ai
85 notes tagged ai in the AI agents topic. How agents are built, tuned and run: loops, harnesses, MCP, skills and coding agents, with what broke and what we measured.
- Agentic engineering: the series map and how to read it
A series on building agentic software for engineers who know the classic lifecycle, and how its pillars, deep dives and reading paths fit together.
- Owner agents, plain functions: where the model sits in our ops estate
Give each domain one owner agent that reports back, keep the steps as plain functions, and add autonomy to a running platform one gated decision at a time.
- No. 43: Tuan Dao on delegating to AI
Tuan Dao, an engineer at Dwarves, on moving from pair programming with AI to delegating to it, and why human review is always the bottleneck.
- #5 Building is cheap now, getting heard is the hard part
Han talks with Ethan Phan, co-founder and CTO of Plot, about reading a pivot signal, staying lean for 2.5 to 3 years, and running an AI-native engineering team where…
- 3,016 delegations to agents: the long briefs cost the most
Median brief 289 words, median run 6.4 minutes, 14.5% needed a follow-up. What 24 days of Claude Code subagent logs say about briefing agents, and where a brief cost…
- Harness engineering notes from a team running agents all day
Nine field notes on running coding agents in production at Dwarves: the harness around the model, how we check and guard it, what we put it to work on, and what it does…
- We called ten technology arcs in June 2025. eight of the 60 experiments we planned got built.
Sixteen months after ten thesis notes, a scored ledger of what we built, which clients paid and what the market did, plus the five-step method that produced the calls.
- We stopped testing engineers on speed
Dwarves cut the weight of timed algorithm problems in its technical interview from 60% to 15%. The dates, the weights, and the new hour.
- Five layers to tune before you fork your agent
What makes AI agentic, how to split a deterministic core from an agentic edge, and the five layers you can tune without rebuilding the harness. Real numbers from circle…
- What the Engelberg report got right about us
Thoughtworks published the findings of its second Future of Software Engineering retreat. Most of what the room converged on is what we have been learning and putting…
- Claude Code 2.1.259 turned our Read() deny rules into a Bash tripwire
Bypass-mode sessions started stalling on 'a Read() deny rule is configured' prompts. What the harness now infers from a Bash command, why a CLAUDE.md rule couldn't stop…
- The debug loop: make the agent measure the browser before it touches the CSS
A repeatable loop for AI-driven UI debugging: name the metric, name the axis, drive a real browser over CDP, read the DOM into a table, kill theories, sweep the suspect,…
- Making our site AI-agent-ready, 50 to 93
We ran memo.d.foundation through is-agentic.com, read its rubric closely, and shipped the fixes that took the score from 50 to 93. Here is the rubric in full and what…
- Our publish step took 110 seconds, then 78. Our release tab took three tries.
Reading a CI log line by line cut memo's publish step from 110 to 78 seconds and exposed three silent bugs. Then agents started deploying all day, and the Releases tab…
- DeepSeek harness, dissected
An architecture read of dsh, DeepSeek's new agent harness: the plugin tree, the patch-layer boot, the log-first session model, and what it means for a Claude Code shop.
- What stays valuable when anyone can build anything
As AI makes building cheap, value concentrates in judgment (inside) and trust (outside): the two inputs it cannot mass-produce. A lens on what to own in the AI era.
- The tells
AI writing gives itself away at the level of structure, not word choice. A field guide to the patterns, and the guideline change that bans them from our posts.
- How knowledge work organizes itself
How societies naturally organize around different types of knowledge work; from those who apply compressed rules to those who derive new principles from scratch.
- The five stages of learning
How human cognition and artificial intelligence both develop increasingly abstract representations, from simple stimulus-response patterns to deep hierarchical…
- E2b breakdown
Technical analysis of E2B, a cloud infrastructure platform that runs AI-generated code in secure, isolated sandboxes using lightweight virtual machines that start in…
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- Cline breakdown
Comprehensive technical analysis of Cline's VS Code extension architecture, covering system design, implementation patterns, and architectural innovations
- Better engineering
A personal take on how AI is changing software development and why better engineering still comes down to human judgment, not just better tools.
- LSTM
Long Short-Term Memory (LSTM) networks are a crucial advancement for sequential data, addressing the exploding and vanishing gradient problems in traditional RNNs. This…
- Composing the Dwarves video pipeline
A behind-the-scenes look at how we built our video pipeline for long-form explainers and short-form content, from script to screen, with AI in the loop.
- Should AI agents transact without human confirmation?
A UX guide to designing agentic AI systems that balance automation with user control.
