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I gave MiMo V2.6 Pro one prompt inside Claude Code. a habit tracker called Pulse. 7-day check grid, streaks, a canvas chart, dark mode, saved in the browser. one prompt. no hand edits. one model. 62 seconds later it opened itself in my browser. 592 lines, one html file,... show more

Muhammad Ayan

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162,531 次观看 • 6 天前 •via X (Twitter)

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MOONSHOT JUST CLONED CLAUDE CODE AND MADE IT FREE. It's called Kimi Code CLI. Open source, MIT license, maintained by the lab that shipped K3 yesterday. And it does things Claude Code doesn't: → drop a screen recording into the chat as input → built-in coder, explore, and plan subagents, each in its own context → plan mode before it touches a single file → MCP servers configured by the agent itself via /mcp-config → plugs into Zed, JetBrains, and VS Code → one binary, no Node setup, starts in milliseconds The CLI costs $0. K3 behind it starts at $3 per million tokens. Grab it for free👇

MOONSHOT JUST CLONED CLAUDE CODE AND MADE IT FREE. It's called Kimi Code CLI. Open source, MIT license, maintained by the lab that shipped K3 yesterday. And it does things Claude Code doesn't: → drop a screen recording into the chat as input → built-in coder, explore, and plan subagents, each in its own context → plan mode before it touches a single file → MCP servers configured by the agent itself via /mcp-config → plugs into Zed, JetBrains, and VS Code → one binary, no Node setup, starts in milliseconds The CLI costs $0. K3 behind it starts at $3 per million tokens. Grab it for free👇

darkzodchi

407,015 次观看 • 2 个月前

GLM 5.2 INPUT FELL FROM $1.40 TO SEVEN CENTS PER MILLION IN NINETY DAYS • what it costs now > $0.07 per million input at the cheapest of 20 providers, a 95% drop in three months. > direct still lists $1.40 in and $4.40 out, with cached input at $0.26. > Same model, same weights, twenty-fold spread depending on the door you walk through. • the free way in > New accounts on get 20M tokens: > 744B MoE, 1M context, MIT weights you can also just download and self-host. Nobody announced this. It happened one provider at a time -> and the model itself never changed. Check which provider you are actually routed through before you top up anywhere ↓

GLM 5.2 INPUT FELL FROM $1.40 TO SEVEN CENTS PER MILLION IN NINETY DAYS • what it costs now > $0.07 per million input at the cheapest of 20 providers, a 95% drop in three months. > direct still lists $1.40 in and $4.40 out, with cached input at $0.26. > Same model, same weights, twenty-fold spread depending on the door you walk through. • the free way in > New accounts on get 20M tokens: > 744B MoE, 1M context, MIT weights you can also just download and self-host. Nobody announced this. It happened one provider at a time -> and the model itself never changed. Check which provider you are actually routed through before you top up anywhere ↓

slash1s

30,276 次观看 • 1 个月前

single RTX 3090. 24 GB VRAM. Qwen3.5-35B-A3B. 4-bit quant, 113 tokens per second at full 262K context harnessing Claude Code locally with no API, no subscription, no proxy. told it what it is. 30 Mamba2 layers, 10 attention, 256 experts, 8 active per token. said "build something that shows off what you can do." it visualized its own architecture. interactive. tokens flowing through layers. 256 experts lighting up on routing. served in the browser from the same GPU running inference. single prompt. then i said level up. 3D. Three.js. separate files. flythrough camera. clickable layers. it planned first, scaffolded 6 files, hit one API bug, fixed it itself, then optimized for smooth framerate. two iterations to a working 3D neural network explorer. llama.cpp just merged a native Anthropic endpoint. Claude Code points at localhost. the whole setup is two commands. no LiteLLM. no proxy config. the open source models coming out of china right now are genuinely changing what's possible on consumer hardware. respect to the Qwen team. this is acceleration.

single RTX 3090. 24 GB VRAM. Qwen3.5-35B-A3B. 4-bit quant, 113 tokens per second at full 262K context harnessing Claude Code locally with no API, no subscription, no proxy. told it what it is. 30 Mamba2 layers, 10 attention, 256 experts, 8 active per token. said "build something that shows off what you can do." it visualized its own architecture. interactive. tokens flowing through layers. 256 experts lighting up on routing. served in the browser from the same GPU running inference. single prompt. then i said level up. 3D. Three.js. separate files. flythrough camera. clickable layers. it planned first, scaffolded 6 files, hit one API bug, fixed it itself, then optimized for smooth framerate. two iterations to a working 3D neural network explorer. llama.cpp just merged a native Anthropic endpoint. Claude Code points at localhost. the whole setup is two commands. no LiteLLM. no proxy config. the open source models coming out of china right now are genuinely changing what's possible on consumer hardware. respect to the Qwen team. this is acceleration.

