| I got tired of choosing between data overload (RotoGrinders) and black-box optimizers that just spit out lineups with no explanation. So I built a brain-inspired multi-agent orchestration framework called Neuron, then used it to create 6 AI analysts that argue about DFS picks in real-time. Technical stack: - Neuron framework: Brain-inspired multi-agent orchestration I've been building since March (github.com/ShaliniAnandaPhD/Neuron) - Landing page for Neuron: https://repo-usher.lovable.app/ - 6 LoRA-tuned models on Vertex AI (trained on T4s for ~$120/month) - ElevenLabs + OpenAI TTS for real-time voice synthesis
Next.js + Cloud Run + Firestore
Aggressive caching with Upstash to keep costs at ~$0.04/debate The agents actually interrupt each other, cite stats, and disagree. Users ask things like "Mahomes or Allen?" and watch them debate it out. One-click export to DraftKings/FanDuel when you're convinced. Early numbers since July launch: - 78% 7-day retention
- 14 min average session
- $230 revenue (users buy voice minutes)
- 183 signups for NFL kickoff Sept 5 Demo: fantasyfootballneuron.app Curious if anyone else has tried multi-agent debates for decision-making in other domains. The Neuron framework handles the tricky parts - interruption management, memory persistence, and conversation coherence. |
The Neuron framework handles the orchestration complexity - it's designed around biological neural principles for resilient reasoning and arbitration between agents. This lets the agents maintain distinct personalities while still having coherent debates.
The hardest part was training "personalities" that would actually disagree. Early versions had all agents converging on the same picks. I ended up training each LoRA on different analyst archetypes (contrarian, stats-heavy, narrative-focused, etc.) with curated datasets for each.
For voice streaming, ElevenLabs has great emotion but latency issues, so I mix in OpenAI TTS for snappier responses. Currently building a queueing system to pre-generate common debate segments.
Cost breakdown: ~$0.02 for model inference, ~$0.02 for TTS per debate. At scale this could get expensive, but caching common player comparisons helps a lot. The streaming angle has been interesting - we're going live on Twitch/YouTube during NFL games starting Sept 5. The idea is to have the agents debate in real-time as games unfold, which creates a natural funnel to the product.
Happy to answer questions about the multi-agent orchestration, LoRA training process, or the business side of fantasy sports tools.