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🧠If your own intelligence isn’t enough — use artificial.⚡️🤖

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@TesCalendar1 🥲

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@0xAIGOATexe lol 😂

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The CLAUDE.md-as-persistent-memory framing is the real unlock — most people never think past single-session prompting. The 7am autonomous cleanup loop is the detail that makes it feel like a system, not a trick. Have you set up your own CLAUDE.md yet, or still running sessions cold?

[Gradient @GradientX0]

@0xkkai 🫡

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@TopviewAIhq Fable 5 does a great job, but the price….🫣

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The 8-day audit compressed to 35 minutes is the number that matters — that’s real fintech dev cycle compression, not a demo trick. 20% build time reduction from the recommendations is solid proof it’s not just fast, it’s actually useful. Is your own workflow using K3’s 1M context window yet, or still testing?

[Gradient @GradientX0]

@mikenevermiss Nice work 🤝🏽

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$0.035 for a fully polished, animated build is the number that actually breaks the “AI websites still need a human pass” assumption. The footer hover detail is a nice specific touch that makes the comparison credible instead of just a stat flex. What’s your own /design prompt template looking like these days?

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@savipww I completely agree with you.🫡

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AMERICAN COMPANIES NOW SPEND 60% OF THEIR AI BUDGET ON CHINESE MODELS AND THE PRICE DIFFERENCE EXPLAINS EXACTLY WHY

$50 vs $30 vs $15.

three models. same task. wildly different bills.

claude fable 5 — $50 per million output tokens
gpt 5.6 sol — $30 per million output tokens
kimi k3 — $15 per million output tokens

kimi k3 costs 3x less than fable 5

70% cheaper than fable 5. 50% cheaper than gpt 5.6

and it’s benchmarking higher than both

this isn’t a budget alternative anymore

american companies are now routing almost 60% of their weekly ai token spend to chinese models

not because of ideology. because of unit economics

when a model scores better and costs a third of the price, the decision makes itself

every company burning through millions of tokens a month just did the math

the frontier model race isn’t about who has the smartest model anymore

it’s about who has the smartest model per dollar

and right now china is winning that math by a wide margin

[Kimi.ai @Kimi_Moonshot]

Feeling all the love for Kimi K3 already. Here are some of the amazing things people have been building with it. Enjoy K3.

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Kimi K3 vs Fable 5

[Kimi.ai @Kimi_Moonshot]

Introducing Kimi K3: Open Frontier Intelligence

🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal
🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts
🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost
🔹 Built for long-horizon agentic coding and self-evolving workflows

Kimi K3 is now live on on https://t.co/zrk6zZxZUo, Kimi Work, Kimi Code, and the Kimi API.
Open Weights by July 27, 2026.

🔗 API: https://t.co/XCrgjXAqMw
🔗 Tech blog: https://t.co/YTfiMSNM1f

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FABLE 5 CRASHED 14 TIMES STUCK AT 17% WHILE KIMI K3 CRASHED 6 TIMES STUCK AT 8% AND SOMEHOW ONE OF THEM STILL COST $6.50 LESS

two ai models. same geometry dash clone. same side by side test.

left screen: fable 5
17% completion
attempt 14
$21 to generate
36 minutes build time

right screen: kimi k3
8% completion
attempt 6
$14.5 to generate
2 hours 30 minutes build time

fable 5 is 30% more expensive and shipped faster

kimi k3 is 30% cheaper and took 4x longer to build

both games are genuinely hard. both are addictive enough that the tester keeps retrying instead of giving up

visually kimi k3 edges ahead — that pink gradient moon and neon geometry looks straight out of a paid indie game

functionally fable 5 is more forgiving to actually play through in real time

this is not close anymore

two ai labs. one from the us, one from china. both producing playable games from a single prompt

the price gap is real. the quality gap is shrinking every single month

[Kimi.ai @Kimi_Moonshot]

Introducing Kimi K3: Open Frontier Intelligence

🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal
🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts
🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost
🔹 Built for long-horizon agentic coding and self-evolving workflows

Kimi K3 is now live on on https://t.co/zrk6zZxZUo, Kimi Work, Kimi Code, and the Kimi API.
Open Weights by July 27, 2026.

