Sensitive content

This media may contain sensitive content.

Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Hello Gemini 2.5 Flash-Lite! So fast, it codes each screen on the fly (Neural OS concept 👇). The frontier isn't always about large models and beating benchmarks. In this case, a super fast & good model can unlock drastic use cases. Read more:

Oriol Vinyals

180,428 subscribers

405,746 görüntüleme • 1 yıl önce •via X (Twitter)

Bilim & Teknoloji Eğitim

Anya Rossi• Live Now

Private livecam show

10 Yorum

Serçiya^ سەرچیا profil fotoğrafı

Serçiya^ سەرچیا1 yıl önce

What are some useful cases for this?

AJ - e/acc 🚀🇨🇴🇵🇷 profil fotoğrafı

AJ - e/acc 🚀🇨🇴🇵🇷1 yıl önce

It's FAST!

HarrisonAIX profil fotoğrafı

HarrisonAIX1 yıl önce

what's the Gemini VScode extension using nowadays? Gemini 2.5 Flash-Lite? From what I gather, users can't justify ultra and pro is a necessity. The extension being updated would be next level.

Chris profil fotoğrafı

Chris1 yıl önce

Apps could just be APIs, where the users can just ask for whatever UI they want and the LLMs will build it

Suman profil fotoğrafı

Suman1 yıl önce

While aiming for better responsiveness with Neural OS ideas, we need to think carefully about how well the model works in different situations, keep user data safe in complex systems, and ensure clear rules for user experiences that change.

Chanchal 💬 profil fotoğrafı

Chanchal 💬1 yıl önce

mind blowing 😲

BlockRadar profil fotoğrafı

BlockRadar1 yıl önce

Alpha tech, WAGMI

Jesahn O profil fotoğrafı

Jesahn O1 yıl önce

Best use cases?

Djasnive Rajaona profil fotoğrafı

Djasnive Rajaona1 yıl önce

@GoogleDeepMind What the heck

Sic Semper Salustri🏴‍☠️ profil fotoğrafı

Sic Semper Salustri🏴‍☠️1 yıl önce

@GoogleDeepMind 👀

Benzer Videolar

Introducing a brand new way to use Jan 💛 You can now search the web, do deep research, and let Jan use your browser. Powered by Jan-v2-VL-Max, 30B model beating Gemini 2.5 Pro & DeepSeek R1 on execution benchmarks. Try it:

👋 Jan

29,403 görüntüleme • 6 ay önce

AI has its PhD and now it’s on the job market. Introducing the AI Productivity Index (APEX), a benchmark that measures how well we’ve automated the most valuable industries in the world. Most benchmarks study abstract capabilities. APEX evaluates model performance on real deliverables across law, finance, consulting, and medicine. The models most capable of doing work today, according to APEX: 🥇 GPT 5 🥈 Grok 4 🥉 Gemini 2.5 Flash Other findings: - GPT 5 demonstrates the strongest performance across all 4 domains - Some cheaper models outperform more expensive models from the same provider (e.g. Gemini 2.5 Flash vs. Gemini 2.5 Pro) - The best open source model, Qwen (7th), performs only 2% behind Grok 4 overall

AI has its PhD and now it’s on the job market. Introducing the AI Productivity Index (APEX), a benchmark that measures how well we’ve automated the most valuable industries in the world. Most benchmarks study abstract capabilities. APEX evaluates model performance on real deliverables across law, finance, consulting, and medicine. The models most capable of doing work today, according to APEX: 🥇 GPT 5 🥈 Grok 4 🥉 Gemini 2.5 Flash Other findings: - GPT 5 demonstrates the strongest performance across all 4 domains - Some cheaper models outperform more expensive models from the same provider (e.g. Gemini 2.5 Flash vs. Gemini 2.5 Pro) - The best open source model, Qwen (7th), performs only 2% behind Grok 4 overall

Brendan (can/do)

451,298 görüntüleme • 9 ay önce

Fast enough to play games ⚡️ We challenged Gemini 3 Flash to a quick drawing game in the G3mini. Because this model is optimized for speed, it can see and guess what you’re drawing while you’re still sketching it.

