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AI is now improving its own model architectures and chips. The power infrastructure it runs on deserves the same attention. We used RL to teach a Kimi base model to design medium-power transformers. It met 93% of unseen specs, helping to compress multi-week engineering efforts into a few minutes... show more

General Trajectory

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31,681 views • 3 days ago •via X (Twitter)

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Fireworks blazing fast LLM inference is now available on Poe! Today, we’re excited to bring the power of the new Mistral 7B Instruct model to the Poe powered by our lightning-fast Fireworks inference platform. You can now have conversations with the Mistral 7B bot and even build your own bots on top of it. Try it here:

Fireworks blazing fast LLM inference is now available on Poe! Today, we’re excited to bring the power of the new Mistral 7B Instruct model to the Poe powered by our lightning-fast Fireworks inference platform. You can now have conversations with the Mistral 7B bot and even build your own bots on top of it. Try it here:

Fireworks AI

93,334 views • 2 years ago

We’re proud to share that Tamarind Bio has been selected to build, host, and operate the inference infrastructure layer for TuneLab2.0, the next evolution of the platform. Eli Lilly and Company TuneLab is a first-of-its-kind, collaborative AI/ML drug discovery platform, bringing models trained on over $1B worth of Lilly proprietary data to the biotech ecosystem. Tamarind will power TuneLab’s scalable drug discovery workflows and model inference.

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Deniz Kavi

43,692 views • 3 months ago

Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.

Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.

Chris

60,278 views • 6 months ago

$Anthropic has revealed that its Claude model is now designing proteins and automating chemistry research on its own. This kind of work used to require specialist scientists and weeks or months of effort. It can now be completed by an AI system in a fraction of the time.$

Anthropic has revealed that its Claude model is now designing proteins and automating chemistry research on its own. This kind of work used to require specialist scientists and weeks or months of effort. It can now be completed by an AI system in a fraction of the time.

Anonymous

59,739 views • 23 days ago

Average US AI lab: > builds the strongest model > gets scared of its own model > spends months arguing about safety > fights other AI labs over who has the strongest model > delays the release for more safety testing > regulators ask for even more safety > switches users to a dumber model > keeps it closed source Average Chinese AI lab: > trains Kimi K3 > builds a 2.8T parameter monster > gives it a 1M context window > beats GPT-5.6 and Claude Fable 5 on some tasks > open-sources the weights > publishes the architecture > refuses to elaborate > leaves

Average US AI lab: > builds the strongest model > gets scared of its own model > spends months arguing about safety > fights other AI labs over who has the strongest model > delays the release for more safety testing > regulators ask for even more safety > switches users to a dumber model > keeps it closed source Average Chinese AI lab: > trains Kimi K3 > builds a 2.8T parameter monster > gives it a 1M context window > beats GPT-5.6 and Claude Fable 5 on some tasks > open-sources the weights > publishes the architecture > refuses to elaborate > leaves

Tornado guy

161,129 views • 1 month ago

1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.

1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.

Sundar Pichai

864,665 views • 1 year ago

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

32,547 views • 20 days ago

The human brain is truly a marvel of nature. If you horribly reductive, and boiled it down to a language model, you'd be looking at roughly 100 trillon parameters running as a sparse MoE architecture Only about 1-5% of neurons fire at any given moment, meaning the brain "activates" maybe 1-5 trillion parameters per inference step. For context, the largest AI models we've built probably top out around 5 trillion parameters. The brain is roughly 100x larger. Even its active params at any given moment are larger than almost every model in existence today. Here's what melts my brain (pun intnended) though Your brain does all of this on about 20 watts of power, less than a dim light bulb. Training a frontier AI model consumes enough electricity to power small cities for months. Running inference across data centers pulls megawatts. Your brain runs 24/7 for 80+ years on the equivalent of a phone charger. We haven't come close to matching the brain's scale. And we're not even in the same universe when it comes to efficiency. Evolution spent 500 million yrs optimizing the most energy-efficient intelligence architecture ever known. we're trying to brute force our way there with compute and electricity. Nature is still the best engineer in the room.

The human brain is truly a marvel of nature. If you horribly reductive, and boiled it down to a language model, you'd be looking at roughly 100 trillon parameters running as a sparse MoE architecture Only about 1-5% of neurons fire at any given moment, meaning the brain "activates" maybe 1-5 trillion parameters per inference step. For context, the largest AI models we've built probably top out around 5 trillion parameters. The brain is roughly 100x larger. Even its active params at any given moment are larger than almost every model in existence today. Here's what melts my brain (pun intnended) though Your brain does all of this on about 20 watts of power, less than a dim light bulb. Training a frontier AI model consumes enough electricity to power small cities for months. Running inference across data centers pulls megawatts. Your brain runs 24/7 for 80+ years on the equivalent of a phone charger. We haven't come close to matching the brain's scale. And we're not even in the same universe when it comes to efficiency. Evolution spent 500 million yrs optimizing the most energy-efficient intelligence architecture ever known. we're trying to brute force our way there with compute and electricity. Nature is still the best engineer in the room.

am.will

130,883 views • 5 months ago

It took a mere 10 minutes of full V12 power (and probably about £6 of fuel) to clear the windscreen under its own power from a very cold start! You can’t see as it’s soo fast but I nearly broke my neck getting to the car!

