Top Tweets for #SN107
OpenAI + Bittensor Subnet 107 (Minos)
https://t.co/NVYTsHJTcR
A Bittensor subnet just co-authored a paper with OpenAI.
@theminos_ai #SN107 is in the author list of OpenAI’s new report: Scientific Computing in the Age of Agentic AI.
Their HelixForge engine, a GPU-native synthetic genome generator built from scratch with coding agents is the most ambitious case study.
On matched benchmarks: ~60× faster end-to-end vs the old CPU workflow.
This is the real $TAO narrative: deAI infra doing work that matters, not just vibes.
Minos → OpenAI paper → agentic genomics at scale.
$TAO / DYOR.
Proud to share: we co-authored a new paper with @OpenAI on scientific computing in the age of agentic AI.
HelixForge, our GPU-native engine for generating synthetic genomes, is featured as the study’s most ambitious and complex system.
An entirely new architecture, built from the ground up.
Minos is building the infrastructure for the age of AI in genomics.
Minos is building the “default lane” for genomic variant detection.
If they nail it, they won’t just participate in genomics... they become the rail everyone else runs on.
The upside for @theminos_ai #SN107 is hiding in plain sight.
@ShizzyUnchained @theminos_ai Most people think AI alpha is only in chatbots… meanwhile Minos is mapping the literal source code of life on #SN107. Chromosome by chromosome is how you build a monopoly on truth.
bittensor:native isn’t just compute, it’s becoming biology’s indexing layer.
Looking very fwd to hearing @theminos_ai on Novelty Search today discussing the future of genomics being built on $TAO. #SN107 is a juggernaut in the making.
This Thursday on Novelty Search :: SN107 Minos
@theminos_ai shows how #SN107 turns genomic variant calling into a Bittensor competition, generating fresh challenge genomes every 72 minutes with hidden synthetic mutations, rewarding miners for optimizing state-of-the-art mutation detection tools.
Join live via Bittensor Discord.
>> Hosted by @const_reborn
This Thursday on Novelty Search :: SN107 Minos
@theminos_ai shows how #SN107 turns genomic variant calling into a Bittensor competition, generating fresh challenge genomes every 72 minutes with hidden synthetic mutations, rewarding miners for optimizing state-of-the-art mutation detection tools.
Join live via Bittensor Discord.
>> Hosted by @const_reborn
Minos MCP Server (beta) is LIVE.
AI agents can now plug directly into #SN107 and watch the subnet in real time:
•live rounds
•miner + validator behavior
•score histories
•network health
This is a big unlock for agentic AI on bittensor:native turning @theminos_ai into a fully observable, machine readable genomics subnet.
We’re getting closer to AI-native science.
The Minos MCP server (beta) is now LIVE!
AI agents can plug directly into SN107 and monitor the subnet as it operates, from live rounds and miner performance to validator activity, score histories, and overall network health.
This opens Minos to the agentic layer of decentralized AI and makes SN107 easier understand.
Minos MCP brings us one step closer to a fully AI-native genomics subnet.
Try it out now 👇
Great discussion on @theminos_ai #SN107 ….changing the Genomics industry in real time. One of my top picks as a possible breakout unicorn on bittensor:native
MINOS: Genomic Sequencing on Bittensor TAO https://t.co/vMqzEAmRfC
Just gonna got out on a limb and say @theminos_ai #SN107 is in the right lane and heavily undervalued.
NEW: Samsung invests $175 million to become top shareholder in U.S. genetics firm Element Biosciences.
https://t.co/Ynva9lreg0
Estamos a unas horas de que de inicio el mayor evento del año para todo el ecosistema de $TAO #Bittensor
Anuncios, lanzamientos, noticias, descubrimientos... Que sorpresas nos darán? #SN44 #SN46 #SN105 #SN107 @webuildscore @zipcodenetwork @theminos_ai @b1m_ai
Toda mi atención esta con ustedes. Muy emocionada 🔥🚀
So @theminos_ai #SN107 is my #2 holding and deservedly so. Top notch 🧬 conversation between @markjeffrey & @centrum_blue.
