Documentation

https://docs.zeus.fyi

Mockingbird Front End GitHub

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.

Usage

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.

Quickstart

mb-simple-demo.mp4

Costs

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.

Features Available via Self Service UI

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

Features Available To Researchers & Partners

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

Unveiling the Next Generation of AI-Powered Workflow Automation

https://medium.zeus.fyi/unveiling-the-next-generation-of-ai-powered-workflow-automation-1f957bc20d3e

zK8s == Kubernetes + Zeus

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

Hosted Docusaurus in 5 Minutes and Under $10/month

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"`
}

How we built a Docusaurus deployment from this.

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,
          },
         },
        },
      },
    },
  }
}

Tutorial & zK8s Overview

https://medium.zeus.fyi/hosted-docusaurus-in-5-minutes-and-under-10-month-af999d7ef90a

Authenticated API in 5 Minutes

Step by step tutorial using our UI

https://medium.com/@zeusfyi/zeus-ui-no-code-kubernetes-authenticated-api-tutorial-c468d5ef0446

zK8s Apps & Clients

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.

`cluster_config_drivers`

`system_config_drivers`

`workload_config_drivers`

Workflow & Proxy Programmable Automation (Rolling releases coming through end of year)

`artemis_workflows`

`iris_programmable_proxy`

API Endpoints

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

Overview

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

Currently Supported Workload Types

Deployments
StatefulSets
Services
ConfigMaps
Ingresses
ServiceMonitors
Secrets

Node Tainting Automation & Infra Provisioning (Servers & SSDs) Deployable & Scalable on Demand.

Pods Controller

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 support@zeus.fyi

Hades Library

Hades is used to interact with Kubernetes workloads via API, and can apply saved Zeus workloads & cookbooks onto your own in house infrastructure.

Hera Client

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.

Cookbooks

Contains common web2 & web3 recipes for deploying applications, and managing infrastructure using zK8s or vanilla Kubernetes.

Zeus UI Highlights

Recent Articles

Introducing Serverless EVMs

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

Secret Sauce: Running Ethereum Validators at Scale for Pennies

Why run them any other way after you’ve read this?

https://medium.com/zeusfyi/secret-sauce-running-ethereum-validators-at-scale-for-pennies-24ac8dad4efd

Automating Validator Status Management in Ethereum in SQL

Using SQL Triggers and Relational States Compliant with Enforcing Consensus Spec

https://medium.zeus.fyi/automating-validator-status-management-in-ethereum-in-sql-part-1-464e40e32b0b

Adaptive RPC Load Balancer Benchmarks

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

High Performance Disks: NVMe in the Cloud

How to use NVMe disks the right way before you spend $$$$

https://medium.zeus.fyi/high-performance-disks-nvme-in-the-cloud-abb2bfc11fd9

Show Me the Stats

Optimal adaptive load balancing in stochastic environments. Recommended reading for scientists, engineers, data driven individuals

https://medium.com/zeusfyi/show-me-the-stats-6740f8d6d0b7

Adaptive RPC Load Balancer

Accurate, Reliable, Performant Node Traffic at Web3 Scale

https://medium.com/zeusfyi/adaptive-rpc-load-balancer-on-quicknode-marketplace-e68bb7c9d8ac

Files

README.md

Latest commit

History