Sudo su

110,464 次观看 • 7 个月前

FABLE 5 + HIGGSFIELD TURN A $35,000 ANIMATED SITE INTO A ONE-SESSION, $12 BUILD. HERE'S EXACTLY HOW. a studio runs this across four people and three weeks. you run it across one chat window and one afternoon. THE BUILD, STAGE BY STAGE: STAGE 1 - THE CONCEPT Claude reads your brief and scripts the scroll before a line of code exists - what the visitor feels at second 3, 15, 40. prompt: "read this brief. script the scroll beat by beat, then scaffold the project with GSAP ScrollTrigger + Lenis." STAGE 2 - THE VISUALS (Higgsfield) every hero shot, transition, and ambient loop comes out of 30+ generative models - matched to the story, not pulled from a stock library. prompt: "generate the hero sting and one b-roll clip per section. 3-5s, high-res, cinematic." STAGE 3 - THE MOTION (Claude Code) Claude writes the ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. zero hand-coded keyframes. prompt: "wire the scroll: pin the hero, scrub the video, reveal each section on scroll. keep it 60fps on mobile." STAGE 4 - THE POLISH six cinematic effects baked in, no config: film grain, particles, vignette, glass cards, color tints, scroll pacing. prompt: "bake in the cinematic layer, then QA load speed, mobile breakpoints, and whether the scroll actually lands - rewrite what doesn't." CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: mcp_servers: higgsfield: url: " one OAuth flow. Claude generates and pulls clips directly - no exporting by hand. THE MATH: → what a studio charges: $6,000-$35,000+ → what it costs you: a Claude sub + a few dollars of Higgsfield credits → what it takes: 4 people + 3 weeks → 1 operator + 1 session the pipeline was the moat. it just became four prompts. Follow me, comment "MATH" and I'll send you the full step-by-step Playbook. full breakdown in the article 👇

FABLE 5 + HIGGSFIELD TURN A $35,000 ANIMATED SITE INTO A ONE-SESSION, $12 BUILD. HERE'S EXACTLY HOW. a studio runs this across four people and three weeks. you run it across one chat window and one afternoon. THE BUILD, STAGE BY STAGE: STAGE 1 - THE CONCEPT Claude reads your brief and scripts the scroll before a line of code exists - what the visitor feels at second 3, 15, 40. prompt: "read this brief. script the scroll beat by beat, then scaffold the project with GSAP ScrollTrigger + Lenis." STAGE 2 - THE VISUALS (Higgsfield) every hero shot, transition, and ambient loop comes out of 30+ generative models - matched to the story, not pulled from a stock library. prompt: "generate the hero sting and one b-roll clip per section. 3-5s, high-res, cinematic." STAGE 3 - THE MOTION (Claude Code) Claude writes the ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. zero hand-coded keyframes. prompt: "wire the scroll: pin the hero, scrub the video, reveal each section on scroll. keep it 60fps on mobile." STAGE 4 - THE POLISH six cinematic effects baked in, no config: film grain, particles, vignette, glass cards, color tints, scroll pacing. prompt: "bake in the cinematic layer, then QA load speed, mobile breakpoints, and whether the scroll actually lands - rewrite what doesn't." CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: mcp_servers: higgsfield: url: " one OAuth flow. Claude generates and pulls clips directly - no exporting by hand. THE MATH: → what a studio charges: $6,000-$35,000+ → what it costs you: a Claude sub + a few dollars of Higgsfield credits → what it takes: 4 people + 3 weeks → 1 operator + 1 session the pipeline was the moat. it just became four prompts. Follow me, comment "MATH" and I'll send you the full step-by-step Playbook. full breakdown in the article 👇