It took a mere 10 minutes of full V12 power (and probably about £6 of fuel) to clear the windscreen under its own power from a very cold start! You can’t see as it’s soo fast but I nearly broke my neck getting to the car!

Lawrence Whittaker

147,342 views • 1 year ago

The M550i uses a very different logic. Its 4.4-litre twin-turbo V8 N63TU3 produces around 523 hp and is paired with all-wheel drive and an automatic transmission tuned for both speed and refinement. Compared with a full M5, the M550i is not positioned as a flagship. It is positioned as a fast 5 Series that sounds more like a usual V8. The engineering emphasis is on torque delivery, stability and comfort rather than maximum lap time. The M550i shows how BMW used to integrate high output V8 power into its core range instead of isolating it inside a specialist model.

The M550i uses a very different logic. Its 4.4-litre twin-turbo V8 N63TU3 produces around 523 hp and is paired with all-wheel drive and an automatic transmission tuned for both speed and refinement. Compared with a full M5, the M550i is not positioned as a flagship. It is positioned as a fast 5 Series that sounds more like a usual V8. The engineering emphasis is on torque delivery, stability and comfort rather than maximum lap time. The M550i shows how BMW used to integrate high output V8 power into its core range instead of isolating it inside a specialist model.

Autowelt

78,567 views • 7 months ago

Someone built an AI you power with a hand crank it is called CrankGPT and its running without any battery, internet or data centre just you turning a handle like it is 1900 SqueezLabs built it using a Raspberry Pi 5 with 8GB of RAM an audio card and a 20 watt hand crank generator it takes about 30 seconds of cranking to boot into a working voice assistant and the onboard capacitor gives you roughly 20 seconds of runtime before you have to start cranking again they have used it to generate small images even write code 🤯 this proves you can run a real AI model on almost no power while everyone is building billion dollar data centres two guys put AI in a box

Someone built an AI you power with a hand crank it is called CrankGPT and its running without any battery, internet or data centre just you turning a handle like it is 1900 SqueezLabs built it using a Raspberry Pi 5 with 8GB of RAM an audio card and a 20 watt hand crank generator it takes about 30 seconds of cranking to boot into a working voice assistant and the onboard capacitor gives you roughly 20 seconds of runtime before you have to start cranking again they have used it to generate small images even write code 🤯 this proves you can run a real AI model on almost no power while everyone is building billion dollar data centres two guys put AI in a box

Sweep

11,610 views • 2 months ago

We've made it to one of Miniatur Wunderland's most famous attractions. The model airport 😁 featuring aircraft taking off and landing, this really is a sight to behold on its own!

We've made it to one of Miniatur Wunderland's most famous attractions. The model airport 😁 featuring aircraft taking off and landing, this really is a sight to behold on its own!

Austin's Rail Adventures

11,051 views • 6 months ago

PAYING PER MODEL IS THE DUMBEST THING IN TECH RIGHT NOW i was paying 3x what i needed to for AI inference the grid lets you buy a quality spec instead of a specific model.. it routes every request in real time to the cheapest option that qualifies swap one url and your code keeps working exactly the same openai-compatible, one line to switch, 200M free tokens to start

PAYING PER MODEL IS THE DUMBEST THING IN TECH RIGHT NOW i was paying 3x what i needed to for AI inference the grid lets you buy a quality spec instead of a specific model.. it routes every request in real time to the cheapest option that qualifies swap one url and your code keeps working exactly the same openai-compatible, one line to switch, 200M free tokens to start

Robin Delta

15,729 views • 3 months ago

Kimi K3 is a wildly creative model. I asked it to design Dieter Rams' Braun T3 radio in adam. It wrote the CAD, then decided the STEP wasn't enough, piped it into Blender on my Mac and lit a full product shoot in three colorways.

Kimi K3 is a wildly creative model. I asked it to design Dieter Rams' Braun T3 radio in adam. It wrote the CAD, then decided the STEP wasn't enough, piped it into Blender on my Mac and lit a full product shoot in three colorways.

Zach Dive

24,322 views • 1 month ago

I asked Astra to recreate Lewis Hamilton’s Ferrari! It went through F1 specs, regulations, videos from F1 engineers and photos of the car, then built a 3D model (w/ blender) with a full breakdown of the parts. It even made clay & wireframe versions too. Every time I think I’ve pushed this model to its limits, it surprises me.

