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Today we’re releasing Greptile Agent v4, our best code review agent yet. It’s catches more tricky bugs than v3, while also having far fewer false positives. We're also updating our pricing to help us support power users.

Daksh Gupta

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105,348 просмотров • 4 месяцев назад •via X (Twitter)

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Greptile has raised $25M to Kill The Bug. The round is led by Benchmark with Eric Vishria joining our board. Today, code is written by humans and a variety of coding agents like Devin, Claude Code, and Cursor. Greptile serves as the independent and universal code review layer. This month alone, Greptile reviewed 500M lines of code for top companies like Brex, Substack, PostHog, Bilt, and even YC’s internal software team, helping prevent 180,000+ bugs. Alongside our Series A - we’re excited to announce Greptile v3 - a brand new agent architecture capable of catching >3x more critical bugs than Greptile v2. Available now to all users. 🧵

Daksh Gupta

686,887 просмотров • 10 месяцев назад

Today I am excited to announce Greptile’s BIGGEST update ever 💥 Greptile is the AI code review bot loved by over 3,000 software teams across the world. We’re launching four things today: 🧠 Long-term memory for the code review agent so it can intelligently learn your company's idiosyncrasies and best practices 🤝 Ability to connect your Jira, Google Docs, and Notion via MCP so Greptile can have more context 🎯 Ability to create highly scoped rules that only apply to certain repos and file patterns 💰 A simpler, more predictable pricing model of flat $30/month/developer. Let’s dive in 🧵

Today I am excited to announce Greptile’s BIGGEST update ever 💥 Greptile is the AI code review bot loved by over 3,000 software teams across the world. We’re launching four things today: 🧠 Long-term memory for the code review agent so it can intelligently learn your company's idiosyncrasies and best practices 🤝 Ability to connect your Jira, Google Docs, and Notion via MCP so Greptile can have more context 🎯 Ability to create highly scoped rules that only apply to certain repos and file patterns 💰 A simpler, more predictable pricing model of flat $30/month/developer. Let’s dive in 🧵

Daksh Gupta

246,046 просмотров • 1 год назад

Anthropic CEO Dario Amodei: "We're a 1-2 year away from AI zooming past us." 90% of Anthropic's own engineers use Claude to ship code today. 50% of entry-level white-collar jobs gone by 2030 "Our lead Claude Code engineer hasn't written a single line of code in 2 months. This is worth more than most paid agent courses combined.

Anthropic CEO Dario Amodei: "We're a 1-2 year away from AI zooming past us." 90% of Anthropic's own engineers use Claude to ship code today. 50% of entry-level white-collar jobs gone by 2030 "Our lead Claude Code engineer hasn't written a single line of code in 2 months. This is worth more than most paid agent courses combined.

Morty

774,492 просмотров • 7 дней назад

Introducing FlowMaker 🌊🤖 A fully open-source, low-code way of building custom agent workflows. Build agents via a drag and drop interface, run it directly in the app, and also directly export it into a deeply custom workflow backed by LlamaIndex 🦙.TS. It’s a fantastic visual tool to help you get started with agents but also gives you full customizability to build the deepest, most advanced workflows, since everything is backed by code. There’s a lot of great low-code agent tools already, but this is a fully-open source template that you can use or clone/customize for your own needs. It was a fun project by Laurie Voss and is intended to give users an easy-on ramp onto our powerful agent orchestration tooling. Also integrates with LlamaCloud in case you want to integrate your own knowledge base into your agent. FlowMaker app: FlowMaker Repo: LlamaCloud:

Introducing FlowMaker 🌊🤖 A fully open-source, low-code way of building custom agent workflows. Build agents via a drag and drop interface, run it directly in the app, and also directly export it into a deeply custom workflow backed by LlamaIndex 🦙.TS. It’s a fantastic visual tool to help you get started with agents but also gives you full customizability to build the deepest, most advanced workflows, since everything is backed by code. There’s a lot of great low-code agent tools already, but this is a fully-open source template that you can use or clone/customize for your own needs. It was a fun project by Laurie Voss and is intended to give users an easy-on ramp onto our powerful agent orchestration tooling. Also integrates with LlamaCloud in case you want to integrate your own knowledge base into your agent. FlowMaker app: FlowMaker Repo: LlamaCloud:

Jerry Liu

20,413 просмотров • 1 год назад

Introducing RAGs v3 🌟: Build a RAG bot that can also search the web 🌐, to find answers that aren’t immediately in its corpus. Do this all in natural language, not code! 💬 Get a better experience than ChatGPT + Bing ⚡️ We used our integration with Metaphor - a search engine designed for LLMs - to pull in relevant text from the internet. Additionally: - you can now view the tools that the agent is using. - note: web search is only accessible for our OpenAI agent Check out the video below, and get started with the repo today. Repo:

Introducing RAGs v3 🌟: Build a RAG bot that can also search the web 🌐, to find answers that aren’t immediately in its corpus. Do this all in natural language, not code! 💬 Get a better experience than ChatGPT + Bing ⚡️ We used our integration with Metaphor - a search engine designed for LLMs - to pull in relevant text from the internet. Additionally: - you can now view the tools that the agent is using. - note: web search is only accessible for our OpenAI agent Check out the video below, and get started with the repo today. Repo:

LlamaIndex 🦙

76,371 просмотров • 2 лет назад

From the moment I got this script, my heart started pounding! THE ANCHOR is more than a movie! It’s 🔥🔥🔥 It’s one of our best stories yet! It’ll shake you to your core, but it’ll also make you feel good. Easter came early! 🎁 TODAY at 5pm WAT / 12 noon EST on Omoni Oboli TV!

From the moment I got this script, my heart started pounding! THE ANCHOR is more than a movie! It’s 🔥🔥🔥 It’s one of our best stories yet! It’ll shake you to your core, but it’ll also make you feel good. Easter came early! 🎁 TODAY at 5pm WAT / 12 noon EST on Omoni Oboli TV!

Omoni Oboli

43,826 просмотров • 3 месяцев назад

🚀New Amazon Q Developer agent for software development is available to customers: This agent is based on a new agent architecture that has exciting results coming from the SWE-bench scores (on the full and verified benchmarks) representing AI models’ ability to resolve real-world coding problems. Interesting aspect of Q Agent is that with these newest updates, Q drove nearly 50% more successful coding tasks completed. What makes Q Dev Agent remarkable? The agent architecture is not just about using the best LLMs (which we do), but also giving the agent the ability to constantly explore multiple paths to find the best way to resolve a particular problem (and back tracking when it has reached dead end like a developer would do). Needless to say, we are just getting started on the developer agent and we are constantly pushing to advance our AI capabilities while maintaining quality, security, privacy, and reliability to keep Amazon Q Developer an innovative and trusted option available to our customers using agents for software development. We highlighted the results of our first SWE-bench submission of Amazon Q Developer back in June blog post; with these updates, our new agent resolves 51% more coding tasks than its previous iteration on the SWE-bench verified dataset, and 43% more on the full dataset. That’s the difference a few months make, and I can’t wait to share what our teams will deliver at re:Invent this December. Here's a quick demo showcasing our new Agent in action:

🚀New Amazon Q Developer agent for software development is available to customers: This agent is based on a new agent architecture that has exciting results coming from the SWE-bench scores (on the full and verified benchmarks) representing AI models’ ability to resolve real-world coding problems. Interesting aspect of Q Agent is that with these newest updates, Q drove nearly 50% more successful coding tasks completed. What makes Q Dev Agent remarkable? The agent architecture is not just about using the best LLMs (which we do), but also giving the agent the ability to constantly explore multiple paths to find the best way to resolve a particular problem (and back tracking when it has reached dead end like a developer would do). Needless to say, we are just getting started on the developer agent and we are constantly pushing to advance our AI capabilities while maintaining quality, security, privacy, and reliability to keep Amazon Q Developer an innovative and trusted option available to our customers using agents for software development. We highlighted the results of our first SWE-bench submission of Amazon Q Developer back in June blog post; with these updates, our new agent resolves 51% more coding tasks than its previous iteration on the SWE-bench verified dataset, and 43% more on the full dataset. That’s the difference a few months make, and I can’t wait to share what our teams will deliver at re:Invent this December. Here's a quick demo showcasing our new Agent in action:

Swami Sivasubramanian

28,946 просмотров • 1 год назад

We've been obsessing a lot about creating the best possible AI support experience Dub.co. And I think we've found the magic formula. The stack: ✦ Mintlify → docs + help articles ✦ Upstash vector → vector search ✦ AI SDK + Claude sonnet → LLM/retrieval ✦ Plain → support infra Here's how it works: We consolidated our docs + help articles on Mintlify (best-in-class docs platform) so all our support content is now in one place. On every push to main, we sync changes to our Upstash Vector DB. We also built a custom chat interface that retrieves content from Upstash Vector and generates responses via AI SDK + Claude sonnet. The best part: The AI agent uses 𝚝𝚘𝚘𝚕() from AI SDK to retrieve the user's workspace / partner data to provide accurate responses. Finally, if the user needs to speak to a human, we generate a 𝚃𝚒𝚌𝚔𝚎𝚝𝚄𝚙𝚕𝚘𝚊𝚍 UI that lets users upload screenshots + add more context, before creating a ticket on Plain. Cherry on top: The chat interface is embeddable, so we're able to embed it both in our Mintlify docs + support portal 👇 Since we launched this, it's been a gamechanger for scaling support – reducing support workload and creating a much better experience for our users. Highly recommend giving this a try!

We've been obsessing a lot about creating the best possible AI support experience Dub.co. And I think we've found the magic formula. The stack: ✦ Mintlify → docs + help articles ✦ Upstash vector → vector search ✦ AI SDK + Claude sonnet → LLM/retrieval ✦ Plain → support infra Here's how it works: We consolidated our docs + help articles on Mintlify (best-in-class docs platform) so all our support content is now in one place. On every push to main, we sync changes to our Upstash Vector DB. We also built a custom chat interface that retrieves content from Upstash Vector and generates responses via AI SDK + Claude sonnet. The best part: The AI agent uses 𝚝𝚘𝚘𝚕() from AI SDK to retrieve the user's workspace / partner data to provide accurate responses. Finally, if the user needs to speak to a human, we generate a 𝚃𝚒𝚌𝚔𝚎𝚝𝚄𝚙𝚕𝚘𝚊𝚍 UI that lets users upload screenshots + add more context, before creating a ticket on Plain. Cherry on top: The chat interface is embeddable, so we're able to embed it both in our Mintlify docs + support portal 👇 Since we launched this, it's been a gamechanger for scaling support – reducing support workload and creating a much better experience for our users. Highly recommend giving this a try!

Steven Tey

22,654 просмотров • 4 месяцев назад

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

LobeHub

185,195 просмотров • 5 месяцев назад

Again, this is why Ridges AI | SN62 will win.. 🔵 "This is an example of code that we are merging into our codebase, this is legitimate code that the agent on our platform wrote, end to end for us, and is improving our platform, without a human in the loop" ⚪️ "Because everyone is competing, its basically improving at a faster rate than everyone else and its also much cheaper" 🔵 "At this point, we're matching the performance of these very expensive incumbents at 1/70th of the cost" Shakeel People don't understand how disruptive this will be.. $TAO

Again, this is why Ridges AI | SN62 will win.. 🔵 "This is an example of code that we are merging into our codebase, this is legitimate code that the agent on our platform wrote, end to end for us, and is improving our platform, without a human in the loop" ⚪️ "Because everyone is competing, its basically improving at a faster rate than everyone else and its also much cheaper" 🔵 "At this point, we're matching the performance of these very expensive incumbents at 1/70th of the cost" Shakeel People don't understand how disruptive this will be.. $TAO

𝗗𝗥𝗘𝗔𝗗 𝗕𝗢𝗡𝗚𝗢

11,209 просмотров • 8 месяцев назад

Excited to announce that Entelligence AI has raised $5M to redefine how engineering teams ship! Today AI writes code faster than ever. But shipping quality products as a team? That's still a challenge. Launching Ask Ellie. An AI agent that triages questions across posthog, sentry, linear, github and more to answer anything about performance, ownership, blockers, and more without jumping between tools. Ellie also beats benchmarks against other code reviewers in catching real issues while also preventing security issues and keeping docs current. Round led Mayfield and Correlation Ventures with support from Embedding VC, DGVentures and an incredible group of leaders from GitLab, Nvidia, Auth0, Zapier, Cisco, Calm, and more!