ZEUS⚡️

47,174 次观看 • 2 个月前

look what a single consumer GPU just built. gave Qwen3.5-35B-A3B one prompt: build a cloud GPU marketplace with pricing cards, deploy templates, and a benchmark leaderboard. it planned the layout, wrote the animations, populated the data, and served it. one shot. one HTML file. then i told it to iterate. split the hero, add a floating GPU with neural network animation. glassmorphism on the cards. done. done. done. three rounds, no confusion, no regressions. 4-bit quantized. 19.7 GB. single RTX 3090. full coding agent claude code harness running on localhost. no API calls leaving my machine. no subscription. no rate limits. earlier today i pointed it at my own production website. it curled the HTML, found every broken link, and told me "pretty shell, empty core. would not recommend." then built a better version from scratch. local inference stops being a demo when you actually steer it. the models are there. they understand intent. but you have to meet them halfway with good prompts, clear context, and real project structure. that's the skill gap now. not the models. the steering. more experiments coming. i genuinely cannot stop playing with this thing.

look what a single consumer GPU just built. gave Qwen3.5-35B-A3B one prompt: build a cloud GPU marketplace with pricing cards, deploy templates, and a benchmark leaderboard. it planned the layout, wrote the animations, populated the data, and served it. one shot. one HTML file. then i told it to iterate. split the hero, add a floating GPU with neural network animation. glassmorphism on the cards. done. done. done. three rounds, no confusion, no regressions. 4-bit quantized. 19.7 GB. single RTX 3090. full coding agent claude code harness running on localhost. no API calls leaving my machine. no subscription. no rate limits. earlier today i pointed it at my own production website. it curled the HTML, found every broken link, and told me "pretty shell, empty core. would not recommend." then built a better version from scratch. local inference stops being a demo when you actually steer it. the models are there. they understand intent. but you have to meet them halfway with good prompts, clear context, and real project structure. that's the skill gap now. not the models. the steering. more experiments coming. i genuinely cannot stop playing with this thing.

Sudo su

37,373 次观看 • 7 个月前

I spend my days explaining to teams why a 770-billion-parameter open-weight model will never fit into their infrastructure. This week, I asked it to code a complete mobile game from a single prompt. The concept is one everyone has probably played before. A hole moving through an open-plan office, viewed from above, swallowing everything in its path. You start tiny, only able to swallow pens and cups. You grow, moving on to keyboards and plants, then chairs and printers, then desks and vending machines. Eventually, you swallow the entire meeting room. 60 seconds on the clock. One HTML file, zero external libraries, zero errors on launch. The video shows the generation and then the actual gameplay. What broke is more instructive than what worked. The structure came out right on the first try: fixed-timestep loop, spatial grid for collisions, tier system, spring camera. The balancing and rendering, not so much. The first version scored 70 points in 13 seconds with a tier threshold at 500, and drew colorful circles and triangles instead of furniture. I had to give it numbers and exact recipes. Speed: 640 pixels per second. Radii: 30, 58, 96, 150, 225. Tier thresholds: 150, 550, 1500, 3500. And for every object, a pixel-perfect drawing recipe. Once I gave it that, it followed the instructions exactly. A model that doesn't execute its own code won't tune itself. But it will execute, down to the exact numbers, what you tell it to build. The model is Hy4 preview, released by Tencent Hunyuan on August 28. 770 billion parameters in total, but 49 billion active per token. And that second number is what determines your serving bill. Native 1M context. Apache 2.0 license. vLLM and SGLang supported from day one, with an official FP8 checkpoint. Text-only preview. The part that matters for deployment is the compression. Tencent describes it as seven times smaller with almost no loss. GGUF builds use mixed per-layer quantization, where calibration data determines the bit width layer by layer. Some layers go as low as 1.31 bits, while others go up to 2.06 bits, averaging 2.38 bits per weight. The model drops from 1.5 TB in BF16 to 213.66 GiB while, according to their measurements, staying in the same performance range on real-world tasks. On this build, they report 204 tokens/s in prefill and 20 tokens/s in decoding, measured on an 8-GPU node. Their numbers, not mine. Their blind evaluation scores 2.99 out of 4 across 203 engineering tasks rated by 163 experts. Ahead of Kimi K3 at 2.94 and GLM-5.3 at 2.92. Their numbers too. My run went through the official hosted studio, not a local build, so I’m not claiming to have benchmarked the compressed GGUF myself. Two honest caveats. None of these builds run on standard llama.cpp. The hyv4 architecture isn't upstream yet, so patches are required. And 214 GiB of resident weights is still a server, not your laptop. This is a preview, and Tencent explicitly asks users to break it and report what fails. So here's my contribution.

