正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Launching today: the world’s first fully optimized LLM routing stack that can be embedded directly into your product, Merge Embedded Routing Stack. Model sovereignty is now table stakes for any AI product. Users and companies are demanding full control over the AI models running in your product. Right now,... no company is able to do this without massive overhead. Until today. Merge makes it easy for your customers to use your product exactly the way they want to. With a single API, you can: 1. Allow customers to bring their own keys, manage their own budgets, and set policies like fallback rules, model allowlists, and more 2. Let each customer connect and switch their own models, right inside your product 3. Optimize each customer for cost, performance, or capability Without Merge, you’d be: - Building routing policies, provider integrations, and LLM key management - Giving your users zero model independence - Receiving endless customer support tickets asking “can I use 5.6 Sol?” Products that fight their customers' model strategy will lose to products that fit it. Merge is the only place that allows your users to produce the exact results they want, with the models they have access to. Give your customers the advantage, without the massive overhead costs.show more

Shensi Ding

6,054 subscribers

331,517 次观看 • 6 天前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Introducing Merge Gateway - Build Your Own Router. You're three sprints into your coding assistant. You pick the most hyped model, integrate, test, deploy. A month later, a new model drops. Now you re-test, re-integrate, re-deploy. Your product didn't change, but the benchmark did. That's how most AI teams operate. Chasing a "best" defined by people who've never seen their product. There is no best model. There's only the right one for your product, users, and use-cases. Build Your Own Router runs on your definition of good. Pick your benchmarks, weigh them, add your own evals. Merge routes every request to your winner. 👉$100 in credits to the first 200 people that comment

Introducing Merge Gateway - Build Your Own Router. You're three sprints into your coding assistant. You pick the most hyped model, integrate, test, deploy. A month later, a new model drops. Now you re-test, re-integrate, re-deploy. Your product didn't change, but the benchmark did. That's how most AI teams operate. Chasing a "best" defined by people who've never seen their product. There is no best model. There's only the right one for your product, users, and use-cases. Build Your Own Router runs on your definition of good. Pick your benchmarks, weigh them, add your own evals. Merge routes every request to your winner. 👉$100 in credits to the first 200 people that comment

Shensi Ding

3,665,672 次观看 • 1 个月前

NEW: Introducing Octane AI Agentic Commerce Quizzes - Increase sales with AI. What is it? A sales quiz AI agent that makes 1-1 personalized sales experiences for every single customer. In real time. Powered by our new AI model CORE-1. Examples: 📸 Want to ask your customer to take a selfie and your AI agent automatically recommends them a full outfit from your catalog? Octane AI agents can do that. 🪞 Want to have an AI agent hand pick out each product for a personalized skin care routine? Want them to upload a selfie to detect their skin tone? Octane AI agents can do can that. 📊 Want to create an incredibly detailed report with graphs and tables and graphics thats generated by AI for each customer? Octane AI agents can do that. We give you the building blocks and you can build anything. And you can build it fast because our AI will do the heavy lifting for you. This is v1 and a representation of where our commerce and quiz technology is headed. Available today to everyone at 🆕 What we are launching today: • Smart Quiz Builder: Have an AI agent plan out and build your Octane AI quiz for you. It can even write custom HTML for beautiful results pages and progress bars. • Smart Products: It can take forever to setup the recommendation logic for a quiz. For those of you who need help, simply add smart products to your Octane AI quiz and your very own AI agent will hand-pick products for each customer who takes your quiz. It’s amazing. • Smart Copy: Instead of showing everyone who takes your quiz the exact same copy, use AI to personalize the quiz for every single person who takes it. Explain why these specific products are perfect for specifically them. • Image Analyzer: Let your customers upload or take a photo during the quiz and have AI analyze it. You can use this for anything from skin tone detection to picking out outfits! • Shopping Assistant: An AI agent that lives on your store that can help your customers at the right time. We have been building quiz software for almost 10 years now and AI is enabling us to make quizzes even more powerful. This is just the v1 of what we will be releasing in this area. We are so excited to see what you create with these new agentic products. Get creative, we think you will be surprised at how many interesting experiences you can create with Octane AI now.

