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📣 Introducing ART Chat 💬 Imagine asking one chat to handle all your onchain research Ask it to: • Pull protocol comps in seconds (e.g. Hyperliquid) • Generate custom charts instantly (BTC vs ETH price) • Query all Artemis-supported metrics for any asset • Find assets that support a... show more

Artemis

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Meet ART Chat — the AI for crypto analysts. Available in Terminal & Sheets/Excel.

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Michael Seibel on how to create a great startup pitch In the clip below, former Y Combinator CEO Michael Seibel breaks down the two types of pitches every startup founder needs: a 30-second elevator pitch and a two-minute pitch for investors. “A lot of people practice 10-minute, 30-minute, hour-long, pitches. I think that’s all garbage. I think you can get all of your points across in two minutes. And one thing I like to tell founders is that the more you talk, the more you have an opportunity to say something that people don’t like.” 30-Second Elevator Pitch: You should be able to explain to anyone you come across what your company does in 30 seconds. This should be three sentences: 1. What does your company do? Assume the person you’re talking to knows nothing. This should be a 1-sentence explanation that your mom or dad can understand (e.g. “We’re Airbnb and we allow you to rent out the extra room in your house” NOT “We’re Airbnb and we’re a marketplace for space”). 2. How big is the market? Do a couple hours of research so that you can give investors a rough approximation of the size of the market you’re in (e.g. Airbnb might give the size of online hotel booking market) 3. How much traction do you have? Ideally you can say something like: “We launched in January and we’re growing 30% month over month. We have $ X sales and Y users.” If you’re pre-launch, you need to convince investors that you’re moving quickly (e.g. “the team came together in January. By March we launched our beta. By April we launched our product.”). Two-Minute Pitch: This pitch is for people you’re actually trying to convince of something (e.g. investors, potential employees, etc.). You basically want to simply explain what you do and then ask for money. There are 5 key components: 1. Clear 30 second pitch (everything mentioned above) 2. Unique insight—what do the biggest players in your market not understand? This should be 2 sentences. 3. How do you make money? 4. Team. If your team has done something that has made investors money, you should mention that (e.g. “we’re the founders of PayPal”). If you haven’t, don’t go on about the awards you’ve won or PhDs you hold. What investors want to hear is: how many founders? (hopefully 2-4) how many of the founders are technical? (hopefully 50% or more engineers) How long have you known each other? (ideally you’ve known each other either personally or professionally for at least 6 months) Are you all full-time? 5. The Big Ask ($$$). You have to know what you’re talking about when you ask for money. Are you raising on a convertible note or a SAFE? What’s the cap of the SAFE? How much money are you raising? What’s the minimum check size? If you don’t know these things, investors won’t think you’re serious or that you haven’t done your homework.

Michael Seibel on how to create a great startup pitch In the clip below, former Y Combinator CEO Michael Seibel breaks down the two types of pitches every startup founder needs: a 30-second elevator pitch and a two-minute pitch for investors. “A lot of people practice 10-minute, 30-minute, hour-long, pitches. I think that’s all garbage. I think you can get all of your points across in two minutes. And one thing I like to tell founders is that the more you talk, the more you have an opportunity to say something that people don’t like.” 30-Second Elevator Pitch: You should be able to explain to anyone you come across what your company does in 30 seconds. This should be three sentences: 1. What does your company do? Assume the person you’re talking to knows nothing. This should be a 1-sentence explanation that your mom or dad can understand (e.g. “We’re Airbnb and we allow you to rent out the extra room in your house” NOT “We’re Airbnb and we’re a marketplace for space”). 2. How big is the market? Do a couple hours of research so that you can give investors a rough approximation of the size of the market you’re in (e.g. Airbnb might give the size of online hotel booking market) 3. How much traction do you have? Ideally you can say something like: “We launched in January and we’re growing 30% month over month. We have $ X sales and Y users.” If you’re pre-launch, you need to convince investors that you’re moving quickly (e.g. “the team came together in January. By March we launched our beta. By April we launched our product.”). Two-Minute Pitch: This pitch is for people you’re actually trying to convince of something (e.g. investors, potential employees, etc.). You basically want to simply explain what you do and then ask for money. There are 5 key components: 1. Clear 30 second pitch (everything mentioned above) 2. Unique insight—what do the biggest players in your market not understand? This should be 2 sentences. 3. How do you make money? 4. Team. If your team has done something that has made investors money, you should mention that (e.g. “we’re the founders of PayPal”). If you haven’t, don’t go on about the awards you’ve won or PhDs you hold. What investors want to hear is: how many founders? (hopefully 2-4) how many of the founders are technical? (hopefully 50% or more engineers) How long have you known each other? (ideally you’ve known each other either personally or professionally for at least 6 months) Are you all full-time? 5. The Big Ask ($$$). You have to know what you’re talking about when you ask for money. Are you raising on a convertible note or a SAFE? What’s the cap of the SAFE? How much money are you raising? What’s the minimum check size? If you don’t know these things, investors won’t think you’re serious or that you haven’t done your homework.

