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Modular has acquired BentoML! 🤝 10K+ orgs use BentoML for production AI, including 50+ Fortune 500 companies. We're pairing their deployment platform with MAX + Mojo's hardware optimization. BentoML stays open source (Apache 2.0), and we’re doubling down on OSS in 2026. Ask BentoML founder Chaoyu Yang and Chris... show more

Modular

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28,702 次观看 • 7 个月前 •via X (Twitter)

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Our cofounder tim built an AI storybook app using Black Forest Labs' FLUX2 and Google Gemma 4 on Modular Cloud. Pick a character, make choices, and the story branches endlessly, with every page written and illustrated in real time. Tim has spent his career obsessing over inference latency, first at Google, now at Modular. Building something his kids use settled it: in a real-time generative app, the inference platform determines the experience as much as the model. The numbers back that up. From 24 hours of production traffic: first prose in 420ms, a full illustration in under 6 seconds, 85% of page turns in 48ms. Create your own story with Inkwell and share it. We're sending swag to our favorites:

Our cofounder tim built an AI storybook app using Black Forest Labs' FLUX2 and Google Gemma 4 on Modular Cloud. Pick a character, make choices, and the story branches endlessly, with every page written and illustrated in real time. Tim has spent his career obsessing over inference latency, first at Google, now at Modular. Building something his kids use settled it: in a real-time generative app, the inference platform determines the experience as much as the model. The numbers back that up. From 24 hours of production traffic: first prose in 420ms, a full illustration in under 6 seconds, 85% of page turns in 48ms. Create your own story with Inkwell and share it. We're sending swag to our favorites:

Modular

14,324 次观看 • 4 个月前

Learn how Inworld AI teamed up with Modular and Oracle Cloud to unlock hardware optionality across Nvidia and AMD GPUs for their groundbreaking TTS models, cutting total cost of model ownership by 70%.

Learn how Inworld AI teamed up with Modular and Oracle Cloud to unlock hardware optionality across Nvidia and AMD GPUs for their groundbreaking TTS models, cutting total cost of model ownership by 70%.

Modular

23,351 次观看 • 1 年前

New chips are shipping faster than ever before, but the ecosystem is being held back by having to rewrite and then re-debug the same code over & over again. Chris Lattner CEO and Co-founder at Modular and EVP of Advanced AI Software & Platforms at Qualcomm, will deliver a keynote at PyTorch Conference North America about an open software platform for heterogeneous compute powered by Mojo and MAX. PyTorch has always been the place where the best models come together, and now there's a way to get those models onto all kinds of hardware. If you're interested in Al and compute, join us at the PyTorch Conference in San Jose, CA. Register now: #PyTorchCon

New chips are shipping faster than ever before, but the ecosystem is being held back by having to rewrite and then re-debug the same code over & over again. Chris Lattner CEO and Co-founder at Modular and EVP of Advanced AI Software & Platforms at Qualcomm, will deliver a keynote at PyTorch Conference North America about an open software platform for heterogeneous compute powered by Mojo and MAX. PyTorch has always been the place where the best models come together, and now there's a way to get those models onto all kinds of hardware. If you're interested in Al and compute, join us at the PyTorch Conference in San Jose, CA. Register now: #PyTorchCon

PyTorch

20,090 次观看 • 7 天前

Missed today's livestream on Modular Platform 25.5? The FOMO is real! We covered the latest and greatest updates to Large Scale Batch Inference, Mojo, MAX, and PyTorch custom ops, plus answered a ton of audience questions. The full replay's waiting!

Missed today's livestream on Modular Platform 25.5? The FOMO is real! We covered the latest and greatest updates to Large Scale Batch Inference, Mojo, MAX, and PyTorch custom ops, plus answered a ton of audience questions. The full replay's waiting!

