https://github.com/zeus-fyi/mockingbird
Mockingbird is a time series controlled AI systems coordinator & workflow executor system, data indexer and searcher, that builds control loops and hierarchical planning rules around higher order goals & objectives using automated evaluation systems and model-to-model communication via synchronized quorums.
Mockingbird is currently a model based supervisor entity, and soon, it will be a Model Supervised Intelligent Network, and now a product you can use, like ChatGPT 4, but with power steering and cruise control.
mb-simple-demo.mp4
Mockingbird is free to use for the first 3 days without a credit card with a valid login account. Though we may restrict account usage if we see abuse. If you connect your own OpenAI API key, you will be responsible for any costs associated with that, otherwise we will use our API key and then charge you 1-to-1 for the cost of the API usage that we incur, however you may have your usage throttled if you exceed our usage limits while using our API key.
- Social Media Indexing (Twitter, Reddit, Discord)
- Retrievals (API, Social Media)
- Analysis/Aggregation Tasks
- Integrated Time Window Search by Aggregation Windows
- Multi-model communication and I/O
- Time series controlled AI workflows, with dynamic time step control
- Workflow + relationship builder (UI)
- Integrated OpenAI + Social integration APIs, natively via Platform Secrets
- Run Scheduler/Controller
- Run Step-by-Step Cycle Execution History
- Run Token Usage
- Human-in-the-loop control trigger actions like API requests
- Automated evaluation systems
- Integrated JSON schema builder
- Additional indexing sources (News, Blogs, Forums, etc)
- Adaptive time series control via Metrics + Evals (PromQL, Adaptive)
- Analysis cache feedback searching (eg. reviewing previous model generated analysis to steer actions)
- Objective building for complex decision making, and milestone tracking
- Decision tree generation for structured decision making
- Unrestricted trigger actions (eg. outside of Human-in-the-loop control)
- Experimental distributed executive task planner
- Tasking single-to-multi-model task planning
- Tasking scheduling, recursive workflows, objective overrides
- Shared workflows contexts, selective contexts, searchable contexts
- Scenario driven workflow generator
- Multi-Model-Single-Model communication via quorums
- Multi-Model-Multi-Model communication via quorums + task planning + synchronization
- Ranked/Promotable-Model-Leader-Model-Follower
- Multi-Model Team vs Multi-Model Team Competitive Scenarios
- Multi-Modal (Text, Image, Video, Audio) analysis
- AI driven compute infrastructure, building, purchasing, maintenance, & monitoring systems for Kubernetes + Cloud Vendor APIs
- PagerDuty integration
https://medium.zeus.fyi/unveiling-the-next-generation-of-ai-powered-workflow-automation-1f957bc20d3e
Here we overview the core concepts needed to understand how you can build, deploy, configure K8s apps using Zeus, with a full walkthrough example of how we created an Ethereum beacon.
https://medium.com/@zeusfyi/zeus-k8s-infra-as-code-concepts-47e690c6e3c5
Developing Kubernetes applications is often complex and time-consuming, with a steep learning curve. However, the advent of zK8s is changing the game, allowing developers to build Kubernetes apps with pure Go code that’s functional, testable, and effortlessly turned into deployable infrastructure.