NEW: Introducing Octane AI Agentic Commerce Quizzes - Increase sales with AI. What is it? A sales quiz AI agent that makes 1-1 personalized sales experiences for every single customer. In real time. Powered by our new AI model CORE-1. Examples: 📸 Want to ask your customer to take a selfie and your AI agent automatically recommends them a full outfit from your catalog? Octane AI agents can do that. 🪞 Want to have an AI agent hand pick out each product for a personalized skin care routine? Want them to upload a selfie to detect their skin tone? Octane AI agents can do can that. 📊 Want to create an incredibly detailed report with graphs and tables and graphics thats generated by AI for each customer? Octane AI agents can do that. We give you the building blocks and you can build anything. And you can build it fast because our AI will do the heavy lifting for you. This is v1 and a representation of where our commerce and quiz technology is headed. Available today to everyone at 🆕 What we are launching today: • Smart Quiz Builder: Have an AI agent plan out and build your Octane AI quiz for you. It can even write custom HTML for beautiful results pages and progress bars. • Smart Products: It can take forever to setup the recommendation logic for a quiz. For those of you who need help, simply add smart products to your Octane AI quiz and your very own AI agent will hand-pick products for each customer who takes your quiz. It’s amazing. • Smart Copy: Instead of showing everyone who takes your quiz the exact same copy, use AI to personalize the quiz for every single person who takes it. Explain why these specific products are perfect for specifically them. • Image Analyzer: Let your customers upload or take a photo during the quiz and have AI analyze it. You can use this for anything from skin tone detection to picking out outfits! • Shopping Assistant: An AI agent that lives on your store that can help your customers at the right time. We have been building quiz software for almost 10 years now and AI is enabling us to make quizzes even more powerful. This is just the v1 of what we will be releasing in this area. We are so excited to see what you create with these new agentic products. Get creative, we think you will be surprised at how many interesting experiences you can create with Octane AI now.

Matt Schlicht

290,828 次观看 • 8 个月前

Scott Belsky on the most common mistake founders make when building a product “Every product has what we call a ‘first-mile experience’, which is the part of your product that the most customers will see. And it’s all drop-off from there. What gets people through the first-mile experience? First, you have to empathize with where that customer is at — whether they’re a consumer or an enterprise customer, in the first 30 seconds of that first mile of your product, I guarantee you, they’re lazy, vain, and selfish. They want to get through it fast. They want to look good to their boss or their friends or feel good about themselves. There needs to be some quick hit of feeling successful in that first mile for them to engage further.” Ironically, the first-mile experience is the last thing many companies and product people will focus on. “Typically it’s the final mile before you launch where you’re like, ‘Oh wait, what should the onboarding be?’ or ‘What should the defaults be?’ That’s like a happenstance conversation towards the end of shipping when in fact that’s the only thing that every customer will ever see. So why not nail that?” Scott takes this point even further: “If you can really just nail the first-mile experience of your product — even if after that it’s all kind of crappy — you’re probably in the top 1% of products out there.” Another interesting point Scott makes here is that optimizing the first-mile experience is something that you’ll have to continually work on because your customers change over time. “Your first cohort of customers that used your product, those were early adopters — and your first-mile experience was nailed for them. But [as you’ve grown] this new cohort of customers that started using your product are no longer early adopters. They’re pragmatists. They’re not coming because they like to test and try new products. They’re coming because their boss told them they had to or they read some blog that said this was the best product in the space, and the first-mile needs to be different for them.” Scott sums his point up as follows: “Spend consistent time, forever on that first-mile experience of your product.” Video source: South Park Commons (2025)

Scott Belsky on the most common mistake founders make when building a product “Every product has what we call a ‘first-mile experience’, which is the part of your product that the most customers will see. And it’s all drop-off from there. What gets people through the first-mile experience? First, you have to empathize with where that customer is at — whether they’re a consumer or an enterprise customer, in the first 30 seconds of that first mile of your product, I guarantee you, they’re lazy, vain, and selfish. They want to get through it fast. They want to look good to their boss or their friends or feel good about themselves. There needs to be some quick hit of feeling successful in that first mile for them to engage further.” Ironically, the first-mile experience is the last thing many companies and product people will focus on. “Typically it’s the final mile before you launch where you’re like, ‘Oh wait, what should the onboarding be?’ or ‘What should the defaults be?’ That’s like a happenstance conversation towards the end of shipping when in fact that’s the only thing that every customer will ever see. So why not nail that?” Scott takes this point even further: “If you can really just nail the first-mile experience of your product — even if after that it’s all kind of crappy — you’re probably in the top 1% of products out there.” Another interesting point Scott makes here is that optimizing the first-mile experience is something that you’ll have to continually work on because your customers change over time. “Your first cohort of customers that used your product, those were early adopters — and your first-mile experience was nailed for them. But [as you’ve grown] this new cohort of customers that started using your product are no longer early adopters. They’re pragmatists. They’re not coming because they like to test and try new products. They’re coming because their boss told them they had to or they read some blog that said this was the best product in the space, and the first-mile needs to be different for them.” Scott sums his point up as follows: “Spend consistent time, forever on that first-mile experience of your product.” Video source: South Park Commons (2025)