Startup Archive

80,580 görüntüleme • 2 yıl önce

Introducing: Curiosity Stack A structured research thinking tool for Claude Cowork. Decode any technology or market signal into 2nd and 3rd order business insights. --> Decompose any industry, business or a topic --> 6 layers to take your through each step --> Just tell it what you want to decode & tool will handhold you at each step, interactive, asking questions at each step to make it immersive --> Connect 17 diff sources to bring your own notes, information, context in the discussion --> Discover global or Indian proxies to a given industry or just ask it break down a hyper around any topic --> Do thesis stress test, 5 curated reading recommendations targeted at the specific topic and layers explored --> Persistent memory across the sessions --> Generate Value Chain diagrams, Interactive Mind-Maps --> If you discover a business in your interested domain but that not listed, setup an alert of your choice, for e.g., alert me when the company files DRHP. --> Export entire discussion into research note If you ever struggled with a topic or domain and didn't know where to start, this might help. Note: You must be CoWork user. The plugin is currently under review with Anthropic. If you prefer to wait and try only after it is approved, for security reasons, please be patient. Detailed note here:

Introducing: Curiosity Stack A structured research thinking tool for Claude Cowork. Decode any technology or market signal into 2nd and 3rd order business insights. --> Decompose any industry, business or a topic --> 6 layers to take your through each step --> Just tell it what you want to decode & tool will handhold you at each step, interactive, asking questions at each step to make it immersive --> Connect 17 diff sources to bring your own notes, information, context in the discussion --> Discover global or Indian proxies to a given industry or just ask it break down a hyper around any topic --> Do thesis stress test, 5 curated reading recommendations targeted at the specific topic and layers explored --> Persistent memory across the sessions --> Generate Value Chain diagrams, Interactive Mind-Maps --> If you discover a business in your interested domain but that not listed, setup an alert of your choice, for e.g., alert me when the company files DRHP. --> Export entire discussion into research note If you ever struggled with a topic or domain and didn't know where to start, this might help. Note: You must be CoWork user. The plugin is currently under review with Anthropic. If you prefer to wait and try only after it is approved, for security reasons, please be patient. Detailed note here:

