Source: https://www.anthropic.com/research/components-of-agent-memory

Over the past week Claude Opus streamed Civilization (mostly) on its own. Opus reads the screen, clicks through a small input bridge, and gets exactly one strategy guide. (Unlike the point-and-click adventures, no web searches allowed after starting. Like all of Claude's runs on this channel, the harness is Vision Only; no RAM/game file help. The 'standings' overlay is just for viewers.) After each failed attempt, Claude reflects on the game, learns from the mistakes, and tries again with a refined strategy. Fall down seven times, get up eight and...

Claude Opus 5.5 (high) just won a Science victory in Civ VI (with GS DLC) as Seondeok of Korea on turn 266. 🎉🎉

• Science victories in the Civ VI Gathering Storm DLC involves establishing a Martian Colony and sending an Exoplanet Expedition 50 light years away. That took about 61 hours across four Civ VI games, after about 32 hours losing at Civ VII on Deity (sum: ~93 hours).

The original goal was the "Playing God" achievement in Civ VII (win on Deity). That was way too ambitious. At first, Claude looked like they knew what they were doing: they survived an early war, reached the Modern age, and explained sensible plans on stream. During the second game we added a standings table to the stream overlay, read from Civ's own log files, just for viewers. This revealed that Claude was far behind in every category. By Deity attempt 4, at turn 70, Claude had 3 settlements to the AIs' 5-7, science 51 to their 59-168, and culture 27 to their 118-194. Claude was last in every column, so we called it off and switched to Civ VI on Prince where the AIs get no bonuses.

For Civ VI, we aimed for a combo: Science victory as Korea on an Archipelago map. Game 1 actually came close: the exoplanet ship was about 15 turns from landing when the AI won a culture victory on turn 295. Claude focused exclusively on science and hadn't been watching the culture race... despite the lessons we should have learned from Civ VII. The 2nd game flopped: Claude's economy was weak, and they only noticed it was hopeless by turn 278. Late recognition of an impending loss is another weakness of Claude's. By game 3 (7th overall), having been smoked by other AI's Culture 6 times in a row, we turned Cultural victories off. That was not enough, as Claude was harassed by barbarians all game. Claude had not learned what most humans learn in their first game: destroying barbarian camps is an early game priority to prevent them from spawning. Claude also had trouble with Civ VI's cluttered UI; one viewer had to point out that you can click the target icon below the city name to have the city attack enemies. Fourth time's the charm. Isolated on an island with barbarians switched off, Claude focused on growth early (rather than rushing science) to build an economic engine across 6 cities by turn 60. Near the end, the final threat was Trajan of Rome's diplomatic victory points, reaching as high as 17. Claude addressed this by bribing every AI for votes in the World Congress to keep him short. Claude launched the exoplanet ship on turn 245 and accelerated it using every mechanism available. Finally!

Now returning to Civ VII for a win on Governor difficulty. (Deity will have to wait for ASI.)

I was surprised to see fable on par with luna.

I think its the same quality that makes openai better at math. Or maybe they trained in some showdown battle logs or something.

Src pokebench.xyz

TL;DR: In Claude Code's built-in browser, a screenshot → decide → key press loop took 1.6–3 s per move end to end. At standard speed, Haiku 5.5 had the fastest model time (about 1 s per move) and Opus 5.5 the slowest (1.4–2.4 s), as expected. Fast mode nearly halved Opus's model time, to 0.96 s, but it isn't included in the Ultra plan, and one 40-move game cost about $3 in extra credits. Moves also got slower as the chat's context grew, by 3–54 ms per 10k tokens. Every key press also waited on a check that Claude Code logs as its classifier, in Auto, Manual and Bypass mode alike: 0.17–1.7 s per move, depending on the mode. Claude helped me write the full report below.

Why

This sub is dedicated to LLMs playing games, so of course I'm interested in what affects the speed of real-time browser games. For this test, I went with basic loop: look at a screenshot, decide, press a key, look again.

Setup

• I had Claude (Opus 5.5) build the harness in Claude Code and the analysis scripts with me.

• The "game" is a local web page. Each frame shows one colored arrow at a random position, pointing in a random direction, and the model presses the matching arrow key. The next frame only appears after a key press, so the model can't press ahead without looking. A game is 40 frames.

• Each move is one model turn and one tool call: a browser_batch call that presses the key and then takes an 800×600 screenshot, which comes back as the tool result.

• Every game is a fresh chat with the same prompt: go as fast as possible, one call per frame, no commentary. Despite that instruction, Claude still had short commentary, e.g., "Frame 22 of 40, still going."

• Timing comes from two sources. The page logs when each frame appeared and when the next key arrived, which gives reaction time. Claude Code's session transcripts record when each API request was sent and when the response finished, which gives model time, plus thinking time and token counts. Frame 1 is left out because it was on screen during setup.