Startup Archive

32,450 次观看 • 1 年前

It’s official – I’m excited to introduce Alloy (), the world’s first tool for prototypes that look exactly like your product. All year, PMs and designers have struggled with off-brand prototypes – built with “app builder” tools that look nothing like their existing app. They’re left with confused stakeholders, prototypes they can’t show customers, and demos where they’re apologizing for the design. Your prototypes should look like your actual product. Starting today, they can. Alloy is AI Prototyping built for Product Management: ➤ Capture your product from the browser in one click ➤ Chat to build your feature ideas in minutes ➤ Share a link with teammates and customers ➤ 30+ integrations for PM teams: Linear, Notion, Jira Product Discovery, and more In lab results, Alloy delivers 3-5x more detail than alternatives when you start from an existing product. It’s powered by groundbreaking technology you won’t find in any other tool. Alloy is now available for you to try for free. Comment “ALLOY” and I’ll DM you an invite with instant access and extra credits.

Simon Kubica

275,447 次观看 • 10 个月前

📢 New in Visual Studio: Bring Your Own Model to Chat We’re expanding flexibility in Copilot for Visual Studio. You can now connect API keys from providers like OpenAI, Anthropic, and Google to run Chat on the model of your choice. This update gives you: ✅ Choice – Access the latest models beyond the built-in defaults. ✅ Customization – Select models that fit your infrastructure and requirements. ✅ Control – Manage API usage, limits, and billing directly with your provider. ✅ Flexibility – Switch seamlessly between Copilot’s built-in models and your own. This release is the first step toward a more open and customizable chat experience in Visual Studio, with more providers and capabilities to come. 🔗

📢 New in Visual Studio: Bring Your Own Model to Chat We’re expanding flexibility in Copilot for Visual Studio. You can now connect API keys from providers like OpenAI, Anthropic, and Google to run Chat on the model of your choice. This update gives you: ✅ Choice – Access the latest models beyond the built-in defaults. ✅ Customization – Select models that fit your infrastructure and requirements. ✅ Control – Manage API usage, limits, and billing directly with your provider. ✅ Flexibility – Switch seamlessly between Copilot’s built-in models and your own. This release is the first step toward a more open and customizable chat experience in Visual Studio, with more providers and capabilities to come. 🔗

Visual Studio

11,873 次观看 • 11 个月前

Michael Seibel on how to get and test startup ideas As the former CEO of Y Combinator puts it in the clip below: “There’s a common misconception that your idea has to be great to start a company, and the first thing I want to do is destroy that misconception.” Michael was one of the cofounders of JustinTV, which later become Twitch and sold to Amazon for almost $1B. Their original idea was to create an online reality TV show—very different from where Twitch eventually ended up. Rather than falling for the trap of thinking that your initial startup idea has to be great, Michael advises founders to start with a problem: “Starting with ideas is tricky because people immediately want to grade your idea. It’s a lot easier to start with a problem and think about how you grade a problem.” Ideally the problem you set out to solve is one you've experienced personally or have some sort of connection to. You should ask yourself: “why am I uniquely qualified to work on this problem?” Is there some unique angle or approach you're taking to the problem that you understand but you don't believe others understand? Peter Thiel argues that “great companies have secrets: specific reasons for success that other people don’t see." After identifying a problem, you’ll want to start thinking about your MVP. What's the first solution you're going to build and release to see if you can help your initial users solve this problem? But don’t fall in love with your MVP. As Michael puts it: “A lot of people fall in love with their product and are not in love with their problem or their customer. I advise the opposite. Be in love with your problem. Be in love with your customer. And treat your product in a way that can change, develop, and improve.” And once you have an MVP, you should have a strong opinion about who your initial customer is and handpick all of your initial users. The goal with an MVP is not to see how many people want to use your product. It's to see if your solution actually solves the problem for your initial target customers. “The best startups very heavily filter the people who are able to use the initial product and make sure that they’re the right type of initial customer.”

Michael Seibel on how to get and test startup ideas As the former CEO of Y Combinator puts it in the clip below: “There’s a common misconception that your idea has to be great to start a company, and the first thing I want to do is destroy that misconception.” Michael was one of the cofounders of JustinTV, which later become Twitch and sold to Amazon for almost $1B. Their original idea was to create an online reality TV show—very different from where Twitch eventually ended up. Rather than falling for the trap of thinking that your initial startup idea has to be great, Michael advises founders to start with a problem: “Starting with ideas is tricky because people immediately want to grade your idea. It’s a lot easier to start with a problem and think about how you grade a problem.” Ideally the problem you set out to solve is one you've experienced personally or have some sort of connection to. You should ask yourself: “why am I uniquely qualified to work on this problem?” Is there some unique angle or approach you're taking to the problem that you understand but you don't believe others understand? Peter Thiel argues that “great companies have secrets: specific reasons for success that other people don’t see." After identifying a problem, you’ll want to start thinking about your MVP. What's the first solution you're going to build and release to see if you can help your initial users solve this problem? But don’t fall in love with your MVP. As Michael puts it: “A lot of people fall in love with their product and are not in love with their problem or their customer. I advise the opposite. Be in love with your problem. Be in love with your customer. And treat your product in a way that can change, develop, and improve.” And once you have an MVP, you should have a strong opinion about who your initial customer is and handpick all of your initial users. The goal with an MVP is not to see how many people want to use your product. It's to see if your solution actually solves the problem for your initial target customers. “The best startups very heavily filter the people who are able to use the initial product and make sure that they’re the right type of initial customer.”