Ameya

17,867 görüntüleme • 5 ay önce

Trade on Hyperliquid with Natural Language 🐉 We just made trading on Hyperliquid faster, simpler, and way more accessible. Cortex now supports Hyperliquid, fully integrated into our natural language interface. That means you can trade directly from chat using plain English: "Long 20x BTC at market, set TP at $110k, SL at $105k" "What is the funding rate for ETH-PERP?" "Close all my positions!" Behind the scenes, Cortex handles everything: parsing your intent, checking your balances, placing the trade on-chain, and tracking the position in real time. No forms, no buttons, no guesswork. What’s New Full Hyperliquid Terminal, Powered by Language - Access funding rates, open interest, volume, orderbook, and positions across all pairs, LIVE. Novel Trade Execution in Natural Language - Market, limit, stop, TP/SL executed in real-time via plain text commands. It’s the fastest way to trade Hyperliquid. - One click, signless position opening and closing. HYPE Onboarding Built In - New to Hyperliquid? Cortex walks you through buying $HYPE, bridging USDC to Arbitrum, and funding your account all in one UI. No need to leave the app or touch a separate bridge. Live Position Tracking - See your active positions update as you trade. Ask “how’s my BTC long doing?” and Cortex tells you: P&L, liquidation risk, everything. - Examine all of your on-chain balances, including HyperCore and HyperEVM. Hyperliquid is one of the fastest-growing protocols in DeFi ever with an incredibly rich, builder-friendly, and growing ecosystem, but onboarding has been a pain: bridging, swapping, funding, trading, etc. fragmented. We fixed that. Cortex brings it all into one place, one interface, and one language: yours. Try It Now! Jump into the Cortex app and type: “Open a 5x long on ETH” We'll handle the rest. Welcome to the new standard in crypto trading. Hyperliquid.

Trade on Hyperliquid with Natural Language 🐉 We just made trading on Hyperliquid faster, simpler, and way more accessible. Cortex now supports Hyperliquid, fully integrated into our natural language interface. That means you can trade directly from chat using plain English: "Long 20x BTC at market, set TP at $110k, SL at $105k" "What is the funding rate for ETH-PERP?" "Close all my positions!" Behind the scenes, Cortex handles everything: parsing your intent, checking your balances, placing the trade on-chain, and tracking the position in real time. No forms, no buttons, no guesswork. What’s New Full Hyperliquid Terminal, Powered by Language - Access funding rates, open interest, volume, orderbook, and positions across all pairs, LIVE. Novel Trade Execution in Natural Language - Market, limit, stop, TP/SL executed in real-time via plain text commands. It’s the fastest way to trade Hyperliquid. - One click, signless position opening and closing. HYPE Onboarding Built In - New to Hyperliquid? Cortex walks you through buying $HYPE, bridging USDC to Arbitrum, and funding your account all in one UI. No need to leave the app or touch a separate bridge. Live Position Tracking - See your active positions update as you trade. Ask “how’s my BTC long doing?” and Cortex tells you: P&L, liquidation risk, everything. - Examine all of your on-chain balances, including HyperCore and HyperEVM. Hyperliquid is one of the fastest-growing protocols in DeFi ever with an incredibly rich, builder-friendly, and growing ecosystem, but onboarding has been a pain: bridging, swapping, funding, trading, etc. fragmented. We fixed that. Cortex brings it all into one place, one interface, and one language: yours. Try It Now! Jump into the Cortex app and type: “Open a 5x long on ETH” We'll handle the rest. Welcome to the new standard in crypto trading. Hyperliquid.

cortexprotocol.hl

23,782 görüntüleme • 1 yıl önce

Etherealize CEO Vivek Raman on how ETH can evolve as money for Wall Street “In the world of tokenized assets, you need an asset that’s money-like and can be used globally that doesn’t have the counterparty risk or an off-chain anchor. That’s what ETH is.” “The more tokenization happens, the more you need a trading pair between those tokenized assets. ETH first found product/market fit in the DeFi ecosystem. Every crypto asset is paired against ETH. Then you saw digital art, which is also paired against ETH. ETH is the most common trading pair and the most common collateral.” “You’ve seen the initial versions of its utility, but that’s just the crypto-native players. Imagine when the Wall Street players start to realize that too… This is how you can start to see the institutional money thesis play out, but it’s early.” ETH is one leg of most high-volume pools on Uniswap V3. On Aave (the largest DeFi lending protocol) ETH alone accounts for ~40% of all loans, according to CoinDesk. As Vivek puts it: “Blockchains are understood. Tokenization and stablecoins are understood. ETH the asset is next.” Watch the full interview with Vivek Raman 👇

