Chris King

What is the GRID Data Platform, and what does it do? I thought I'd start my 'miniblog' series off with some of the fundamentals. @GRIDesports builds and operates the GRID Data Platform, technology designed to collect, enrich and distribute data from games. The overarching goal being to empower a sustainable, data-centric ecosystem. Let’s dive into a bit more detail about each of these steps in the pipeline. 1. Data Collection 🧲 Enter, GRID Game SDK. This software is provided to game developers, allowing them to extract data (in real-time) about what is happening in the game, down to the most granular details. These details are then transmitted in small chunks, reducing the infrastructure load and easing integration efforts. 2. Data Enrichment 🏦 As the data arrives at the platform, the first thing necessary is to aggregate all of the small details, building up one comprehensive, usable state. Once this is done, further enrichment occurs, which includes calculating derivative data points, generating deeper statistics and insights, and running the data through models to generate predictions. 3. Data Distribution 📨 What use would this all be if no one could use it? So finally, the GRID Data Platform has a suite of APIs (the GRID Data Feeds) that enable people to consume the different types of enriched data produced (live data, statistics, predictions, etc.), so that they can build their products on top of it. Additionally, the GRID Data Portal framework makes rich UIs available on top of the data, making it accessible for a variety of use cases. That's what the GRID Data Platform does, in a nutshell. There’s a lot of fun challenges involved in the details of scaling and operating such a platform. These will be some tales for another day! #esports #gaming #data #platform #engineering

I've been talking about the importance of official data accessibility in esports for many years now. In the age extremely cheap automation, this is simpler and doesn't just apply to esports: Accessibility of official data is key.

@arvidkahl Amazing news, massive congratulations Arvid!

A few days ago, I got the best birthday present ever: I became a dad. My son and I share a birthday. Still can't believe it! 🥰 Everyone's healthy and happy. And I can't imagine any greater joy. I'm in tears most of the day. Time to stock up on dad jokes. Got a good one? 🤣

100% great advice. For a while I wanted to start a coding bootcamp that purely taught and progressed students by contributions to open source projects. It would be much more effective at training developers rapidly than the "build a terminal noughts and crosses in Java" projects.

@fatih Absolutely true. Gets you (sometimes better than) real-world professional job experience. Plus, there's endless fun and interesting projects to work on! I've rarely interviewed a poor candidate that came from an OSS background.

People also ask me all the time what to do. I think it was around 2014/2015 when I told them, get good at contributing to big open source projects. This can be Kubernetes, Vitess, a Programming Language, you pick it up. Working on these projects teach you a lot of things. Not just coding, but also how to talk and navigate human issues. How to deliver code, how TO not break existing stuff and so on. With Agents and LLM tools, I think it became even more important if you want to be a step ahead of everyone + also have experience. It actually reminds me of a medieval guild. You join as an apprentice, do the hard work, learn the tools, make mistakes (under guidance), and slowly earn trust of other people through your contributions. I still don't understand how people just totally skip contributing and growing with OSS projects. There are many ways, but this one is solid, and free as well. All it takes is your hard work.

@GergelyOrosz I agree. Brings everyone forward though, those best eng teams will be the ones that will push on and set the next bar.

Unpopular option: most change that AI tools will bring for software engineers are likely to be making the practices that the best eng teams did until now, the baseline for those that want to stay competitive + move fast Things like product-minded engineers, testing, o11y, CD etc

@joshpuckett This would actually be really cool 😂 Time to feudal rush my next MVP!

ok which one of you is ready to make an Age of Empires 2 like interface for Claude Code and make a billion dollars??

@geoffreylitt Soon enough (and already the case for some tasks) "I did it" will just be assumed that it was with AI assistance. Might be more necessary to have a shorthand way of letting people know you did it the old fashioned way!

We need a shorthand way of saying: "An AI did the work, but I vouch for the result" Saying "I did it" feels slightly sketchy, but saying "Claude did it" feels like avoiding responsibility

@deedydas I do think we’ll be at a point soon where it truly is indistinguishable in most cases - so it’s key that there’s a widespread sense that the human message matters. This will help us stay on the right track.

@deedydas An important topic. Thanks for writing about it. We need solutions (technical and non-technical) in place to identify AI generated content, but we also need to keep highlighting why this is important. Easy for AI momentum overwhelm the discussion until it’s too late.

@EcZachly Robust, dependable and trustworthy data pipelines are a prerequisite to efficient adoption of AI (or indeed any automation that delivers tangible value for an org). In a time where accessibility of automation is exploding, greater need for data engineers makes sense.