Startup Archive

101,023 次观看 • 2 年前

Rahul Vohra on how to measure product/market fit Rahul Vohra is the founder and CEO of Superhuman. He was looking for a metric to measure product/market fit so that he and his team could optimize, and he came across the following methodology from Sean Ellis: Simply ask your users: “How would you feel if you could no longer use the product?” with three options: (1) not disappointed, (2) somewhat disappointed, or (3) very disappointed. It turns out that the benchmark for product/market fit across hundreds of venture-backed startups is 40% of respondents saying “very disappointed”. And as Rahul puts it: “If more than 40% of your users would be very disappointed without your product, then you should focus on growing your company. If less than 40% of your users would be very disappointed without your product, then you’ll probably struggle to grow.” 40% may not sound like a lot, but it’s an incredibly hard benchmark to beat. For example, Slack posed this to 731 customers early in the company’s history, and 51% said they would be very disappointed without Slack. One might expect a terrific product like Slack to have a score of 60-80%, but that wasn’t the case. Rahul’s explanation of why the response options are focused on disappointment rather than happiness is interesting too: “I think the reason behind that is that if you ask people how they feel about a product and you give them positive potential responses, I think it invites more bias. People are more likely to be polite. And it also doesn’t get to the heart of the matter which is: how necessary has your product become in people’s lives? If you’re trying to build a company that’s going to stand the test of time, you really do have to build a product that matters and that people ultimately come to depend on because it’s just so incredible at what it does. And that’s what this question gets to the heart of.”

Rahul Vohra on how to measure product/market fit Rahul Vohra is the founder and CEO of Superhuman. He was looking for a metric to measure product/market fit so that he and his team could optimize, and he came across the following methodology from Sean Ellis: Simply ask your users: “How would you feel if you could no longer use the product?” with three options: (1) not disappointed, (2) somewhat disappointed, or (3) very disappointed. It turns out that the benchmark for product/market fit across hundreds of venture-backed startups is 40% of respondents saying “very disappointed”. And as Rahul puts it: “If more than 40% of your users would be very disappointed without your product, then you should focus on growing your company. If less than 40% of your users would be very disappointed without your product, then you’ll probably struggle to grow.” 40% may not sound like a lot, but it’s an incredibly hard benchmark to beat. For example, Slack posed this to 731 customers early in the company’s history, and 51% said they would be very disappointed without Slack. One might expect a terrific product like Slack to have a score of 60-80%, but that wasn’t the case. Rahul’s explanation of why the response options are focused on disappointment rather than happiness is interesting too: “I think the reason behind that is that if you ask people how they feel about a product and you give them positive potential responses, I think it invites more bias. People are more likely to be polite. And it also doesn’t get to the heart of the matter which is: how necessary has your product become in people’s lives? If you’re trying to build a company that’s going to stand the test of time, you really do have to build a product that matters and that people ultimately come to depend on because it’s just so incredible at what it does. And that’s what this question gets to the heart of.”

Michael McGuiness

67,106 次观看 • 2 年前

Q: How do you decide which customers to listen to? As Superhuman founder & CEO Rahul Vohra puts it: “In a world where you’re drowning in feedback—and most startups are drowning in feedback—you have to filter it down to only the stuff that’s going to increase the number of people who fall in love with your product.” Most startups will listen to all feedback from on-the-fence customers, but this isn’t targeted enough and will often lead to a muddled, incoherent product. As Rahul argues in the clip below, you need to identify the main benefit of your product—for Superhuman this was speed. And then focus on the feedback of on-the-fence users who also view this as the main benefit—there’s often something small holding them back. Users for whom your main benefit does not resonate (e.g. Superhuman users who value offline capabilities rather than speed), are unlikely to ever fall in love with your product. When Superhuman ran this analysis in 2015, they found that the main thing holding back users who viewed speed as the main benefit was their lack of a mobile app. Probing further, they found some less obvious and more interesting requests, such as integrations, attachment handling, calendering, unified inbox and read receipts. With a clear understanding of their main benefit and missing features, they were able to move this cohort of users from on-the-fence into the territory of enthusiastic advocates. As Rahul puts it in his Product Market Fit Engine article: “To increase your product/market fit score, spend half your time doubling down on what users already love and the other half on addressing what’s holding others back.” But make sure you’re focusing on users who love the main benefit of your product. Users who don’t are unlikely to ever fall in love with your product.