Fast enough to play games ⚡️ We challenged Gemini 3 Flash to a quick drawing game in the G3mini. Because this model is optimized for speed, it can see and guess what you’re drawing while you’re still sketching it.

Google

119,117 görüntüleme • 6 ay önce

Playing Pokemon with LLMs became a benchmark, but what about Super Mario? Turns out Google DeepMind Gemini 2.0 Flash can play Super Mario in real-time due to its fast latency, multimodal input and long context! 🎮 Gemini 2.0 Flash receives screenshots of the game and then generates Python code (PyAutoGUI commands) for each screenshot to control the game for a short period (the next 1 or 2 seconds). Add a while loop to it and you have it play in real-time. 🤯

Playing Pokemon with LLMs became a benchmark, but what about Super Mario? Turns out Google DeepMind Gemini 2.0 Flash can play Super Mario in real-time due to its fast latency, multimodal input and long context! 🎮 Gemini 2.0 Flash receives screenshots of the game and then generates Python code (PyAutoGUI commands) for each screenshot to control the game for a short period (the next 1 or 2 seconds). Add a while loop to it and you have it play in real-time. 🤯

Philipp Schmid

153,067 görüntüleme • 1 yıl önce

🚨 Gemini 3.5 Flash checkpoint notes: The old “cool Gemini UI” vibe feels mostly gone. It is more GPTified now. Good: fast, smart for a Flash model. Bad: it keeps filling websites with HUDs/panels, even when the prompt asks for restraint. Biggest issue: prompt adherence. I told it multiple times not to use web, and it still did. Because of that, exact knowledge cutoff is hard to verify confidently.

🚨 Gemini 3.5 Flash checkpoint notes: The old “cool Gemini UI” vibe feels mostly gone. It is more GPTified now. Good: fast, smart for a Flash model. Bad: it keeps filling websites with HUDs/panels, even when the prompt asks for restraint. Biggest issue: prompt adherence. I told it multiple times not to use web, and it still did. Because of that, exact knowledge cutoff is hard to verify confidently.

Chetaslua

127,743 görüntüleme • 2 ay önce

Love this example! Congrats to Google DeepMind! I successfully replicated the Boggle game on SWE Arena. TLDR: Gemini 2.0 Pro won! In the case of Claude 3.5 Sonnet vs Gemini 2.0 Pro, both are given the image and prompt to generate the algo to get the results. The image and prompt are exactly the same. Try it out on SWE Arena: BTW can we get a bigger streaming rate limit for our API, so that ppl can better compare the latest Gemini models with other frontier models? Jeff Dean Logan Kilpatrick 👀

Love this example! Congrats to Google DeepMind! I successfully replicated the Boggle game on SWE Arena. TLDR: Gemini 2.0 Pro won! In the case of Claude 3.5 Sonnet vs Gemini 2.0 Pro, both are given the image and prompt to generate the algo to get the results. The image and prompt are exactly the same. Try it out on SWE Arena: BTW can we get a bigger streaming rate limit for our API, so that ppl can better compare the latest Gemini models with other frontier models? Jeff Dean Logan Kilpatrick 👀

Terry Yue Zhuo

48,220 görüntüleme • 1 yıl önce

✨Spotlight: Top 4 Code Arena open models Open models are moving fast, and this community is directly shaping how that progress shows up in real use. Your builds, votes, and experiments surface what frontier models can actually do in practice. To celebrate the momentum, we’re spotlighting the top open models in Code Arena (WebDev) 🧵👇 🔥 MiniMax (official) — MiniMax-M2.1 🤖 Z.ai — GLM-4.7 ✨ DeepSeek — Deepseek-v3.2-thinking 🚀 Xiaomi MiMo — Mimo-v2-flash (non-thinking) Let’s keep building in the open and raising the bar together 🚀

