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Kimi K2.7 Code just made Kimi K2.6 painfully outdated we tested Kimi K2.7 Code against Kimi K2.6, GPT 5.5, and Claude Opus 4.8 across Lorenz attractors, solar systems, and water waves in our previous comparison, Kimi K2.6 struggles with physical motion, now K2.7 gave the most realistic rendering of...

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GMI Cloud

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87,301 просмотров • 3 месяцев назад •via X (Twitter)

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Kimi K2.6 just cooked Claude design 😭 Kimi already ships the video hero section when Claude is still rendering effects way faster iterations and… 9x cheaper see prompt in the comments 🧵👇
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Kimi K2.6 just cooked Claude design 😭 Kimi already ships the video hero section when Claude is still rendering effects way faster iterations and… 9x cheaper see prompt in the comments 🧵👇

Darshal Jaitwar

148,046 просмотров • 4 месяцев назад

we compared Gemini 3.1 Pro, Opus 4.7, and GPT 5.5 to Kimi K2.6, Xiaomi Mimo v2.5, and Qwen 3.6 Max in average, closed source are faster. GPT 5.5 and Opus 4.7 the fastest. Kimi K2.6 comes after. It keeps up thanks to its native INT4 quantization. MiMo took the longest, but the refinement and aesthetic only ranks after Gemini 3.1. as if a mini Gemini. Its slow cuz it's trained for long-horizon agentic work. Claude Opus 4.7 is blooming in a very different style, probably because Anthropic trains for taste, not just accuracy.
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we compared Gemini 3.1 Pro, Opus 4.7, and GPT 5.5 to Kimi K2.6, Xiaomi Mimo v2.5, and Qwen 3.6 Max in average, closed source are faster. GPT 5.5 and Opus 4.7 the fastest. Kimi K2.6 comes after. It keeps up thanks to its native INT4 quantization. MiMo took the longest, but the refinement and aesthetic only ranks after Gemini 3.1. as if a mini Gemini. Its slow cuz it's trained for long-horizon agentic work. Claude Opus 4.7 is blooming in a very different style, probably because Anthropic trains for taste, not just accuracy.

GMI Cloud

39,221 просмотров • 4 месяцев назад

I tested Kimi K3 vs Claude Opus 4.8 Same prompt, an armory bay with lighting, props, and detail. Top is Kimi K3, bottom is Opus 4.8. It's not even close. Kimi K3 built a full scene with textures, proper lighting, ammo crates, weapon racks, working detail everywhere. Opus 4.8 gave me a near empty room with a couple of floating tables. No doubt it beats Opus 4.8. Kimi K3 is Fable 5 level, and it's clearly better than GPT-5.6 Sol at 3D and games. An open weight model just matched the best closed models on the market. Let that sink in.
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I tested Kimi K3 vs Claude Opus 4.8 Same prompt, an armory bay with lighting, props, and detail. Top is Kimi K3, bottom is Opus 4.8. It's not even close. Kimi K3 built a full scene with textures, proper lighting, ammo crates, weapon racks, working detail everywhere. Opus 4.8 gave me a near empty room with a couple of floating tables. No doubt it beats Opus 4.8. Kimi K3 is Fable 5 level, and it's clearly better than GPT-5.6 Sol at 3D and games. An open weight model just matched the best closed models on the market. Let that sink in.

Bhavy☄️

479,144 просмотров • 2 месяцев назад

Kimi K3 ranks #6 on ReactBench - It performs better than Opus 4.8 while being over 2x cheaper (!!) - Scores 33%, jump of +10% compared to K2.7 - Lags behind the frontier (Fable and Sol) in frontend production-readiness:
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Kimi K3 ranks #6 on ReactBench - It performs better than Opus 4.8 while being over 2x cheaper (!!) - Scores 33%, jump of +10% compared to K2.7 - Lags behind the frontier (Fable and Sol) in frontend production-readiness:

Aiden Bai

75,250 просмотров • 1 месяц назад

> be Kimi Founder > Zhilin Yang. nobody in the West knows your name. > build K1. K2. K2.5. K2.6. K2.7. > each one sets the open-source record > everyone says China can't reach the frontier > raise $2B. hit $20B valuation. > keep your head down. keep shipping. > July 16. drop K3. > 2.8 trillion parameters. > the biggest open model ever released. > 1 million token context. native multimodal. > Kimi Delta Attention — 6.3x faster decoding. > Artificial Analysis ranks it #4 overall. > right behind GPT-5.6 Sol and Fable 5. > open weights July 27. free for anyone. > the closed labs charge for the frontier. > you gave it away. > different game.
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> be Kimi Founder > Zhilin Yang. nobody in the West knows your name. > build K1. K2. K2.5. K2.6. K2.7. > each one sets the open-source record > everyone says China can't reach the frontier > raise $2B. hit $20B valuation. > keep your head down. keep shipping. > July 16. drop K3. > 2.8 trillion parameters. > the biggest open model ever released. > 1 million token context. native multimodal. > Kimi Delta Attention — 6.3x faster decoding. > Artificial Analysis ranks it #4 overall. > right behind GPT-5.6 Sol and Fable 5. > open weights July 27. free for anyone. > the closed labs charge for the frontier. > you gave it away. > different game.