Q: How do you decide which customers to listen to? As Superhuman founder & CEO Rahul Vohra puts it: “In a world where you’re drowning in feedback—and most startups are drowning in feedback—you have to filter it down to only the stuff that’s going to increase the number of people who fall in love with your product.” Most startups will listen to all feedback from on-the-fence customers, but this isn’t targeted enough and will often lead to a muddled, incoherent product. As Rahul argues in the clip below, you need to identify the main benefit of your product—for Superhuman this was speed. And then focus on the feedback of on-the-fence users who also view this as the main benefit—there’s often something small holding them back. Users for whom your main benefit does not resonate (e.g. Superhuman users who value offline capabilities rather than speed), are unlikely to ever fall in love with your product. When Superhuman ran this analysis in 2015, they found that the main thing holding back users who viewed speed as the main benefit was their lack of a mobile app. Probing further, they found some less obvious and more interesting requests, such as integrations, attachment handling, calendering, unified inbox and read receipts. With a clear understanding of their main benefit and missing features, they were able to move this cohort of users from on-the-fence into the territory of enthusiastic advocates. As Rahul puts it in his Product Market Fit Engine article: “To increase your product/market fit score, spend half your time doubling down on what users already love and the other half on addressing what’s holding others back.” But make sure you’re focusing on users who love the main benefit of your product. Users who don’t are unlikely to ever fall in love with your product.

Michael McGuiness

89,872 次观看 • 2 年前

Many startups find early pull, but only a few break out. Why? They stop at product/market fit and never find business model fit. Business model fit isn’t just about how much you charge for your product, but HOW you charge for it, and how you align customer value with your pricing. There’s a lot of advice out there about seat-based vs. usage-based vs. outcome-based pricing, but all of that is just noise. The search for business model fit is as specific to your company as the search for product market fit. You need to align your acquisition strategy and monetization strategy with your customer and product. It’s all one interconnected system. Take Slack for example: many associate Slack’s early success with its fun, creative design and fresh approach to communication. That’s what drew people to the product. But a big part of what made Slack a breakout success was business model fit. Freemium wasn’t just an acquisition tactic, it was essential to driving the revenue flywheel, viral expansion, and adoption in the enterprise. Obvious in retrospect, but controversial and against the grain of what others were doing at the time. The best founders don’t just chase product/market fit, they design a business model to make their product unstoppable. If that’s you, we want to meet you a16z speedrun 🧊 - link in replies

Many startups find early pull, but only a few break out. Why? They stop at product/market fit and never find business model fit. Business model fit isn’t just about how much you charge for your product, but HOW you charge for it, and how you align customer value with your pricing. There’s a lot of advice out there about seat-based vs. usage-based vs. outcome-based pricing, but all of that is just noise. The search for business model fit is as specific to your company as the search for product market fit. You need to align your acquisition strategy and monetization strategy with your customer and product. It’s all one interconnected system. Take Slack for example: many associate Slack’s early success with its fun, creative design and fresh approach to communication. That’s what drew people to the product. But a big part of what made Slack a breakout success was business model fit. Freemium wasn’t just an acquisition tactic, it was essential to driving the revenue flywheel, viral expansion, and adoption in the enterprise. Obvious in retrospect, but controversial and against the grain of what others were doing at the time. The best founders don’t just chase product/market fit, they design a business model to make their product unstoppable. If that’s you, we want to meet you a16z speedrun 🧊 - link in replies

Fareed Mosavat

24,820 次观看 • 10 个月前

Alex Karp went on TV and said his enterprise customers are done letting AI companies quietly steal the alpha out of their business, and the CEO of Together AI explained on CNBC exactly how that theft works. Karp said his customers are livid. They feel they pay for tokens that create nothing while handing over the secret sauce of their own company. Then Vipul Ved Prakash laid out the mechanism. Every time you send your data to a company with very smart models, you hand them the recipe to rebuild what you do. Your data is the thing that makes you money. And you are pushing it straight into a competitor's system. The smarter the models get, the more they learn from what you feed them. This is why companies are moving to open models, and it was never really about price. You can download an open model, run it inside your own walls, and never let your data leave the building. It scores 81 against the top closed model's 85, and runs up to sixty times cheaper. Half the Fortune 500 is already running open models from Hugging Face. The story was that the labs win because they own the best models. The companies paying them just realized they owned the best data the whole time.

