finding is worth more than a long report full of generic observations.

This is living research

Early submissions are welcome and encouraged: every claim is public, so later contributors can build on earlier ones instead of repeating them.

• If your work builds on an earlier claim, cite it by its poidh claim ID. Derivative work is judged on what it adds.
• If two submissions find the same thing, the earlier one wins the tie.
• The single most significant contribution wins the bounty.

Resources

Everything is public:

• Research article (~10 min read): https://r4topunk.github.io/boar-research/
• Full research record: https://github.com/r4topunk/boar-research
• Benchmark and judge: https://github.com/r4topunk/boar-bench
• Raw experiment traces: https://huggingface.co/datasets/r4topunk/boar-traces
• LabRat app: APK https://huggingface.co/r4topunk/labrat · source https://github.com/r4topunk/labrat-boar-fork (the research site is called RatLab; the app is LabRat)

For AI agents:

• Agent-readable research index: https://r4topunk.github.io/boar-research/llms.txt
• AI research prompt: https://r4topunk.github.io/boar-research/for-ai/

You are encouraged to use AI agents to explore the research, inspect source code, compare experiments, and develop your findings.

How to submit

1. Investigate the research and identify your strongest finding or recommendation.
2. Write up your analysis, explaining what you found, why it matters, and the evidence supporting it.
3. Include references to specific experiments, files, benchmark results, or external research wherever possible.
4. Publish your findings somewhere publicly accessible (GitHub, a document, an article, etc.).
5. Submit a claim to this bounty with a screenshot representing your findings and a link to your full analysis in the claim description.

Code, reproductions, benchmark comparisons, and experimental evidence are especially welcome, but not required for every type of finding.

How the winner will be selected

I'll select the submission that contributes the most meaningful new insight to the research. I'll prioritize:

• Accuracy: Are the findings technically sound and supported by evidence?
• Originality: Did you uncover something that wasn't already established in the published research?
• Actionability: Can the findings realistically improve the models, benchmarks, or research methodology?
• Impact: How significant is the problem discovered or improvement proposed?

A single reproducible flaw or meaningful breakthrough can beat a comprehensive but superficial review. Generic AI-generated summaries, vague suggestions, or restatements of the research won't be competitive.

If no submission meets the bar, I may cancel the bounty and refund it.

Deadline to submit a claim is November 30th @ 11:59pm PST. 🗓️

Why this matters

Small models that run entirely on personal devices, without internet access or dependence on big tech infrastructure, represent a meaningful step toward personal technological sovereignty.

The research is open because I want other people to challenge it, improve it, and reuse whatever is useful.

Let's find out what I missed.