Excited to announce that Entelligence AI has raised $5M to redefine how engineering teams ship! Today AI writes code faster than ever. But shipping quality products as a team? That's still a challenge. Launching Ask Ellie. An AI agent that triages questions across posthog, sentry, linear, github and more to answer anything about performance, ownership, blockers, and more without jumping between tools. Ellie also beats benchmarks against other code reviewers in catching real issues while also preventing security issues and keeping docs current. Round led Mayfield and Correlation Ventures with support from Embedding VC, DGVentures and an incredible group of leaders from GitLab, Nvidia, Auth0, Zapier, Cisco, Calm, and more!

Aiswarya Sankar

102,971 просмотров • 6 месяцев назад

We’re excited to announce our brand-new Agentic AI podcast — fully curated and generated by OptimAI Agent, leveraging the power of our OptimAI Data Network. This is just the start; stay tuned for more exciting updates. + More utilities showcasing how OptimAI Agent empowers creators worldwide. + OptimAI Edge Node for iOS is coming We've also seen many questions regarding our roadmap and TGE: 👉Roadmap: Kindly dive into our whitepaper and docs to clearly see our vision, mission, and ongoing development. 👉 TGE: Our primary goal is delivering long-term value and real utility, not rushing token launches. A token without purpose creates fleeting excitement; we’re committed to sustained growth, genuine community benefits, and meaningful results. Stay strong, build with us, and together we'll create lasting value. Real Utility First. Rewards Will Follow. Let’s #BUIDL something amazing, together! 👉OptimAI Lite Node: 👉OptimAI Edge Node:

OptimAI Network

55,313 просмотров • 1 год назад

We're launching Glif V2 today ...and it created 5 launch videos for us! Glif is a creative super agent: just tell it what you want to make and it produces incredible outputs using virtually every available AI model. Create ads, marketing content, films, short form content, voiceovers, music, and more. All in one conversation. Easy to start, endlessly deep. We're also announcing our $17.5M seed led by a16z and USV. Creatives: You're not cooked. You're the chef now.

We're launching Glif V2 today ...and it created 5 launch videos for us! Glif is a creative super agent: just tell it what you want to make and it produces incredible outputs using virtually every available AI model. Create ads, marketing content, films, short form content, voiceovers, music, and more. All in one conversation. Easy to start, endlessly deep. We're also announcing our $17.5M seed led by a16z and USV. Creatives: You're not cooked. You're the chef now.

fabian

268,231 просмотров • 3 месяцев назад

Today, KOHO announced $130M in new capital at a $1.33B valuation. We’re very happy to welcome Mubadala, Savano Capital, tobi lutke, CEO, Founder of Shopify, and Michael Linford, COO of Affirm, to the KOHO story. We’re also grateful for the continued support of Drive Capital, Portage Ventures, Eldridge, BDC Capital, and HOOPP. One of my favourite things about KOHO is that the only way we win is if millions of Canadians choose us. We cannot out-spend or out-market. We have to out-build. The progress we have made reflects the progress our users have made. So, while we feel very proud of what’s behind us, we’re much more humbled by what’s in front of us. With this capital, we’ve earned the right to keep building on our promise to users. That job remains monumental, and we’re very honoured that we get to do the work. I want to thank the team for their incredible work these past few years. The world doesn’t yet know how talented, smart and kind the KOHO Collective is - but they will. I want to thank our investors for their continued trust and support. We’re lucky to have partners who recognize and support the need to take the longer view here. More than anything, I want to thank our users. We’ve come a long way with much farther to go. We’ll keep earning your trust every damn day. Back to work 🔥

Today, KOHO announced $130M in new capital at a $1.33B valuation. We’re very happy to welcome Mubadala, Savano Capital, tobi lutke, CEO, Founder of Shopify, and Michael Linford, COO of Affirm, to the KOHO story. We’re also grateful for the continued support of Drive Capital, Portage Ventures, Eldridge, BDC Capital, and HOOPP. One of my favourite things about KOHO is that the only way we win is if millions of Canadians choose us. We cannot out-spend or out-market. We have to out-build. The progress we have made reflects the progress our users have made. So, while we feel very proud of what’s behind us, we’re much more humbled by what’s in front of us. With this capital, we’ve earned the right to keep building on our promise to users. That job remains monumental, and we’re very honoured that we get to do the work. I want to thank the team for their incredible work these past few years. The world doesn’t yet know how talented, smart and kind the KOHO Collective is - but they will. I want to thank our investors for their continued trust and support. We’re lucky to have partners who recognize and support the need to take the longer view here. More than anything, I want to thank our users. We’ve come a long way with much farther to go. We’ll keep earning your trust every damn day. Back to work 🔥

Daniel Eberhard

25,801 просмотров • 1 месяц назад

Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.

Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.

Bret Taylor

1,060,880 просмотров • 4 месяцев назад

Five years ago, Wayne L. and I founded HeyGen on a simple, yet powerful belief: everyone has a story to tell. But most people avoid the lens, and premium production isn’t in every budget. So we set out to democratize video creation and make visual storytelling accessible to everyone. Today, we take the next, most significant leap in that journey with three big announcements: 1. A new HeyGen Our new brand reflects our evolution from an AI video tool to an indispensable creative operating system. You’ll see our new look reflected on our website and in the app. 2. Video Agent public beta Video Agent is now in public beta and available to all HeyGen users. It’s the creative operating system that transforms a single prompt into a complete, publishable video across all your needs. 3. Alisa joins HeyGen To accelerate our mission, we have acquired Alisa by Genova Labs, a true pioneer in intelligent multimedia agents. With this new addition, we are also thrilled to welcome its founder, Bin Liu, to lead our new Video Agent product. Join us as we welcome the Alisa team and write the next chapter of HeyGen, together.

Joshua Xu

3,267,347 просмотров • 10 месяцев назад

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Sam Rodriques

724,960 просмотров • 1 год назад

Webacy is now on TON 💎 🎉 The security suite you know and love is now on TON: → DYOR Anything on TON: paste any Jetton, wallet or address into DYOR for real time risk exposure - keeps you safe and secure while trading 👀 → API: Integrate Webacy's EmbeddedSafety™ APIs to your TON projects with real time Jetton Analysis, Ownership Analysis, Fake Contract Analysis, AI-Code Engine and more 🔥 → It wouldn't be a TON announcement without TON community support: join our TON announcement channel to be the first to know what's happening in the TON eco. → We're also launching our very own [REDACTED] to assist with trading, a new and novel [redacted]/[redacted] score and more 👀 To start our TON support right, we’re working with the folks over at STON.fi who will be using Webacy’s EmbeddedSafety™ APIs to keep their end-users safe! Let's make TON safer TODAY!

Webacy is now on TON 💎 🎉 The security suite you know and love is now on TON: → DYOR Anything on TON: paste any Jetton, wallet or address into DYOR for real time risk exposure - keeps you safe and secure while trading 👀 → API: Integrate Webacy's EmbeddedSafety™ APIs to your TON projects with real time Jetton Analysis, Ownership Analysis, Fake Contract Analysis, AI-Code Engine and more 🔥 → It wouldn't be a TON announcement without TON community support: join our TON announcement channel to be the first to know what's happening in the TON eco. → We're also launching our very own [REDACTED] to assist with trading, a new and novel [redacted]/[redacted] score and more 👀 To start our TON support right, we’re working with the folks over at STON.fi who will be using Webacy’s EmbeddedSafety™ APIs to keep their end-users safe! Let's make TON safer TODAY!

Webacy

14,000 просмотров • 1 год назад

AI writes all your code. Does it still work? That's the gap: coding agents are scaling output, but the same with bugs and incidents. The biggest bottleneck is QA: how can you make sure the code your agents just wrote doesn’t break everything? The best QA are the ones who know everything about your product. Today, we give that knowledge to Momentic’s most capable agent. Every Linear ticket, every PRD in Notion, every PR - Momentic knows all. In just the past few weeks, our agents analyzed 70k+ test failures and created 600 tests. Achieving a 73% PR merge rate, and it’s growing fast. We’re grateful for the trust of our customers, including Notion, Xero, Webflow, Retool, Runway, Bilt, and more.

AI writes all your code. Does it still work? That's the gap: coding agents are scaling output, but the same with bugs and incidents. The biggest bottleneck is QA: how can you make sure the code your agents just wrote doesn’t break everything? The best QA are the ones who know everything about your product. Today, we give that knowledge to Momentic’s most capable agent. Every Linear ticket, every PRD in Notion, every PR - Momentic knows all. In just the past few weeks, our agents analyzed 70k+ test failures and created 600 tests. Achieving a 73% PR merge rate, and it’s growing fast. We’re grateful for the trust of our customers, including Notion, Xero, Webflow, Retool, Runway, Bilt, and more.

wei-wei

966,030 просмотров • 1 месяц назад