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Covering the future of artificial intelligence—daily.

[0xAtlas @0xAtlas072]

This isn't a demo, it's a screen recording of an actual production codebase called SWINEMETRICS, software for tracking pig batches, weights, health records and herd logs on a farm

The person running it typed one instruction and let Kimi K3 work through a full todo list on its own, reading files, running greps, testing commands, fixing a batch intake bug, and generating its own documentation along the way

At one point a command straight up fails and exits with an error code, and the model just keeps going, digging into the actual function definitions until it finds where the real problem sits

What makes this worth noting is the plan behind it. Kimi K3 shows up on budget coding subscriptions for as little as 10 dollars a month bundled with other open models, while tools built around closed frontier models often charge two to four times that for less usage

A year ago this kind of long, autonomous debugging session was something you paid a premium subscription for and hoped the model didn't wander off task. Now it's running quietly in the background on a discount plan, chewing through a legacy farm app one bug at a time

Save this post, the interesting AI story right now isn't the flashy demo, it's regular people running frontier level coding agents on the cheapest plan available

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https://t.co/bnIr1mYE9k

[0xAtlas @0xAtlas072]

A 1.56 terabyte AI model just got compressed down to 594 gigabytes, and it still keeps almost 79% of its accuracy

Kimi K3 at full precision needs 1.56 terabytes of storage, more than most people's entire hard drive collection combined

Compressed with 1-bit quantization it shrinks to 594 gigabytes, a 62% cut in size, while holding onto roughly 79% top-1 accuracy. Push it to 2-bit instead and accuracy climbs to about 90% at 861 gigabytes

That's the difference between something only a handful of labs can touch and something you can actually run on a Mac Studio or a single NVIDIA DGX station with 128GB of RAM

The economics flip the usual cloud AI story too. Local deployment costs land around 50 dollars against roughly 250 dollars for the same workload through third party cloud APIs, which adds up to over 50,000 dollars a year saved at scale

This is happening while headlines are already calling Kimi K3 a real rival to OpenAI and Anthropic, and it fits a bigger pattern. China's AI ecosystem stopped being just DeepSeek a while ago, Alibaba's Qwen, Moonshot's Kimi, Zhipu's GLM and MiniMax are all shipping frontier level models in the same few months

The strategy was never to outspend anyone. It's to make frontier AI cheap enough and open enough that adoption happens on its own

Save this post, the AI race stopped being one company against another a while ago, now it's entire ecosystems racing each other

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https://t.co/bnIr1mYE9k

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Kimi K3 just showed up inside GitHub Copilot and Databricks, and a rumor is going around that ByteDance is training something almost twice its size

A few weeks ago Kimi K3 was just a strong open weight model with 2.8 trillion parameters. Now it is showing up everywhere

It went GA inside GitHub Copilot, rolling out across VS Code, Visual Studio, JetBrains, Xcode and the Copilot CLI, priced at 3 dollars per million input tokens and 15 dollars per million output, the same tier as Sonnet class pricing

It also landed on Databricks through Unity AI Gateway, so companies can run it directly next to their own data with enterprise access controls, no separate deployment needed

On benchmarks it sits close enough to Claude Fable 5 and GPT-5.6 that the score gap is basically noise, and around the same time, US labs quietly stopped disclosing how big their own models actually are

Now there are reports that ByteDance is training something even bigger, over 5 trillion parameters, nearly double the size of Kimi K3, reportedly built from scratch instead of distilled off western models

A year ago open weight Chinese models were treated like a curiosity. Now they're built into the coding tools millions of developers use daily, and the next one might be twice the size

Save this post, this stopped being a one time launch story a while ago, it's starting to look like a pattern

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https://t.co/bnIr1mYE9k

[0xAtlas @0xAtlas072]

Two AI models built the exact same extreme sports website, and only one of them broke halfway through

Someone gave Kimi K3 and Fable 5 the identical prompt, build a landing page for a high altitude expedition gear brand

Both models nailed the vibe immediately. Bold typography, mountain line art, a cold color palette, the kind of look a real outdoor brand would pay a design agency thousands for

Kimi K3 kept building out full sections, a product grid with prices for axes, suits and tents, a stats block with wind speed and summit numbers, a testimonials panel labeled word from the ridge

Fable 5's version looked just as sharp on the surface, but a few screens in the layout cracked. A heading for camp III collapsed directly on top of the Lhotse Face title, both fighting for the same space

That is the part that actually matters here. On a glance these outputs look interchangeable, but only one of them held together through a full multi page build

And that gap only gets bigger the more complex the app gets. If reliability swings depending on which page the model happens to render, the model is not really the product anymore, the whole pipeline underneath it is

Save this post, the real test for these tools is never the first screenshot, it's whether page ten still holds together

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https://t.co/bnIr1mYE9k

[0xAtlas @0xAtlas072]