Kirill

413,271 просмотров • 1 месяц назад

KIMI K2.6 SERVERS BURN 30 MILLION LITERS OF WATER A MONTH. INDIE DEVS USE THE SAME MODEL FOR $30 IN TOKENS TO LAUNCH $20,000/MONTH APPS IN A WEEKEND kimi k2.6 sits at number 1 on the openrouter leaderboard processing 1.58 trillion tokens a week. more than claude sonnet 4.6 and deepseek combined indie developers who launched in 2024-2025 are making $10,000-20,000 a month solo. no team, no office, $20 in starting costs. most aren't even senior devs the stack is next.js, supabase, stripe and kimi k2.6. you give the model 5 open source repos as reference and it assembles the product from the best parts of each 3,000 paying users at $9.99 a month is $29,970 in revenue. infrastructure costs $1,235. net profit lands at $28,735 with a 95% margin most people will bookmark this and forget. the ones who ship this weekend get a 6-12 month head start in app store rankings over everyone who starts later bookmark this and read the article below
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KIMI K2.6 SERVERS BURN 30 MILLION LITERS OF WATER A MONTH. INDIE DEVS USE THE SAME MODEL FOR $30 IN TOKENS TO LAUNCH $20,000/MONTH APPS IN A WEEKEND kimi k2.6 sits at number 1 on the openrouter leaderboard processing 1.58 trillion tokens a week. more than claude sonnet 4.6 and deepseek combined indie developers who launched in 2024-2025 are making $10,000-20,000 a month solo. no team, no office, $20 in starting costs. most aren't even senior devs the stack is next.js, supabase, stripe and kimi k2.6. you give the model 5 open source repos as reference and it assembles the product from the best parts of each 3,000 paying users at $9.99 a month is $29,970 in revenue. infrastructure costs $1,235. net profit lands at $28,735 with a 95% margin most people will bookmark this and forget. the ones who ship this weekend get a 6-12 month head start in app store rankings over everyone who starts later bookmark this and read the article below

starmex

31,826 просмотров • 3 месяцев назад

We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.
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We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.

elvis

47,678 просмотров • 4 месяцев назад

1-bit Kimi K3 performs at Opus 5 level on 3D physics! We ran our Atomic Chat quant of Kimi K3 locally on 4x B200 against three cloud models and gave them all the same task, to build a giant anvil drop test as a single HTML file with real physics Outputs: K3 1bit (local): 15.8K tokens, $0 API cost Kimi K3 (API): 15.3K tokens, $0.30 API cost Opus 5: 22.8K tokens, $0.77 API cost GPT 5.6: 14.5K tokens, $0.72 API cost All four got the physics right. But only Kimi made a working winch. The drum turns and the chain drags the flat car off the pad. Opus 5 drew the most detail, road markings and sparks on the hit. And you can run a model at this level on your own box now. That still feels insane to us
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1-bit Kimi K3 performs at Opus 5 level on 3D physics! We ran our Atomic Chat quant of Kimi K3 locally on 4x B200 against three cloud models and gave them all the same task, to build a giant anvil drop test as a single HTML file with real physics Outputs: K3 1bit (local): 15.8K tokens, $0 API cost Kimi K3 (API): 15.3K tokens, $0.30 API cost Opus 5: 22.8K tokens, $0.77 API cost GPT 5.6: 14.5K tokens, $0.72 API cost All four got the physics right. But only Kimi made a working winch. The drum turns and the chain drags the flat car off the pad. Opus 5 drew the most detail, road markings and sparks on the hit. And you can run a model at this level on your own box now. That still feels insane to us

atomic.chat

53,845 просмотров • 1 месяц назад

Claude Opus 4.6 is available for all users in Orchids right now! Tag Orchids with any app idea and we’ll give you 100k in free credits to test it out. Comparisons to Opus 4.5, GPT-5.2-Codex, Gemini 3 Flash, and Kimi K2.5 👇 Prompt: Make me an interactive, 3d model of the solar system.
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Claude Opus 4.6 is available for all users in Orchids right now! Tag Orchids with any app idea and we’ll give you 100k in free credits to test it out. Comparisons to Opus 4.5, GPT-5.2-Codex, Gemini 3 Flash, and Kimi K2.5 👇 Prompt: Make me an interactive, 3d model of the solar system.