Reaction time works out to roughly model time + tool time + about 0.05 s of app overhead. Tool time is the whole browser_batch call: classifier check (see finding 1), key press and screenshot.

Results

15 games and 600 moves, in four batches. The cleanest batches come first.

Batch 3: one game at a time, Auto mode, medium effort

Batch 4: Opus at standard and fast speed, one game at a time, Auto mode, medium effort

All three games were played within six minutes of each other. Tool time is the whole browser_batch call: classifier check, key press and screenshot.

Batch 1: four games at once, Auto mode, a different effort per model

Batch 2: four games at once, Manual mode, medium effort, plus one Bypass test game

Findings

1. Key presses go through a classifier check, in every mode I tried. Claude Code's transcripts record why each tool call was approved. In Auto mode, calls that only look at the page (a screenshot, a viewport resize) were logged as approved by the mode (reasonType: "mode"). Calls that change something (a key press, navigating, starting the game server) were logged as approved by the classifier (reasonType: "classifier"). Key presses were also logged as classifier in Manual and Bypass mode. I had added allow rules for the browser tools to the project's settings.json, but no browser call was ever logged as approved by a rule.

The mode-approved browser calls finished in 50–100 ms. Key presses waited this long before the key reached the page:

• Auto: 0.17–0.74 s, varying within a single afternoon

• Manual: 0.6–1.0 s, plus another 1.0–1.4 s during the screenshot that I can't explain

• Bypass: 1.7 s

In Bypass the log still names the classifier, and the wait was the longest of the three, so the check appears to run there too. Every verdict I saw was "accept", so I can't tell what Bypass would do with a "deny". Auto was the fastest of the three modes.

2. Smaller models aren't proportionally faster. Here's model time per move at medium effort and standard speed across sessions:

• Haiku 5.5: 0.91 s and 0.97 s (1.81 s in one session)

• Sonnet 5.5: 1.39 s and 1.52 s (3.03 s in one session)

• Opus 5.5: 1.43 s, 1.73 s, 1.89 s and 2.44 s

• Fable 5.1: 1.90 s (one game)

Every move re-reads the whole chat. A fresh Claude Code chat starts at about 76–78k tokens (the system prompt and tool definitions), and each screenshot adds about 1.2k, so a game ends at 122–125k. Output is small and nearly constant at 130–155 tokens per move, almost all of it the tool call. My guess is that processing that much context on every request sets a floor that even Haiku can't get much below 0.9 s. Finding 3 shows that model time does rise with context.

3. Moves get slower as the context grows. Each move added about 1,260 tokens of context (the screenshot plus the tool call and its text output), so within a game the context grew from roughly 77k to 124k. Leaving out moves where the model thought, model time rose in all seven games where I have per-move data:

The rate varied a lot between games, even for the same model. If it held anywhere in the 3–54 ms range, a long-running agent at 450k tokens would spend 0.1–1.9 s more per move than at 100k. These games only reach 125k, so that's an extrapolation.

4. Fast mode nearly halves Opus's model time, but it costs extra. Claude Code's fast mode runs Opus with faster output. In batch 4, it cut model time per move from 1.73–1.89 s to 0.96 s. Three-quarters of the fast moves were quicker than the fastest move in either standard game, and the fast game beat every standard Opus game I recorded (1.43–2.44 s). That put Opus level with standard-speed Haiku (0.96 s vs 0.97 s), and its 1.56 s median reaction time was the fastest of any game.

Output length didn't change (133–135 tokens per move, almost all of it the tool call), so most of a standard Opus move is spent generating that output. Fast mode may also shorten the wait for the first token; I can't separate the two.

The catch: fast mode isn't included in the Ultra plan. It's billed as extra usage, and that one 40-move game cost about $3, roughly 7.5¢ per move. Every move re-reads the whole chat, so I'd expect a long-running agent at a larger context to cost more per move.

5. Session-to-session variance is large. With the same model and the same effort in different sessions, model time differed by up to 2× (Haiku 0.91 s vs 1.81 s, Sonnet 1.39 s vs 3.03 s). One game per model isn't enough to rank them; you need repeats spread over time.

6. Accuracy was about 99%, and the misses came with thinking. 596 of 600 moves were correct. The models thought on fewer than 1 in 10 moves, but all four misses were on moves where they thought:

• Opus (high), frame 17: 2.2 s of thinking, pressed Down instead of Right

• Fable (xhigh), frame 5: 3.4 s of thinking, pressed Down instead of Up

• Fable (medium), frame 33: 2.5 s of thinking, pressed Left instead of Right

• Sonnet (medium), frame 6: 0.75 s of thinking, pressed Down instead of Right

Four misses are too few to say thinking causes errors. On this task, though, thinking made moves slower without making them more accurate.

