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I gave Kimi K2.6 and K2.7 Code the exact same prompt to animate a letter burning to ash. Pure HTML canvas, zero libraries.

90,027 次观看 • 4 个月前 •via X (Twitter)

41 条评论

Jake Harris 的头像
Jake Harris4 个月前

Fable 5 Max for you...

stevibe 的头像
stevibe4 个月前

Whoa

stevibe 的头像
stevibe4 个月前

Prompt: Create a single HTML file with a canvas animation of a handwritten letter burning. Show an aged, slightly yellowed sheet of paper with visible handwritten cursive text (procedurally drawn lines are fine) resting on a dark wooden desk. After 2 seconds, a flame ignites at the bottom-right corner and spreads organically across the page — the burn front should advance with an irregular, noisy edge, never a straight line. Just ahead of the flames, the paper should darken and brown (scorching), then char black, then disappear entirely, revealing the desk beneath. Render the fire with layered particles: a bright white-yellow core, orange mid-flame, and translucent red tips that flicker and lick upward. Glowing embers should detach from the burn edge and drift upward on turbulent air currents, fading from orange to gray. Add wisps of semi-transparent smoke rising and dispersing above the flames, and a warm flickering light that the fire casts onto the surrounding desk. The entire page should be consumed in roughly 15 seconds, leaving only a few glowing ash fragments that slowly dim. 60fps, no external libraries.

Daniel 的头像
Daniel4 个月前

K2.6 looks better?

stevibe 的头像
stevibe4 个月前

Honestly both are solid, but I'm really into the ignition randomness of K2.7 Code.

Daniel 的头像
Daniel4 个月前

I like the burning effect more of 2.6. 2.7 seems to add random sparkles with no realistic positioning imo

Nishant Gaurav 的头像
Nishant Gaurav4 个月前

Tried with Gemini 3👀

stevibe 的头像
stevibe4 个月前

This is awesome!

Fab 的头像
Fab4 个月前

qwen-3.6-27B comparison .. this is a 37x smaller model :)

Regi Kusumaatmadja 的头像
Regi Kusumaatmadja4 个月前

fire ignition from k2.7 code is dope

Jay-F. 😎 的头像
Jay-F. 😎4 个月前

K2.6 is the most accurate and realistic. It simulates the actual way such a paper would burn. @jakeharrisdev used fable 5 Max which is trash but it got one factor right. It left ashes which K2.6 omitted. K2.6 gets the combustion right. Fable 5 max gets the after effects right Speaking from experience during my Prometheus phase.

ShitCockaSays 的头像
ShitCockaSays4 个月前

We should tar and feather whoever started this one-shot obsession for showcasing LLMs. And we should do the same to any person who shows n=1 when doing it themselves.

Ivan Fioravanti ᯅ 的头像
Ivan Fioravanti ᯅ4 个月前

Love this one! Stevibe super Hero!

Mike 的头像
Mike4 个月前

K2.6 looks more realistic

Ebenezer Don 🐘 的头像
Ebenezer Don 🐘4 个月前

K 2.7 is good. Probably not the best example for it, cause here, 2.6 looks better/more realistic

KrayzeeK2 的头像
KrayzeeK24 个月前

I've burnt paper , 2.6 is more realistic

Alex Sebastian 的头像
Alex Sebastian4 个月前

The THINKING TRACES man....!!! K2.7 Code is very similar to Nex-N2 Pro!! - It uses "We" instead of "I" - Uses "Need" extensively - Uses "maybe" extensively - Concise, caveman style Very interesting.

CypherDom 的头像
CypherDom4 个月前

2.6 is better

0xrobinhood007 🟡 的头像
0xrobinhood007 🟡4 个月前

2.6 seems a bit more realistic, I also like the fact that it left some after effect (shadow) after burning

AI Mastery Guide 的头像
AI Mastery Guide4 个月前

Zero libraries and it pulls off fire physics like that 👀 Wild how much K2.7 Code improved in just one version.

