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This week in ML Engineering:

How to Write an Agent Skill

This week I published a new post on how to write agent skills! And this comes with a skill... to write skills 🙃 Basically a post on how I stopped writing bash scripts, and the learnings and best practices on what we can call... non-deterministic programming?

What is interesting is that I still write it as I was writing code, but in English (haven't tried Spanish).

I propose 6 principles that I believe lead to high quality skills:

  1. The description is a router, so it says when to use the skill in the words you'd actually type
  2. The body is steps, and anything that isn't a step gets deleted
  3. Scripts carry the fixed sequences, and the agent carries the judgement calls
  4. SKILL.md holds only what every run needs
  5. Verification is a step of the procedure, for the output and for the skill itself
  6. (Optional) A human feedback loop, where the skill ends by reporting its own friction upstream

I have to say also that principle 6 is the one that has felt magical; a skill that reports its own friction is one that actually gets better every week.

The post goes through each one with a good and a bad example, plus the real skills we open sourced along the way in the Agent Skills Marketplace. Check it out and let me know what you think!

NVIDIA Buys Hugging Face

Huge news for the European AI ecosystem 🚀 NVIDIA has agreed to buy Hugging Face for $12.9 billion!

It seems there is still no official confirmation from NVIDIA or HF, however CNBC's own sources confirm the talks, and if it completes this would put the de-facto home of open source AI, with its model hub, Transformers library and datasets, under the ownership of the company that sells the hardware it all runs on.

We have to say this is a huge win for Europe; Hugging Face was founded by French founders and has grown into one of the most important platforms in AI, and a $12.9 billion outcome at the centre of NVIDIA's open-model strategy is quite the milestone for the European ecosystem.

There are of course open questions, and the community is already asking whether NVIDIA-optimised models will get preferential treatment.

It will be interesting to see whether the open ethos that made Hugging Face what it is survives the integration.

Uber on the Agentic Software Factory

The Software Factory era is here... Uber has published (yet another) one of the most detailed pictures on agentic engineering, and how it's embedded in their dev lifecycle.

Over 70% of Uber's pull requests are now attributed to local or cloud agents, with 3,600+ agent skills across the SDLC running more than 30K executions per day. Weekly active users grew 7x since February, and agentic requests 9.4x.

The really interesting part is the cost engineering; they decompose spend into users x sessions x turns x requests x tokens x price, and attack each factor: prompt-cache TTL tuning, CLI-resolved MCP calls, and code-mode batching that they claim saves 55-100% of tool-execution tokens. The result is cost per 1,000 requests down 34%, and cost per session down 52% from peak.

Similar to what we've seen on mapping engineering context, Uber grounds their agents with an AI Context Graph: 24M nodes and 80M edges connecting 30+ internal systems, exposed through 1,000+ MCP tools.

For production ML practitioners this is one of the most concrete looks yet at what agentic engineering looks like at scale, and the honest caveat that your mileage may vary makes it more credible rather than less!

GLM-5.3 Open Weights Are Out

The GLM-5.3 open weights are out! Z.ai launched the model earlier this month and has now released the weights on Hugging Face after what they describe as safety evaluation and hardening:

From their report it seems their improvements come from scaled post-training on synthesized long-horizon environments; they claim open-source SOTA on Terminal Bench 3.0 and Agents Last Exam + and better results than Claude Opus 4.8 on their in-house code bench at less than half the output tokens.

The part everyone is talking about is the emergent cyber capability: as post-training scaled, exploitation skills grew faster than expected, and working with security teams the model surfaced 2,436 vulnerabilities across 269 open source projects, the oldest dating back to 1981, now tracked in a public disclosure ledger.

It is quite interesting to see a lab publish this openly, and it is clear that open-weights releases are now frontier-relevant for ML security as much as for coding.

OpenAI's Jalapeño Inference Chip

Can anyone actually challenge NVIDIA on inference? OpenAI and Broadcom said "hold my beer" with "Jalapeño", OpenAI's first Intelligence Processor - these names are getting out of hand:

This chip was co-designed from a blank slate for LLM inference and taken from design to tape-out in nine months with help from OpenAI's own models.

From early testing they report substantially better performance per watt than the current state of the art, and SemiAnalysis were invited into the lab to benchmark it, measuring it ahead of every NVIDIA, AMD and Google chip they tested on perf per watt.

Worth noting the numbers were provided by OpenAI and the agentic long-context suites remain untested.

Deployment is planned at gigawatt scale from late 2026. Quite the week to be NVIDIA: buying the model hub while the chip moat gets its first real test!

Upcoming MLOps Events

The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below.

You can also find our upcoming events and past talk recordings on the talks and events page.

Events we are speaking at this year:

Other relevant events:

In case you missed our talks, check our recordings below:

Open Source MLOps Tools

Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 20,000 ⭐ github stars. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. Here's a few featured open source libraries that we maintain:

  • SARC - Provides wrappers for popular agentic frameworks to enable guardrails and constraints that are enforced through the flow.
  • KAOS - K8s Agent Orchestration Service for managing the KAOS in large-scale distributed agentic systems.
  • Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced data processing usecases.
  • Production ML Tools - A curated list of tools to deploy, monitor and optimize machine learning systems at scale.
  • AI Policy List - A mature list that maps the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and beyond.
  • Agentic Systems Tools - A new list that aims to map the emerging ecosystem of agentic systems with tools and frameworks for scaling this domain

Please do support some of our open source projects by sharing, contributing or adding a star ⭐

About us

The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning.

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