Modular

80,670 次观看 • 1 年前

Mojo🔥 combines the usability of Python with the performance of C, unlocking unparalleled programmability of AI hardware and extensibility of AI models. Also, it's up to 35000x faster than Python 🤯 and … deploys 🏎

Mojo🔥 combines the usability of Python with the performance of C, unlocking unparalleled programmability of AI hardware and extensibility of AI models. Also, it's up to 35000x faster than Python 🤯 and … deploys 🏎

Modular

124,558 次观看 • 3 年前

Here are 10 AI video editor GitHub repos worth bookmarking: 1. Shotcut Most actively maintained open source video editor in 2026. 14K stars. Cross-platform with AI-assisted features. Just shipped a new release April 30, 2026. 2. Kdenlive The closest open source alternative to Adobe Premiere Pro. Multi-track editing, proxy editing, VST audio, and customizable workspace. Best for professional workflows. 3. OpenShot The easiest entry point for beginners. Drag and drop, 400+ transitions, 3D titles, and AI-assisted trimming. 5,700 stars. 4. Blender Not just 3D. Blender's video sequence editor and compositing pipeline is used in professional film production. 18,300 stars. Unmatched for VFX. 5. Recordly Screen recorder with auto-zoom, cursor polish, webcam overlays, and styled frames built in. Built for demo videos and walkthroughs. 6. Wan2.1 Alibaba's open source text-to-video model. Cinema-grade 1080p generation. Apache 2.0. The gold standard for open source video generation in 2026. 7. HunyuanVideo Tencent's 13B parameter open source video model. 11.9K stars. Handles 720p and 1080p with high temporal coherence. 8. CogVideoX Apache 2.0 licensed. Loads natively via Hugging Face Diffusers. Strong prompt following and smooth frame transitions. Needs 16GB VRAM minimum. 12.5K stars. 9. Open-Sora Most starred open source video generation project at 24K stars. Full training pipeline for $200K. Production-level output quality. 10. Mochi 1 Focused entirely on motion quality. The most natural-looking physics of any open source video model. Water, fabric, and human gestures without AI jitter. Apache 2.0.

Here are 10 AI video editor GitHub repos worth bookmarking: 1. Shotcut Most actively maintained open source video editor in 2026. 14K stars. Cross-platform with AI-assisted features. Just shipped a new release April 30, 2026. 2. Kdenlive The closest open source alternative to Adobe Premiere Pro. Multi-track editing, proxy editing, VST audio, and customizable workspace. Best for professional workflows. 3. OpenShot The easiest entry point for beginners. Drag and drop, 400+ transitions, 3D titles, and AI-assisted trimming. 5,700 stars. 4. Blender Not just 3D. Blender's video sequence editor and compositing pipeline is used in professional film production. 18,300 stars. Unmatched for VFX. 5. Recordly Screen recorder with auto-zoom, cursor polish, webcam overlays, and styled frames built in. Built for demo videos and walkthroughs. 6. Wan2.1 Alibaba's open source text-to-video model. Cinema-grade 1080p generation. Apache 2.0. The gold standard for open source video generation in 2026. 7. HunyuanVideo Tencent's 13B parameter open source video model. 11.9K stars. Handles 720p and 1080p with high temporal coherence. 8. CogVideoX Apache 2.0 licensed. Loads natively via Hugging Face Diffusers. Strong prompt following and smooth frame transitions. Needs 16GB VRAM minimum. 12.5K stars. 9. Open-Sora Most starred open source video generation project at 24K stars. Full training pipeline for $200K. Production-level output quality. 10. Mochi 1 Focused entirely on motion quality. The most natural-looking physics of any open source video model. Water, fabric, and human gestures without AI jitter. Apache 2.0.

Kanika

17,726 次观看 • 3 个月前

Introducing Superblocks 2.0: AI-generated enterprise apps – finally under IT control. Vibe-coded apps just became the #1 attack vector in the enterprise. Business teams are building on production data, while IT has zero visibility. No reviews. No audits. No permissions. No control. AI hackers are about to get 100x better. Anthropic proved it with Mythos. Superblocks 2.0 is the only platform to take back control: > Business teams build AI-powered apps with permissions baked in. > IT and Security can audit everything and lock down anything, instantly. > Engineering sets the standards. Every app follows them. Instacart, SoFi, and LinkedIn run Superblocks in production today. And larger organizations we can't yet name are too: A Fortune 500 just shut down 2,500 Replit users to standardize on Superblocks, running the platform air-gapped in their AWS environment. A 150,000-employee global services firm replaced Lovable with Superblocks to unlock AI-built apps on restricted internal systems. Every IT leader we’ve demoed to using Replit, Lovable or v0 asked for early access. Today we open access to the world. The genie is out of the bottle on employee vibe coding. Let it run wild, or take back control –

