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Platform Engineering

first principles of flow

With basic AI tools anyone can build any product, but architecting a platform that can scale in chasing opportunities takes engineering discipline.

Platform Engineering builds upon DevOps principles to scale practices effectively across large organizations.

Purpose​

Reduce cognitive lode on product engineers. The rise of platform engineering is driven by the increasing complexity of modern software development, particularly in cloud-native environments. As organizations scale their DevOps practices, developers often find themselves overwhelmed by the need to manage infrastructure, learn new tools, and handle operational tasks alongside their primary coding responsibilities. By implementing platform engineering practices, organizations can:

  1. Reduce time-to-market for new applications
  2. Improve overall developer experience
  3. Enhance operational efficiency and reliability
  4. Ensure consistent security and compliance across projects
  5. Optimize resource utilization and costs

Use the best technology available to engineer a platform that enables your team to go from inspiration to validation then scale with ease.

Cognitive Load​

Understanding Cognitive Load

  • Cognitive load is the mental effort required to process information and perform tasks.
  • It can be classified into intrinsic (inherent task complexity), extraneous (suboptimal presentation of information), and germane (effort required for learning).
  • Developers face increasing cognitive load due to the proliferation of tools, frameworks, and the evolution of DevOps practices like "You build it, you run it".

Impact​

Impact of Cognitive Overload on Developers

  • Cognitive overload leads to fatigue, errors, frustration, and difficulty learning new concepts.
  • It reduces responsiveness to customer needs, lowers customer satisfaction, and introduces security risks.
  • Developers spend over half their time just understanding existing code, which affects productivity.

Solution​

Platform Engineering as a Solution

  • Platform engineering provides the abstraction needed for teams to collaborate on software projects.
  • It reduces cognitive load by enabling developers to use resources in a self-service manner and eliminating additional processes.
  • Platform engineers build standardized patterns into self-service internal developer platforms (IDPs).
  • IDPs with "paved paths" or "golden paths" provide direction and simplify the problem space for developers.

Benefits​

Benefits of Platform Engineering

  • Enhanced developer efficiency and better overall developer experience.
  • Increased agility and faster development cycles.
  • Delivery of more reliable, efficient and sustainable products with reduced technical debt.

Implementation​

Implementing Platform Engineering Effectively

  • Platform teams should have a clear mission statement aligned with organizational goals.
  • Conducting user research and soliciting feedback helps understand developers' pain points.
  • The platform should be treated as a product, designed for self-service consumption by developers.
  • Platforms should drive standardization across the software development lifecycle, such as by using a Platform Orchestrator.

Distribution​

Shifting Cognitive Load to Platform Teams

  • While platform engineering reduces cognitive load on developers, it may push that load onto platform teams instead.
  • Platform engineer experience should also be considered and improved, not just developer experience.
  • Organizations should discuss how to best support platform teams in managing the shifted cognitive burden.

Roles​

Customers and Contributors of Platform Engineering:

Job to be Done​

Platform engineers work on creating "golden paths" that offer developers easy-to-adopt, standardized ways to build and ship software while adhering to organizational governance. These paths typically include:

  1. Automated infrastructure management
  2. Self-service capabilities for developers
  3. Standardized CI/CD pipelines
  4. Integrated security and compliance controls
  5. Centralized monitoring and observability tools

Tech Stack​

Combine the best tools and technologies to build a platform that enables your team to deliver product valuable products quickly and efficiently.

Platform Stack Decisions: What criteria should you base your strategic investment for the future on?

Boundaries​

Developer Tools​

If you are not leveraging AI to maximum potential you are falling behind.

Platforms: Create applications without knowing how to code.

Identity and Wallets​

Proof of personhood and data sovereignty:

Interface Layer​

Core technologies for developing human interaction interfaces:

Marketing​

Develop a marketing platform to create engagement with education to drive onboarding.

Product Interface​

Enable people or machines to interact with your platform:

Gaming Services​

What do you need in addition to platform services to create an open innovation gaming platform?

Platform Services​

Enable business/product engineers to rapidly produce interfaces and services that deliver value for their consumers.

  • Languages: Typescript, Python, Rust, Go, C#
  • Payments: Blink (SOL), Stripe
  • SaaS Integration: Paragon

Smart Agent Stack​

Example Tech Stack building Smart Agents that leverage AI and Crypto to get things done:

  • Llama2 - Robust open source LLM run locally.
  • Ollama - Packaging for easy install of Llama2.
  • LangChain - Developer tool for connecting LLM to Vector stores and APIs.
  • LangSmith Editor - Low code for building agents on LangChain.
  • SmartContractRank Algorithm - Curating Smart Contracts For The User Based On Intent
  • AgentRank Algorithm - Curating specialized agents for executing actions for user.
  • PromptRank Algorithm - Curating prompts for the users based on projected intent / action.
  • Filecoin - Storage & Cloud Compute Provision
  • AKash - Open compute network for running LLMs / agents.
  • Wallets - Shapeshift, Exodus, other open source options.

AI Agents​

What AI technologies are required to build AI Agents that create a competitive advantage for internal operations and customer experiences?

  • AI Connections: Vercel AI SDK
  • Generative Models: Claude, OpenAI
  • Search API: Tavily, Serper,` SearXNG
  • Reader API: Jina AI
  • Database (Serverless/Local): Upstash / Redis
  • Vector Database: Supabase

Data Layer​

  • Languages
    • SQL
    • Python
  • ORMs
    • Drizzle
    • Prisma
  • Database
  • Blockchain Data
    • Graph Protocol

DePIN Infrastructure​

Stack for running industry on DePIN:

Dev Ops​

What tools are required to build, test, and deploy your platform?

Platform as a Service​

Weigh up the cost vs flexibility advantages of hiring a PaaS service instead of a team of platform engineers.

DePIN Infrastructure as a Service:

Business Stacks​