software engineer backend · full-stack · agents Berlin

Sanath Samarasinghe - Software Engineer

sanath@dev:~
A garden, not a blog

This site is a digital garden, a set of notes on engineering, AI and the things I'm building, that link to each other and grow over time. The notes index groups them by topic and sorts by when each one was last tended. Wander with the file tree on the left, or hit ⌘K to jump anywhere.

Recently tended

Dates are the last update, or the publish date when a note has never been revised. The full list, grouped by topic, is the notes index. The dated feed still lists every post by publish date.

Oct 31 26
From Loop to Harness seedling
A short map from the agent loop to the harness, the memory flush, the MCP tool list, and the judgment note.
#ai#development#tools#mcp
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Oct 31 26
Hybrid Search Keeps Showing Up seedling
Keyword search and vector search show up as a pair in the RAG note and in OpenClaw's memory index. This seedling lines the two up.
#ai#rag#openclaw#development
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Oct 31 26
Two Ways to Give an Agent Memory seedling
A memory MCP server stores project facts the model saves and retrieves. OpenClaw stores memory as Markdown files the session already loads. This seedling compares the two.
#ai#mcp#openclaw#development
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Oct 31 26
How OpenClaw Gives AI Agents Persistent Memory With Plain Markdown
A deep dive into how OpenClaw's file-based memory system gives AI assistants genuine continuity between sessions using Markdown files, semantic search, and automatic memory flush.
#ai#openclaw#development#tools
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Now

Right now I'm building Draph and an agent runtime, writing about evals for agentic systems, and going deep on small language models. My About note has the full picture - current focus, the stack I reach for, and work history.

  • Building - Draph - diagram & flowchart tool with AI-assisted layout. Meso - an agent runtime.
  • Writing - A long post on evals for agentic systems - why standard approaches fall short when tools have side effects.
  • Learning - How to build my own SLM - going deep on the fundamentals of small language models.
  • Location - Berlin, DE.

Open source

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