This AI turns a full slide deck into something you build in minutes instead of hours, and it beats GPT and Claude while doing it

Kimi K3 just quietly became one of the strongest models out there and most people have not noticed yet

On internal knowledge work benchmarks it beat GPT 5.5 and Claude Opus 4.8 across the board. 75.5 versus 70.6 versus 65.9 on general reasoning, 73.5 versus 68.2 versus 66.9 on deck building, 62.6 versus 60.7 versus 58.4 on finance tasks

Then there is Kimi Design, upload a messy PDF or PowerPoint and it turns it into a full polished slide deck, charts, layout, everything included, work that used to eat up hours turns into minutes

And the model itself is completely free to use

That combination is rare. Most labs make you pick one, either the model is strong or it is accessible, rarely both

If a free tool is already beating paid frontier models on real work tasks, the pressure on the big subscription based AI products just went up a notch

Save this post if you have not tried Kimi K3 yet, it might replace more of your workflow than you expect

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https://t.co/bnIr1mYE9k

[0xAtlas @0xAtlas072]

Two AI models built the same website, one costs over eleven times more, and you cannot tell them apart

Someone ran the exact same prompt through Kimi K3 and Fable 5, build a creative agency landing page from scratch

Kimi K3 cost 3.5 cents. Fable 5 cost 40 cents. That is more than eleven times the price for the same task

Both models produced a full page, hero section, a four step process breakdown, a closing call to action, contact details, footer, all in the same dark minimal style creative studios charge thousands for

Side by side the two sites are almost impossible to tell apart. Different names, different taglines, same level of polish

This is the part that should worry the expensive labs. People are not paying for quality anymore because the cheap model already matches it, they are paying for a brand name on the invoice

If a task like this runs millions of times a day across every startup building landing pages, that price gap turns into real money fast

Save this post, the real AI price war is not about who is smartest anymore, it is about who still has a reason to charge more

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https://t.co/bnIr1mYE9k

[0xAtlas @0xAtlas072]

A Chinese startup just built an AI model that scores almost as high as the best labs in the world, and this time nobody can just dismiss it

Kimi K3 just dropped and something strange happened, US labs have quietly stopped disclosing their model sizes

On the latest benchmark leaderboard, Kimi K3 scored 57, sitting right in the middle of a tight cluster with Claude Fable 5 at 60, GPT-5.6 Sol at 59 and 58, Claude Opus 4.8 at 56, and several others between 54 and 56

A handful of points used to separate hobbyist AI from frontier AI, now it barely separates the top nine models on the planet

The real difference is what Kimi K3 actually is. It's open, you can download the full weights, run it on your own servers and fine tune it however you want, something none of the big US labs let you do with their top models

Pricing sits at 3 dollars per million input tokens and 15 dollars per million output, a fraction of what closed frontier models charge for similar context windows

The catch, this is not something you casually run on a laptop. Loading K3 reportedly needs at least 16 H200 GPUs, a huge jump from Kimi K2 which enthusiasts could run on a single Mac

Open weight used to mean open to anyone. Now it increasingly means open to whoever owns a data center

The full model weights are set to be released by July 27, and once they land, expect Wall Street to start paying attention too

Save this post, the gap between the US and China on AI just got a lot harder to explain away

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https://t.co/bnIr1mYE9k

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Google Cloud just gave companies a way to run China's biggest AI model without sending a single byte outside their own walls

Kimi K3 is a 2.8 trillion parameter open weight model from Moonshot AI, one of the largest models anyone has ever released to the public

Normally if you want to use it you call the Moonshot hosted API, and your data leaves your infrastructure and lands on someone else's servers

Google Cloud just announced Day 0 support for Kimi K3, meaning the full model can now run inside your own project and VPC

Same model, completely different control plane, your app talks to Kimi K3 running on your own network, with regional residency, encryption and access controls all under your rules

This flips the usual tradeoff. Normally the biggest models only exist behind someone else's API and you just have to trust them with your data

Now a frontier scale open model can sit fully inside your own cloud project like it was built in house

That is a massive deal for banks, hospitals, governments, or any company that legally cannot send data outside its own walls

Save this post, self hosting a model this size used to be something only a handful of giants could pull off

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A model that weighs almost 1.6 terabytes can now run on your own computer, and you can't tell the difference from the top AI labs

Kimi K3 from Moonshot AI is a 2.8 trillion parameter model with context up to 1 million tokens

At full size it weighs around 1.56 terabytes, running that on a regular machine used to sound impossible

The Unsloth team compressed it down to 1-bit and 2-bit versions, cutting the size by almost 62 percent while accuracy stayed at 78.9 and 90 percent

Here's where it gets interesting. The compressed Kimi K3 was given the same task as GPT-5.6 and Claude Opus 5, write the code for a fish tank animation

