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How powerful is Gemini 3.5 Flash? In a recent research preview, we pushed it to the extreme, powering 93 subagents across 15,314 model calls that wrote a custom kernel, filesystem, and drivers from scratch. 12 hours later, it booted Doom. 🖥️ This is just a glimpse of the model's... show more

Google Antigravity

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248,786 views • 1 month ago •via X (Twitter)

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Introducing Gemini 3.5 Flash ⚡️ Normally, Gemini 3.5 Flash is 4x faster than other models with frontier performance. For a limited time, Antigravity is serving it 12x faster thanks to custom inference tricks, delivering incredible speed for your workflows. 🚀 See the performance in this demo: generating pixel art from photos, orchestrating multi-agent workflows to write and register sprites, and spawning browser subagents to auto-test rendering: 👇

Introducing Gemini 3.5 Flash ⚡️ Normally, Gemini 3.5 Flash is 4x faster than other models with frontier performance. For a limited time, Antigravity is serving it 12x faster thanks to custom inference tricks, delivering incredible speed for your workflows. 🚀 See the performance in this demo: generating pixel art from photos, orchestrating multi-agent workflows to write and register sprites, and spawning browser subagents to auto-test rendering: 👇

Google Antigravity

131,891 views • 2 months ago

Gemini 3 Pro is the best model in the world for multimodal understanding. One of its most exciting capabilities is document understanding and reasoning. This means you can convert information in any format and into the medium that works best for you. Gemini 3 also has leading multilingual capabilities, enabling it to process, reason and even capture cultural relevance across a variety of languages. For example, here Gemini 3 is translating handwritten recipes in Korean and English to build a digital family cookbook in different languages.

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Google AI

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We’re launching the first in a series of Embodied Reasoning offerings: Perceptron Egocentric. It achieves SOTA over the best robotic annotation pipelines built on Gemini 3.5 Flash and Gemini Robotics-ER 1.6. Early access is available to partners.

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Ever wondered what an industrial-grade AI film workflow actually looks like? We spent 72 hours tracking the top teams at the Hackathon to see how they go from concept to final cut under extreme pressure. See how TapNow is powering the next generation of cinema. 🎬 Want the direct link to the full project breakdown? Reply "WORKFLOW" below and I'll DM it to you.

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This is a wild use case! I used Gamma + n8n to automatically generate a complete presentation on AI Agents research. In just minutes! It combines web search (for research), GPT-5 (narrative), and Gamma (for slide content generation). Full workflow breakdown below 👇

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UPDATE: Melissa has exploded from a Tropical Storm to a CATEGORY 3 MAJOR HURRICANE in just 12 HOURS, with the Hurricane Hunters confirming winds of 115 MPH and a new low pressure of 967 mbar. It is difficult to convey just how extreme this kind of intensification is.

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Head of Anthropic Labs: "Fable is one of those models you'll just remember - like Sonnet 3.5, Opus 4, Opus 4.5. Fable is way smarter than me. It finishes a task, lists its trade-offs, and I have to ask it to explain them to me." in 20-minute talk, the Anthropic head reveals the full capabilities of the Fable 5 model in practice. Watch it now, then read how to use this frontier model in the article below

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The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.

The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.

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I just ran a full research project without doing any research. Lemma by Analemma did everything: 1. Explore I typed a question. It mapped the entire field. Concepts, references, key papers. Done. 2. Survey I asked for a literature review. It gave me 50+ pages with real citations. Not a summary. A publication-grade draft. 3. Code I described an experiment. It wrote the code, ran it, showed results. 4. FARS I gave it a research direction. It generated hypotheses, planned experiments, ran them, and wrote the full paper. Zero input from me after that. This is the first tool I've used that actually runs research instead of just helping you do it. FARS already ran live and produced 100 papers in 10 days. One every 2 hours. Watch it here: This is what "Vibe Research" looks like.

I just ran a full research project without doing any research. Lemma by Analemma did everything: 1. Explore I typed a question. It mapped the entire field. Concepts, references, key papers. Done. 2. Survey I asked for a literature review. It gave me 50+ pages with real citations. Not a summary. A publication-grade draft. 3. Code I described an experiment. It wrote the code, ran it, showed results. 4. FARS I gave it a research direction. It generated hypotheses, planned experiments, ran them, and wrote the full paper. Zero input from me after that. This is the first tool I've used that actually runs research instead of just helping you do it. FARS already ran live and produced 100 papers in 10 days. One every 2 hours. Watch it here: This is what "Vibe Research" looks like.