Hash Rate - Ep. 172: Minos Subnet 107
🧙 Guest: @centrum_blue of @theminos_ai
02:27 The Challenge of Private Genetic Data
07:04 The Mutation Detector
10:59 Synthetic Genomes
14:21 The Role of Miners
22:27 Why Subnet?
26:29 Competitive Landscape
29:53 Synthetic Genomes and Digital Twins
34:00 Tokenomics
46:31 Marketing
48:51 Mamad's Journey and Vision
Current variant-calling tools in genomics are highly configurable, but many pipelines still run them with default settings without deeply searching the hyperparameter space.
Minos is changing that static approach.
We are turning routine genomic benchmarking into measurable improvements in variant-calling accuracy.
🧬 @theminos_ai #SN107 might be one of the most underpriced asymmetric bets in Bittensor right now.
Decentralized genomic variant calling isn’t just a niche, it’s a massive unlock. Instead of trusting blackbox labs, Minos turns SNP + INDEL detection into a competitive market where accuracy = rewards.
That’s how you get real signal.
New price × emission model aligning incentives
Transparent dev velocity + frequent releases
At $3.2M mcap, this is still early.
If Minos lands even ONE real world integration (biotech, research, personalized medicine), the narrative flips fast from “experimental subnet” → “critical genomic infra.”
Short-term volatility is noise.
Long-term, this is blockchain securing truth in biology.
Minos is a core holding and will continue to add on any dips.
If you’re active in the $TAO subnets, you should have @theminos_ai #SN107 in your portfolio.
Just went live and the future looks bright. Still my biggest position in the $TAO ecosystem.
When Minos went live, FreeBayes, one of the most popular genome analysis tools, quickly started outperforming the competition. At first it looked like it was simply better.
However, the network found it was overcalling by flooding genomic regions with excessive mutation calls to boost its score randomly, not accurately.
This exposed a real limitation. Widely used genomic tools had never been tested under adversarial pressure, as most rely on rule-based systems that break in unmeasured ways.
Minos is building infrastructure to stress-test these tools, expose their failures, and train the next generation of robust AI genomic models.
🚨 @theminos_ai #SN107 NERDS AMA TL;DR
- From idea → execution: SN107 is LIVE, validating 30K+ genomes in days
- Const backed Mamad early — not hype, real domain conviction
- Live incentives = live exploits… and rapid hardening
- Elegant design: hidden mutations, miners hunt, validators score → ungameable truth set
- Chromosome 20 = FDA-grade proving ground, not training wheels
- HelixForge-Pheno = sleeper unlock: synthetic disease genomes at scale (90% PRS correlation 🤯)
- While OpenAI/Anthropic struggle with genomics, Minos is building the data layer on bittensor:native
- Roadmap: DNA → multi-layer biology → personalized medicine AI
- Prototype by Oct? Customers at ASHG? Timeline just compressed hard
- This isn’t theory anymore ,,,,it’s running, paying, scaling
Phase 1 is live. The moat is data. The thesis just leveled up.
Bullish on what @theminos_ai #SN107 is building here. 27,000+ evaluations in just 6 days on chromosome 20 isn’t just speed ….it’s proof of a scalable feedback loop for genomic learning.
Starting compact, iterating fast and exposing real signal between variant callers is exactly how you lay the groundwork for AI that can truly reason over human genetics.
This is how personalized medicine goes from theory to inevitability on bittensor:native
The future of personalized medicine depends on AI models that can learn from genomes through a feedback loop.
In the first 6 days, we ran over 27,000 evaluations on chromosome 20 alone.
We started bootstrapping with chromosome 20 because it is compact enough for fast iteration, but complex enough to reveal real differences between variant-calling tools.
This is how we build the foundation for AI models that can reason over human genetic data.
#SN107 @theminos_ai is definitely one to watch in the $TAO ecosystem.
It’s still very early, but the project looks promising.
The future of personalized medicine depends on AI models that can learn from genomes through a feedback loop.
In the first 6 days, we ran over 27,000 evaluations on chromosome 20 alone.
We started bootstrapping with chromosome 20 because it is compact enough for fast iteration, but complex enough to reveal real differences between variant-calling tools.
This is how we build the foundation for AI models that can reason over human genetic data.
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