I spend my days explaining to teams why a 770-billion-parameter open-weight model will never fit into their infrastructure. This week, I asked it to code a complete mobile game from a single prompt. The concept is one everyone has probably played before. A hole moving through an open-plan office, viewed from above, swallowing everything in its path. You start tiny, only able to swallow pens and cups. You grow, moving on to keyboards and plants, then chairs and printers, then desks and vending machines. Eventually, you swallow the entire meeting room. 60 seconds on the clock. One HTML file, zero external libraries, zero errors on launch. The video shows the generation and then the actual gameplay. What broke is more instructive than what worked. The structure came out right on the first try: fixed-timestep loop, spatial grid for collisions, tier system, spring camera. The balancing and rendering, not so much. The first version scored 70 points in 13 seconds with a tier threshold at 500, and drew colorful circles and triangles instead of furniture. I had to give it numbers and exact recipes. Speed: 640 pixels per second. Radii: 30, 58, 96, 150, 225. Tier thresholds: 150, 550, 1500, 3500. And for every object, a pixel-perfect drawing recipe. Once I gave it that, it followed the instructions exactly. A model that doesn't execute its own code won't tune itself. But it will execute, down to the exact numbers, what you tell it to build. The model is Hy4 preview, released by Tencent Hunyuan on August 28. 770 billion parameters in total, but 49 billion active per token. And that second number is what determines your serving bill. Native 1M context. Apache 2.0 license. vLLM and SGLang supported from day one, with an official FP8 checkpoint. Text-only preview. The part that matters for deployment is the compression. Tencent describes it as seven times smaller with almost no loss. GGUF builds use mixed per-layer quantization, where calibration data determines the bit width layer by layer. Some layers go as low as 1.31 bits, while others go up to 2.06 bits, averaging 2.38 bits per weight. The model drops from 1.5 TB in BF16 to 213.66 GiB while, according to their measurements, staying in the same performance range on real-world tasks. On this build, they report 204 tokens/s in prefill and 20 tokens/s in decoding, measured on an 8-GPU node. Their numbers, not mine. Their blind evaluation scores 2.99 out of 4 across 203 engineering tasks rated by 163 experts. Ahead of Kimi K3 at 2.94 and GLM-5.3 at 2.92. Their numbers too. My run went through the official hosted studio, not a local build, so I’m not claiming to have benchmarked the compressed GGUF myself. Two honest caveats. None of these builds run on standard llama.cpp. The hyv4 architecture isn't upstream yet, so patches are required. And 214 GiB of resident weights is still a server, not your laptop. This is a preview, and Tencent explicitly asks users to break it and report what fails. So here's my contribution.

Alexa Benchmark

16,315 次观看 • 26 天前

Grok Build is seriously addictive. I can't put it down. I had my 8-year-old nephew build a game with it, Starship Breakthrough. He sent all the prompts himself; I just gave a little guidance on the side. He knows zero code, and when he finished his first real game, he was over the moon. One tip: when you start a new project in Grok Build, don't have it execute right away. Say "I want to build a ___, design it first, then build it." Let it lay out a plan, you review it, then it executes — the results come out noticeably more solid. Everyone should give it a try.

Grok Build is seriously addictive. I can't put it down. I had my 8-year-old nephew build a game with it, Starship Breakthrough. He sent all the prompts himself; I just gave a little guidance on the side. He knows zero code, and when he finished his first real game, he was over the moon. One tip: when you start a new project in Grok Build, don't have it execute right away. Say "I want to build a ___, design it first, then build it." Let it lay out a plan, you review it, then it executes — the results come out noticeably more solid. Everyone should give it a try.