Alex Karp went on TV and said his enterprise customers are done letting AI companies quietly steal the alpha out of their business, and the CEO of Together AI explained on CNBC exactly how that theft works. Karp said his customers are livid. They feel they pay for tokens that create nothing while handing over the secret sauce of their own company. Then Vipul Ved Prakash laid out the mechanism. Every time you send your data to a company with very smart models, you hand them the recipe to rebuild what you do. Your data is the thing that makes you money. And you are pushing it straight into a competitor's system. The smarter the models get, the more they learn from what you feed them. This is why companies are moving to open models, and it was never really about price. You can download an open model, run it inside your own walls, and never let your data leave the building. It scores 81 against the top closed model's 85, and runs up to sixty times cheaper. Half the Fortune 500 is already running open models from Hugging Face. The story was that the labs win because they own the best models. The companies paying them just realized they owned the best data the whole time.

Ihtesham Ali

17,433 次观看 • 20 天前

The customer can use your product in the wrong way. That doesn't mean the customer is wrong. Our first batch of hand mixers sold out. With almost zero customer complaints. But we noticed something interesting. Many customers were using it to mix large quantities of atta. More than what the product was originally designed for. The motor would get hot and trip. Technically, that's abnormal use. But customers don't care about what's written in the manual. They care about getting the job done. So instead of telling customers to change their behavior... We changed the product: - More copper in the motor - More power - Higher temperature tolerance That's how manufacturing works. You don't wait for 100 complaints. You improve after the first signal. Every batch should be better than the last.

The customer can use your product in the wrong way. That doesn't mean the customer is wrong. Our first batch of hand mixers sold out. With almost zero customer complaints. But we noticed something interesting. Many customers were using it to mix large quantities of atta. More than what the product was originally designed for. The motor would get hot and trip. Technically, that's abnormal use. But customers don't care about what's written in the manual. They care about getting the job done. So instead of telling customers to change their behavior... We changed the product: - More copper in the motor - More power - Higher temperature tolerance That's how manufacturing works. You don't wait for 100 complaints. You improve after the first signal. Every batch should be better than the last.

Prakash Dadlani

85,695 次观看 • 28 天前

Today we are publicly launching Adora and announcing US$7m in funding to help product teams visualize their entire product experience. Adora helps world-class teams like Canva, Notion, Replit ⠕, Granola and to deeply understand their user-journeys, uncover friction and center their team around a visual source-of-truth. Today we are opening the doors to everyone 👋 In a world where teams ship faster than ever, it’s near impossible to know what your own product looks like. Different teams building for different users – free vs paid, different experiments, markets and languages. Adora automatically maps your entire live product and website: every journey, screen, modal and variation in real-time. Our AI continuously scans your journeys and scans sessions replays to identify usability issues and opportunities to improve your product. Pinging you directly on Slack so you can catch critical issues before they snowball. Getting set up is easy and there’s no manual event tagging required - everything from your journeys down to every button in your product are automatically captured. We’re so proud of the product we’ve built over the last 2.5 years, and we’re excited to help you build product experiences your users adore 💜

Today we are publicly launching Adora and announcing US$7m in funding to help product teams visualize their entire product experience. Adora helps world-class teams like Canva, Notion, Replit ⠕, Granola and to deeply understand their user-journeys, uncover friction and center their team around a visual source-of-truth. Today we are opening the doors to everyone 👋 In a world where teams ship faster than ever, it’s near impossible to know what your own product looks like. Different teams building for different users – free vs paid, different experiments, markets and languages. Adora automatically maps your entire live product and website: every journey, screen, modal and variation in real-time. Our AI continuously scans your journeys and scans sessions replays to identify usability issues and opportunities to improve your product. Pinging you directly on Slack so you can catch critical issues before they snowball. Getting set up is easy and there’s no manual event tagging required - everything from your journeys down to every button in your product are automatically captured. We’re so proud of the product we’ve built over the last 2.5 years, and we’re excited to help you build product experiences your users adore 💜

omar ✌️

70,412 次观看 • 5 个月前

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Santiago

39,101 次观看 • 2 年前

Introducing Index – the new planning tool for Product Managers on Linear. ➡️ Product leaders have long deserved a modern experience for product planning and discovery. Something powerful, flexible, and integrated with the latest tools. That’s why we built Index (Index), the new standard for Product Management: 1. Every layout at your fingertips Breeze through planning with a whiteboard, table, roadmap, or board available every step of the way. One place to go, infinite flexibility, and context on your projects carried throughout. 2. Built for B2B Product Management Natively connect to your customer context, leveraging their requests, insights, and feedback all in one place. Index connects to Hubspot, Intercom, Dovetail, Attio, Gong, and more – so you can back up your vision with customer evidence. 3. The product-native companion to Linear To build a true companion to Linear for PMs, we knew we had to raise the bar. Interactions are instant, every layout is collaborative, and it fits Linear like a glove with native sync. We're live today, and you can pre-order now at