Orchids

22,774 просмотров • 7 месяцев назад

a moonshot engineer leaked the benchmark anthropic, openai and xai all buried the same week: kimi k3 beat opus 5, gpt-5.6 and grok 4.6 at $0.94 a task. stop paying anthropic $200 a month for opus 5 and openai $200 for gpt-5.6 when kimi does the same work for $8 the leak showed kimi k3 winning 9 of 12 categories against opus 5, gpt-5.6 and grok 4.6. within 48 hours all three labs quietly pushed pricing pages and one very specific comparison chart off their sites. nobody announced anything. they just deleted, which tells you everything the four numbers they scrubbed: cost per task · $0.94 vs $1.80 -> opus 5 charges $1.80 to finish one task. gpt-5.6 $1.04. grok 4.6 $0.61. kimi k3 $0.94 and it landed 487 of 500 clean -> anthropic is billing you double for a model that lost the benchmark it paid to promote the weights · free, sitting on huggingface right now -> the entire model is a public download. pull it, keep it, run it forever, nobody can switch it off -> a model you can hold cannot be rented at $200 a month. that single fact is what three labs deleted a chart over the switch · one line of bash -> moonshot ships an anthropic-compatible endpoint. one env variable and claude code points at kimi -> same cli, same keybindings, same /model. you change a url, opus 5 never knows it lost the seat the bill · $400 down to $8 -> opus 5 max plus gpt-5.6 pro is $400 a month. kimi runs the same daily work for $8 metered -> that is a 98% cut for output that beat both of them 9 categories to 3 here is the part they will fight me on: the frontier tax died the week this leaked and all three labs know it. once the weights are public the price has a ceiling, because anyone can serve the same model. anthropic, openai and xai are charging 2025 prices on a lead that ended in a benchmark they deleted instead of answered drop your $400/mo ai stack to $8. the run above is kimi k3 finishing the task opus 5 bills $1.80 for. the full breakdown is in the article below
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a moonshot engineer leaked the benchmark anthropic, openai and xai all buried the same week: kimi k3 beat opus 5, gpt-5.6 and grok 4.6 at $0.94 a task. stop paying anthropic $200 a month for opus 5 and openai $200 for gpt-5.6 when kimi does the same work for $8 the leak showed kimi k3 winning 9 of 12 categories against opus 5, gpt-5.6 and grok 4.6. within 48 hours all three labs quietly pushed pricing pages and one very specific comparison chart off their sites. nobody announced anything. they just deleted, which tells you everything the four numbers they scrubbed: cost per task · $0.94 vs $1.80 -> opus 5 charges $1.80 to finish one task. gpt-5.6 $1.04. grok 4.6 $0.61. kimi k3 $0.94 and it landed 487 of 500 clean -> anthropic is billing you double for a model that lost the benchmark it paid to promote the weights · free, sitting on huggingface right now -> the entire model is a public download. pull it, keep it, run it forever, nobody can switch it off -> a model you can hold cannot be rented at $200 a month. that single fact is what three labs deleted a chart over the switch · one line of bash -> moonshot ships an anthropic-compatible endpoint. one env variable and claude code points at kimi -> same cli, same keybindings, same /model. you change a url, opus 5 never knows it lost the seat the bill · $400 down to $8 -> opus 5 max plus gpt-5.6 pro is $400 a month. kimi runs the same daily work for $8 metered -> that is a 98% cut for output that beat both of them 9 categories to 3 here is the part they will fight me on: the frontier tax died the week this leaked and all three labs know it. once the weights are public the price has a ceiling, because anyone can serve the same model. anthropic, openai and xai are charging 2025 prices on a lead that ended in a benchmark they deleted instead of answered drop your $400/mo ai stack to $8. the run above is kimi k3 finishing the task opus 5 bills $1.80 for. the full breakdown is in the article below

starmex

32,547 просмотров • 25 дней назад

KIMI K3 VS OPUS 4.8 SIMULATING A 3D PARTICLE SYSTEM same prompt, same target: thousands of particles reacting to gravity and to each other in real time → K3: more organic distribution, smoother motion, particles actually cluster and drift like something physical → Opus: more rigid pattern, less natural, movement reads more like a grid than a simulation K3: $0.65 in API. Opus 4.8: ~$1.30 this is simulated physics, not just aesthetics, and it's the kind of detail that separates a demo from something you'd actually ship
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KIMI K3 VS OPUS 4.8 SIMULATING A 3D PARTICLE SYSTEM same prompt, same target: thousands of particles reacting to gravity and to each other in real time → K3: more organic distribution, smoother motion, particles actually cluster and drift like something physical → Opus: more rigid pattern, less natural, movement reads more like a grid than a simulation K3: $0.65 in API. Opus 4.8: ~$1.30 this is simulated physics, not just aesthetics, and it's the kind of detail that separates a demo from something you'd actually ship