WY

43,825 次观看 • 4 个月前

20 days ago, I connected Claude Code to my newly created instagram handle.. I gained 4.3M views and 6500+ followers in less than a month [ i post Ai generated animated stories ] Full workflow: i let claude study my account before i write another reel.. This is the cleanest content workflow i've built on claude. give it your IG first. 4 prompts handle the rest.. niche research, the reel script, the hook, and the daily automation.. the whole loop is basically, give claude your IG → find what's working → write retention-optimized scripts → engineer the hook → automate the daily output.. ▫️ Setup: give claude your instagram open claude code. claude code has a built-in web tool that browses any public URL. or install any agentic browser like Browser Harness or Firecrawl or Comet browser paste this with your handle filled in: "Browse and pull the last 30 reels and posts. Analyze my recurring topics, top-performing hooks, formats, and engagement patterns. Then map out my actual audience and what they consistently respond to." claude reads your profile, pulls every reel down, and now has the context to personalize every prompt below to YOUR account, not a generic niche. if you're on claude desktop, the same works with firecrawl MCP connected. ▫️ Prompt 1 find what actually goes viral in your niche: "Analyze the highest-performing Instagram Reels, TikToks, and Reddit posts in the [niche] niche from the last 30 days. Identify repeating hooks, visual styles, emotional triggers, and content formats that consistently generate high engagement. Then summarize the 5 strongest content angles optimized for AI-generated content and short-form videos." run this after the setup. you get 5 angles backed by what's already working in your niche, cross-checked against what's already working on YOUR account. ▫️ Prompt 2 write a high-retention reel script "Write a short-form Instagram Reel script about [topic] with an aggressive hook in the first 2 seconds. Create immediate curiosity, tension, or controversy to stop scrolling, then deliver a fast and satisfying payoff. Keep it under 30 seconds and optimize the structure for watch time, replays, comments, and shares. Finish with a subtle CTA." the line that matters: "optimize the structure for watch time, replays, comments, and shares." claude writes for the metrics, not just the word count. ▫️ Prompt 3 engineer better hooks "Study the top-performing Reels in [niche] and break down the hook structure, pacing, and emotional triggers used in the first 3 seconds. Then generate 5 new hook variations that are even more curiosity-driven, emotionally charged, and optimized to stop scrolling instantly. Focus on triggers like surprise, fear, ego, urgency, or desire." most reels die in the first 2 seconds. this prompt has claude reverse-engineer what already works, then give you 5 sharper versions to swap in. ▫️ Prompt 4 automate the whole workflow "Build a complete AI-powered content workflow for Instagram in the [niche] niche. The system should identify trending topics daily, generate high-retention scripts, create matching AI visuals, turn them into short-form videos, and generate optimized captions and hashtags. Structure everything as a repeatable workflow designed for consistent daily posting and growth." once the niche and script structure are validated, this turns it into a daily loop. one prompt that handles topic → script → visual → video → caption. these 4 prompts are the building blocks. the setup is what makes them yours. your real value is in the [niche] you plug in. content workflow built in one weekend, daily posting on autopilot from monday.

Axel Bitblaze 🪓

201,810 次观看 • 3 个月前

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.

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 次观看 • 1 个月前

A CHINESE GUY STOPPED PAYING WEB DESIGNERS $1,800 PER LANDING PAGE AND BUILT THE SAME KIND OF SITE WITH CLAUDE CODE IN ONE AFTERNOON FOR LESS THAN $70 claude was not just writing code. it was doing the job of a designer, copywriter and frontend dev in one window. in 4 hours, one rough idea turned into the layout, color system, font choices, page sections and the full html/css/js build the real trick was the references. he gave claude 5 screenshots, made it ask 7 questions first, then pushed it toward one clear visual direction instead of accepting another generic ai-looking template the first version already looked solid. the second pass made it feel expensive: better typography, a darker palette, mobile cleanup, cursor effects, 6 micro-interactions and custom hero visuals his old workflow was burning $1,200 on design, $500 on frontend work and another $150 on small fixes every time he needed a new page. now the whole test costs less than $70 and the site still looks like something a $5,000 agency would ship the edge is not “ai builds websites.” the edge is that one person can now brief, critique, polish and launch in one afternoon without waiting 10 days for a designer to send version one

A CHINESE GUY STOPPED PAYING WEB DESIGNERS $1,800 PER LANDING PAGE AND BUILT THE SAME KIND OF SITE WITH CLAUDE CODE IN ONE AFTERNOON FOR LESS THAN $70 claude was not just writing code. it was doing the job of a designer, copywriter and frontend dev in one window. in 4 hours, one rough idea turned into the layout, color system, font choices, page sections and the full html/css/js build the real trick was the references. he gave claude 5 screenshots, made it ask 7 questions first, then pushed it toward one clear visual direction instead of accepting another generic ai-looking template the first version already looked solid. the second pass made it feel expensive: better typography, a darker palette, mobile cleanup, cursor effects, 6 micro-interactions and custom hero visuals his old workflow was burning $1,200 on design, $500 on frontend work and another $150 on small fixes every time he needed a new page. now the whole test costs less than $70 and the site still looks like something a $5,000 agency would ship the edge is not “ai builds websites.” the edge is that one person can now brief, critique, polish and launch in one afternoon without waiting 10 days for a designer to send version one