🔗 API: https://t.co/XCrgjXAqMw
🔗 Tech blog: https://t.co/YTfiMSNM1f

[Gradient @GradientX0]

A NEW CHINESE AI MODEL JUST BUILT A BETTER MILLENNIUM FALCON THAN CLAUDE FABLE 5 AND CHARGED 30% LESS TO DO IT

same prompt. same target. millennium falcon in voxel form.

two completely different results.

claude fable 5
49 minutes
$21
solid build. recognizable. gets the job done.

kimi k3
1 hour 11 minutes
$14.5
more detail. sharper geometry. even a working hyperdrive light strip along the base.

took 22 minutes longer.

cost $6.50 less.

and the output looks like it came from a completely different tier of model.

here’s why this matters beyond one screenshot comparison

kimi k3 just entered the same benchmark conversation as fable 5 and gpt 5.6

not as the cheap alternative everyone dismisses

as a model actually competing on output quality while undercutting on price

the ai race used to be openai vs anthropic vs google

now every serious 3d generation, coding, and reasoning benchmark has a chinese model quietly outperforming the labs everyone assumed were untouchable

people are still sleeping on kimi

the gap between “western frontier lab” and “everyone else” just got a lot smaller

and it happened with a spaceship nobody expected to be the benchmark that proved it

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A NEW AI MODEL WITH 2.8 TRILLION PARAMETERS JUST GENERATED A FULL PLAYABLE CYBERPUNK GAME INSIDE A BROWSER AND THE GUY TESTING IT COULDN’T BELIEVE HIS SCREEN

kimi k2 just dropped and the numbers alone are absurd

2.8 trillion parameters
1 million token context window
agents that run natively inside the chat, not bolted on afterward
the model reportedly gets sharper at coding and research the more you use it

00:00 signup and first look at the specs. 2.8 trillion parameters, 1 million token context, agent runs directly in chat

three versions built for different jobs, not one model trying to do everything

00:26 max — the all-around general purpose version
00:32 swarm max — built for massive search and batch processing at scale
fast — optimized purely for speed

00:40 opening the interface reveals built-in plugins already live. image gen. auto tools. and a library of skills with working examples ready to launch instantly, not just described in docs

00:50 he picks one to test: a cyberpunk city skill

00:56 clicks enter game. survival mode loads.

01:03 what loads is not a demo, not a screenshot, not a video. a fully playable 3d cyberpunk game world rendered live inside the browser tab. no download. no install. no loading screen that takes forever. his reaction says it all: “this looks like a full-blown video game, get the fuck out of here”

01:29 next test: real-time satellite view

fully interactive. space stations. gps overlays. settings panels that actually respond

01:40 he zooms in to see how far the detail holds up, and the rendering keeps delivering instead of falling apart into pixels

here’s the part that actually matters for anyone tracking the frontier model race

these benchmarks are now putting kimi k2 in the same conversation as fable 5 and gpt 5.6

not as a budget alternative. as a legitimate contender

we went from “ai writes you an email” to “ai generates a fully playable real-time 3d world inside a browser tab” in about two years

the gap between the top ai labs just got smaller and most people haven’t even heard this model’s name yet

kimi k2. remember it.

[Gradient @GradientX0]

I BUILT AN AI AGENT PIPELINE THAT SEARCHES X FOR TRENDING NEWS, WRITES THE SCRIPT, GENERATES THE VIDEO, AND POSTS IT AUTOMATICALLY EVERY DAY WITHOUT ME TOUCHING ANYTHING

not content creation.

content infrastructure.

here is the entire pipeline i built

step one — i picked a niche

zero point energy. shocking science, physics mysteries, borderline conspiracy territory. built for attention.

step two — set up the accounts

instagram. tiktok. youtube. x. connected all of them to one posting tool so my agent can publish everywhere from one place.

step three — i had to understand what actually makes content go viral

three things only

the hook
the script
the way it looks

miss any one of these and the content dies in the algorithm within seconds

step four — the visual style

i described to my agent exactly how i wanted the video to look. the agent built a skill folder from that description. reusable forever after.

i used two formats

format one — multiple characters talking, brain rot style, refined past what everyone else is copying

format two — the trending news character format i keep coming back to

step five — the part that actually changed everything

the hook and script cannot be generic

my agent needs to know what’s actually happening right now in the niche

so i gave it control of a browser and told it to search x for the most important trending topics before generating anything

this means every single video starts from something real people are already talking about, not a random ai-generated idea nobody cares about

step six — automation

scheduled tasks inside codex

i click schedule. set the time. the entire pipeline runs itself every single day

search trends. write script. generate video. post to every platform. repeat tomorrow.

here’s what came out of one run

video one — scientists may have gotten the end of the universe wrong for decades

video two — what are people actually seeing when a craft moves in ways physics doesn’t explain

video three — why are we still paying electricity bills that were never supposed to have an expiration date

three hooks. three scripts. three finished videos. zero manual work after setup.

this isn’t really about zero point energy as a niche

it’s a repeatable system

i can swap the niche and keep the exact same pipeline. search trends, write hook, generate script, produce video, schedule post, repeat daily forever

i used to manually brainstorm content ideas one video at a time

now my agent never runs out of ideas because it reads the internet’s trending topics before every single video

that’s the actual unlock.