Jade

32,766 просмотров • 1 месяц назад

Kimi K3 vs GPT-5.6 vs Claude Fable 5 A game is one of the hardest places to fake working code. A web app can hide bugs behind a polished screenshot. A game can't. Bad collision, clipping, jitter, broken physics, awkward controls, or a snowboard sinking into the terrain become obvious within seconds. That's why I like using games to evaluate coding models. I gave Kimi K3 a single prompt to build a snowboard game in Godot, and just 20 seconds of gameplay told me more than a page of benchmark scores ever could. Benchmarks matter, but watching a model build something interactive reveals a different side of its capabilities. If the gameplay feels right, the systems work together, and the experience holds up under real input, that's a much more convincing demonstration than any leaderboard.
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Kimi K3 vs GPT-5.6 vs Claude Fable 5 A game is one of the hardest places to fake working code. A web app can hide bugs behind a polished screenshot. A game can't. Bad collision, clipping, jitter, broken physics, awkward controls, or a snowboard sinking into the terrain become obvious within seconds. That's why I like using games to evaluate coding models. I gave Kimi K3 a single prompt to build a snowboard game in Godot, and just 20 seconds of gameplay told me more than a page of benchmark scores ever could. Benchmarks matter, but watching a model build something interactive reveals a different side of its capabilities. If the gameplay feels right, the systems work together, and the experience holds up under real input, that's a much more convincing demonstration than any leaderboard.

FHILY👑

36,766 просмотров • 1 месяц назад

You can now use GPT 5.5, Gemini 3.7 Flash, Kimi K3 and 47 other AI models completely free😱 No subscription. No credit card. Even the API usage costs $0. AIHubMix just opened a free catalog with 50 AI models. Some of the available models: • Ox Alpha • Gemini 3.7 Flash • GLM 5.2 • Kimi K3 • MiniMax M3 • GPT 5.5 • 40+ more And you don’t need separate API keys for each model. Setup takes 2 minutes: > Step 1: Go to > Create an account using your email or OAuth. No card needed. Step 2: Create one API key > The same key works with every free and paid model. Step 3: Add it to any OpenAI-compatible tool Base URL: Then choose any model ending in -free, such as: coding-glm-5.2-free gpt-5.5-free That’s it. One API key. 50 AI models. $0 for both input and output. Save this. You might need a free multi-model setup later.
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You can now use GPT 5.5, Gemini 3.7 Flash, Kimi K3 and 47 other AI models completely free😱 No subscription. No credit card. Even the API usage costs $0. AIHubMix just opened a free catalog with 50 AI models. Some of the available models: • Ox Alpha • Gemini 3.7 Flash • GLM 5.2 • Kimi K3 • MiniMax M3 • GPT 5.5 • 40+ more And you don’t need separate API keys for each model. Setup takes 2 minutes: > Step 1: Go to > Create an account using your email or OAuth. No card needed. Step 2: Create one API key > The same key works with every free and paid model. Step 3: Add it to any OpenAI-compatible tool Base URL: Then choose any model ending in -free, such as: coding-glm-5.2-free gpt-5.5-free That’s it. One API key. 50 AI models. $0 for both input and output. Save this. You might need a free multi-model setup later.

CDG

15,388 просмотров • 23 дней назад

Nearly a year of Code Arena: WebDev progress compressed into 15 seconds. Each line follows the highest-scoring model from top labs over time, showing the pace of improvement across the ecosystem. In just the last year, the model in the leading spot increased score by +340 pts and the number of frontier labs competing for the top spot expanded from 6 to 10. Anthropic has dominated throughout the year. Although standout releases have jumped to the top spot, most notably the Chinese open-source model Kimi K3 from Kimi.ai in July. Today, GPT-6 Astra by OpenAI leads with 1796 pts, followed by Claude Fable 5.1 at 1764 pts. The next closest lab is 103 pts away, Qwen with 1685 pts. Code Arena: WebDev ranks models through head-to-head user preference on real front-end web development tasks. These votes drive the leaderboard that is tracking the frontier.
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Nearly a year of Code Arena: WebDev progress compressed into 15 seconds. Each line follows the highest-scoring model from top labs over time, showing the pace of improvement across the ecosystem. In just the last year, the model in the leading spot increased score by +340 pts and the number of frontier labs competing for the top spot expanded from 6 to 10. Anthropic has dominated throughout the year. Although standout releases have jumped to the top spot, most notably the Chinese open-source model Kimi K3 from Kimi.ai in July. Today, GPT-6 Astra by OpenAI leads with 1796 pts, followed by Claude Fable 5.1 at 1764 pts. The next closest lab is 103 pts away, Qwen with 1685 pts. Code Arena: WebDev ranks models through head-to-head user preference on real front-end web development tasks. These votes drive the leaderboard that is tracking the frontier.