Introducing Superblocks 2.0: AI-generated enterprise apps – finally under IT control. Vibe-coded apps just became the #1 attack vector in the enterprise. Business teams are building on production data, while IT has zero visibility. No reviews. No audits. No permissions. No control. AI hackers are about to get 100x better. Anthropic proved it with Mythos. Superblocks 2.0 is the only platform to take back control: > Business teams build AI-powered apps with permissions baked in. > IT and Security can audit everything and lock down anything, instantly. > Engineering sets the standards. Every app follows them. Instacart, SoFi, and LinkedIn run Superblocks in production today. And larger organizations we can't yet name are too: A Fortune 500 just shut down 2,500 Replit users to standardize on Superblocks, running the platform air-gapped in their AWS environment. A 150,000-employee global services firm replaced Lovable with Superblocks to unlock AI-built apps on restricted internal systems. Every IT leader we’ve demoed to using Replit, Lovable or v0 asked for early access. Today we open access to the world. The genie is out of the bottle on employee vibe coding. Let it run wild, or take back control –

Brad Menezes

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AMD's Anush Elangovan explains why he thinks his company's open source ethos combined with agentic AI superpowers their leverage as a company: Because AMD publishes a lot of technical details about its hardware, when engineers use AI tools, the models already “understand” AMD’s systems and can help write code for them, debug them, or even generate new tools. And that makes developers more productive on AMD hardware without AMD having to do all the work internally. "AMD has had this ethos of open source, which really plays to our advantage. Every frontier model that I use has already seen every bit of AMD source code." "It'll rewrite my spec for me because it's already in the training data. Which you can't get from closed ecosystems." "In fact I built a virtual GPU simulator just based off our public specs, and now I'm running it on the GPU. So now I can run cross-generational GPU simulations on existing hardware." "We have that advantage. And we've run a Dev Day contest where we generated more tokens on AMD — Triton kernels and HIP kernels — than existed on the internet at the time." "So now that's all part of the pre-training data. It's a superpower because now you're open source, and you're agentically accelerating this process."

AMD's Anush Elangovan explains why he thinks his company's open source ethos combined with agentic AI superpowers their leverage as a company: Because AMD publishes a lot of technical details about its hardware, when engineers use AI tools, the models already “understand” AMD’s systems and can help write code for them, debug them, or even generate new tools. And that makes developers more productive on AMD hardware without AMD having to do all the work internally. "AMD has had this ethos of open source, which really plays to our advantage. Every frontier model that I use has already seen every bit of AMD source code." "It'll rewrite my spec for me because it's already in the training data. Which you can't get from closed ecosystems." "In fact I built a virtual GPU simulator just based off our public specs, and now I'm running it on the GPU. So now I can run cross-generational GPU simulations on existing hardware." "We have that advantage. And we've run a Dev Day contest where we generated more tokens on AMD — Triton kernels and HIP kernels — than existed on the internet at the time." "So now that's all part of the pre-training data. It's a superpower because now you're open source, and you're agentically accelerating this process."

TBPN

34,469 次观看 • 4 个月前

ModCon is back. August 18th. San Francisco. A full day of AI infrastructure talks, launches, and workshops, with speakers including Dylan Patel of SemiAnalysis , Jerry Liu of LlamaIndex 🦙, 👩‍💻 Paige Bailey of Google DeepMind, and Sid Sheth of @dMatrix. Limited spots. Early-bird pricing ends July 1st. Get your ticket:

ModCon is back. August 18th. San Francisco. A full day of AI infrastructure talks, launches, and workshops, with speakers including Dylan Patel of SemiAnalysis , Jerry Liu of LlamaIndex 🦙, 👩‍💻 Paige Bailey of Google DeepMind, and Sid Sheth of @dMatrix. Limited spots. Early-bird pricing ends July 1st. Get your ticket:

Modular

11,609 次观看 • 3 个月前

Today ReUI Pro goes live! 🔥 Design-forward shadcn/ui platform with MCP fluency for agents. AI made UI generation instant. It didn't make it production-ready. You still spend hours picking blocks, adjusting details, and fine-tuning the last mile. We spent six months building the agent-ready layer for shadcn/ui that closes the gap. Inside: ☑️ Free Hosted MCP with Agent Skills. ☑️ 1,019 open-source shadcn/ui components, free forever. ☑️ 17 open-source ReUI primitives(Data Grid, Filters,File Upload, Stepper and more). ☑️ 485 Pro blocks across over 50 categories. ☑️ 562 hand-crafted interactive icons in 4 styles. ☑️ Default Base UI and legacy Radix by WorkOS versions. Real credit to shadcn for the ecosystem! The registry model changed how we all build and ship UI, and ReUI stands on that foundation. Below is a thread on the 10 things we're most proud of👇

Today ReUI Pro goes live! 🔥 Design-forward shadcn/ui platform with MCP fluency for agents. AI made UI generation instant. It didn't make it production-ready. You still spend hours picking blocks, adjusting details, and fine-tuning the last mile. We spent six months building the agent-ready layer for shadcn/ui that closes the gap. Inside: ☑️ Free Hosted MCP with Agent Skills. ☑️ 1,019 open-source shadcn/ui components, free forever. ☑️ 17 open-source ReUI primitives(Data Grid, Filters,File Upload, Stepper and more). ☑️ 485 Pro blocks across over 50 categories. ☑️ 562 hand-crafted interactive icons in 4 styles. ☑️ Default Base UI and legacy Radix by WorkOS versions. Real credit to shadcn for the ecosystem! The registry model changed how we all build and ship UI, and ReUI stands on that foundation. Below is a thread on the 10 things we're most proud of👇

ReUI

50,965 次观看 • 2 个月前

Jason Calacanis @jason on compute cost: "Yeah, people are running Kimi on the last generation of hardware, and that's plentiful. I think—and I'll make this prediction here—that you're going to see some of the major customers of Anthropic and major customers of OpenAI (I'm talking about the 8- and 9-figure customers, people spending $50 million, $100 million a year) leaving. They're going to be leaving because they don't trust those companies to not steal the application layer and to compete with them. ElevenLabs, Figma, Lovable—they're all gonna leave, and they're all gonna take Kimi, or they're gonna fork it or DeepSeek. They're all... I know for a fact they're all working on their own models currently. I know from my team; my team has installed Kimi. It is 90% cheaper already. Not sure where you get your data from, but go on OpenRouter. And what OpenRouter does is you pick Kimi stacks, and then you get all the providers there, and then you pick which provider—hold on, let me finish—and you pick them based on their data retention and other issues, and you can dynamically pick the lowest one. That's going to be a massive headwind against these companies. Massive. And I'm seeing it: 9 out of 10 startups I talked to in our portfolio—and found a university when I was just in Japan last week—run the next one, they're all working on open-source, they're all embracing it, and those big companies are embracing it." Via The All-In Podcast

Jason Calacanis @jason on compute cost: "Yeah, people are running Kimi on the last generation of hardware, and that's plentiful. I think—and I'll make this prediction here—that you're going to see some of the major customers of Anthropic and major customers of OpenAI (I'm talking about the 8- and 9-figure customers, people spending $50 million, $100 million a year) leaving. They're going to be leaving because they don't trust those companies to not steal the application layer and to compete with them. ElevenLabs, Figma, Lovable—they're all gonna leave, and they're all gonna take Kimi, or they're gonna fork it or DeepSeek. They're all... I know for a fact they're all working on their own models currently. I know from my team; my team has installed Kimi. It is 90% cheaper already. Not sure where you get your data from, but go on OpenRouter. And what OpenRouter does is you pick Kimi stacks, and then you get all the providers there, and then you pick which provider—hold on, let me finish—and you pick them based on their data retention and other issues, and you can dynamically pick the lowest one. That's going to be a massive headwind against these companies. Massive. And I'm seeing it: 9 out of 10 startups I talked to in our portfolio—and found a university when I was just in Japan last week—run the next one, they're all working on open-source, they're all embracing it, and those big companies are embracing it." Via The All-In Podcast