type WorkloadDefinition struct {
WorkloadName string `json:"workloadName"`
ReplicaCount int `json:"replicaCount"`
Containers zk8s_templates.Containers `json:"containers"`
FilePath filepaths.Path `json:"-"`
}
type Containers map[string]Container
type Container struct {
IsInitContainer bool `json:"isInitContainer"`
ImagePullPolicy string `json:"imagePullPolicy,omitempty"`
DockerImage DockerImage `json:"dockerImage"`
}
type DockerImage struct {
ImageName string `json:"imageName"`
Cmd string `json:"cmd"`
Args string `json:"args"`
ResourceRequirements ResourceRequirements `json:"resourceRequirements,omitempty"`
EnvVars []EnvVar `json:"envVars,omitempty"`
Ports []Port `json:"ports,omitempty"`
VolumeMounts []VolumeMount `json:"volumeMounts,omitempty"`
}wd := zeus_cluster_config_drivers.WorkloadDefinition{
WorkloadName: "docusaurus-template",
ReplicaCount: 1,
Containers: zk8s_templates.Containers{
docusaurusTemplate: zk8s_templates.Container{
ImagePullPolicy: "Always",
DockerImage: zk8s_templates.DockerImage{
ImageName: "docker.io/zeusfyi/docusaurus-template:latest",
ResourceRequirements: zk8s_templates.ResourceRequirements{
CPU: "100m",
Memory: "500Mi",
},
Ports: []zk8s_templates.Port{
{
Name: "http",
Number: "3000",
Protocol: "TCP",
IngressEnabledPort: true,
ProbeSettings: zk8s_templates.ProbeSettings{
UseForLivenessProbe: true,
UseForReadinessProbe: true,
UseTcpSocket: true,
},
},
},
},
},
}
}https://medium.zeus.fyi/hosted-docusaurus-in-5-minutes-and-under-10-month-af999d7ef90a
Step by step tutorial using our UI
https://medium.com/@zeusfyi/zeus-ui-no-code-kubernetes-authenticated-api-tutorial-c468d5ef0446
zK8s is an expressive language for cloud infrastructure, used for building, assembling, and keeping them running over their entire lifecycle. Enabling cost efficient, effortless large scale infra automation, coordination, customization, and control.
Workflow & Proxy Programmable Automation (Rolling releases coming through end of year)
Documentation and code examples are found here API_README.md
How to use the test suite to setup your own api calls README.md
The test directory contains useful mocks and tools for interacting with the API. It also contains a useful config-sample.yaml, convert this to config.yaml and set your bearer token here, which then allows you to use the demo code to create your first api request in seconds
- Automates translation of kubernetes yaml configurations into representative SQL models
- Users upload these infrastructure configurations via API where they are stored in the DB
- Users can then query the contents of these infrastructure components, deploy, mutate or destroy them on demand
- Deployments
- StatefulSets
- Services
- ConfigMaps
- Ingresses
- ServiceMonitors
- Secrets
- Node Tainting Automation & Infra Provisioning (Servers & SSDs) Deployable & Scalable on Demand.
- GetPods
- GetPodLogs
- PortforwardReqToPods
- DeletePods
Not every possible field type is supported, but the most common ones are, and even a decent amount of the uncommon ones. If you find a field you need isn't supported please send us an email at [email protected]
Hades is used to interact with Kubernetes workloads via API, and can apply saved Zeus workloads & cookbooks onto your own in house infrastructure.
This client uses the OpenAI API to generate code with AI. This service is available at OpenAI cost, so just pay for the token cost, otherwise it is free to use.
Contains common web2 & web3 recipes for deploying applications, and managing infrastructure using zK8s or vanilla Kubernetes.
Foundry’s Anvil as an EVM Simulation Environment on Demand.
Democratizing access to simulation technology at scale by lowering the cost of simulation to near zero.
For safer smart contracts, less exploits, more robust engineering by using isolated ephemeral environments per each test.
https://medium.zeus.fyi/introducing-serverless-evms-0035549a5e7f
Why run them any other way after you’ve read this?
Using SQL Triggers and Relational States Compliant with Enforcing Consensus Spec
Over several weeks on a production load monitoring Uniswap prices
When should you consider using the Adaptive RPC Load Balancer?
TLDR:
- You need to scale your application to handle more requests than a single endpoint can handle
- You need to reduce the error rate of your application
- Especially if you are still paying for the request even if it fails
- You need to improve the reliability of your application
- You want to run multi-step procedures in a single request
https://medium.zeus.fyi/adaptive-rpc-load-balancer-benchmarks-c7aa3aa0d42a
How to use NVMe disks the right way before you spend $$$$
https://medium.zeus.fyi/high-performance-disks-nvme-in-the-cloud-abb2bfc11fd9
Optimal adaptive load balancing in stochastic environments. Recommended reading for scientists, engineers, data driven individuals
https://medium.com/zeusfyi/show-me-the-stats-6740f8d6d0b7
Accurate, Reliable, Performant Node Traffic at Web3 Scale
https://medium.com/zeusfyi/adaptive-rpc-load-balancer-on-quicknode-marketplace-e68bb7c9d8ac