Arena.ai

90,773 просмотров • 3 дней назад

anthropic will sell you opus 5 at $200 a month. openai will sell you gpt-5.6 at $200 a month. neither will tell you stanford and berkeley published the 5 principles to build a $100k/mo ai company on kimi k3 for $10 stanford and berkeley spent years figuring out what actually separates ai systems that work in production from ai systems that die in demos. they published the findings. anthropic and openai priced their frontier subs like nobody would read the papers. the papers are free this is dspy plus verifiers plus decomposition plus skills plus mcp. five principles from stanford, berkeley and moonshot that turn a $10/mo kimi k3 sub into an ai analyst that runs unattended. the model is public. the system is the moat five moves that turn kimi k3 into the $100k/mo company: P1 don't prompt, program (stanford dspy) -> stanford proved hand-tuned prompts don't scale. define a pipeline as modules, let the optimizer tune them -> the compiled pipeline beat expert few-shot on multi-step tasks. one line of dspy replaces a month of prompt engineering P2 don't trust the model, build verifiers (berkeley 2026) -> a compiler either accepts or rejects. a test either passes or fails. that is a verifier -> berkeley: test-suite reward hit 42.2% pass@1 on swe-bench. hybrid verifiers hit 51.0% best@26. no bigger model, just a real check P3 don't scale agents, decompose them (stanford ai index 2026) -> stanford found multi-agent gains only 2-4 percentage points. two coding agents sometimes did worse than one -> the win is role decomposition, not count. researcher, writer, reviewer, verifier, clear input, clear output, no overlap P4 don't repeat expertise, encode it as skills (kimi code) -> every session starting from zero is institutional knowledge you lost. a skill.md file makes kimi activate the workflow automatically -> week one you write the skill. month six it encodes more institutional memory than most junior employees carry P5 don't keep ai in chat, connect it to tools (mcp) -> a model that only sees what you paste is a consultant working blindfolded. mcp connects kimi to your crm, db, github, linear, slack -> the model is public. the data is yours. the connections are your moat my position, and it is the arguable one: the next $100k/mo ai company will not win because it got early access to a frontier model. it will win because it followed 5 papers that anthropic and openai are quietly hoping you never read drop your $200/mo ai sub to $10. the swarm above is what 300 kimi k3 agents look like running those 5 principles. the full playbook is in the article below
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anthropic will sell you opus 5 at $200 a month. openai will sell you gpt-5.6 at $200 a month. neither will tell you stanford and berkeley published the 5 principles to build a $100k/mo ai company on kimi k3 for $10 stanford and berkeley spent years figuring out what actually separates ai systems that work in production from ai systems that die in demos. they published the findings. anthropic and openai priced their frontier subs like nobody would read the papers. the papers are free this is dspy plus verifiers plus decomposition plus skills plus mcp. five principles from stanford, berkeley and moonshot that turn a $10/mo kimi k3 sub into an ai analyst that runs unattended. the model is public. the system is the moat five moves that turn kimi k3 into the $100k/mo company: P1 don't prompt, program (stanford dspy) -> stanford proved hand-tuned prompts don't scale. define a pipeline as modules, let the optimizer tune them -> the compiled pipeline beat expert few-shot on multi-step tasks. one line of dspy replaces a month of prompt engineering P2 don't trust the model, build verifiers (berkeley 2026) -> a compiler either accepts or rejects. a test either passes or fails. that is a verifier -> berkeley: test-suite reward hit 42.2% pass@1 on swe-bench. hybrid verifiers hit 51.0% best@26. no bigger model, just a real check P3 don't scale agents, decompose them (stanford ai index 2026) -> stanford found multi-agent gains only 2-4 percentage points. two coding agents sometimes did worse than one -> the win is role decomposition, not count. researcher, writer, reviewer, verifier, clear input, clear output, no overlap P4 don't repeat expertise, encode it as skills (kimi code) -> every session starting from zero is institutional knowledge you lost. a skill.md file makes kimi activate the workflow automatically -> week one you write the skill. month six it encodes more institutional memory than most junior employees carry P5 don't keep ai in chat, connect it to tools (mcp) -> a model that only sees what you paste is a consultant working blindfolded. mcp connects kimi to your crm, db, github, linear, slack -> the model is public. the data is yours. the connections are your moat my position, and it is the arguable one: the next $100k/mo ai company will not win because it got early access to a frontier model. it will win because it followed 5 papers that anthropic and openai are quietly hoping you never read drop your $200/mo ai sub to $10. the swarm above is what 300 kimi k3 agents look like running those 5 principles. the full playbook is in the article below