Gipp 🦅

304,719 次观看 • 3 个月前

19-year-old from china makes $9,000/month designing product sites and ships each one in an afternoon. here's his exact setup the whole thing runs on two tools that each do one job: > brief written by hand: 5 min > Moonchild builds the design system, then every screen from it: 20 min > MCP hands the design to Claude as real structure, not a screenshot: instant > Claude Code reads those exact tokens and builds the live app: 20 min > second Claude session reviews the build for drift: 10 min total: about an hour. screen five still matches screen one. no agency, no dev, no design team the trick is MCP. the design tool passes Claude the actual colors, components and layout, so it builds from the source instead of guessing from a picture. full pipeline, every prompt, in the article above.

19-year-old from china makes $9,000/month designing product sites and ships each one in an afternoon. here's his exact setup the whole thing runs on two tools that each do one job: > brief written by hand: 5 min > Moonchild builds the design system, then every screen from it: 20 min > MCP hands the design to Claude as real structure, not a screenshot: instant > Claude Code reads those exact tokens and builds the live app: 20 min > second Claude session reviews the build for drift: 10 min total: about an hour. screen five still matches screen one. no agency, no dev, no design team the trick is MCP. the design tool passes Claude the actual colors, components and layout, so it builds from the source instead of guessing from a picture. full pipeline, every prompt, in the article above.

Ridark

19,477 次观看 • 3 个月前

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.

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 次观看 • 2 个月前

Okay, motion designers are 100% cooked. Saw on x that Opus 5.5 could do advanced animations and I was skeptical at first. So I gave it a one sentence prompt, and it one shot this in 15 minutes. Will add the prompt below, but this is genuinely mind blowing. This level of work would have costed $8,000 - $10,000 with a motion design team and 3-4 week turnaround with multiple revisions. Claude did it in one go with this prompt: "make a dynamic 15-second motion graphics video that shows what an incredible motion designer you are, like it's your showreel for a résumé. go all out." The only thing remaining for humans is creativity and critical thinking.

Okay, motion designers are 100% cooked. Saw on x that Opus 5.5 could do advanced animations and I was skeptical at first. So I gave it a one sentence prompt, and it one shot this in 15 minutes. Will add the prompt below, but this is genuinely mind blowing. This level of work would have costed $8,000 - $10,000 with a motion design team and 3-4 week turnaround with multiple revisions. Claude did it in one go with this prompt: "make a dynamic 15-second motion graphics video that shows what an incredible motion designer you are, like it's your showreel for a résumé. go all out." The only thing remaining for humans is creativity and critical thinking.

Leon Abboud

109,218 次观看 • 5 天前

🚨Gemini 3.6 Flash is trash I tested it on a 3D Golden Gate Bridge, and the results were awful. • I had to re-prompt it three times because it repeatedly ignored the instructions. • First attempt, instead of creating the requested .html file, it first tried to build the experience inside the Gemini app using simulations. • Then second attempt it started placing images from the web into the chat rather than actually producing the file. • Even after getting it to complete the task, the final output was dramatically worse than Gemini 3.1 Pro, which is 5 months old and now not even a top 10 model on leaderboards. This feels like a regression from Gemini 3.5 Flash and honestly, it is one of the weakest models I have tested in the past few months. Has anyone else tested Gemini 3.6 Flash yet, and are you seeing the same thing?

🚨Gemini 3.6 Flash is trash I tested it on a 3D Golden Gate Bridge, and the results were awful. • I had to re-prompt it three times because it repeatedly ignored the instructions. • First attempt, instead of creating the requested .html file, it first tried to build the experience inside the Gemini app using simulations. • Then second attempt it started placing images from the web into the chat rather than actually producing the file. • Even after getting it to complete the task, the final output was dramatically worse than Gemini 3.1 Pro, which is 5 months old and now not even a top 10 model on leaderboards. This feels like a regression from Gemini 3.5 Flash and honestly, it is one of the weakest models I have tested in the past few months. Has anyone else tested Gemini 3.6 Flash yet, and are you seeing the same thing?