- AI-powered consulting sprints for rapid clarity
AI-powered consulting sprints for rapid clarity and breakthrough
- AI agent explained: LLMs, workflows and agents
Most people confuse LLMs, workflows, and agents, but understanding these three levels unlocks the real potential of AI automation. The key difference lies in who makes…
- Prompt formula
Most prompts fail because they lack structure, but a six-part formula transforms vague requests into precise AI instructions. The difference lies in knowing which…
- Speed beats perfectionism in AI adaptation
Developers who adapt fast to AI are thriving. Those who overthink are falling behind.
- Seeding your content for AI: a writer’s guide
This guide shows writers practical tactics for sharing content on platforms like Reddit, YouTube, and Quora to boost citations and stay visible in AI-generated answers.
- Stand up GEO tracking tools: a simple guide
Not sure if your content is showing up in AI search? This guide walks you through simple GEO tracking methods so you can see what works and keep your memos in play.
- Generative engine optimization
Learn the essential principles of Generative Engine Optimization (GEO): structure content for AI, boost your reference rate, and get LLMs to quote your pages directly in…
- Staying visible in AI search with GEO
Why generative engine optimization (GEO) decides whose knowledge AI answers cite, what the daily work of staying visible involves, and how to choose an AI visibility…
- Understanding Tidewave.ai
Discover how Tidewave connects AI assistants to your web framework's runtime through MCP, enabling real-time interaction with your application. Learn about its features,…
- Engineers in the AI landscape: architects, tinkerers, planners, and the vibers
Exploring the new archetypes of software engineers in the age of AI: architects, tinkerers, planners, and vibers.
- Composing the future, part 1: agent basics, LangGraph and Autogen
What makes a system agentic, why multi-agent beats single-agent, how orchestration works, and a close look at LangGraph and Microsoft Autogen.
- Composing the future, part 2: OpenAI's agent tools, MCP and fast-agent
How OpenAI's Swarm and newer agent tools work, why Model Context Protocol matters for tool access, and how fast-agent builds MCP-native agents.
- Composing the future, part 3: Mastra and CrewAI
Two frameworks with opposite instincts: Mastra for TypeScript teams building web-native agents, and CrewAI for role-playing crews of Python agents.
- Composing the future, part 4: LlamaIndex and the framework face-off
LlamaIndex's agent features for document-heavy work, then a side-by-side comparison of the frameworks on differentiators, strengths and ease of development.
- Composing the future, part 5: agent patterns, open problems and how to choose
The common agent composition patterns, the problems that remain unsolved, where the field is heading, and guidance for picking a framework.
- Mapping the path to AGI
Dissecting the evolution of AI agents from eager human-in-the-loop systems to fully autonomous entities, and how each stage automates the core tasks of exploration,…
- Skillmaxing
A guide for talents on how to cultivate the mindset and skills to become a proven, AI-augmented engineer, inspired by the principles of vetting top-tier individuals.
- Vetting proven talents
Guidelines and criteria for vetting high-level engineering talents, especially those leveraging AI.
- How to use Claude to build the system prompt
Building the use case: how to use Claude to build the system prompt
- Build custom AI agent with ElizaOS
This guide shows how to build a custom AI Agent with ElizaOS.
- Authorization for remote MCP servers over SSE
This guide explores implementing robust authorization for Model Context Protocol (MCP) over Server-Sent Events (SSE) transport, providing a standardized framework for…
- Tool-level security for remote MCP servers, part 1: designing role-based access control
Why remote MCP servers need tool-level access control, the security architecture, and the data model and tool registry behind role-based access.
- Tool-level security for remote MCP servers, part 2: enforcement, identity and practice
How an MCP server enforces permissions and filters data, connects to existing identity systems, and rolls out access control in practice.
- Let AI ask you
Most people give AI commands and expect perfect results, but the most effective approach flips this dynamic. Letting AI ask you questions transforms shallow outputs into…
- Your AI approach
Most people struggle with AI tool paralysis and prompt fatigue, but building an AI-native workflow comes down to three focused strategies. The key is systems over tools.
- Synthetic users in UX research
Explore AI-generated user profiles in UX research and their potential benefits and limitations. Learn when synthetic users can complement real user research and best…
- Working with legacy code
Learn how to navigate large, established codebases without losing your sanity. Discover technical strategies, cultural insights, and AI assistance for thriving in…
- Building chatbot agent to streamline project management
A technical case study detailing the implementation of an AI chatbot agent in a project management platform. Learn how the team leveraged LangChain, LangGraph, and GPT-4…
- Building data pipeline for OGIF transcriber
A technical case study of creating an automated system that downloads videos, processes audio, and generates transcripts using AI services like Groq and OpenAI.