starmex

31,358 просмотров • 29 дней назад

Fable 5 totally crushed our new contest, but it cost 6x more than Opus 4.8! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: — A train derailing off a broken bridge into the water — Two cars jumping off ramps and colliding mid-air over a canyon — A monster truck crushing a row of parked cars Outputs: Fable 5: 62,158 tokens, $3.12 GPT 5.5: 37,753 tokens, $1.14 Opus 4.8: 22,280 tokens, $0.56 GLM 5.2: 36,246 tokens, $0.08 Fable 5 did all three scenes at A+. The crashes looked real, things fell and broke the right way, and nothing went through the ground or floated. GPT 5.5 was the closest to Fable. In the Bigfoot show, we think GPT was even a little better. GLM 5.2 did not win any scene, but it was the cheapest by far. Fable is the best pick for quality, but you pay more for it.
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Fable 5 totally crushed our new contest, but it cost 6x more than Opus 4.8! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: — A train derailing off a broken bridge into the water — Two cars jumping off ramps and colliding mid-air over a canyon — A monster truck crushing a row of parked cars Outputs: Fable 5: 62,158 tokens, $3.12 GPT 5.5: 37,753 tokens, $1.14 Opus 4.8: 22,280 tokens, $0.56 GLM 5.2: 36,246 tokens, $0.08 Fable 5 did all three scenes at A+. The crashes looked real, things fell and broke the right way, and nothing went through the ground or floated. GPT 5.5 was the closest to Fable. In the Bigfoot show, we think GPT was even a little better. GLM 5.2 did not win any scene, but it was the cheapest by far. Fable is the best pick for quality, but you pay more for it.

atomic.chat

2,840,996 просмотров • 2 месяцев назад

A 24-year-old thrift reseller from Shenzhen spent $31 and one evening photographing 12 outfits from her own closet against a plain wall. Tomorrow she had a 40-second lookbook clip with 900,000 views - every outfit turned into a brand's technical spec sheet, three angles, measurements and all. She took one front photo of each outfit. Kimi K3 added the specs and Seedance 2.5 created the other angles and blueprint effect. Her 12 outfits now looked studio-made. The format already had momentum. She took a visual formula that was catching on: a plain backdrop, a girl standing still, technical lines that make an old T-shirt look like it belongs in an archive. 12 looks in one sitting is a record nobody was chasing. Most accounts post 2 or 3. The $31 covered one month of Kimi K3 and Seedance 2.5, combined. By midnight, the clip was live on 3 platforms. Then she kept the blueprint style and swapped the outfit. New pieces, new colorway chips, same three angles. One evening in front of a wall. 12 looks that keep pulling views.
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A 24-year-old thrift reseller from Shenzhen spent $31 and one evening photographing 12 outfits from her own closet against a plain wall. Tomorrow she had a 40-second lookbook clip with 900,000 views - every outfit turned into a brand's technical spec sheet, three angles, measurements and all. She took one front photo of each outfit. Kimi K3 added the specs and Seedance 2.5 created the other angles and blueprint effect. Her 12 outfits now looked studio-made. The format already had momentum. She took a visual formula that was catching on: a plain backdrop, a girl standing still, technical lines that make an old T-shirt look like it belongs in an archive. 12 looks in one sitting is a record nobody was chasing. Most accounts post 2 or 3. The $31 covered one month of Kimi K3 and Seedance 2.5, combined. By midnight, the clip was live on 3 platforms. Then she kept the blueprint style and swapped the outfit. New pieces, new colorway chips, same three angles. One evening in front of a wall. 12 looks that keep pulling views.

Fokki

97,153 просмотров • 28 дней назад

New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.
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New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