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Gemini 2.0 Flash Experimental has the ability to produce native audio in a variety of styles and languages - all from scratch. 🗣️ Here’s how this is different to traditional text-to-speech systems ↓

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Google DeepMind

125,794 views • 1 year ago

Gemini 3.5 Flash is a step forward for Google on speed and agentic capabilities but comes at a trade-off of being higher cost than prior models We have measured up to ~280 output tokens/sec, placing it on the speed/intelligence Pareto frontier and well ahead of Gemini 3 Flash. It also shows a major uplift on agentic tasks, reaching ~1650 ELO on GDPVal-AA. The trade-off: cost is up ~5x versus Gemini 3 Flash, driven by higher token prices (3x higher than Gemini 3 Flash) and higher token usage. In this video, Declan Jackson, Member of Technical Staff at Artificial Analysis, breaks it down.

Gemini 3.5 Flash is a step forward for Google on speed and agentic capabilities but comes at a trade-off of being higher cost than prior models We have measured up to ~280 output tokens/sec, placing it on the speed/intelligence Pareto frontier and well ahead of Gemini 3 Flash. It also shows a major uplift on agentic tasks, reaching ~1650 ELO on GDPVal-AA. The trade-off: cost is up ~5x versus Gemini 3 Flash, driven by higher token prices (3x higher than Gemini 3 Flash) and higher token usage. In this video, Declan Jackson, Member of Technical Staff at Artificial Analysis, breaks it down.

Artificial Analysis

16,055 views • 1 month ago

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

kwindla

40,319 views • 1 month ago

ROUTE LLM - SUPER INTELLIGENCE IN THE MAKING RouteLLM combines the superpowers of o1-preview, Sonnet 3.5, Gemini Flash, o1-mini, 4o, and 4o-mini! Based on your query, it optimizes for latency, cost, and quality of the response. The cool thing about RouteLLM is it combines. - coding ability of Sonnet 3.5 - reasoning ability of o1-preview - hard-coding ability of o1-mini - 1M context of Gemini - speed of 4o-mini Similar to ensemble models that are very effective in classical ML, RouteLLM and similar models will be the most effective in the LLM ecosystem

ROUTE LLM - SUPER INTELLIGENCE IN THE MAKING RouteLLM combines the superpowers of o1-preview, Sonnet 3.5, Gemini Flash, o1-mini, 4o, and 4o-mini! Based on your query, it optimizes for latency, cost, and quality of the response. The cool thing about RouteLLM is it combines. - coding ability of Sonnet 3.5 - reasoning ability of o1-preview - hard-coding ability of o1-mini - 1M context of Gemini - speed of 4o-mini Similar to ensemble models that are very effective in classical ML, RouteLLM and similar models will be the most effective in the LLM ecosystem

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ordering groceries collaboratively should be as seamless as messaging in a group chat. this is the type of experience that is hard to go back from once you experience it, and it's just a small glimpse into all the things Twin can do. see how it works below!

ordering groceries collaboratively should be as seamless as messaging in a group chat. this is the type of experience that is hard to go back from once you experience it, and it's just a small glimpse into all the things Twin can do. see how it works below!

Kevin Bai

14,153 views • 3 months ago

Been using the new Claude 3.5 model as a tester and now that it is out, I can say it is very very impressive, and the "artifacts" that it generates are like a simpler version of Code Interpreter This is a real-time video of me creating a playable game and editing it with Claude

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Ethan Mollick

162,293 views • 2 years ago

🔥 Running Kimi K3 (Kimi.ai) inside Claude Code is a game-changer. Claude is the best coding harness out there, and now you can pair it with Kimi's powerful (and affordable) K3 model running 24/7 on a VPS. Loops, subagents, and max performance. Watch the full setup guide 👇

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Boxmining

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Gemini-2.5-Pro-preview-05-06 is now my top coding model. It beats o3 and Claude 3.7 Sonnet on several of my hard prompts. One example prompt: "Code simulation of water in a bucket that is rocking back and forth." See how it crushes o3 and Sonnet. Google, call it Gemini 3!

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Yuchen Jin

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