Etherealize CEO Vivek Raman on how ETH can evolve as money for Wall Street “In the world of tokenized assets, you need an asset that’s money-like and can be used globally that doesn’t have the counterparty risk or an off-chain anchor. That’s what ETH is.” “The more tokenization happens, the more you need a trading pair between those tokenized assets. ETH first found product/market fit in the DeFi ecosystem. Every crypto asset is paired against ETH. Then you saw digital art, which is also paired against ETH. ETH is the most common trading pair and the most common collateral.” “You’ve seen the initial versions of its utility, but that’s just the crypto-native players. Imagine when the Wall Street players start to realize that too… This is how you can start to see the institutional money thesis play out, but it’s early.” ETH is one leg of most high-volume pools on Uniswap V3. On Aave (the largest DeFi lending protocol) ETH alone accounts for ~40% of all loans, according to CoinDesk. As Vivek puts it: “Blockchains are understood. Tokenization and stablecoins are understood. ETH the asset is next.” Watch the full interview with Vivek Raman 👇

Etherealize

13,458 görüntüleme • 3 ay önce

We’ve been in private beta with a human + AI workspace similar to Buzz, with a few really important differences: (We previously built a company backed by Slack that sold to Atlassian, so we've been thinking about this problem for a while) - Agents reply by default unless you specifically flip to “team chat.” This feels 10x better than having to @ mention the agent every time. - It’s all in the cloud, with shared compute spread across a set of VMs. Way smoother onboarding, and yes, you can still connect your claude/codex subscriptions - You don’t create an army of agents. Number of agents is a vanity metric. Instead, you create shared computers, each one is a “space” where you define shared memory, connectors, skills, automations, etc. The Type agent pulls the appropriate context from the space where you have the conversation. - You can connect it to Slack to make it easy for your team to adopt and slowly move over rather than having to rip out Slack day 1. We’re working with a couple dozen forward thinking companies, who have told us “the whole team loves type”, “it’s the most used app on my computer”, and “type gives you superpowers” Here's a demo of how it works. Reply if you want to test it out!

We’ve been in private beta with a human + AI workspace similar to Buzz, with a few really important differences: (We previously built a company backed by Slack that sold to Atlassian, so we've been thinking about this problem for a while) - Agents reply by default unless you specifically flip to “team chat.” This feels 10x better than having to @ mention the agent every time. - It’s all in the cloud, with shared compute spread across a set of VMs. Way smoother onboarding, and yes, you can still connect your claude/codex subscriptions - You don’t create an army of agents. Number of agents is a vanity metric. Instead, you create shared computers, each one is a “space” where you define shared memory, connectors, skills, automations, etc. The Type agent pulls the appropriate context from the space where you have the conversation. - You can connect it to Slack to make it easy for your team to adopt and slowly move over rather than having to rip out Slack day 1. We’re working with a couple dozen forward thinking companies, who have told us “the whole team loves type”, “it’s the most used app on my computer”, and “type gives you superpowers” Here's a demo of how it works. Reply if you want to test it out!

Fletcher Richman

371,755 görüntüleme • 1 ay önce

Why BlackRock CEO Larry Fink calls Ethereum "The Toll Road to Tokenization" "What he meant by that is assets will be tokenized, people will transact, and then each of those transactions will pay a fee to the Ethereum network. Part of that fee will be burned and decrease the supply of ETH. The other part will be paid to stakers. So it's kind of like a toll road for anyone who wants to transact with tokenized assets." "The first example of product/market fit with tokenization was stablecoins. There are a lot of reasons why a tokenized dollar is better than one on fiat rails. But the big things are accessibility -- it gives people around the world who don't have access to the US financial system access to dollars -- and reducing the friction of settling those transactions. If you've had to send a wire or ACH transfer, it takes days. Stablecoins settle instantly, so it's way more efficient. You've even heard Jamie Dimon come around to the advantages of stablecoins." "Stablecoins are really just tokenized dollars, and then that's going to happen with almost every asset class. Stocks will be next. People are talking a bout tokenizing art and real estate. The theme is 'the tokenization of everything.' And as all of these assets move on chain because more people can access them and the friction of transacting with them is reduced, trillions of dollars of assets will move on-chain... and Ethereum will be the global settlement layer for all of those transactions, and all of those transactions will generate a fee to ETH holders, which is why your ETH will compound."