elvis

11,303 просмотров • 27 дней назад

Made with Seedance 2.5 Prompt: Style: Luxury Korean skincare, minimal, clean, soft ivory and blush-pink palette, premium CGI, glossy liquid physics, elegant editorial beauty commercial, photorealistic, slow-motion. Product: Use the exact attached Anua Rice 70+ Glow Milky Toner bottle as the hero product. Preserve the bottle shape, proportions, cap, label, typography, colors, and all packaging details exactly. No redesign or altered text. ⸻ Scene 1 — Hero Introduction The exact Anua Rice 70+ Glow Milky Toner bottle slowly descends from above against a pristine ivory-white background with subtle warm pink gradients. Fine water droplets gently float around the bottle, catching soft light. Camera: Slow cinematic push-in. Lighting: Soft diffused studio lighting, delicate glossy reflections, premium beauty-commercial look. Scene 2 — Milky Water Splash The bottle gracefully settles into a pool of crystal-clear water. A perfectly balanced, elegant splash rises around the base in slow motion, while subtle blush-pink reflections shimmer through the liquid. Camera: 120fps macro cinematic shot. Details: Realistic water physics, fine droplets, glass-like refractions, soft highlights. Scene 3 — Rotating Beauty Shot The bottle slowly rotates 360° while suspended just above reflective water. Tiny water droplets orbit naturally around it as the background transitions from ivory white into an ultra-soft blush-pink gradient. Camera: Smooth luxury product turntable movement. Details: Sharp product focus, shallow depth of field, realistic reflections. Scene 4 — Milky Toner Texture Extreme macro shot of the toner dispensing from the bottle. A silky, translucent milky-white drop forms and slowly falls downward. Camera: Macro lens tracking the falling drop. Details: Ultra-realistic liquid simulation, smooth viscosity, glossy surface tension, soft pink reflections. Scene 5 — Ripple & Hydration The milky toner drop lands gently on a perfectly calm water surface, creating elegant concentric ripples. Tiny micro-bubbles rise through the water as light softly reflects across the surface. Camera: Slow-motion macro shot from water level. Mood: Calm, fresh, luxurious, hydrating. Scene 6 — Ingredient & Benefit Reveal The exact bottle remains perfectly centered above a glossy reflective surface. Minimal elegant typography appears around the product. Left Side ✓ Rice Water 70+ ✓ Nourishing Glow Right Side ✓ Hydrated, Radiant Skin ✓ Smooth Milky Finish Typography is refined, minimal, and premium, with generous spacing. Do not obstruct the product. Scene 7 — Milky Texture Beauty Shot Extreme macro shot of the Anua milky toner spreading smoothly across a pristine glass surface. The texture looks silky, lightweight, and luminous, forming delicate waves with tiny air bubbles. Lighting: Soft Korean beauty editorial lighting with glossy highlights. Camera: Slow macro tracking shot. Scene 8 — Product in Hand An elegant feminine hand gently picks up the exact toner bottle. Natural soft sunlight enters from the side, creating subtle highlights along the bottle and cap. Camera: Slow graceful rotation around the hand and product. Background: Minimal warm-white studio environment with a faint blush-pink gradient. Scene 9 — Final Hero Shot The exact Anua Rice 70+ Glow Milky Toner bottle stands perfectly centered on a glossy reflective water surface. Soft blush-pink mist gently fills the background. A few water droplets float around the bottle while subtle sparkles create a premium finishing touch. Camera: Very slow push-in toward the label. Lighting: Soft diffused luxury studio lighting, realistic reflections, cinematic depth of field. Final look: High-end Korean skincare campaign, photorealistic product CGI, elegant, minimal, luxurious, soft pink-and-ivory aesthetic, realistic water and liquid physics, no distortion, no extra products, no altered packaging, 3:4
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Made with Seedance 2.5 Prompt: Style: Luxury Korean skincare, minimal, clean, soft ivory and blush-pink palette, premium CGI, glossy liquid physics, elegant editorial beauty commercial, photorealistic, slow-motion. Product: Use the exact attached Anua Rice 70+ Glow Milky Toner bottle as the hero product. Preserve the bottle shape, proportions, cap, label, typography, colors, and all packaging details exactly. No redesign or altered text. ⸻ Scene 1 — Hero Introduction The exact Anua Rice 70+ Glow Milky Toner bottle slowly descends from above against a pristine ivory-white background with subtle warm pink gradients. Fine water droplets gently float around the bottle, catching soft light. Camera: Slow cinematic push-in. Lighting: Soft diffused studio lighting, delicate glossy reflections, premium beauty-commercial look. Scene 2 — Milky Water Splash The bottle gracefully settles into a pool of crystal-clear water. A perfectly balanced, elegant splash rises around the base in slow motion, while subtle blush-pink reflections shimmer through the liquid. Camera: 120fps macro cinematic shot. Details: Realistic water physics, fine droplets, glass-like refractions, soft highlights. Scene 3 — Rotating Beauty Shot The bottle slowly rotates 360° while suspended just above reflective water. Tiny water droplets orbit naturally around it as the background transitions from ivory white into an ultra-soft blush-pink gradient. Camera: Smooth luxury product turntable movement. Details: Sharp product focus, shallow depth of field, realistic reflections. Scene 4 — Milky Toner Texture Extreme macro shot of the toner dispensing from the bottle. A silky, translucent milky-white drop forms and slowly falls downward. Camera: Macro lens tracking the falling drop. Details: Ultra-realistic liquid simulation, smooth viscosity, glossy surface tension, soft pink reflections. Scene 5 — Ripple & Hydration The milky toner drop lands gently on a perfectly calm water surface, creating elegant concentric ripples. Tiny micro-bubbles rise through the water as light softly reflects across the surface. Camera: Slow-motion macro shot from water level. Mood: Calm, fresh, luxurious, hydrating. Scene 6 — Ingredient & Benefit Reveal The exact bottle remains perfectly centered above a glossy reflective surface. Minimal elegant typography appears around the product. Left Side ✓ Rice Water 70+ ✓ Nourishing Glow Right Side ✓ Hydrated, Radiant Skin ✓ Smooth Milky Finish Typography is refined, minimal, and premium, with generous spacing. Do not obstruct the product. Scene 7 — Milky Texture Beauty Shot Extreme macro shot of the Anua milky toner spreading smoothly across a pristine glass surface. The texture looks silky, lightweight, and luminous, forming delicate waves with tiny air bubbles. Lighting: Soft Korean beauty editorial lighting with glossy highlights. Camera: Slow macro tracking shot. Scene 8 — Product in Hand An elegant feminine hand gently picks up the exact toner bottle. Natural soft sunlight enters from the side, creating subtle highlights along the bottle and cap. Camera: Slow graceful rotation around the hand and product. Background: Minimal warm-white studio environment with a faint blush-pink gradient. Scene 9 — Final Hero Shot The exact Anua Rice 70+ Glow Milky Toner bottle stands perfectly centered on a glossy reflective water surface. Soft blush-pink mist gently fills the background. A few water droplets float around the bottle while subtle sparkles create a premium finishing touch. Camera: Very slow push-in toward the label. Lighting: Soft diffused luxury studio lighting, realistic reflections, cinematic depth of field. Final look: High-end Korean skincare campaign, photorealistic product CGI, elegant, minimal, luxurious, soft pink-and-ivory aesthetic, realistic water and liquid physics, no distortion, no extra products, no altered packaging, 3:4

