RLMs are the new reasoning models
Harness, Memory, Context Fragments, & the Bitter Lesson
this is a work in progress mental dump on interesting intersections between how we use and design a harness, implications for memory being accumulated over long timescales, and the search bitter lesson we can’t escape
this
Your harness, your memory
You Don't Know LLM Training: Principles, Pipelines, and New Practices
You Don't Know AI Agents: Principles, Architecture, and Engineering Practices
I started a new chapter of my Agentic Engineering Patternw guide about anti-patterns - things NOT to do
So far I only have one: Inflicting unreviewed code on collaborators, aka dumping a thousand line PR without even making sure it works first https://t.co/rg6LVi9zkk
Agents: Inner Loop vs Outer Loop
Lessons from Building AI Agents for Financial Services
Agent Engineering: A New Discipline
If you’ve built an agent, you know that the delta between “it works on my machine” and “it works in production” can be huge. Traditional software assumes you mostly know the inputs and can define the outputs. Agents give you neither: users can say literally anything, and the space
Agents Should Be More Opinionated | vtrivedy
The best agent products aren't the most flexible, they're the most opinionated. Learn why agents need fewer knobs, not more, and how to design around model intelligence spikes.
Agent Engineering 101
A practical guide to Agent Engineering: the intersection of software, systems and security engineering.
Six Principles for Production AI Agents
Practical lessons from building production agentic systems
Docs for AI agents
Utkarsh Kanwat - AI Engineer
AI Engineer at ANZ Bank working on intelligent systems, LLM optimization, and scalable ML platforms.
Enterprise Agentic AI Hierarchy of Needs
The crucial layers of infrastructure that make up a production grade Agentic AI system.
Which agent framework should you use? I tried 7. The winners will surprise you 🤯
I rewrote my "tech writer" agent in 7 frameworks: Agno, Autogen, Google ADK, Atomic Agents, DSPy, Langgraph, and Pydantic AI. You'll NEVER guess the winners.
GitHub - business-science/awesome-generative-ai-data-scientist: A curated list of 100+ resources for building and deploying generative AI specifically focusing on helping you become a Generative AI Data Scientist with LLMs
A curated list of 100+ resources for building and deploying generative AI specifically focusing on helping you become a Generative AI Data Scientist with LLMs - business-science/awesome-generative-...
A Visual Guide to LLM Agents
Explore the main components of what makes LLM Agents special.
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.
Agents interact with their environment and typically consist of several important components
chain-of-thought
This is where planning comes in. Planning in LLM Agents involves breaking a given task up into actionable steps.
reasoning” and “thinking” a bit loosely as we can argue whether this is human-like thinking or merely breaking the answer down to structured steps.
without any examples (zero-shot prompting)
Providing examples (also called few-shot prompting7)
ReAct
Reflecting
These Multi-Agent systems usually consist of specialized Agents, each equipped with their own toolset and overseen by a supervisor.
three LLM roles
SELF-REFINE
To enable planning in LLM Agents, let’s first look at the foundation of this technique, namely reasoning.
Chat bot considerations
Agents
Foundation models enable many new application interfaces, but one that has especially grown in popularity is the conversational interface, such as with chatbots and assistants. The conversational interface makes it easier for users to give feedback but harder for developers to extract signals. This post will discuss what conversational AI feedback looks like and how to design a system to collect the right feedback without hurting user experience.
Building effective agents \ Anthropic
A post for developers with advice and workflows for building effective AI agents
AI Agent In Production - Insights from the market
Explore the capabilities of AI Agents and their real-world applications. CrewAI showcases the power and versatility of AI technologies across various sectors.
The Problem with Reasoners
A new tool that blends your everyday work apps into one. It's the all-in-one workspace for you and your team
Agentless is a great example of how a more constrained agent is better than a general agent for specific tasks 💡 - it achieves much higher scores on SWE-Bench Lite for bug-fixing than other agent approaches 🛠️
The whole point is to not let the agent do everything, but to do a…
— Jerry Liu (@jerryjliu0)
(12) Pedro Domingos on X: "Calling an LLM an agent doesn’t suddenly make it more intelligent." / X
— Pedro Domingos (@pmddomingos)
LLM Resource Hub
A comprehensive collection of Large Language Model (LLM) resources, tools, and learning materials.
I've been building agents for almost 1.5 years and can confidently 99% of the "ai browsing" demos are useless.
the reality is consumers won't have millions of AI agents working 24/7 for them, bargaining and shopping to save $20. there will just be an ai shopping app.
compute…
— Sully (@SullyOmarr)
Roadmaps
Community driven roadmaps, articles and guides for developers to grow in their career.
The architecture of today's LLM applications
Here’s everything you need to know to build your first LLM app and problem spaces you can start exploring today.