P Equity Research 📰

45,064 次观看 • 1 个月前

Alibaba just released a coding model that hits 82 percent on SWE-Bench Verified. That is the highest score ever published for an open-source model. The weights are free. The license is Apache 2.0. You can run it today. The model is Qwen 4 Coder 32B. Here is what 82 percent on SWE-Bench Verified actually means. SWE-Bench Verified tests whether an AI can autonomously resolve real bugs pulled from real production GitHub repositories. Not synthetic exercises. Real open-source projects that real teams depend on. A model gets a bug report, reads the code, writes a fix, and either passes the test suite or it does not. At 82 percent, Qwen 4 Coder 32B resolves 82 out of every 100 real production bugs it is given. Without a human guiding it. On code it has never seen before. For comparison: Qwen 4 Coder 32B: 82 percent SWE-Bench Verified. Open source. Apache 2.0. Claude Fable 5: 80.3 percent SWE-Bench Pro. $10 input / $50 output per million tokens. Currently suspended. GPT-5.6 Sol: Competitive on Terminal-Bench. $5 input / $30 output per million tokens. An open-weight model that you can download and run for free just beat both of them on the benchmark designed to measure real software engineering capability. Here is the architecture. Qwen 4 Coder 32B is a 32 billion parameter dense model. Not a Mixture-of-Experts. Every parameter is active on every request. This matters for inference: a dense 32B model runs on 22 gigabytes of VRAM, which fits on a single high-end consumer GPU or a MacBook Pro with 64GB of unified memory. The smaller variant, Qwen 4 Coder 4B, runs at approximately 135 tokens per second on an M5 Max and fits inside 8 gigabytes of RAM. For a model with usable coding capability, that is a new bar for what fits in a single laptop. The training methodology continued Alibaba's approach of reinforcement learning on verifiable coding tasks. The model gets rewarded when its code passes tests. It gets penalized when it fails. Over millions of training steps, the model learns to write code that actually runs rather than code that looks plausible. License: Apache 2.0. Full commercial use. No attribution requirement. No revenue threshold. No monthly active user ceiling. Weights: Hugging Face, available today. Runs on: vLLM, Ollama, SGLang, and any standard GGUF-compatible inference engine. Qwen 4 32B also runs at approximately 135 tokens per second on an M5 Max chip, setting a new bar for what a sub-8GB model can do on Apple Silicon. The open-source coding model just beat the best closed-source model in the world on the benchmark designed to test whether AI can actually do software engineering. The weights are free. The subscription is optional. Source: Autom8Labs AI Insight July 2026, State of Open Source LLMs June 2026, Kunal Ganglani blog June 2026.

Alibaba just released a coding model that hits 82 percent on SWE-Bench Verified. That is the highest score ever published for an open-source model. The weights are free. The license is Apache 2.0. You can run it today. The model is Qwen 4 Coder 32B. Here is what 82 percent on SWE-Bench Verified actually means. SWE-Bench Verified tests whether an AI can autonomously resolve real bugs pulled from real production GitHub repositories. Not synthetic exercises. Real open-source projects that real teams depend on. A model gets a bug report, reads the code, writes a fix, and either passes the test suite or it does not. At 82 percent, Qwen 4 Coder 32B resolves 82 out of every 100 real production bugs it is given. Without a human guiding it. On code it has never seen before. For comparison: Qwen 4 Coder 32B: 82 percent SWE-Bench Verified. Open source. Apache 2.0. Claude Fable 5: 80.3 percent SWE-Bench Pro. $10 input / $50 output per million tokens. Currently suspended. GPT-5.6 Sol: Competitive on Terminal-Bench. $5 input / $30 output per million tokens. An open-weight model that you can download and run for free just beat both of them on the benchmark designed to measure real software engineering capability. Here is the architecture. Qwen 4 Coder 32B is a 32 billion parameter dense model. Not a Mixture-of-Experts. Every parameter is active on every request. This matters for inference: a dense 32B model runs on 22 gigabytes of VRAM, which fits on a single high-end consumer GPU or a MacBook Pro with 64GB of unified memory. The smaller variant, Qwen 4 Coder 4B, runs at approximately 135 tokens per second on an M5 Max and fits inside 8 gigabytes of RAM. For a model with usable coding capability, that is a new bar for what fits in a single laptop. The training methodology continued Alibaba's approach of reinforcement learning on verifiable coding tasks. The model gets rewarded when its code passes tests. It gets penalized when it fails. Over millions of training steps, the model learns to write code that actually runs rather than code that looks plausible. License: Apache 2.0. Full commercial use. No attribution requirement. No revenue threshold. No monthly active user ceiling. Weights: Hugging Face, available today. Runs on: vLLM, Ollama, SGLang, and any standard GGUF-compatible inference engine. Qwen 4 32B also runs at approximately 135 tokens per second on an M5 Max chip, setting a new bar for what a sub-8GB model can do on Apple Silicon. The open-source coding model just beat the best closed-source model in the world on the benchmark designed to test whether AI can actually do software engineering. The weights are free. The subscription is optional. Source: Autom8Labs AI Insight July 2026, State of Open Source LLMs June 2026, Kunal Ganglani blog June 2026.