✨Spotlight: Top 4 Code Arena open models Open models are moving fast, and this community is directly shaping how that progress shows up in real use. Your builds, votes, and experiments surface what frontier models can actually do in practice. To celebrate the momentum, we’re spotlighting the top open models in Code Arena (WebDev) 🧵👇 🔥 MiniMax (official) — MiniMax-M2.1 🤖 Z.ai — GLM-4.7 ✨ DeepSeek — Deepseek-v3.2-thinking 🚀 Xiaomi MiMo — Mimo-v2-flash (non-thinking) Let’s keep building in the open and raising the bar together 🚀

Arena.ai

16,742 görüntüleme • 6 ay önce

I just had to make a new video of GLM-4.7-Flash - Helping me refactor VLLM studio - Did a data analytics report for work - Managed to search my tweets - Made me a fully playable Pacman in 1 shot - Great at browser use This model is too good to be this small, the full thing will fit on a macbook, it's fast, precise and can do pretty much anything Sonnet can. It somehow benches higher than Sonnet 4, 3.7, Opus 4.5 no thinking, all the GPT models from last year and more.

I just had to make a new video of GLM-4.7-Flash - Helping me refactor VLLM studio - Did a data analytics report for work - Managed to search my tweets - Made me a fully playable Pacman in 1 shot - Great at browser use This model is too good to be this small, the full thing will fit on a macbook, it's fast, precise and can do pretty much anything Sonnet can. It somehow benches higher than Sonnet 4, 3.7, Opus 4.5 no thinking, all the GPT models from last year and more.

0xSero

38,263 görüntüleme • 5 ay önce

Sundar Pichai just dropped a surprisingly candid look into Google DeepMind’s current AI heartbeat. Here’s the essence of the clip: • Google is now operating on a steady six month cadence, pushing the frontier twice a year • Sundar openly admits that meaningful leaps are getting harder • Gemini 2.5 Pro was already a beast, so improving on it is no small task • Developers love the Flash models because they scale efficiently for millions of users • Sundar is genuinely excited about Gemini 3.0 Flash and even hints it might be their best model yet • While the public focuses on current releases, internal teams are already pre training the next generation • This culture of relentless innovation is pushing the entire stack toward major breakthroughs heading into 2026 It really feels like we’re watching the early stages of a world where AI takes over more and more of the work so humans can focus on… well, living.

Sundar Pichai just dropped a surprisingly candid look into Google DeepMind’s current AI heartbeat. Here’s the essence of the clip: • Google is now operating on a steady six month cadence, pushing the frontier twice a year • Sundar openly admits that meaningful leaps are getting harder • Gemini 2.5 Pro was already a beast, so improving on it is no small task • Developers love the Flash models because they scale efficiently for millions of users • Sundar is genuinely excited about Gemini 3.0 Flash and even hints it might be their best model yet • While the public focuses on current releases, internal teams are already pre training the next generation • This culture of relentless innovation is pushing the entire stack toward major breakthroughs heading into 2026 It really feels like we’re watching the early stages of a world where AI takes over more and more of the work so humans can focus on… well, living.

VraserX e/acc

162,001 görüntüleme • 7 ay önce

🚨 New Paper Hybrid Neural World Models. We build neural world models for discontinuous environments and our method discovers when they're making error, with no ground truth. Our neural models run physics fast, leaping many simulator steps in a single pass. How we find points where not to trust it: ask the model for the same future two ways, and the disagreement between the two answers marks where it's failing without needing labels. The same recipe holds on three systems with nothing in common: a chemical wave, compressible gas with shocks, rigid bodies colliding in 3D. 👇

🚨 New Paper Hybrid Neural World Models. We build neural world models for discontinuous environments and our method discovers when they're making error, with no ground truth. Our neural models run physics fast, leaping many simulator steps in a single pass. How we find points where not to trust it: ask the model for the same future two ways, and the disagreement between the two answers marks where it's failing without needing labels. The same recipe holds on three systems with nothing in common: a chemical wave, compressible gas with shocks, rigid bodies colliding in 3D. 👇