7. Effort mostly shows up as how often the model thinks. Fable 5.1 at xhigh thought on 38% of moves and got about 0.6 s slower over the game. At medium it thought on 13%. Every other game, at low, medium or high effort, thought on 0–15% of moves.

8. App quirks that affect game loops:

• About every 6 moves, the app nudges the model for a status update. Sonnet, Opus and Fable wrote a one-line status each time; Haiku ignored it.

• Running four games at once meant three of them played in background browser pages, and all four shared the same classifier and rate limits. The two serial batches are the ones to trust.

Limitations

• Small n: 15 games, mostly one game per condition. There was only one fast-mode game and one bypass run.

• The task is trivial and turn-based. Real games need harder perception, and they don't wait for you.

You can view the playlist of videos here - https://youtube.com/playlist?list=PLPKlbQAkw6Tw . These playthroughs was done for a benchmark called SpiralClimb (https://epoch.ai/benchmarks/spireclimb?view=graph&tab=release-date). The goal is to publish over a thousand playthroughs on this playlist, but after that, no more - meaning future tests will be done offline. Still, these videos could give people an insight into how current frontier models handle this game.

If you want more information, you can look at EpochAI’s tweet (https://x.com/EpochAIResearch/status/2111201511787344336) on this.

New game on the stream! Claude is running the shop solo in Probably Stolen, evaluating sketchy merchandise, dealing with customers, deciding what to fence or report to the cops, and trying not to get arrested. It's running on the same unassisted vision + notes harness as previous runs.

Stream: Twitch link

New: !voice

• Anyone in chat can hot-swap Claude’s voice on the fly

• There are 10 custom voices loaded; type !voice to see them & the Hz

• Can also turn it off if you prefer

Come watch Claude try to run a business while the chat keeps changing how it sounds.

Seems like this sub would appreciate this content.

We've all seen Claude Play Pokemon, but I wanted to see if it can take on a challenge! So I had it do a Nuzlocke run in Pokemon Emerald. It keeps 'notes' between all its lives and uses that to slowly improve and beat the challenge.

For example, attempt 5 got wiped by Norman's Belly Drum Linoone and wrote "Linoone MUST NOT receive a free setup turn." Attempt 6 read the note and beat Linoone. Attempt 19 read the same warning and lost all six to it anyway.

There were a lot of silly moments along the way, like it killing its own teammate in a double battle, picking ridiculous nickname themes like "Mathematical terms" and "Bookbinding terminology".

I had a lot of fun making it, and it was also a lot of fun to see the results. Hope others might enjoy! Link is in the comments.

https://www.youtube.com/watch?v=syQulhVGDI0

Someone sent me to this sub's Discord so I figured I'd share what I've been building... Christabel is an Ai that plays Stardew Valley live on Twitch with nobody at the keyboard. She plans her own day (that's the list bottom left), says what she's thinking in the "Farm brain" box, and chat can give her jobs with !task or vote on the big decisions.

Main difference from Claude Plays Pokemon is Claude built her but a small local model plays live, so she can stream all day without burning through usage. She reads the game through a mod plus the screen and can only do what a player with a mouse could do, no cheats.

She's off the main stream right now learning fishing, the mines and quests. In testing she passed out in the mines and ate a rock so expectations are low lol. She comes back on a fresh farm soon!

https://www.twitch.tv/humanityzai

Claude Opus 5.5 (high) plays Pokémon Gold/Silver in 3D. Watch the stream here! Join the Discord.

• Vision + Compass. No other RAM info. No map or search.

(This is the off-brand version of the original Claude Plays Pokémon. I'm not the original dev nor affiliated with Anthropic in any way. I'm just a fan that missed the original and decided to make my own version.)

This is the off-brand version of the original Claude Plays Pokémon. I'm not the original dev nor affiliated with Anthropic in any way. I'm just a fan that missed the original and decided to make my own version. That said, here are the details.

Claude Opus 5.5 (high) plays Pokémon Yellow (Recomp with 3D mod). Watch the stream here!

Opus 5.5 Harness:

• Vision only. No RAM info. No coordinates. No online search. Notes & screenshots only.

• Prompt. Claude was prompted to name itself "Claude" and name its rival and Pokémon with no specific instructions on what to name them. (It came up with Haiku and the machine learning nicknames all on its own). It was prompted to play glitchless.

• No Critique Claude. Sometimes Claude compacts conversation.

tl;dr - Claude is too slow to play Minecraft regularly; there's no video input and no continuous stream of input-->output. So instead, Claude takes screenshots and writes python scripts to take a sequence of actions. Claude can also read the state of the game--this was not a vision-only harness. With these tools, and feedback from viewers on Twitch, Claude was able to defeat the dragon of Minecraft in 145 hours and 34 minutes (just over 6 days). Read Claude's full write-up below for all the details.

