What Happens to Your AI App at 9am on Monday?
I made 20% of my LLM gateway's calls fail on purpose and it dropped none of 200. Then I simulated 50 clients at 9am on Monday and 20 of them got an error.
I build AI agents that survive the real world.
I'm a Staff Developer Advocate at DigitalOcean. I build AI apps and agents, contribute to the open-source tools developers use to ship them, and write about what I learn.
Voice assistant for macOS
Nix lives in my Mac's menu bar. I say "Hey Nix", ask something, and it answers out loud. The thinking is done by whichever coding agent I'm signed in to (Claude Code, Codex or Antigravity), so it can read files, run commands and use my tools, within permissions I set. Speech recognition runs on the Mac with Whisper, five times faster than a hosted service (265ms against 1.3s), and the audio never leaves the machine.
Official integration
I wrote DigitalOcean's official LangChain integration from the first commit, and I maintain it. LangChain apps use it to call models on Gradient serverless inference, and it streams responses and reports token usage. I added LangChain 1.x support, reviewed and released the 1.0 move to the new pydo SDK before the old SDK retired, and I review and merge community pull requests, such as the one that added tool calling.
$ pip install langchain-gradient
from langchain_gradient import ChatGradient
llm = ChatGradient(model="llama3.3-70b-instruct")
for chunk in llm.stream("What is RAG?"):
print(chunk.content, end="")
Benchmark
I benchmarked six agent memory backends over 50-turn conversations. Qdrant, pgvector and the LangGraph store kept 89 to 94% recall at turn 50, for a third of the cost of keeping the full history. Mem0 cost more than the full history, and a rolling summary came last on both recall and cost.
| Backend | Recall | Cost |
|---|---|---|
| Buffer | 100% | $1,437 |
| Mem0 | 97% | $1,865 |
| LangGraph store (best value) | 94% | $487 |
| pgvector (best value) | 91% | $493 |
| Qdrant (best value) | 89% | $476 |
| Summary | 74% | $3,619 |
I made 20% of my LLM gateway's calls fail on purpose and it dropped none of 200. Then I simulated 50 clients at 9am on Monday and 20 of them got an error.
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I give talks and run hands-on workshops on AI agents and taking LLM apps to production. I also review agent architectures for a few teams.
I'm always up for a meetup, conference or workshop. The easiest way to reach out is below.
I'm always happy to talk about AI agents, developer tools, talks and workshops. Book a time, or pick whichever channel suits you.