H A J R A

18,104 просмотров • 5 дней назад

How a 22-year-old developer built a full 3D Jet Ski racing game in just 40 minutes with zero manual coding He used Claude Opus 5 to generate physics, WebGL 3D graphics, HUD, and audio in a single prompt and turned single-prompt gamedev into a high-margin income stream. Costs: $423 He launched a single-prompt generation workflow that built the entire HTML5 project from scratch: Top layer: A Three.js and WebGL rendering pipeline dynamically creates 3D water physics, real-time wave dynamics, dynamic lighting, and jet ski fluid mechanics, all written autonomously inside one output file without external frameworks. Bottom layer: The Claude Opus 5 engine processed a massive 690-million-token context window to generate the complete gameplay logic, collision handling, dynamic sound generation, controls, and UI layout directly from a detailed initial system prompt. The trend of single-prompt 3D game creation is rapidly exploding across media and indie development. The author monetizes this tech stack through three main channels: 1. Viral Content & Media Systems: Short-form breakdown videos driving massive reach, monetized via promo placements, prompt-pack access, and private developer communities. 2. Rapid Hypercasual Prototyping: Testing 10+ WebGL mechanics per day, flipping fully functional browser games on itch io or CodeCanyon, and licensing prototypes directly to casual game portals. 3. Interactive WebGL Client Solutions: Delivering custom 3D promotional browser games and interactive brand experiences for clients in 48 hours instead of weeks. First month results: > WebGL games generated: 24 > Viral impressions generated: 3.8M+ > Total revenue across licensing & content: $21,400 The AI completely automated the core development lifecycle: Claude Opus 5 built the physics engine, rendered 3D graphics in WebGL, hooked up audio controllers, and generated interactive browser logic with zero manual line-by-line coding. Bookmark it and check article 👇
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How a 22-year-old developer built a full 3D Jet Ski racing game in just 40 minutes with zero manual coding He used Claude Opus 5 to generate physics, WebGL 3D graphics, HUD, and audio in a single prompt and turned single-prompt gamedev into a high-margin income stream. Costs: $423 He launched a single-prompt generation workflow that built the entire HTML5 project from scratch: Top layer: A Three.js and WebGL rendering pipeline dynamically creates 3D water physics, real-time wave dynamics, dynamic lighting, and jet ski fluid mechanics, all written autonomously inside one output file without external frameworks. Bottom layer: The Claude Opus 5 engine processed a massive 690-million-token context window to generate the complete gameplay logic, collision handling, dynamic sound generation, controls, and UI layout directly from a detailed initial system prompt. The trend of single-prompt 3D game creation is rapidly exploding across media and indie development. The author monetizes this tech stack through three main channels: 1. Viral Content & Media Systems: Short-form breakdown videos driving massive reach, monetized via promo placements, prompt-pack access, and private developer communities. 2. Rapid Hypercasual Prototyping: Testing 10+ WebGL mechanics per day, flipping fully functional browser games on itch io or CodeCanyon, and licensing prototypes directly to casual game portals. 3. Interactive WebGL Client Solutions: Delivering custom 3D promotional browser games and interactive brand experiences for clients in 48 hours instead of weeks. First month results: > WebGL games generated: 24 > Viral impressions generated: 3.8M+ > Total revenue across licensing & content: $21,400 The AI completely automated the core development lifecycle: Claude Opus 5 built the physics engine, rendered 3D graphics in WebGL, hooked up audio controllers, and generated interactive browser logic with zero manual line-by-line coding. Bookmark it and check article 👇

Ridark

11,592 просмотров • 1 месяц назад