AI vs people: Tailwind css (business)

After Stack Overflow, here is another (small) business being "destroyed" by AI.

The very business model that created content used for AI "free" training 
in being destroyed by free/cheap use of AI to replace business. 

A good summary of the situation:

Tailwind is in DEEP trouble - YouTube by Maximilian Schwarzmüller

Now developers don't need premium tools and support, when AI can generate code almost free.

At the end, who benefits?
Only $billionare investors?

And here is another deeply troubling conversation:

How to Save the World with Elon Musk

Yes, there are benefits of USING AI tools by people to be more productive.

But here is discussion of REPLACING people with AI & Robots, 
and keeping PEOPLE as PETS, with some "free money" to consume AI/robot generated products.
To treat people like dogs of cats, but without affection. 

Just because something is possible, does not mean something is good.

Unfortunately, such technology revolutions often cause people revolt.

tools: LangGraph: AI agent with state machine, and similar

The state machine + agent pattern is powerful, and LangGraph has popularized it, but it's not unique to them.

Similar Approaches

Direct competitors with explicit graph/state concepts:

• Autogen (Microsoft) - Uses a different model (multi-agent conversation) but has added StateFlow for explicit state machine control
• CrewAI - More task/role-based but recently added "Flows" which provide state machine semantics
• Rivet (Ironclad) - Visual node-based graph editor for AI workflows, compiles to JSON that can run from any language
• Llama Index Workflows - Their newer workflow system has explicit state and conditional branching

Workflow engines adding AI capabilities:

• Temporal - You can absolutely build agent loops with Temporal activities; it gives you durability and fault tolerance that LangGraph lacks out of the box
• Inngest - Similar to Temporal but lighter weight, good for AI agent patterns
• Prefect / Dagster - Data pipeline tools that can orchestrate agent-style flows

Custom - you could implement the same patterns. The core insight is just: state + transitions + conditional routing based on LLM outputs. Step Functions with a "loop until done" pattern and dynamic choice states.

LangGraph Language Support

Native SDKs:

• Python (primary, most features)
• JavaScript/TypeScript (solid, slightly behind Python in features)

LangGraph Platform (Cloud/Self-hosted):

• Exposes a REST API, so you can call it from any language
• But you still need to define your graphs in Python or JS

So if you wanted to orchestrate from, say, a .NET service, you'd either define the graph in Python and call the LangGraph API

LangGraph

• Website: https://www.langchain.com/langgraph
• GitHub: https://github.com/langchain-ai/langgraph
• Docs: https://docs.langchain.com/langgraph-platform
• Languages: Python, JavaScript/TypeScript (native SDKs); REST API for any language

Similar Tools / Competitors

AutoGen (Microsoft)

• Website: https://www.microsoft.com/en-us/research/project/autogen/
• GitHub: https://github.com/microsoft/autogen
• Docs: https://microsoft.github.io/autogen/stable/
• Languages: Python, C#/.NET

CrewAI

• Website: https://www.crewai.com/
• GitHub: https://github.com/crewAIInc/crewAI
• Docs: https://docs.crewai.com
• Languages: Python only

Rivet (Ironclad)

• Website: https://rivet.ironcladapp.com/
• GitHub: https://github.com/Ironclad/rivet
• Docs: https://rivet.ironcladapp.com/docs
• Languages: Visual editor exports to JSON; TypeScript library for runtime (@ironclad/rivet-core, @ironclad/rivet-node)

LlamaIndex Workflows

• Website: https://www.llamaindex.ai/workflows
• GitHub: Part of LlamaIndex core
• Docs: https://docs.llamaindex.ai/en/stable/module_guides/workflow/
• Languages: Python, TypeScript

Workflow Engines (general-purpose, AI-capable)

Temporal

• Website: https://temporal.io/
• AI Landing Page: https://temporal.io/solutions/ai
• GitHub: https://github.com/temporalio/temporal
• Languages: Go, Python, Java, TypeScript, .NET, Ruby
• Note: OpenAI's Codex agent and Replit's Agent 3 are built on Temporal Temporal

Inngest

• Website: https://www.inngest.com/
• AI Landing Page: https://www.inngest.com/ai
• GitHub: https://github.com/inngest/inngest
• Languages: TypeScript/JavaScript, Python, Go

Who's trying similar things:

Tool	Approach	Gap vs LangGraph
CrewAI Flows	Event-driven with @listen, @router decorators	Less explicit graph structure, more implicit event wiring
LlamaIndex Workflows	@step + events, state via Context	Similar feel, but events rather than edges — less visual
Semantic Kernel Process Framework	Stateful processes, Dapr/Orleans for distribution	More enterprise/Microsoft stack oriented
Rivet	Visual graph, but agents are nodes not routers	Agent doesn't decide routing — you wire it statically
Temporal	You can build it, but it's DIY	No native "LLM picks next step" primitive