Lossfunk

14,909 görüntüleme • 1 ay önce

This week, we tested 3 latest models in our Game Arena Benchmark: → O3 → O4-mini → Gemini 2.5 Flash Across 4 games—Phoenix Wright, Sokoban, Candy Crush, and 2048—O3 dominated the zero-shot leaderboard, ranking #1 or #2 in nearly every task and outperforming previous SOTA models like O3-mini and Gemini 2.5 Flash. 🔥 Beyond our customized tests, we took on a real challenge: Sokoban (1989)—the classic, unforgiving original. 🗣️ Many say O3 shows strong image reasoning, but does it really? Let’s see what our Game Arena Benchmark reveals. 👇

This week, we tested 3 latest models in our Game Arena Benchmark: → O3 → O4-mini → Gemini 2.5 Flash Across 4 games—Phoenix Wright, Sokoban, Candy Crush, and 2048—O3 dominated the zero-shot leaderboard, ranking #1 or #2 in nearly every task and outperforming previous SOTA models like O3-mini and Gemini 2.5 Flash. 🔥 Beyond our customized tests, we took on a real challenge: Sokoban (1989)—the classic, unforgiving original. 🗣️ Many say O3 shows strong image reasoning, but does it really? Let’s see what our Game Arena Benchmark reveals. 👇

Hao AI Lab

14,636 görüntüleme • 1 yıl önce

Libghostty can now be used to fuzz TUIs, thanks to Oskar Wickström + Antithesis. They already found bugs in multiple including btop. I always imagined libghostty would be useful for testing TUIs, super happy to see this is both practical and valuable. This is another example of where speed matters! "Why does Ghostty need to be so fast?" Well, if you're running hundreds or thousands of unit tests that each use a clean in-memory terminal, you want that to be fast. If you're fuzz testing and trying to push an unlimited amount of data through a terminal, you want that terminal to be fast. So many people got hung up on "why does my terminal _GUI_ need to be fast" without connecting one more dot and realizing the GUI is only fast if the core is fast, and the core being fast unlocks a hell of a lot more. Like this.

Libghostty can now be used to fuzz TUIs, thanks to Oskar Wickström + Antithesis. They already found bugs in multiple including btop. I always imagined libghostty would be useful for testing TUIs, super happy to see this is both practical and valuable. This is another example of where speed matters! "Why does Ghostty need to be so fast?" Well, if you're running hundreds or thousands of unit tests that each use a clean in-memory terminal, you want that to be fast. If you're fuzz testing and trying to push an unlimited amount of data through a terminal, you want that terminal to be fast. So many people got hung up on "why does my terminal _GUI_ need to be fast" without connecting one more dot and realizing the GUI is only fast if the core is fast, and the core being fast unlocks a hell of a lot more. Like this.

Mitchell Hashimoto

71,584 görüntüleme • 2 ay önce

THE AI RACE IS OVER. DUST CLEARED. GROK 4 FAST ALREADY HIT 67 BILLION TOKENS Grok 4 Fast just bulldozed the leaderboard, clocking 67.1 billion tokens and leaving rivals gasping for bandwidth! Leaderboard Mode: • Qwen3’s 80B trails at 23B – miles behind • Gemini 2.5 Flash barely flickers in 3rd • Claude? Still waiting at the loading screen • TrustAI charts show Grok’s surge lighting up November It’s not just an AI model anymore – it’s a speed-run on the entire industry! Source: TrustAI, xAI, Medium

THE AI RACE IS OVER. DUST CLEARED. GROK 4 FAST ALREADY HIT 67 BILLION TOKENS Grok 4 Fast just bulldozed the leaderboard, clocking 67.1 billion tokens and leaving rivals gasping for bandwidth! Leaderboard Mode: • Qwen3’s 80B trails at 23B – miles behind • Gemini 2.5 Flash barely flickers in 3rd • Claude? Still waiting at the loading screen • TrustAI charts show Grok’s surge lighting up November It’s not just an AI model anymore – it’s a speed-run on the entire industry! Source: TrustAI, xAI, Medium

