Issue #395 - The ML Engineer 🤖

World Models in Gaming with Epic Games, Model's Pareto with Raschka and Databricks, META Enters the Coding Model Arena, Top 30 Papers to Read in ML, SpaceXAI Trains Grok 4.5 from Cursor Data + more 🚀

Jul 13, 2026

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This week in ML Engineering:

World Models in Gaming with Epic Games
Model’s Pareto with Raschka and Databricks
META Enters the Coding Model Arena
Top 30 Papers to Read in ML
SpaceXAI Trains Grok 4.5 from Cursor Data
Open Source ML Frameworks
Awesome AI Guidelines to check out this week
+ more 🚀

World Models in Gaming with Epic Games

Researchers from Epic Games & General Intuition have released a new AI world model that lets you play online/free a version of Rocket League that runs purely on an ML model. This is bascially a 5B latent diffusion world model that generates a four-player 2v2 Rocket League match from synchronized video context and each player’s actions. It’s pretty cool to see how these models are trained on controller inputs, predicting the next frame; in this case it was trained on ~10,000 hours of bot gameplay, the model jointly renders four mutually consistent viewpoints at 20 frames per second on one Nvidia B200 GPU. The experiments indicate that pretrained visual representations and diffusion forcing materially reduce long-horizon drift, and that multiplayer conditioning improves the treatment of off-screen agents and shared physical events relative to single-view modelling; this is cool because it shows that adding the actions and viewpoints of other players can improve the model’s estimate of the shared game state. The way that they evaluated it was also pretty interesting, as they did not rely only on visual-quality metrics, but also measured whether actions could be recovered from generated video and whether internal representations preserved information about car and ball positions. This is a super exciting space as learned simulators could eventually support multi-agent training, policy evaluation and interactive environments without requiring direct access to the original game engine.

Model’s Pareto with Raschka and Databricks

It’s pretty cool to see coding models now being presented on a Pareto-like curve across task-completion vs cost instead of just a leaderboard with one percentage per model; here’s Raschka’s and DBX’s takes: Sebastian Raschka dropped a super interesting chart that shows the model curves across reasoning settings, showing that the highest-scoring configuration is not necessarily the appropriate choice under a fixed budget. Databricks also dropped a super interesting analysis bechmarked on a multi-million-line codebase, which plots overall pass rate against mean cost per task subject to the harness. These two are slightly different but complementary takes, and they are super interesting as they are making it clear that evaluating models alone is no longer enough; we need to also consider other parameters that will likely become growingly important as models start seeing diminishing returns. For example, it was super interesting to see that using Pi as the harness results in 1.20 and 2.08 times cheaper than the corresponding native tools in the reported comparisons. Similarly the jumps from Sol / Terra / Luna across the various reasoning levels, showing the tradeoffs across each, and also the consideration between switchign across model families vs taking a cost hit for consistency. It is clearly now expected that this will be a growing trend, and I am excited to see more and more practical benchmarks that are taken from hands on exercises as opposed to purely benchmarks that are being gamed.

META Enters the Coding Model Arena

Meta has entered the AI race with their very own coding model with support for 1m context and highly subsidised token API costs (for now!). Meta reports 80% on OSWorld, 69% on WebArena, 88.1 on MCP Atlas, and 61.5 on SWE-Bench Pro, however, we’ll have to see how it performs in practise once it hits the ground running with the community. Here’s the full model report, which shows how interesting insights of the “unmitigated model”, which echoes the same risks that other providers mention like anthropic on the pre-release mythos, so likely we’ll be interacting with a highly nerfed model (e.g. after the “US Customs Approval”). It is indeed interesting to see that everyone in the AI space is reducing towards the same average when it comes to offerings, the question will be whether there is indeed a real differentiator on the model/harness, or whether, at the end, the main competitive advantage will be critical mass pricing discounts.

Top 30 Papers to Read in ML

Here’s the reconstructed list of 30 papers that Ilya Sutskever shared with John Carmack, and this contains what we can see as the 30 must-read classics in ML. This recommendation list covers the major lines of deep-learning research, including convolutional and recurrent networks, residual connections, attention and Transformers, external memory, graph message passing, scaling laws, pipeline parallelism, and information-theoretic accounts of learning. It’s also nice to see that for each paper there’s a brief short explanation (beyond the abstract), so for ML practitioners, this can be a great TODO list for a compact curriculum for understanding architectural and systems concepts that continue to shape the current AI revolution.

TSpaceXAI Trains Grok 4.5 from Cursor Data

X / Xai / SpaceXAI (or whatever is twitter’s latest nickname) has trained a new coding model by using the entire data from Cursor, and released it as Grok 4.5 - and it’s offered on a highly subsidised / competitive pricing (for now!). The model is a MoE achitecture trained with trillions of tokens from Cursor interaction data together with STEM and research material, followed by standard reinforcement learning tuning. SpaceXAI reports pretty impressive scores across all benchmarks, with the main driver being efficiency as they are serving throughput of 80 tokens per second and an average of 15,954 output tokens per SWE-Bench Pro task. The API provides a 500k context window, configurable reasoning and tool interfaces at $2 per million input tokens and $6 per million output tokens, which is clearly highly subsidised to gain initial traction. It is interesting to see that although model training is not exactly commoditised, the MOAT that was being spearheaded by OpenAI is no longer far ahead, but the differentiating model is getting narrower - it will be interesting to see whether Anthropic/OpenAI will continue relying on model perf as their MOAT, as at the end there’s a threshold where price competitiveness seems to win, and the lockdown to a harness is not as strong as it initially was.

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.

Events we are speaking at this year:

Signals Conference - September @ Berlin
World Summit AI Europe - September @ Amsterdam

Other relevant events:

KubeCon Europe - March @ Amsterdam
PyData Berlin - April @ Frankfurt
Databricks Summit - June @ San Francisco
World Developer Congress - July @ Berlin
EuroPython 2026 - July @ Prague
EuroSciPy 2026 - July @ Krakow
AI Infra Summit 2026 - Sept @ California
Code.Talks 2026 - Nov @ Hamburg
MLOps World 2026 - Nov @ Austin

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.

The Machine Learning Engineer

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