Harman

41,278 次观看 • 2 个月前

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!

Workshop AI

29,379 次观看 • 6 个月前

pretty incredible to see Patrick Collison casually mentioning to Sam Altman the agentic infra we built in January for Tempo and Paradigm to transform how we work. we indeed use this to orchestrate pretty much anything at both companies. neither paradigm nor tempo can live without this now. it feels like the stone age without it. and because our organizations are so workflow heavy / data rich and we are <100 people, these are the perfect organizations to max deploy ai. it is natively multiplayer, harness agnostic, has secure secret management & firewalls, durable workflows, observable so it can self-improve/heal and most importantly - all self-hosted on bare metal & self-built, end to end. no third parties involved. we have two deploys, one for each company on different machines, but they both have a shared core codebase that's modular and extensible for each company's idiosyncracies. we're looking to do way more things with it. proactive agents remains an open problem for us. we hope to open source it very soon as i've mentioned a few times before. we're adding 1 special eng to that team, dm me + your proof of work if you're epic at infrastructure / security and love hacking things quickly.

pretty incredible to see Patrick Collison casually mentioning to Sam Altman the agentic infra we built in January for Tempo and Paradigm to transform how we work. we indeed use this to orchestrate pretty much anything at both companies. neither paradigm nor tempo can live without this now. it feels like the stone age without it. and because our organizations are so workflow heavy / data rich and we are <100 people, these are the perfect organizations to max deploy ai. it is natively multiplayer, harness agnostic, has secure secret management & firewalls, durable workflows, observable so it can self-improve/heal and most importantly - all self-hosted on bare metal & self-built, end to end. no third parties involved. we have two deploys, one for each company on different machines, but they both have a shared core codebase that's modular and extensible for each company's idiosyncracies. we're looking to do way more things with it. proactive agents remains an open problem for us. we hope to open source it very soon as i've mentioned a few times before. we're adding 1 special eng to that team, dm me + your proof of work if you're epic at infrastructure / security and love hacking things quickly.

Georgios Konstantopoulos

113,567 次观看 • 4 个月前

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 次观看 • 1 年前

Today is the biggest product launch in Mutiny's history. Every B2B CMO I talk to says that AI has turned their marketing playbooks upside down: • Outbound effectiveness is plummeting as AI spam floods inboxes • Ads that used to work are now just more noise • Inbound leads are down as LLMs hijack their SEO traffic But what if instead of spamming buyers with AI, we used AI to deeply understand our buyers and build genuine relationships with them? That’s why we built 𝗔𝗜 𝗳𝗼𝗿 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗚𝗧𝗠, the fastest place to launch breakthrough campaigns for target accounts. We use AI agents to: • Research accounts to identify needs and uncover themes across accounts • Create personalized landing pages and ads based on what’s relevant for each account • Arm sales with personalized content and engagement insights for their accounts Companies like Uber, GitLab, Twilio, and LaunchDarkly are transforming how they connect with enterprise buyers with Mutiny. Early results are insane! In just 60 days, LaunchDarkly used the product to: • Book 45 enterprise meetings with Fortune 500 accounts • Exceeded pipeline goals by 50% • Operated at 12X the average ABM productivity Even established enterprises with more complex workflows like BMC Software have hit the ground running, launching breakthrough campaigns in hours. We're building a world where go-to-market teams can move at the speed of thought, executing every creative idea instantly without dependencies. Link to the launch blog post: #B2BMarketing #AI #EnterpriseGTM #ABM