Nitin Gupta 的头像
Nitin Gupta4 个月前

The initial reactions of Kimi K2.7 are really good. Can't wait to try it. @opencode can you give me free access to it? 😁

Homunculus XII 的头像
Homunculus XII4 个月前

Real letter burners know Kimi K2.6 was more realistic.

Gautham Yaramasa 的头像
Gautham Yaramasa4 个月前

right one feel bit realistic...

Nazmi Efe Armutcu 的头像
Nazmi Efe Armutcu4 个月前

actually the old one seems better 0_0

Nick Venturi 的头像
Nick Venturi4 个月前

drop the results i want to see the lag

Ola 的头像
Ola4 个月前

I hope opencode gets this on the go plan soon

John Shina 的头像
John Shina4 个月前

There's a clear improvement.

jay vi 的头像
jay vi4 个月前

Both are pretty bad haha, k2.7 is much less so though. Very interesting problem! How do claude models fare on this benchmark?

Joman 👺 的头像
Joman 👺4 个月前

what provider are you using?

stevibe 的头像
stevibe4 个月前

OpenRouter -> MoonshotAI

DMTeta 的头像
DMTeta4 个月前

Kimi 2.6 did it better than 2.7

Can Matrix 的头像
Can Matrix4 个月前

Wow very different

Carlos Tello 的头像
Carlos Tello4 个月前

More realistic 2.6

dalu 的头像
dalu4 个月前

GLM-5.2

jpp 的头像
jpp4 个月前

liked 2.6 better in terms of looks did you track how much time each took? did you have to correct or was it a one shot?

Sebastian Buzdugan 的头像
Sebastian Buzdugan4 个月前

watched identical canvas burn effects diverge once mobile forced a 2k particle budget

Silvio Ney 的头像
Silvio Ney4 个月前

@ivanfioravanti Could you count token usage for both?

stevibe 的头像
stevibe4 个月前

@ivanfioravanti I didn't count it this time. I'll have it next time.

Moondv 的头像
Moondv4 个月前

2.6이 더 깔롱한데?

vr8vr8 的头像
vr8vr84 个月前

kim 2.7 is really good. Test it too and was amazed.

Studio Zamudio One 的头像
Studio Zamudio One4 个月前

Wait, wait, wait, wait, and wait! K2.7 Code? WTH?!

相关视频

I designed a new test specifically for multimodal models: fill out a paper form. And it's much harder than it sounds. This isn't typing into an electronic field that captures your text. The form is just an image. The model has to place each form element: text, checkmarks — at the correct pixel position on the canvas itself. Results: 🟢 Kimi K2.6 → done in 3:45, 16.7k output tokens 🟡 Step 3.7 Flash → half the fields, 57k output tokens 🔴 Gemini 3.5 Flash → 489k output tokens, never finished. I had to kill it. Gemini burned ~29x more output tokens than Kimi on the exact same task, and Kimi's was the only form that actually looked filled out. The test, a mocked application form, contains some challenging parts, such as one-character-per-box fields. I provided every model the same set of tools: > get canvas size > drop probe markers to find coordinates > add text > add checkmarks > move elements > take a screenshot anytime to check their own work > ... etc So it's vision + spatial reasoning + tool use + long context, all at once. Small models (Qwen, Gemma) can't really complete this test, so I skipped them. What happened: > Kimi nailed name, DOB, ID, gender, marital status, nationality, email, phone, address, postal code — placement slightly loose, but content correct. 15 turns. Clean. > Step got maybe half right — fields dropped, "United States" landed in the email line, data floating outside boxes. Burned 1.24M input tokens doing it (81 turns of re-reading the canvas). > Gemini almost got there visually... then spiraled. By turn 40 it was issuing a delete_elements call wiping element IDs 365–425, basically erasing its own work. 31 minutes, 489k output tokens, still streaming. Terminated. The takeaway isn't "Gemini bad." This test is indeed difficult. But token efficiency is capability now. A model that needs 30x the tokens and still can't converge is going to be 30x the cost in production. Kimi K2.6 just quietly did the thing.

stevibe

25,494 次观看 • 4 个月前