Why BlackRock CEO Larry Fink calls Ethereum "The Toll Road to Tokenization" "What he meant by that is assets will be tokenized, people will transact, and then each of those transactions will pay a fee to the Ethereum network. Part of that fee will be burned and decrease the supply of ETH. The other part will be paid to stakers. So it's kind of like a toll road for anyone who wants to transact with tokenized assets." "The first example of product/market fit with tokenization was stablecoins. There are a lot of reasons why a tokenized dollar is better than one on fiat rails. But the big things are accessibility -- it gives people around the world who don't have access to the US financial system access to dollars -- and reducing the friction of settling those transactions. If you've had to send a wire or ACH transfer, it takes days. Stablecoins settle instantly, so it's way more efficient. You've even heard Jamie Dimon come around to the advantages of stablecoins." "Stablecoins are really just tokenized dollars, and then that's going to happen with almost every asset class. Stocks will be next. People are talking a bout tokenizing art and real estate. The theme is 'the tokenization of everything.' And as all of these assets move on chain because more people can access them and the friction of transacting with them is reduced, trillions of dollars of assets will move on-chain... and Ethereum will be the global settlement layer for all of those transactions, and all of those transactions will generate a fee to ETH holders, which is why your ETH will compound."

Etherealize

10,793 görüntüleme • 4 ay önce

How to extract data from papers for literature review in seconds? 1. Go to 2. Upload 2-3 papers you already know are relevant 3. Start with clicking on one paper 4. ResearchCollab do the following for the paper ✦ Provide a short overview of the paper ✦ Identify weaknesses in the paper ✦ Share papers that contrast the existing paper ✦ Extract meta data about the paper ✦ Evaluate each part (e.g., methodology) of the paper ✦ Provide AI chat to extract any other data 5. Now click on the Related Papers button at top right 6. You will get papers related to the seed paper 7. You can click on each paper and read its abstract 8. If you find it relevant, just add it to your list of papers 9. Do the same for the other 2 seed papers 10. This way you will collect all relevant papers 11. And extract data from those papers After this, analyse the data and report the findings. Try ResearchCollab today:

How to extract data from papers for literature review in seconds? 1. Go to 2. Upload 2-3 papers you already know are relevant 3. Start with clicking on one paper 4. ResearchCollab do the following for the paper ✦ Provide a short overview of the paper ✦ Identify weaknesses in the paper ✦ Share papers that contrast the existing paper ✦ Extract meta data about the paper ✦ Evaluate each part (e.g., methodology) of the paper ✦ Provide AI chat to extract any other data 5. Now click on the Related Papers button at top right 6. You will get papers related to the seed paper 7. You can click on each paper and read its abstract 8. If you find it relevant, just add it to your list of papers 9. Do the same for the other 2 seed papers 10. This way you will collect all relevant papers 11. And extract data from those papers After this, analyse the data and report the findings. Try ResearchCollab today:

Faheem Ullah

26,494 görüntüleme • 6 ay önce

Two years ago, we started with a simple question. What will it take to simplify the integration of blockchain primitives with global financial rails? Today we’re launching LI.FI Intents to help make that possible for every business in the world. A product we know this industry needs, a product we’ve built to solve some of the biggest challenges global fintechs face as they move into digital assets, a product we believe companies don’t need tomorrow, or even today, they needed it yesterday. How do we enable stablecoin payments? How do we unlock access to real-world assets for our users? How do we ensure we only touch compliant liquidity across blockchains? These are some of the biggest questions leadership teams across fintech are asking themselves right now. They’re the same questions we asked ourselves. And we knew they had to be solved. LI.FI Intents is our answer. Every touchpoint of the stack is modular. The entire product is fully customizable to fit ANY business need. From the order type, to who the market maker can be, to how the transaction will be settled on blockchains, to which service providers your business trusts - everything can be shaped around how you want your product to work. As a builder myself, this is the kind of flexibility and abstraction I’ve been looking for in this industry ever since we started LI.FI 5 years ago. And I’m incredibly proud that the LI.FI team is delivering it. LI.FI Intents is built to make crypto products easier to launch for businesses that want to move onchain, but have been slowed down by the complexity of it all. The enterprise stack for onchain finance. For all intents and purposes.