Today is the biggest product launch in Mutiny's history. Every B2B CMO I talk to says that AI has turned their marketing playbooks upside down: • Outbound effectiveness is plummeting as AI spam floods inboxes • Ads that used to work are now just more noise • Inbound leads are down as LLMs hijack their SEO traffic But what if instead of spamming buyers with AI, we used AI to deeply understand our buyers and build genuine relationships with them? That’s why we built 𝗔𝗜 𝗳𝗼𝗿 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗚𝗧𝗠, the fastest place to launch breakthrough campaigns for target accounts. We use AI agents to: • Research accounts to identify needs and uncover themes across accounts • Create personalized landing pages and ads based on what’s relevant for each account • Arm sales with personalized content and engagement insights for their accounts Companies like Uber, GitLab, Twilio, and LaunchDarkly are transforming how they connect with enterprise buyers with Mutiny. Early results are insane! In just 60 days, LaunchDarkly used the product to: • Book 45 enterprise meetings with Fortune 500 accounts • Exceeded pipeline goals by 50% • Operated at 12X the average ABM productivity Even established enterprises with more complex workflows like BMC Software have hit the ground running, launching breakthrough campaigns in hours. We're building a world where go-to-market teams can move at the speed of thought, executing every creative idea instantly without dependencies. Link to the launch blog post: #B2BMarketing #AI #EnterpriseGTM #ABM

Jaleh Rezaei

27,841 次观看 • 1 年前

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

Dan Shipper 📧

67,958 次观看 • 5 个月前

On Friday, I hosted a Space with Jonathan Ross, the founder and CEO of Groq Inc - a company I invested in that is building custom chips for AI inference. Jonathan, a former high-school dropout, entered the chip industry while working on ad optimization at Google’s New York office. Jonathan overheard the speech recognition team complaining that they couldn't get enough compute. These were the early days of AI, and machine learning wasn’t really a thing yet. So he asked for some budget from Google and started putting together a chip-based machine learning accelerator for them. During the day, Jonathan would work in the normal ads part of the business, and at night, he would work with the accelerator team. After winning approval from Google, Jonathan and his team built a new chip called the Tensor Processing Unit, and began deploying it across Google’s data centers within a year. The TPU was a huge success within Google, eventually underpinning more than 50% of all of Google’s compute power. When the other hyper-scalers learned of this success, they tried to hire Jonathan to build custom chips for them too. During this process, it became increasingly clear to Jonathan that a gap would emerge between companies that had access to next-gen compute and companies that didn’t. So he founded Groq and set out to build a chip that would be available to everyone. I led Groq’s founding investment in 2016, and since then, Jonathan and his team have developed several types of AI hardware including the Language Processing Unit (LPU), a new type of silicon that is hyper-efficient at running inference for LLMs. In our conversation on Friday, we discussed the founding story of Groq, what you need for great AI hardware, large language models, and some of the implications for the key players in AI. It’s one of the most interesting conversations I’ve had on AI with a lot of learnings. You can listen to our conversation below:

On Friday, I hosted a Space with Jonathan Ross, the founder and CEO of Groq Inc - a company I invested in that is building custom chips for AI inference. Jonathan, a former high-school dropout, entered the chip industry while working on ad optimization at Google’s New York office. Jonathan overheard the speech recognition team complaining that they couldn't get enough compute. These were the early days of AI, and machine learning wasn’t really a thing yet. So he asked for some budget from Google and started putting together a chip-based machine learning accelerator for them. During the day, Jonathan would work in the normal ads part of the business, and at night, he would work with the accelerator team. After winning approval from Google, Jonathan and his team built a new chip called the Tensor Processing Unit, and began deploying it across Google’s data centers within a year. The TPU was a huge success within Google, eventually underpinning more than 50% of all of Google’s compute power. When the other hyper-scalers learned of this success, they tried to hire Jonathan to build custom chips for them too. During this process, it became increasingly clear to Jonathan that a gap would emerge between companies that had access to next-gen compute and companies that didn’t. So he founded Groq and set out to build a chip that would be available to everyone. I led Groq’s founding investment in 2016, and since then, Jonathan and his team have developed several types of AI hardware including the Language Processing Unit (LPU), a new type of silicon that is hyper-efficient at running inference for LLMs. In our conversation on Friday, we discussed the founding story of Groq, what you need for great AI hardware, large language models, and some of the implications for the key players in AI. It’s one of the most interesting conversations I’ve had on AI with a lot of learnings. You can listen to our conversation below:

Chamath Palihapitiya

326,752 次观看 • 2 年前