I asked Astra to recreate Lewis Hamilton’s Ferrari! It went through F1 specs, regulations, videos from F1 engineers and photos of the car, then built a 3D model (w/ blender) with a full breakdown of the parts. It even made clay & wireframe versions too. Every time I think I’ve pushed this model to its limits, it surprises me.

Vaibhav (VB) Srivastav

97,085 views • 6 days ago

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.

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 views • 4 months ago

We just launched Claude Sonnet 4.5 and “Imagine with Claude”, a 5-day preview that creates dynamic experiences for you in real-time (and the first product I’ve worked on at Anthropic!). It pioneers the concept of “model-as-backend”, using a model to not only generate interfaces on the fly but also power all the functionality behind it – all made possible by our newest model, Claude Sonnet 4.5. Here’s a "choose your own adventure" version of my founder journey, as retold by Imagine with Claude:

We just launched Claude Sonnet 4.5 and “Imagine with Claude”, a 5-day preview that creates dynamic experiences for you in real-time (and the first product I’ve worked on at Anthropic!). It pioneers the concept of “model-as-backend”, using a model to not only generate interfaces on the fly but also power all the functionality behind it – all made possible by our newest model, Claude Sonnet 4.5. Here’s a "choose your own adventure" version of my founder journey, as retold by Imagine with Claude:

Sean Strong

115,693 views • 11 months ago

I began working on Transformers in 2018 at Stanford AI Lab -- training and inference in the early days was anything but fun. Today it takes 3 lines of code and zero setup time to use a multi-B LLM on Replit ⠕. Releasing Replit ModelFarm is one of my career dreams come true. 1/ 🧵

I began working on Transformers in 2018 at Stanford AI Lab -- training and inference in the early days was anything but fun. Today it takes 3 lines of code and zero setup time to use a multi-B LLM on Replit ⠕. Releasing Replit ModelFarm is one of my career dreams come true. 1/ 🧵

Michele Catasta

109,926 views • 3 years ago

Grok 3 is now available on Alchemist AI. Explore the power of Grok in your workflow. As an Ideation Model: Grok 3 turns rough ideas into structured, detailed prompts. It breaks down your intent into clear building blocks, understands nuance and context, and opens up multiple creative directions to explore. As a Generation Model: Grok 3 is built for logic-heavy outputs and multi-step generation. From game mechanics to character systems and worldbuilding, it’s capable of producing full-fledged games and applications with depth and precision.

Grok 3 is now available on Alchemist AI. Explore the power of Grok in your workflow. As an Ideation Model: Grok 3 turns rough ideas into structured, detailed prompts. It breaks down your intent into clear building blocks, understands nuance and context, and opens up multiple creative directions to explore. As a Generation Model: Grok 3 is built for logic-heavy outputs and multi-step generation. From game mechanics to character systems and worldbuilding, it’s capable of producing full-fledged games and applications with depth and precision.

ALCHEMIST AI 🔮

16,075 views • 1 year ago

NVIDIA might have just declared war on the cloud GPU business For years, AI builders had one option Rent compute Pay every month Watch the bill grow every time usage increased Now NVIDIA is putting serious AI hardware directly on people's desks Small enough to fit next to a monitor Powerful enough to run workloads that used to require expensive cloud infrastructure That's why this launch is getting so much attention The real story isn't the hardware specs It's the business model shift Every month, developers send money to cloud providers for inference, testing, fine-tuning and AI applications The question nobody can answer yet is what happens if enough developers decide they'd rather buy infrastructure once than rent it forever Because if local AI hardware keeps getting more powerful, the economics start changing very quickly Cloud providers built empires on renting access to compute NVIDIA is betting more people will eventually want to own it And that's a much bigger story than a new piece of hardware sitting on a desk

NVIDIA might have just declared war on the cloud GPU business For years, AI builders had one option Rent compute Pay every month Watch the bill grow every time usage increased Now NVIDIA is putting serious AI hardware directly on people's desks Small enough to fit next to a monitor Powerful enough to run workloads that used to require expensive cloud infrastructure That's why this launch is getting so much attention The real story isn't the hardware specs It's the business model shift Every month, developers send money to cloud providers for inference, testing, fine-tuning and AI applications The question nobody can answer yet is what happens if enough developers decide they'd rather buy infrastructure once than rent it forever Because if local AI hardware keeps getting more powerful, the economics start changing very quickly Cloud providers built empires on renting access to compute NVIDIA is betting more people will eventually want to own it And that's a much bigger story than a new piece of hardware sitting on a desk

beamnxw ./

30,361 views • 3 months ago