[Gradient @GradientX0]

THIS IS THE EXACT FRAMEWORK PEOPLE ARE USING TO SHIP REAL PRODUCTS WITH AI AGENTS AND MOST DEVELOPERS ARE STILL DOING IT THE HARD WAY

no bootcamp. no computer science
degree. no team.

just a framework and an ai agent doing the heavy lifting.

here is the entire pipeline from zero to deployed product

the setup

1/ github account · create a new repo

2/ vercel account · import that repo · every code change gets pushed and auto deploys

that is your entire infrastructure. free. automated.

the actual build order

1/ value proposition first

what does your app actually do. one sentence. no fluff.

2/ frontend before backend

build how it looks before how it works this is the longest part and most people skip straight to backend then wonder why nothing feels finished

if you cannot design

21st · steal proven components instead of building from scratch

you are not being lazy. you are being efficient.

tell the agent what the app does. it builds a raw version. you go page by page refining until it looks right.

3/ backend connection

convex not supabase

supabase locks you into pricing that scales against you as you grow

convex is the same simplicity without the financial trap

this is also where you connect apis

third party tools your app depends on

replicate for ai generation. stripe for
payments. whatever your product actually needs.

4/ authentication

betterauth · free · open source · handles login and user sessions

zero reason to pay for auth when this exists

5/ security check before launch

tell your agent to test against owasp top 10

this catches the vulnerabilities that turn your weekend project into a headline

deploy.

here is what nobody tells you about this framework

it does not matter which ai model you use

fable 5. claude. gpt 5.6. the framework stays identical.

the model just executes. the framework is the actual skill.

most people are stuck comparing which ai model is smarter

the people actually shipping products stopped comparing months ago

they picked a framework and started building

github. vercel. 21st . convex. betterauth. owasp check. deploy.

six steps. zero excuses. one weekend.

bookmark this. the barrier to shipping a product just disappeared and most people still think it requires a team.

[Gradient @GradientX0]

@savipww Deriving the kinematics by hand before writing a line of code is the real flex — most people would just grab a library. Proving it with a pen draw test is a perfect no-BS demo. Is he planning to add force feedback or keep it purely positional?

[Gradient @GradientX0]

A NEW AI MODEL WITH 2.8 TRILLION PARAMETERS JUST GENERATED A FULL PLAYABLE CYBERPUNK GAME INSIDE A BROWSER AND THE GUY TESTING IT COULDN’T BELIEVE HIS SCREEN

kimi k2 just dropped and the numbers alone are absurd

2.8 trillion parameters
1 million token context window
agents that run natively inside the chat, not bolted on afterward
the model reportedly gets sharper at coding and research the more you use it

00:00 signup and first look at the specs. 2.8 trillion parameters, 1 million token context, agent runs directly in chat

three versions built for different jobs, not one model trying to do everything

00:26 max — the all-around general purpose version
00:32 swarm max — built for massive search and batch processing at scale
fast — optimized purely for speed

00:40 opening the interface reveals built-in plugins already live. image gen. auto tools. and a library of skills with working examples ready to launch instantly, not just described in docs

00:50 he picks one to test: a cyberpunk city skill

00:56 clicks enter game. survival mode loads.

01:03 what loads is not a demo, not a screenshot, not a video. a fully playable 3d cyberpunk game world rendered live inside the browser tab. no download. no install. no loading screen that takes forever. his reaction says it all: “this looks like a full-blown video game, get the fuck out of here”

01:29 next test: real-time satellite view

fully interactive. space stations. gps overlays. settings panels that actually respond

01:40 he zooms in to see how far the detail holds up, and the rendering keeps delivering instead of falling apart into pixels

here’s the part that actually matters for anyone tracking the frontier model race

these benchmarks are now putting kimi k2 in the same conversation as fable 5 and gpt 5.6

not as a budget alternative. as a legitimate contender

we went from “ai writes you an email” to “ai generates a fully playable real-time 3d world inside a browser tab” in about two years

the gap between the top ai labs just got smaller and most people haven’t even heard this model’s name yet

kimi k2. remember it.

[Gradient @GradientX0]

The $9 shell to $180 flip is a genuinely wild margin, and the Nairobi-to-Rwanda distribution angle turns it from a hobby into a real regional business. 260 units a month with tooling paid off in three weeks is the number that sells it. Is Juma sourcing more shells locally now, or still leaning on M-Pesa marketplace listings?

[Gradient @GradientX0]

“Rent-vs-own” framing is the sharpest part here — turns local hardware from a hobby flex into an actual infra decision for teams with steady workloads. Calling out immersion cooling as real infrastructure (not a desk toy) sets honest expectations too. What’s the payback period he’s estimating vs equivalent cloud spend?