Introducing Index – the new planning tool for Product Managers on Linear. ➡️ Product leaders have long deserved a modern experience for product planning and discovery. Something powerful, flexible, and integrated with the latest tools. That’s why we built Index (Index), the new standard for Product Management: 1. Every layout at your fingertips Breeze through planning with a whiteboard, table, roadmap, or board available every step of the way. One place to go, infinite flexibility, and context on your projects carried throughout. 2. Built for B2B Product Management Natively connect to your customer context, leveraging their requests, insights, and feedback all in one place. Index connects to Hubspot, Intercom, Dovetail, Attio, Gong, and more – so you can back up your vision with customer evidence. 3. The product-native companion to Linear To build a true companion to Linear for PMs, we knew we had to raise the bar. Interactions are instant, every layout is collaborative, and it fits Linear like a glove with native sync. We're live today, and you can pre-order now at

Simon Kubica

66,073 次观看 • 1 年前

The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

Dan Shipper 📧

58,714 次观看 • 4 个月前

Today Sam Asante and I are releasing Typeahead 2.0 on Product Hunt. Sam and I started this company because we think local AI software is going to become a much bigger part of how people use their computers. Typeahead is the first product from that thesis. The first version made the idea clear. AI autocomplete should work in the apps where writing already happens. Since then, we have been shipping toward the product that idea deserves. The app feels faster and more natural. Suggestions are easier to control. The experience is more native to the Mac. It's getting better at staying out of the way until the exact moment it can help. That matters because writing is personal. The best AI writing help keeps you in the sentence. You stay in flow and keep your voice. The machine helps without taking over. Typeahead works across your Mac, runs locally, works offline, and is built to feel like software you own. We’re live on Product Hunt today.

Today Sam Asante and I are releasing Typeahead 2.0 on Product Hunt. Sam and I started this company because we think local AI software is going to become a much bigger part of how people use their computers. Typeahead is the first product from that thesis. The first version made the idea clear. AI autocomplete should work in the apps where writing already happens. Since then, we have been shipping toward the product that idea deserves. The app feels faster and more natural. Suggestions are easier to control. The experience is more native to the Mac. It's getting better at staying out of the way until the exact moment it can help. That matters because writing is personal. The best AI writing help keeps you in the sentence. You stay in flow and keep your voice. The machine helps without taking over. Typeahead works across your Mac, runs locally, works offline, and is built to feel like software you own. We’re live on Product Hunt today.

Hiten Shah

27,150 次观看 • 18 天前

Your data is your edge, but only if your AI is built on it. Rent a generic model and so can your competitor. The companies with an edge are deploying custom models that they own and improve over time. Our co-founder Rhythm Garg recently stopped by South Park Commons to share how companies are owning their intelligence with Applied Compute.

Your data is your edge, but only if your AI is built on it. Rent a generic model and so can your competitor. The companies with an edge are deploying custom models that they own and improve over time. Our co-founder Rhythm Garg recently stopped by South Park Commons to share how companies are owning their intelligence with Applied Compute.

Applied Compute

118,922 次观看 • 1 个月前

The most dangerous thing a company can do right now is rent intelligence from the same place as its competitors (Save this). You cannot rent intelligence from the same place that rents it to your competitor as Chamath Palihapitiya points out. If every company in an industry is feeding their workflows into the same frontier model, they are all converging on the same outputs, the same decisions, the same product improvements. The model becomes the equalizer and everyone pays a premium to become more mediocre. This is happening exactly as Chamath predicted, and the evidence is now concrete. Anthropic and OpenAI have established what analysts are now openly calling an emerging model layer duopoly. Anthropic crossed $45 billion ARR in may 2026, more than tripling from $9 billion at the end of 2025, OpenAI was at roughly $24 to $33 billion ARR at the same time. Together, the two companies combined could hit $160 to $240 billion ARR by end of 2026 and Anthropic and OpenAI now control 88% of enterprise LLM spend. That concentration is the structural problem Chamath is pointing at. And Anthropic isn't just winning on merit because it's actively lobbying for regulatory outcomes that would make that duopoly permanent. Dario Amodei has explicitly framed open source models as unsafe, pushing a safety agenda that, if enshrined in regulation, would effectively make it illegal for enterprises to use the cheaper, private, sovereign alternatives locking them into a closed model dependency by government decree rather than by choice. So you have market forces producing a duopoly, and potential regulatory capture moving to enforce it from the top down. This is exactly why the Nvidia Palantir partnership is not just a product announcement but rather a strategic counter to that duopoly. The logic is straightforward from both sides because If you're Palantir, sitting at the application layer, the last thing you want is to be permanently beholden to Anthropic or OpenAI for the intelligence that powers your product. You want competitive model options, sovereignty and be able to tell enterprise customers they can run AI on their own infrastructure with their own data without any of it touching a frontier lab's servers. If you're Nvidia, sitting at the chip layer, an Anthropic-OpenAI duopoly is an existential concentration risk. Right now, Meta, Google, Microsoft, Amazon, and dozens of other companies buy Nvidia's hardware. If the model layer consolidates into two players, both of which are building their own chips Nvidia faces a monopsony where its best customers are building the tools to displace it. A healthy open source ecosystem where thousands of enterprises train, fine tune, and deploy their own models is Nvidia's ideal market structure. More buyers, more diversity, more demand, less pricing leverage from any single customer.

