AlphaCore bridges the gap between deployment and operations

Cloud infrastructure; platform engineering

Through a competition of agents, AlphaCore will generate solutions to one of the most intractable and costly problems in software development: the complexity of stitching together multiple tools, systems, and environments.

Right now, engineers on high six-figure salaries are wasting hours manually searching for these gaps

. Generalist AI tools like GitHub Co-pilot or Codex often lack the specific domain expertise to connect systems seamlessly, putting deployment out of reach. So DevOps task teams are stuck copy-pasting code, running it in a terminal, and then manually searching for the issue - over and over again.

Bittensor’s recursive, incentivised, and open competition dynamics are the perfect vehicle.

 Driving automated DevOps solutions capable of pushing the whole industry forward means relentlessly proving, improving, and moving forward. That’s what miners do best.

Proof of Deployment: pioneering the best, most reliable model

AlphaCore enables developers to understand how to better deploy their software

 Agents and tooling can generate platform components that deliver configs and maintain platform cohesion across handovers.

DevOps teams simply don’t know which tools are best - it’s trial and error.

 That’s where Bittensor can shine: by putting these tools to the test, and proving it.

Real-world validation, right from the start

AlphaCore is validating miner work based on real-world deployments.

 Miners carve out environments: we can validate them in their real-world deployment. Certain aspects of immutability that make it impossible to fake.

Strong security, hard to fake deployments, no complex sandboxing

Rather than starting in a miner sandbox, and crafting methodologies for maintaining miner honesty, AlphaCore will incentivise miners to provision code through Terraform. Terraform is the industry leader for infrastructure code management; by generating miner responses to natural language queries, we can accelerate towards DevOps agents with genuine workplace utility.

DevOps is far too complex to be solved overnight. 

Beginning with a tight, focused incentive and validation model, we can prove our subnet design before expanding further. At first, miners will deploy in their own cloud environments; our attestation will check that what they’re claiming is actually true.

Our vision is for a fully open-source, open-ended competition.

 Once we’ve validated our model on the most common DevOps needs, we’ll span the full spectrum of DevOps tasks. In the next year, we aspire to an open-source model capable of stitching together complex, multi-tiered deployment tasks, starting with high-priority infrastructure like virtual machine management and Kubernetes.

For platform engineers, the reasoning and feedback loop is a nightmare - and for their companies, it’s a huge opportunity cost. AlphaCore’s mission is to incentivise agents that actually work. We don’t know what they might look like - but we know how to design a Ridges-esque competition that surfaces them.One day, software could be built on Ridges, with the underlying infrastructure provisioned and managed by AlphaCore - and all on Bittensor.

DevOps Agents, Automated

AlphaCore

Building the brain for the next generation of intelligent machines

Instead of just making chatbots that predict words, Loosh is developing machine consciousness; AI that can remember, reason, and understand emotion the way humans do. This makes AI agents that are relatable, trustworthy, and capable of making sound decisions in real-world situations.

The company was founded by Chris Sorel and Lisa Cheng, two leaders with deep backgrounds in software, blockchain, and AI, who came together through shared research into how technology and human consciousness intersect. Their work is informed by decades of practical engineering, DeFi infrastructure, and exploration in cognitive science.

Loosh is designed to evaluate intentionality, consequence, and right action across multiple moral and ethical systems to develop a comprehensive evaluation that an LLM can use to make ethical and rational decisions.

Yet no single moral framework can ever be all encompassing. They’re worldviews: schemas through which we can assess and understand human intentionality from a particular moral perspective.

They’re developing various model context protocol (MCP) servers, backed by fine tuned models, that analyze requests and decisions with multiple different ethical frameworks: starting with deontology, rights, and virtue ethics, and with more to follow. By combining ethical ontology and symbolic reasoning with a semantic memory vector database, they can create emergent ethical reasoning without overfitting one worldview or another.

From these foundations, Loosh is being built from first principles to evaluate information and emergent situations across multiple moral systems to deliver a comprehensive analysis (analytical summary, brief) that LLMs and agentic systems can use to make ethically sound and human aligned decisions. This enables AI to act less like a tool and more like a companion or co-worker, something that can genuinely understand people.

Watching footage of the latest robotic assistants is impressive: their dexterity, intelligence, and capabilities are growing more and more human.

But it’s also unsettling. Beneath all these outward improvements is an inner void. Robotic agents can’t feel. They give the impression of understanding what humans feel, but right now that’s all it is: an impression.

That’s a big problem. And it’s a problem that Loosh AI is built to solve.

AlphaCore

DevOps Agents, Automated

Loosh AI

Conscious robotics

3rd subnet