Data engineering is projected to grow faster than AI engineering over the next decade, according to the World Economic Forum! AI is not going to replace data engineering; it will make it increasingly more valuable! Things like Claude Code will make "building pipelines" easier, but data engineering is so much more than building pipelines! Data engineers in 2030 are: - Able to handle all types of data: structured, semi-structured, and unstructured - Integrating private data into AI in a privacy-compliant and efficient way using multi-tenant architectures - Using coding agents to increase the speed at which they build pipelines - Crushing data siloes with data lakehouse architectures like Iceberg and Delta. Getting the entire company to agree upon business definitions Data engineering is one of the few "safe" roles in the coming decade!

@lexfridman I’d love a deep dive into data acquisition: sources and challenges for both pre-training and RAG, the current landscape, and emerging sustainable business models for high-quality data. Looking forward to listening!

Doing a long, super-technical podcast on the state-of-the-art in AI. Let me know if you have question, topic suggestions. Everything from details of LLM training pipeline & architectures, to coding, robotics, scaling, compute, business, geopolitics, etc. Besides topics & questions... add papers, blogs, posts, rants, perspectives that you'd like to see covered.

Really interesting concept

@rakyll I’ve been thinking about this too and agree. As you mentioned elsewhere - code won’t be the moat that results in software sales of the future, data will be.

I spent the last two weeks building apps that I always wished existed. From chord progression designers to screen recorders. I think we are going into an era where every app will be built on demand. Buying software makes sense if it's solving a problem that cannot be generated.

@readswithravi I love this book, it’s been on my default recommendation list for a long time now.

This is one of the best books on productivity that a young person must read.

@mitchellh @RogerAlsing Great example. Using AI while still thinking critically about the issue being solved, and the best way to deploy the AI in doing so (which included giving visibility to you on how it was used). This is the way!

Slop drives me crazy and it feels like 95+% of bug reports, but man, AI code analysis is getting really good. There are users out there reporting bugs that don't know ANYTHING about our stack, but are great AI drivers and producing some high quality issue reports. This person (linked below) was experiencing Ghostty crashes and took it upon themselves to use AI to write a python script that can decode our crash files, match them up with our dsym files, and analyze the codebase for attempting to find the root cause, and extracted that into an Agent Skill. They then came into Discord, warned us they don't know Zig at all, don't know macOS dev at all, don't know terminals at all, and that they used AI, but that they thought critically about the issues and believed they were real and asked if we'd accept them. I took a look at one, was impressed, and said send them all. This fixed 4 real crashing cases that I was able to manually verify and write a fix for from someone who -- on paper -- had no fucking clue what they were talking about. And yet, they drove an AI with expert skill. I want to call out that in addition to driving AI with expert skill, they navigated the terrain with expert skill as well. They didn't just toss slop up on our repo. They came to Discord as a human, reached out as a human, and talked to other humans about what they've done. They were careful and thoughtful about the process. People like this give me hope for what is possible. But it really, really depends on high quality people like this. Most today -- to continue the analogy -- are unfortunately driving like a teenager who has only driven toy go-karts. Examples: github.com/ghostty-org/gh…

@GergelyOrosz I struggle to understand anyone assuming anything about the use of AI that applies to them (especially in their profession). Those not starting to use it to validate such assumptions (and having a strategy to re-validate them, given the pace of progress) will get left behind.

I keep being amused by devs who assume AI is worthless and useless and is not used to produce production software. In the comments there are already so many examples. And if you need ones with ~90%+ AI generation that’s used by tens of millions, generating $1B+: Claude Code

A very Happy New Year to all 🎇 As the first day of 2026 comes to a close, I've had some nice time reflecting on some key moments of 2025. A few of my personal highlights: ✅ Successful GRID product launches that set the direction for the coming year: GRID Play and GRID Bet. 🎙️ Speaking on a panel ("You had me at in-game data" :-)) at GRID's own Gamescom side-event. 💡 The Product Engineering full-team meet up with plenty in-depth discussion on the most key technical challenges, and how we're going to solve them. Looking forward - 2026 is a year I'm really excited for. Not only are there are many more GRID milestones in the works (#staytuned 📻), but it's also just exceptionally exciting to be working in technology at such a transformative and impactful time in history. Thank you to everyone who made 2025 what it was, let's make 2026 just as great 🫶

The software industry's AI transformation is fascinating. There's still plenty for engineers to do (and plenty that can go wrong if they don't do it), but the empowerment these tools offer when used well is mesmerising. What is extremely interesting is the development of techniques and best-practices that work at scale in order to channel this investment into sustained high value output. Lots to do here, but the reward is huge!

Very cool, keep up the great work!

@Cloud9 @GRIDesports @jetbrains There’s still plenty of time to build something awesome with official esports data!

Want to work with esports data? @GRIDesports has you covered! Join the Cloud9 x @JetBrains Sky's the Limit Hackathon today! Details below! 👇