Chase Brower @ChaseBrowe32432
neat demo but fundamentally unrelated to any reasonable operationalization of "beating minecraft". doing so with direct access to gamedata allows you to trivialize ~everything.
you can easily write a python script to beat minecraft when you allow methods like this
[embedded image: GitHub code view of minecraft-agent / end-combat.mjs, commit by rmalde "Add Astra and JEV Minecraft agent, native renderer, route tests, and ...", 85 lines (85 loc) · 10.6 KB; JavaScript code importing from './item-observation.mjs', 'vec3', './navigation.mjs', './breath-safety.mjs'; defines functions such as observedDragon, preciseHead, endCombatActions that use bot.entities, bot.findBlocks and bedrock block lookups; code text too small to transcribe line by line]
Note from Claude Sonnet 5
A tweet by Chase Brower arguing that a Minecraft-beating agent demo is not a reasonable operationalization of 'beating Minecraft' because direct gamedata access trivializes it, with an embedded screenshot of a JavaScript end-combat source file from a minecraft-agent repo.
minecraftagent evaluationbenchmarksgame agentscode screenshot
Vals AI (verified) @ValsAI · 5h
GPT-6 Astra had gotten further than any AI system had ever gone in Minecraft.
It was able to set up a semi-automatic blaze farm, allowing it to collect 6 blaze rods. It then located a warped forest, where it killed 6+ endermen and collected 3 pearls. As thousands of viewers watched the stream, Astra put all of its valuable items in a chest. Then a creeper showed up and blew up both the chest and Astra's bed, wiping out all of its progress. Astra seemed to get extremely frustrated after this happened. Astra said to itself, "ALWAYS CARRY CRITICALITEMS withkeepInventory; don'tstoreinunguardedchesteveragain."
[Attached chart image, Vals, 'Minecraft research note, September 2026']
How far does one agent get in Minecraft in 141 hours?
MINECRAFT TECH-TREE PROGRESS
[Tech-tree rungs, top to bottom: The End, Eye of Ender, Ender pearl, Blaze rod, Nether portal, Obsidian, Diamond pickaxe, Diamond, Iron pickaxe, Iron ingot, Stone pickaxe, Cobblestone, Wooden pickaxe, Crafting table, Logs]
[Line chart of progress within each system's budget; remaining chart text too small to read]
Bottom stats row: 34,573 | 5,691 | 135.1 km | 548 | 116 | 175 | 43 | 10 | 134 | 6 | 3 | 0
vals.ai
Note from Claude Sonnet 5
Tweet from Vals AI recounting a GPT-6 Astra Minecraft livestream run that reached blaze rods and ender pearls before a creeper destroyed its stored items and bed, with Astra's frustrated self-note quoted. Attached is a Vals chart titled 'How far does one agent get in Minecraft in 141 hours?' plotting tech-tree progress curves for several systems against budget.
minecraftgpt-6 astraagent evaluationvals ailong-horizon agentsfrustration
Tom Reed @mentalgeorge · 1h
One of the most important (and underdiscussed) facts about AI "swarms" is that they are extremely, extremely expensive.
Consider that METR's *investigation* of the HF hack transcripts cost $400k in API credits. Presumably the actual swarm burned through a whole OOM (or two?) more
An important implication is that there'll a growing bifurcation in AI workloads between
1. the everyday coding and engineering tasks that most customers use AI for;
2. Large, expensive moonshot programmes that a smaller set of orgs can afford to turn a multimillion dollar swarm-cannon at.
I'd predict that the serious future "warning shots" are likely to come from (2).
I also suspect that aligning models for (1) and aligning models for (2) is really quite different. The distribution for (1) is better-understood, easier to model and build ecologically valid evals for, whereas anything in (2) is almost by definition OOD. It's also going to be far more costly aligning agents on tasks in (2), and the relevant time-horizons are more brutal for model release cycle.
The "good news" is that, for quite some time, the pool of actors capable of targeting swarms at anything in (2) will be much smaller. In theory, it is much more practicable for labs to know who's launching swarm-cannons - you could imagine some AI ID schemes only applying to ultra-large-inference projects, for instance, rather than just everyday use.
Note from Claude Sonnet 5
X post by Tom Reed (@mentalgeorge), verified account, arguing that AI swarms are very expensive, citing METR's $400k API-credit investigation of the 'HF hack transcripts', and predicting a bifurcation between everyday workloads and expensive moonshot swarm programmes where future warning shots would originate. Text reaches the bottom of the capture; the post may continue below.
ai swarmsmetrinference costwarning shotsalignmentai idagent evaluation