- Quantization for large language models
As large language models (LLMs) continue to evolve, their parameter counts grow exponentially, with some models reaching trillions of parameters. This exponential growth…
- Project reports system: a case study
An in-depth look at Dwarves' monthly Project Reports system - a lean, efficient system that transforms communication data into actionable intelligence for Operations…
- Natural language to database queries: Text-to-MongoDB
An exploration of natural language to database query systems using MongoDB, examining system prompts and implications for data engineering and agentic workflows.
- ReAct(reason + act) in LLM
Working with Large Language Models (LLMs) may seem straightforward at first glance. Initially, we might think that simply prompting the AI model with a task is…
- ReWOO: Reasoning without observation - a deeper look
In the process of improving Large Language Model (LLM) performance, many techniques have been proposed. The Augmented Language Model (ALM) approach boosted LLM accuracy…
- Model selection
Learn how to choose the right AI model for your needs. Explore key factors like accuracy, privacy, and cost. Compare commercial vs open-source options and API vs…
- Use cases for LLM applications
Explore the diverse applications of large language models (LLMs) and AI in both enterprise and consumer sectors. Learn about key use cases across data analysis, content…
- The rise of AI applications with LLM
Discover how the rapid surge in artificial intelligence, led by models like ChatGPT, Claude, and Gemini, is reshaping industries and democratizing AI development. This…
- Prevent prompt injection
Nowadays, Large Language Models (LLMs) have become integral to various applications. However, with great power comes great responsibility, and the rise of LLMs has…
- Journey of thought prompting: Harnessing AI to craft better prompts
Journey of Thought Prompting is an innovative technique that leverages AI to craft more effective prompts for large language models. This approach uses the analytical…
- What is Generative UI?
An introduction to Generative UI (genUI), a user interface that generates interactive elements in response to user needs using AI, enhancing UX in chat applications. The…
- Function calling in AI agents
Function calling is a critical component in the architecture of AI agents, facilitating the integration of external functionalities and resources. This note explores how…
- Streamlining internal tool development with managed LLMOps: A Dify case study
Discover how managed LLMOps platforms like Dify streamline the development of AI-powered internal tools. Learn about the challenges of integrating LLMs, the benefits of…
- Building agent supervisors to generate insights
In the rapidly evolving field of artificial intelligence, the concept of agent supervisors has emerged as a powerful approach to orchestrating multiple AI agents for…
- How we crafted the OGIF summarizer bot to streamline weekly knowledge-sharing
Introducing the OGIF Memo Summarizer, a chatbot we developed using Dify in collaboration with @innno_. This tool transcribes YouTube videos and extracts key points from…
- Proximal policy optimization
Proximal policy optimization (PPO) is an algorithm that aims to improve the stability of training by avoiding overly large policy updates. It is a popular and effective…
- Explaining gradient descent in machine learning with a simple analogy
Gradient descent is a fundamental optimization algorithm in machine learning. It's a way for models to learn from data and improve their accuracy by gradually adjusting…
- A grand unified theory of the AI hype cycle
An exploration of the cyclical nature of AI development, tracing the rise and fall of new technologies within the field, and how this pattern has repeated throughout…
- Developing rapidly with generative AI
Generative AI overview and the different stages of building an LLM-powered feature
- A story map for building with LLMs
A comprehensive guide outlining the journey for engineers developing applications on top of Large Language Models (LLMs), covering key stages from understanding AI…
- Adversarial prompting in prompt engineering
An overview of adversarial prompting in prompt engineering, focusing on understanding risks associated with Large Language Models (LLMs) and strategies for mitigating…
- LLM's accuracy - self refinement
An overview of self-refinement, a technique where Large Language Models (LLMs) evaluate and improve their own output without the need for supervised data or…
- Reward model
A Reward model is a critical component in Reinforcement Learning for Large Language Models (LLMs), designed to evaluate and score the quality of generated responses. It…
- Q learning
An introduction to Q-learning, a model-free reinforcement learning algorithm used to learn optimal policies in Markov Decision Processes.
- Building blocks of a RAG pipeline
How chunking, hierarchical tree summaries, re-ranking, query caching and vector-store chat memory fit together around a vector database in an LLM application.
- Introduction to reinforcement learning and its application with LLMs
An introduction to Reinforcement Learning (RL), a machine learning method where an agent learns to make decisions by interacting with an environment. This article covers…
- How we created an AI powered interview system using OpenAI's ChatGPT
Discover InterviewGPT, an AI-powered ChatGPT system that automates technical interviews by generating tailored questions and evaluating answers for engineers at all…
- Choosing a vector database for LLM applications
An overview of vector databases and their application in LLM systems. This article covers the history and core concepts of vector databases, their use cases,…
- Foundation models: the latest advancement in AI
An overview of foundation models, their versatility in generative AI tasks, and their impact across various domains like NLP, image generation, and scientific research.…