Lumina

72,529 次观看 • 2 个月前

JENSEN HUANG UNVEILED A BOARD THAT RUNS 1 TRILLION PARAMETER AI MODELS. THE $249 NVIDIA BOX UNDER YOUR DESK KILLS A $200/MONTH AI BILL FOR $5 IN ELECTRICITY jensen held it up on stage with one hand and called it the architecture that runs the future of ai. that same technology now ships in a $249 box smaller than your wallet the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

JENSEN HUANG UNVEILED A BOARD THAT RUNS 1 TRILLION PARAMETER AI MODELS. THE $249 NVIDIA BOX UNDER YOUR DESK KILLS A $200/MONTH AI BILL FOR $5 IN ELECTRICITY jensen held it up on stage with one hand and called it the architecture that runs the future of ai. that same technology now ships in a $249 box smaller than your wallet the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

54,492 次观看 • 4 个月前

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

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

33,100 次观看 • 1 个月前

New open source library: kugiri (区切り). Already running in production on several of my sites, now public. It splits text into lines, words and characters, the thing every text reveal needs. The difference is where it cuts. Most splitters guess where a line breaks, and the guess falls apart the moment the text does something real: text-wrap: balance, hyphenation, a drop cap, a float, a link that wraps, RTL, Japanese. kugiri does not guess. It asks the browser where the lines already are and cuts exactly there, so the split text looks pixel for pixel like the text before it. No animation, no scroll watching, no opinions. Every unit gets an index and the library gets out of the way. Animate with CSS, the Web Animations API, GSAP or Motion. One file, no dependencies, about 7 kB gzipped. The demo is also the test suite: fifty situations, each checked against what the browser painted, in Chromium, WebKit and Firefox. Demo: npm: Source:

New open source library: kugiri (区切り). Already running in production on several of my sites, now public. It splits text into lines, words and characters, the thing every text reveal needs. The difference is where it cuts. Most splitters guess where a line breaks, and the guess falls apart the moment the text does something real: text-wrap: balance, hyphenation, a drop cap, a float, a link that wraps, RTL, Japanese. kugiri does not guess. It asks the browser where the lines already are and cuts exactly there, so the split text looks pixel for pixel like the text before it. No animation, no scroll watching, no opinions. Every unit gets an index and the library gets out of the way. Animate with CSS, the Web Animations API, GSAP or Motion. One file, no dependencies, about 7 kB gzipped. The demo is also the test suite: fifty situations, each checked against what the browser painted, in Chromium, WebKit and Firefox. Demo: npm: Source:

Edoardo Lunardi

46,449 次观看 • 25 天前

THREE 3090s ON ONE BOARD GIVE YOU 72GB OF VRAM AND KILL YOUR $200 CLAUDE CODE AND $200 OPENAI BILL people are pulling three used 3090s off ebay for around $2,100 total and stacking them in one tower to build a dedicated ai rig. that pools 72gb of vram for less than what a single rtx 5090 retails for alibaba shipped qwen 3.6 27b in april under apache 2.0. on realworldqa vision it scores 84.1 against claude 4.5 opus at 77.0. on ifbench instructions it lands at 76.5 against claude's 58.0 a single 3090 already runs qwen 3.6 27b with eight gigs of headroom. three of them in parallel handle larger models like deepseek r1 70b and qwen 235b without breaking a sweat a heavy ai user pays $200 claude code, $200 chatgpt pro plus $40 cursor and gemini. that's $5,280 a year and the rig pays itself off before month nine on $8 a month in electricity setup is one shell command for ollama, one to pull the model, one environment variable to point claude code at localhost. cli stays identical, nothing leaves the network, requests stop costing money bookmark this and read the article below

THREE 3090s ON ONE BOARD GIVE YOU 72GB OF VRAM AND KILL YOUR $200 CLAUDE CODE AND $200 OPENAI BILL people are pulling three used 3090s off ebay for around $2,100 total and stacking them in one tower to build a dedicated ai rig. that pools 72gb of vram for less than what a single rtx 5090 retails for alibaba shipped qwen 3.6 27b in april under apache 2.0. on realworldqa vision it scores 84.1 against claude 4.5 opus at 77.0. on ifbench instructions it lands at 76.5 against claude's 58.0 a single 3090 already runs qwen 3.6 27b with eight gigs of headroom. three of them in parallel handle larger models like deepseek r1 70b and qwen 235b without breaking a sweat a heavy ai user pays $200 claude code, $200 chatgpt pro plus $40 cursor and gemini. that's $5,280 a year and the rig pays itself off before month nine on $8 a month in electricity setup is one shell command for ollama, one to pull the model, one environment variable to point claude code at localhost. cli stays identical, nothing leaves the network, requests stop costing money bookmark this and read the article below