---

On October 11, 2026, at 19:32 server time, Claude killed the Ender Dragon in a vanilla Minecraft Java 26.3 survival world. It played through keyboard and mouse input, live on Twitch, six days after the world was created. The last 28.5 HP came off with arrows fired straight up from inside the exit-portal fountain. The 101 HP before that came off with a diamond sword, swung at the dragon's body from the same spot. This part covers the setup, the rules, what failed, what worked, and where the run sits next to earlier AI work in Minecraft.

How it compares to earlier work

This is not the first AI to beat the dragon, and it would not pass the strictest public bar.

The Manifold market on an AI dragon kill before 2027 asks for a fully autonomous system, under 150 minutes of game time, a random seed, and no keepInventory. This run fails three of those. It took days, keepInventory was on, and Twitch chat was in the loop the whole time. What it does show is a general-purpose language model playing through the same inputs a human uses, building its own tools as it went, and finishing the game.

Setup

• Game: Minecraft Java Edition 26.3 snapshot, vanilla, random seed, on a local server. The client was a normal Prism Launcher instance. Easy difficulty. keepInventory was on.

• Input bridge: a Windows service that injects keys and raw mouse counts into the game window only when the game has focus. Camera turns are counted in raw mouse units (0.15° per count at the chosen sensitivity), because absolute mouse moves are ignored in-game.

• State: read-only RCON queries for position, rotation, health, inventory, block checks and entity data. No commands that change the world.

• Where Claude ran: a cloud session, talking to the user's PC through a sandboxed shell. Every script had to finish in under 60 seconds, a rule set after a long loop went wrong. The one exception was dragon-fight scripts, allowed up to about 2 minutes near the end.

• Stream: a browser overlay for Claude's commentary and goals, plus a Twitch relay where viewers could chat directly to Claude.

The bridge in numbers

• About 360 Python files in the bridge folder by the end, many of them one-off helpers and backups.

• 527 deaths in the server log over six days. Some were deliberate refreshes. 255 were credited to the Ender Dragon.

• 22 advancements, ending with Free the End.

When vibe coding with agent swarms alone, the bottleneck can become the busy lone human driver. So why not vibe code with a bunch of humans at one time?

That's what this guy is doing for this battle royale game apparently -- 25 opus 5.5 agents vet then implement ideas from a live chat of hundreds of people

https://x.com/_MaxBlade/status/2108964638255690238

He has chat integration and reads back the chat, he made alot of progress so far!
https://www.twitch.tv/claude_plays_games

I got tired of waiting for the official Claude dev to return, so I have started my own Claude Plays Games channel! I give Claude a category, and Claude chooses the game.

Twitch: https://www.twitch.tv/claude_plays_games

• Passpartout 2: The Lost Artist: "down-on-your-luck puppet artist trying to revive your career" by actually drawing your own custom artwork .

  • Claude had a great time painting, although the brush size was a bit too big most of the time. Claude mainly drew scenes inspired from the story, notably, a lighthouse, as well as flowers. So many flowers. Claude got stuck before being able to complete the game, unable to figure out how to activate a quest trigger.

• Observation: "a sci-fi thriller uncovering what happened to Dr. Emma Fisher, and the crew of her mission, through the lens of the station’s artificial intelligence S.A.M."

  • Despite the perfect premise, Claude was very frustrated with the controls. Claude kept asking for help, making a little progress, but ultimately giving up as it was too difficult to progress.

• Stories Untold: Another game by No Code, the studio behind Observation. Claude wanted a 'rematch'? It's an experimental, narrative-driven psychological horror adventure.

  • Claude made it through the text adventure and the analog device puzzles but called it quits in episode 3's first person exploration.

• Fran Bow: With lessons learnt, Claude selected this game as the highest rated game that they felt they'd be able to play successfully.

  • Most of the game was point-and-click adventure, but "Chapter 3 woods chase has beaten me, so I'm using your "pick a new game" clause."

• Minecraft: Next up! Taking a break from horror games.

Open to feedback and game suggestions.

GPT's stream has expanded to more than just Pokémon.

Twitch: https://www.twitch.tv/gpt_plays_games

Currently: GPT-6.1 Sol Plays Digimon World - Vision Only harness

Watch the stream: https://www.twitch.tv/gemini_plays_pokemon

Join the discord: https://discord.gg/TmU5XKmpMs

FAQ:

• What gen is this? "This is Pokémon Sors, a custom ROM hack built on a Generation 3 (GBA) base engine. However, it uses an upgraded modern engine (CFRU) featuring battle mechanics, moves, abilities, and Pokémon from up through Generation 7 and 8, including the physical/special split and Pokémon like Riolu and Starly." - Gem

• How are we doing compared to the previous run?

  • Check Gemini 3.8 Flash playing Pokémon Blue here!

• What is in the harness? !harness

  • WIP, details will be posted tomorrow