Mike's Notes
The MLOps Community is fantastic, and it has a regular newsletter from Demetrios.
100% better than anything coming out of NZ or Australia. I attend MLOps events remotely whenever possible. Now I know where to find the talent capable of working on Pipi in future.
This is taken from a recent newsletter. Interesting how deterministic and probabilistic contexts are handled in generative AI.
All Pipi Engines are both deterministic and probabilistic as required.
The April 21 2026, MLOps Community Netherlands workshop video and slides are available.
Resources
- https://youtu.be/jXtnhyro-QE?si=DSPK2k81xKB_HL4w
- https://presentation-astro-xi.vercel.app/present?start=title
References
- Reference
Repository
- Home > Ajabbi Research > Library > Subscriptions > MLOps Community
- Home > Handbook >
Last Updated
10/05/2026
Context Engineering for Coding Agents - Fausto's Amsterdam workshop
Demetrios founded the largest community dealing with productizing AI and ML models. In April 2020, he fell into leading the MLOps community (more than 75k ML practitioners come together to learn and share experiences), which aims to bring clarity around the operational side of Machine Learning and AI. Since diving into the ML/AI world, he has become fascinated by Voice AI agents and is exploring the technical challenges that come with creating them.
Fausto's workshop focused on the part of agent systems you can control: what gets injected into the context window, when it gets loaded, and what should live somewhere else. You are not training the model on a daily basis, so the engineering work shifts to managing context, memory, tools, and retrieval.
His rule of thumb was to keep context usage under 25%, regardless of whether you are working with a 200k or 1M token model. Past that, things get slower, more expensive, and more error-prone, with cleaner options than letting the context window bloat.
The lens for the rest of the talk was a brain analogy. Attention is finite, memory shapes attention, and knowledge does not get embedded in a vacuum. An agent will only notice the right things if you have given it the right priors.
From there, Fausto split context into three categories.
- Deterministic context includes CLAUDE.md, project rules, hooks on lifecycle events, auto-memory, and scheduled loops. His point on rules was especially practical: coding conventions belong in path-scoped or file-extension-scoped rules, not dumped into CLAUDE.md.
- Human context covers chat turns, slash commands, and references.
- Probabilistic context covers sub-agents, retrieval, MCPs, skills, and observers. Sub-agents are useful because they do not inherit CLAUDE.md, memory, or the default system prompt, which makes them better suited for specific non-coding tasks. Skills are markdown plus optional scripts, and can also wrap calls to other models when Claude cannot handle the job. Fausto's example was routing native video analysis through Gemini.
Two practical tips came out of this section. Turn on deferred tool loading, a single flag that reduces what gets injected at session start. And lean toward project scope over user scope for skills and MCPs, so the agent has the full descriptions it needs to choose well at runtime.
The second half made the case for treating long-term memory as a folder of markdown files rather than defaulting to a vector store, inspired by Karpathy's wiki memory idea.
The structure is an index plus raw source files, processed summaries, and a policy that decides what to ingest and retrieve. Concepts that recur get weighted up. Concepts that go unused decay over time. An observer agent watches the session and either pulls relevant knowledge into the active context or pushes new findings into the wiki.
To show the difference in practice, Fausto ran two Claude Code sessions on the same cellular automata task. Same model, same skills, same sub-agents, same CLAUDE.md. The only difference was that one had a populated wiki and the other did not. Under a five-minute timer, the wiki run pulled the concepts it needed and produced a working visualization. The default run fell back on parametric memory and live search, then ran out of time.
Context Engineering for Coding Agents: Hands-on Lab + Agent Build-Off
A hands-on workshop on configuring Claude Code for real work.
Claude Code is powerful out of the box. But there's a gap between using it and mastering it. Closing that gap is mostly a matter of context: what the agent knows, where its memory lives, which rules it follows, and how its output gets verified before it ships.
This workshop is three hours of hands-on practice to optimize your Claude environment. After a short primer you join a pre-assigned team and build out a Claude Code setup in a prepared sandbox — working on memory, rules, hooks, retrieval, and prompt quality. In the final stretch we drop an unknown technical drawing on the screen and your system gets one run at it. Scores go on a live leaderboard, winners are announced in the room.
What you'll practice
- Shaping what the agent sees and remembers
- Writing rules and hooks that actually fire
- Designing memory so it finds what matters
- Judging output before it leaves the loop
Format
One continuous team build, one unknown challenge, one live leaderboard, closing with pizza and drinks. — The system you build in 60 minutes is the score you receive.Agenda
- 5:00 PM — Walking dinner & Drinks
- please be on time :)
- 5:30 – 5:45 PM — Welcome by Prosus
- Opening and framing for the evening.
- 5:45 – 6:00 PM — Opening
- 6:00 – 7:00 PM — Theory + Mini-Demos
- 7:00 – 8:00 PM — Build Lab
- 8:00 – 8:15 PM — Drawing Reveal + One-Shot Run
- An unknown technical drawing is broadcast into every team sandbox. Each system gets a single run. Whatever it produces is what gets scored.
- 8:15 – 8:20 PM — Processing Break
- Last runs finish, evaluator collects output.
- 8:20 – 8:30 PM — Live Leaderboard + Winners
- Scores land on screen. Jury walks through the top runs and the most interesting architectural choices.
- 8:30 – 9:00 PM — Pizza + Networking
- Pizza, drinks, Q&A.
Who this is for
Technical practitioners who have already used Claude Code (or a comparable coding agent) and want to push past the default setup. Comfortable on the command line, and with Github, fluent enough in Python to read and edit a small repo.What we provide
- A pre-configured cloud sandbox per team, with Claude Code installed and API access included. Minimum requirement is to have a Claude Pro account — and bring your laptop.
- Registering is not a confirmed seat — we curate the room and send confirmations separately
YouTube 2:27:28
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