starmex

16,719 次观看 • 3 个月前

Someone posted the full code of a Chinese Polymarket bot on GitHub and deleted it within hours I saved it before it went down This is the bot behind gabagool22, $868,000 profit, 28,620 predictions, all green And now I understand exactly how it works The strategy is pure arbitrage, no prediction needed, no direction bets: → Buy YES when price drops below 48 cents → Buy NO when price drops below 48 cents → Combined cost under 97 cents, payout always $1.00 → Profit locked regardless of outcome 7.5% per pair. 96 markets per day on BTC alone. Running 24/7. The bot combines 4 open source repos into one hybrid engine: API client, thread-safe position tracker, gas optimizer, and risk manager with pre-trade validation It targets 15-minute BTC, ETH, and SOL markets simultaneously Start with $100, set your thresholds, run dry-mode first, then go live Profile: Full code: Copy:

Woody

26,359 次观看 • 5 个月前

I just built a Google Ads Builder in Claude Code that turns one URL into a complete, launch-ready campaign 🤯 Point it at your website and it builds the whole Google Search campaign — keywords, ad groups, every headline, the negative list — structured the way a good paid-search manager would. All inside Claude Code. Perfect for DTC brands and agencies who need Search live but don't have days to build it right. If you're staring at a blank Google Ads account, researching keywords one browser tab at a time, writing 15 headlines per ad group to a 30-character limit, guessing at a negative list while budget quietly leaks on junk clicks... This builds the entire campaign from a single input — your homepage URL: → Reads your site and works out what you sell and who for → Groups keywords into tight, high-intent ad groups → Writes every Responsive Search Ad — 15 headlines, 4 descriptions, to Google's exact limits → Builds a negative-keyword list so you stop paying for junk clicks → Adds sitelinks, callouts, match types, bidding, and a budget split → Exports a Google Ads Editor CSV you import in one click No blank-account paralysis. No keyword rabbit holes. No character-counting 60 headlines by hand. What you get: → A launch-ready campaign from just your URL → Tight ad groups built for Quality Score, not a keyword dump → Every ad written to Google's limits and policy → A one-click CSV import plus a full campaign dashboard Built 100% in Claude Code. No API keys, no Google Ads login. And I'm not sending a playbook this time — I'm giving the whole skill away for free. The actual file. Install it and build your own campaign in 5 minutes. Want the skill? > Like this post > Comment "BUILD" And I'll send it over (must be following so I can DM)

I just built a Google Ads Builder in Claude Code that turns one URL into a complete, launch-ready campaign 🤯 Point it at your website and it builds the whole Google Search campaign — keywords, ad groups, every headline, the negative list — structured the way a good paid-search manager would. All inside Claude Code. Perfect for DTC brands and agencies who need Search live but don't have days to build it right. If you're staring at a blank Google Ads account, researching keywords one browser tab at a time, writing 15 headlines per ad group to a 30-character limit, guessing at a negative list while budget quietly leaks on junk clicks... This builds the entire campaign from a single input — your homepage URL: → Reads your site and works out what you sell and who for → Groups keywords into tight, high-intent ad groups → Writes every Responsive Search Ad — 15 headlines, 4 descriptions, to Google's exact limits → Builds a negative-keyword list so you stop paying for junk clicks → Adds sitelinks, callouts, match types, bidding, and a budget split → Exports a Google Ads Editor CSV you import in one click No blank-account paralysis. No keyword rabbit holes. No character-counting 60 headlines by hand. What you get: → A launch-ready campaign from just your URL → Tight ad groups built for Quality Score, not a keyword dump → Every ad written to Google's limits and policy → A one-click CSV import plus a full campaign dashboard Built 100% in Claude Code. No API keys, no Google Ads login. And I'm not sending a playbook this time — I'm giving the whole skill away for free. The actual file. Install it and build your own campaign in 5 minutes. Want the skill? > Like this post > Comment "BUILD" And I'll send it over (must be following so I can DM)

Mike Futia

61,042 次观看 • 2 个月前