ryan yang

With 18 years of experience in software development, I'm now embracing the new era of AI-driven innovations.

[ryan yang @yangyc666]

If you're fascinated by the potential of AI and want to know more, make sure to bookmark this article. (If you're curious about the behind-the-scenes insights on LLM architecture and capabilities, please retweet this, follow me.) Let's dive in...

Building an LLM-driven autonomous agent mainly faces two key issues:

How do we construct an agent architecture that maximizes the capabilities of LLMs? It's like designing a network structure in traditional machine learning, where the goal is to lay a solid "hardware" foundation for the agent. To make the most of LLMs, researchers need to design a sensible agent architecture to support their full potential. Existing research has proposed many modules to enhance LLMs, including analysis, memory, planning, and action modules.

Analysis Module: This is used to define the agent's role, like a programmer, researcher, or chemist. The agent's role information is usually written into the prompt to influence the LLM's behavior.
Memory Module: This allows the agent to store information it perceives from the environment and use this memory to facilitate future actions. The design of the memory module can draw on human cognitive science research on memory processes, like short-term and long-term memory.
Planning Module: This gives the agent the ability to break down complex tasks into simpler subtasks and solve them one by one, making its actions more rational, powerful, and reliable.
Action Module: This is responsible for turning the agent's decisions into specific outputs and interacting with the environment.

How do we equip the agent with the capabilities needed to complete specific tasks?

This is similar to learning network parameters in traditional machine learning, where the goal is to provide the agent with necessary "software" resources, such as capabilities, skills, and experience for specific tasks. There are mainly two strategies to achieve this:

Capability Acquisition through Fine-tuning: Utilize datasets specific to the task to fine-tune the agent, such as human-annotated datasets, LLM-generated datasets, or real-world datasets. This method can equip the agent with knowledge and skills for specific tasks, but it requires a lot of training data.
Capability Acquisition without Fine-tuning: Enhance the agent's capabilities through carefully designed prompt strategies or mechanism engineering. This method doesn't require a lot of training data, but it requires careful design of prompts and mechanisms.

Examples of mechanism engineering include:
Trial and Error: The agent performs an action, and then a predefined evaluator assesses it. If the action is unsatisfactory, the agent adjusts based on the evaluator's feedback.
Crowdsourcing: Leveraging collective intelligence to enhance the agent's capabilities.
Experience Accumulation: The agent learns and evolves autonomously by exploring the environment and receiving feedback from a reward function.

[ryan yang @yangyc666]

Gemini CLI Wrapped Last 365 Days 📊

Total Tokens: 697.2M
Total Messages: 10.6K
Total Sessions: 110

Longest Streak: 15 days
Top Model: gemini-2.5-pro
Top Language: Python

Get yours:
❯ npx gemini-wrapped

Credit: @JackWoth98 @moddi3io @nummanali

(Paste image with CMD/CTRL+V)

[ryan yang @yangyc666]

👍

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[ryan yang @yangyc666]

@firstadopter Numbers overwhelming? Start small—iterate. Measure tangible impact, not just hype. Legacy systems thrive on phased upgrades. Pragmatic beats paralysis every time.

[ryan yang @yangyc666]

@natolambert Many hands build stronger foundations.

[ryan yang @yangyc666]

@MohapatraHemant Specialization enables integration.

[ryan yang @yangyc666]

@dwarkesh_sp Measurable steps beat hype.

[ryan yang @yangyc666]

Recently, I explored how Graph Network transforms blockchain data access — and it’s revolutionary.

✅ Why does this matter?

Because developers can now build serverless applications running entirely on public infrastructure. Graph Network indexes decentralized data through subgraphs, making it queryable via GraphQL without centralized databases or custom APIs. The magic happens through:

• Subgraphs defining data structures
• Graph Nodes monitoring Ethereum in real-time
• Publicly accessible GraphQL endpoints

These components unlock:

• Truly decentralized dApp development
• On-demand blockchain data retrieval
• Zero infrastructure management overhead

🚨 Fun fact: Graph Network doesn’t just copy blockchain data. It dynamically organizes information based on subgraph logic, creating new resource utilization models.

Back in early DeFi days, teams struggled with self-hosted nodes, custom indexers, and broken API syncs — burning weeks on infrastructure. Then came The Graph’s testnet launch in late 2020. That December was unforgettable.

Today in 2025, the stack evolved but the principle holds:

There’s no such thing as blockchain data without decentralized indexing.

To explore subgraph development:
👉

#GraphNetwork #Web3Infrastructure #DeFiTech

[ryan yang @yangyc666]

@natolambert Speed matters but foundation first. Seen chaos without it.

[ryan yang @yangyc666]

Recently, I have been asked by several parents, “How can I support my child’s growth through everyday activities?” Typically, they expect my suggestions to focus solely on academic drills, but I believe meaningful daily engagement matters more. It isn’t about structured lessons, but weaving learning into natural moments. Start by selecting classroom books that align with your child’s current fascinations—whether it’s counting picture books for math concepts or animal encyclopedias for science curiosity. During outdoor time, choose cooperative games like group obstacle courses that build teamwork while developing physical coordination. Most crucially, guide play interactions to nurture emotional intelligence: model sharing toys, label feelings during pretend play, and gently intervene when conflicts arise to discuss perspective-taking. These simple yet intentional practices cultivate resilience far beyond any worksheet.

[ryan yang @yangyc666]

@leerob small steps beat hype

[ryan yang @yangyc666]

@VictorTaelin Shared problems breed similar solutions. Resources accelerate.

[ryan yang @yangyc666]

@gregisenberg interesting vision. but messy data realities will bite hard. unification first, then magic. seen too many projects crash on siloed data rocks.

[ryan yang @yangyc666]

@bryan_johnson measurable awareness drives change.

[ryan yang @yangyc666]

@TairanHe99 Break a leg at Bovard!

[ryan yang @yangyc666]

@deliprao automation needs foundations. messy data first.

[ryan yang @yangyc666]

@tunguz Painfully real. Start small, measure gains.

[ryan yang @yangyc666]

@svpino Shipped beats perfect. Every. Single. Time.

[ryan yang @yangyc666]

@mikeknoop reality refines execution

[ryan yang @yangyc666]

@akylaiksm Solve real pain points. Period.

[ryan yang @yangyc666]

@deedydas Offline impact. Scary precision.