Builds

The Agent Stack #025 — Monday Build Distributed tracing just got its AI moment. Jaeger v2 dropped with OpenTelemetry at its core, specifically targeting the chaos of multi-agent workflows. The timing isn’t coincidental. Traditional observability breaks down when you have agents calling other agents calling APIs calling more agents. You end up with a mess of logs that tell you nothing about why your agent decided to delete the production database (yes, that actually happened this week). ...

The Agent Stack #022 — Monday Build NVIDIA just dropped Nemotron OCR v2, and it’s not the model that matters—it’s how they built it. They generated millions of synthetic text images to train a multilingual OCR system that beats commercial APIs. Here’s how to steal their playbook. The Synthetic Data Factory Traditional OCR training requires massive datasets of real documents. Expensive, slow, and you’re stuck with whatever languages and fonts exist in your training set. NVIDIA flipped this: generate infinite training data instead. ...

The Agent Stack #019 — Monday Build The OpenClaw drama last week wasn’t just about Anthropic flexing their pricing muscles. It highlighted the biggest pain point for AI agent builders: your brilliant agent becomes useless when it hits rate limits or gets temporarily banned. Here’s how to build agents that keep working when APIs fail. The Problem with Single-Provider Agents Most developers build agents that depend entirely on one LLM provider. When Claude goes down or your API key gets throttled, everything stops. OpenClaw’s creator learned this the hard way when Anthropic temporarily cut off access. ...

The Agent Stack #016 — Monday Build Most agent memory solutions want you to spin up vector databases and embedding services. MemWeave just dropped on GitHub with a different approach: Markdown files and SQLite. That’s it. The sachinsharma9780/memweave repo shows how to build persistent agent memory without any external dependencies. No Pinecone subscriptions. No ChromaDB containers. Just files your agent can read and search locally. Here’s the core architecture. Your agent writes memories as structured Markdown: ...

The Agent Stack #013 — Monday Build Bluesky just dropped Attie, and it’s the most interesting social architecture I’ve seen this year. While everyone’s talking about OpenAI killing Sora, builders should focus on what Bluesky’s actually built: an AI agent that customises social feeds using their AT Protocol. This isn’t just another chatbot wrapper. It’s Claude running on top of a decentralised social protocol, letting users build custom algorithms in plain English. “Show me startup news but filter out crypto drama” becomes actual feed logic. ...

The Agent Stack #011 — Monday Build Three markdown files and a script tag. That’s apparently all you need to turn any website into an AI agent interface. The Agent Kernel breakthrough Agent Kernel landed on HN this week with a deceptively simple promise. Three markdown files that make any AI agent stateful. No complex orchestration frameworks. No heavyweight architectures. Here’s how it works: agent-kernel/ ├── context.md # System prompt and agent personality ├── memory.md # Persistent knowledge and learnings └── conversation.md # Current session history The genius is in the simplicity. Each file has a specific job. The agent reads all three before responding, writes updates to memory.md when it learns something new, and appends to conversation.md for session continuity. ...

The Agent Stack #008 — Monday Build NVIDIA just dropped something builders should care about. Their NeMo Retriever introduces “agentic retrieval” - moving beyond simple semantic similarity to actually reason about what information you need. Building Beyond Basic RAG Traditional RAG is broken. You throw documents at a vector database, hope semantic similarity finds the right chunks, and pray your LLM can piece together coherent answers. NVIDIA’s approach flips this. ...

The Agent Stack #006 — Monday Build Chrome 146 just shipped WebMCP support. That means any website can now expose tools for your AI agents to use. The real story isn’t the API—it’s what happens when agents start hitting paid services at scale. Most builders are flying blind on costs until their OpenAI bill arrives. Build: Cost estimation before deployment The Agent-Audit repo tackles this head-on. It’s a linter that estimates costs before your agent runs wild. ...