The most dangerous thing a company can do right now is rent intelligence from the same place as its competitors (Save this). You cannot rent intelligence from the same place that rents it to your competitor as Chamath Palihapitiya points out. If every company in an industry is feeding their workflows into the same frontier model, they are all converging on the same outputs, the same decisions, the same product improvements. The model becomes the equalizer and everyone pays a premium to become more mediocre. This is happening exactly as Chamath predicted, and the evidence is now concrete. Anthropic and OpenAI have established what analysts are now openly calling an emerging model layer duopoly. Anthropic crossed $45 billion ARR in may 2026, more than tripling from $9 billion at the end of 2025, OpenAI was at roughly $24 to $33 billion ARR at the same time. Together, the two companies combined could hit $160 to $240 billion ARR by end of 2026 and Anthropic and OpenAI now control 88% of enterprise LLM spend. That concentration is the structural problem Chamath is pointing at. And Anthropic isn't just winning on merit because it's actively lobbying for regulatory outcomes that would make that duopoly permanent. Dario Amodei has explicitly framed open source models as unsafe, pushing a safety agenda that, if enshrined in regulation, would effectively make it illegal for enterprises to use the cheaper, private, sovereign alternatives locking them into a closed model dependency by government decree rather than by choice. So you have market forces producing a duopoly, and potential regulatory capture moving to enforce it from the top down. This is exactly why the Nvidia Palantir partnership is not just a product announcement but rather a strategic counter to that duopoly. The logic is straightforward from both sides because If you're Palantir, sitting at the application layer, the last thing you want is to be permanently beholden to Anthropic or OpenAI for the intelligence that powers your product. You want competitive model options, sovereignty and be able to tell enterprise customers they can run AI on their own infrastructure with their own data without any of it touching a frontier lab's servers. If you're Nvidia, sitting at the chip layer, an Anthropic-OpenAI duopoly is an existential concentration risk. Right now, Meta, Google, Microsoft, Amazon, and dozens of other companies buy Nvidia's hardware. If the model layer consolidates into two players, both of which are building their own chips Nvidia faces a monopsony where its best customers are building the tools to displace it. A healthy open source ecosystem where thousands of enterprises train, fine tune, and deploy their own models is Nvidia's ideal market structure. More buyers, more diversity, more demand, less pricing leverage from any single customer.

Milk Road AI

33,493 次观看 • 18 天前

Today, I’m excited to announce our new product: Merge Agent Handler! With the growth of AI, we've seen demand for both synced connectors to power enterprise search and deterministic use cases, and live connectors that let agents take real-time actions. And now, with Merge, you can have both. Merge Agent Handler securely connects your AI agents to thousands of pre-built tools via MCP. The platform also comes with authentication, credential management, monitoring, logging, and enterprise-grade security. Huge shout out to the team who worked tirelessly to bring this to life.

Today, I’m excited to announce our new product: Merge Agent Handler! With the growth of AI, we've seen demand for both synced connectors to power enterprise search and deterministic use cases, and live connectors that let agents take real-time actions. And now, with Merge, you can have both. Merge Agent Handler securely connects your AI agents to thousands of pre-built tools via MCP. The platform also comes with authentication, credential management, monitoring, logging, and enterprise-grade security. Huge shout out to the team who worked tirelessly to bring this to life.

Shensi Ding

45,064 次观看 • 9 个月前

the best way to make your product photos stand out on your website is to turn them into animations... that's why I just built an AI system that takes any product image and makes it look like a professional product video here’s what it can do: – animate still photos so they feel alive – keep the product consistent + realistic – generate multiple variations you can use across your store and socials once it’s built, you can: – make your product pages look premium without hiring a video team – catch more attention than static images ever could – have unlimited product videos ready in minutes comment “video” and I’ll DM you the walkthrough (must be following)

the best way to make your product photos stand out on your website is to turn them into animations... that's why I just built an AI system that takes any product image and makes it look like a professional product video here’s what it can do: – animate still photos so they feel alive – keep the product consistent + realistic – generate multiple variations you can use across your store and socials once it’s built, you can: – make your product pages look premium without hiring a video team – catch more attention than static images ever could – have unlimited product videos ready in minutes comment “video” and I’ll DM you the walkthrough (must be following)

Tyler

65,824 次观看 • 9 个月前