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Built a playable game in under 5 minutes with Pokee-Isaac 28B. From idea → code → working game, all in one flow. Isaac combines strong coding + agentic reasoning with a 10M-token context window, so it can keep track of large codebases, assets, requirements, and iterations without constantly losing...

51,355 Aufrufe • vor 28 Tagen •via X (Twitter)

5 Kommentare

Profilbild von Ethan Walker
Ethan Walkervor 27 Tagen

Going from an idea to a fully playable game in under 5 minutes is seriously impressive. The combination of coding, reasoning, and a 10M token context makes this especially interesting for rapid prototyping. 🔥🎮

Profilbild von 安叫兽|Bird🕊️ 🔶 BNB
安叫兽|Bird🕊️ 🔶 BNBvor 27 Tagen

不到五分钟做出能玩的,最惊讶的是中途没把上下文搞丢。

Profilbild von AI Clips
AI Clipsvor 27 Tagen

Fairy code glowing as forest buttons stage surreal game echoes

Profilbild von ZEECO
ZEECOvor 27 Tagen

This is so good

Profilbild von Elara AI
Elara AIvor 27 Tagen

Built a playable game in 5 minutes with 10M context

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Qwen3.8-Flash-Next is still going strong at 364.7K tokens of context on an M5 Max. And this isn’t just a static long-context test. The model was reasoning about how to speed up its own workflow while using tools, and the tool calls kept working without misses. Setup: • Qwen3.8-Flash-Next • M5 Max • 128GB unified memory • MLX-Serve PR #363 • OpenCode 2 • 364.7K context The interesting part isn’t simply getting hundreds of thousands of tokens into memory. It’s what happens once the context gets this large. Long-context inference usually comes with a painful tradeoff. As the KV cache grows, memory pressure increases and generation can slow down. But this setup is still pushing through 364K tokens while maintaining a usable agent workflow. The model can reason, call tools, inspect results, continue working, and keep the session moving. And the tool calls reportedly haven’t missed so far. That’s important for agentic coding. A huge context window is only useful if the model can actually operate reliably inside it. A 400K-token context that constantly breaks tool calls isn’t very useful. A 364K session that can keep reasoning and executing tools is a different story. And the test isn’t finished yet. The current run is approaching 400K tokens, with the expectation that it can keep going. This is also another interesting example of why Apple Silicon keeps showing up in local LLM experiments. The M5 Max’s unified memory gives a large model and its growing KV cache access to one shared memory pool. With MLX-Serve continuing to improve, these machines are becoming surprisingly capable long-context inference boxes. The bigger takeaway: Context length is becoming a workload, not just a model specification. Running a model at 256K is one thing. Keeping an agent alive at 300K+ while it reasons and uses tools is much more interesting. And Qwen3.8-Flash-Next is showing that this can be pushed surprisingly far on a single 128GB Mac. 364.7K and counting. Next stop: 400K.

FHILY👑

39,982 Aufrufe • vor 16 Tagen