The output landed on the same level, even though this version is a fraction of the size and runs locally instead of in the cloud

Access to top tier AI used to mean a subscription and a cloud server

Now a stripped down but still powerful version can just be downloaded and run on your own hardware

Save this post, local models at this level sounded like science fiction just a couple months ago

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https://t.co/bnIr1mYE9k

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A small startup in China just gave away for free what OpenAI charges $20 a month for

It's called Kimi

Nobody expected much from the team behind it, way smaller than OpenAI, nowhere near the same budget

But the answers hold up right next to ChatGPT, and anyone can use it for free, no subscription

While the big players build paywalls around AI, this team went the other way

Now picture a kid who can't afford a tutor. Until recently, a top tier AI assistant was something only money could buy

Now that same kid has the same intelligence in his pocket as kids from rich families, and it costs nothing

That's why this matters. Not because there's one more chatbot out there, but because the balance of power in AI is starting to shift

Save this post, a year from now people will be talking about Kimi as much as they talk about ChatGPT today

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https://t.co/bnIr1mYE9k

[0xAtlas @0xAtlas072]

A 1-bit quantized model running locally just built a better physics scene than Claude Opus 5. And it used a third of the tokens.

Someone ran a blind 4-way benchmark: Kimi K3 in two flavors, a 1-bit quantized version running locally and the full API version, against Claude Opus 5 and GPT 5.6. Same prompt, same physics engine, same construction site scene with a gantry crane, tires, cones and crates scattered around.

The gap wasn't in how it looked. It was in the cost of getting there.

Opus 5 needed around 22,000 tokens to finish its scene. GPT 5.6 did it in about 14,500. Kimi K3's local 1-bit build landed right in that same range, under 15,000 tokens, while running on a machine with no API call at all.

That's the part that actually matters. A quantized, locally hosted version of K3 wasn't just "good enough." It kept pace with a frontier closed model using roughly a third of the tokens Opus 5 burned through, and it didn't need a server farm to do it.

Watching all four build in real time side by side, the structures converge on the same idea: a yellow gantry frame over a work site. But K3 gets there lighter, cheaper and just as sturdy.

Open weight, running locally, beating the leaderboard darling on efficiency. That's not a demo anymore, that's a shift.

#KimiK3 #OpenWeights #AIbenchmark #LocalAI #ClaudeOpus

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https://t.co/bnIr1mYE9k

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An AI agent just opened 3 browser tabs, searched Twitter, Reddit and Hacker News at the same time, and handed back a finished spreadsheet. No human touched the mouse.

Moonshot just quietly shipped Kimi WebBridge, and it's less a feature than a full browser takeover.

You give the Kimi Code CLI one line: go find out what people are saying about the Kimi K2.6 launch across X, Reddit and Hacker News, then compile it into an Excel file.

That's it. From there the agent takes the wheel, literally. It opens tabs labeled agent:twitter, agent:reddit, agent:hackernews, runs all three searches in parallel, reads every post, and starts building a report while you watch the browser move on its own.

The output wasn't a wall of text either. It came back with an actual formatted workbook: 17 posts from X, 16 from Reddit, 13 from Hacker News, total engagement numbers, top post stats, average engagement per platform, and a "key themes" section written in plain English summarizing things like cost efficiency, open source dominance and the geopolitical narrative around Chinese open models.

Somewhere in the same session it was also filling out an actual Google Form survey and reading random web pages, like it was just another tab in someone's normal workday.

This isn't a chatbot answering questions anymore. It's an agent operating a browser the way a person would, except it doesn't get bored on tab 40.

#KimiWebBridge #MoonshotAI #KimiK3 #AIagents #Automation

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https://t.co/bnIr1mYE9k

[0xAtlas @0xAtlas072]

AI just replaced an entire engineering department in 9 days. For real.

A 190-room hotel. Full model of piping, electrical, ductwork and fire protection systems.

Before: 3 engineers, 6 weeks, about $47,000.
Now: 1 engineer reviewing AI output, 9 days, about $10,500.
The method is stupidly simple: hook Kimi K3 up to the modeling software, let it read blueprints and plain English descriptions directly, and it builds the model itself, instead of humans placing every component by hand.

Change orders during construction dropped 60-80%.

This isn't an investor demo. This is a real job site, real money, real time saved.

You can actually see it happening in the clip: the model grows layer by layer, first the building skeleton, then the pipes, then the ducts, then everything auto-resolves collisions that usually take a team weeks to catch manually.

When AI starts genuinely tackling "real world complex systems," the changes are just getting started.

#kimi #kimik3 #k3 #AI #BIM

[0xAtlas @0xAtlas072]

https://t.co/bnIr1mYE9k

[0xAtlas @0xAtlas072]

https://t.co/bnIr1mYE9k