Mario Nawfal

196,729 görüntüleme • 8 ay önce

How to use 50+ API keys (models) for FREE on OpenClaw API??? - go to - login or register your account - click on "more models" - click on "use case" and select what you need it for - choose the model and open it - click on "view code" → "Generate API key" many models don't allow direct deploy, so use the "view code" button to generate API access basically Nvidia NIM gives you the ability to test almost any model from their list for FREE some of them are not worse than GPT 5.2 or Claude Opus 4.6, some might even perform better depending on the task how to understand if a model is efficient and compare it with others??? - go to - type the model name in search - click on "benchmarks" - you’ll see performance tests and rankings this way you can easily compare free models with paid ones of course there are RPM limits, on many models it’s around ~40 requests per minute each model is different, after generating the API key, RPM limits are shown in the top-right corner nothing stops you from using them, many work perfectly fine, super solid option for first tests and for learning OpenClaw or any other system where you need an AI API model

How to use 50+ API keys (models) for FREE on OpenClaw API??? - go to - login or register your account - click on "more models" - click on "use case" and select what you need it for - choose the model and open it - click on "view code" → "Generate API key" many models don't allow direct deploy, so use the "view code" button to generate API access basically Nvidia NIM gives you the ability to test almost any model from their list for FREE some of them are not worse than GPT 5.2 or Claude Opus 4.6, some might even perform better depending on the task how to understand if a model is efficient and compare it with others??? - go to - type the model name in search - click on "benchmarks" - you’ll see performance tests and rankings this way you can easily compare free models with paid ones of course there are RPM limits, on many models it’s around ~40 requests per minute each model is different, after generating the API key, RPM limits are shown in the top-right corner nothing stops you from using them, many work perfectly fine, super solid option for first tests and for learning OpenClaw or any other system where you need an AI API model

Ronin

58,624 görüntüleme • 4 ay önce

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 görüntüleme • 1 ay önce

You can now try Llama 3.1 405B for free (link below)! This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. First, here is the link where you can play with Llama 3.1 for free: The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model Fast and easy! As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

You can now try Llama 3.1 405B for free (link below)! This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. First, here is the link where you can play with Llama 3.1 for free: The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model Fast and easy! As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

Santiago

55,609 görüntüleme • 1 yıl önce

Imagine an AI application that can type anywhere you can and use the full context of what's on your screen. This is the application we all deserve (at least if you have macOS.) Check out Omnipilot. It's an app that works with every other macOS application and uses Claude Sonet 3.5 in the background—it also supports Gemini and GPT-4o. Here is the idea: You can use the tool to ask questions about anything on your screen. Or you can use it to autocomplete the text you are typing. You don't need to copy and paste anymore or waste your time providing context to a model. It sees what you see. It works right where you are. That's pretty cool. Here are a couple of cool examples: • Use it to reply to an email • Use it in the terminal to autocomplete a command • Use it to finish a document • Use to send a message on Slack AI at the system level is bonkers. You can read a ton more on their Product Hunt launch page: Thanks to the Omnipilot team for collaborating with me on this post!

Imagine an AI application that can type anywhere you can and use the full context of what's on your screen. This is the application we all deserve (at least if you have macOS.) Check out Omnipilot. It's an app that works with every other macOS application and uses Claude Sonet 3.5 in the background—it also supports Gemini and GPT-4o. Here is the idea: You can use the tool to ask questions about anything on your screen. Or you can use it to autocomplete the text you are typing. You don't need to copy and paste anymore or waste your time providing context to a model. It sees what you see. It works right where you are. That's pretty cool. Here are a couple of cool examples: • Use it to reply to an email • Use it in the terminal to autocomplete a command • Use it to finish a document • Use to send a message on Slack AI at the system level is bonkers. You can read a ton more on their Product Hunt launch page: Thanks to the Omnipilot team for collaborating with me on this post!

Santiago

72,306 görüntüleme • 2 yıl önce