Two years ago, we started with a simple question. What will it take to simplify the integration of blockchain primitives with global financial rails? Today we’re launching LI.FI Intents to help make that possible for every business in the world. A product we know this industry needs, a product we’ve built to solve some of the biggest challenges global fintechs face as they move into digital assets, a product we believe companies don’t need tomorrow, or even today, they needed it yesterday. How do we enable stablecoin payments? How do we unlock access to real-world assets for our users? How do we ensure we only touch compliant liquidity across blockchains? These are some of the biggest questions leadership teams across fintech are asking themselves right now. They’re the same questions we asked ourselves. And we knew they had to be solved. LI.FI Intents is our answer. Every touchpoint of the stack is modular. The entire product is fully customizable to fit ANY business need. From the order type, to who the market maker can be, to how the transaction will be settled on blockchains, to which service providers your business trusts - everything can be shaped around how you want your product to work. As a builder myself, this is the kind of flexibility and abstraction I’ve been looking for in this industry ever since we started LI.FI 5 years ago. And I’m incredibly proud that the LI.FI team is delivering it. LI.FI Intents is built to make crypto products easier to launch for businesses that want to move onchain, but have been slowed down by the complexity of it all. The enterprise stack for onchain finance. For all intents and purposes.

Philipp Zentner | LI.FI

16,632 görüntüleme • 3 ay önce

Stripe CEO Patrick Collison shares the tactics he used for finding product/market fit “We tried very hard to understand in granular detail what exactly it was that people were doing, where they were tripping up and so on.” Patrick gives some examples of specific tactics: • A public chat room to provide support to people integrating Stripe • For the first 10 users of Stripe, every API request sent an email to the founders so they could better understand how users were using their product and see if users were doing anything weird • All errors generated a high-priority email to the founders. This created a pleasant user experience where 15 minutes after hitting an error, Patrick could reach out to them and let them know the issue was fixed “These are all kind of examples of a general pattern of trying to be hyper-attentive to all the micro details of what people were doing in the product and iterating rapidly in response to it. Generally speaking, I think pre-product/market fit metrics are actually relatively unhelpful because probably not that many people are using your product. If it’s 20 users, you can in some sense afford to just look at everything they’re doing to understand what’s working and what isn’t.” Another example of this Patrick gives is embedding a text input on each of their web pages with placeholder text prompting users to give them useful feedback(e.g. “The worst thing about Stripe is…”, “The worst thing about this page is…”, or “I really hate the way Stripe does…”). As Patrick explains: “At that stage, you have to be kind of masochistic. We’d always be waking up to all these emails telling us all the terrible things about Stripe. But that was a helpful to-do list for the day ahead. Video source: Y Combinator (2018)

Stripe CEO Patrick Collison shares the tactics he used for finding product/market fit “We tried very hard to understand in granular detail what exactly it was that people were doing, where they were tripping up and so on.” Patrick gives some examples of specific tactics: • A public chat room to provide support to people integrating Stripe • For the first 10 users of Stripe, every API request sent an email to the founders so they could better understand how users were using their product and see if users were doing anything weird • All errors generated a high-priority email to the founders. This created a pleasant user experience where 15 minutes after hitting an error, Patrick could reach out to them and let them know the issue was fixed “These are all kind of examples of a general pattern of trying to be hyper-attentive to all the micro details of what people were doing in the product and iterating rapidly in response to it. Generally speaking, I think pre-product/market fit metrics are actually relatively unhelpful because probably not that many people are using your product. If it’s 20 users, you can in some sense afford to just look at everything they’re doing to understand what’s working and what isn’t.” Another example of this Patrick gives is embedding a text input on each of their web pages with placeholder text prompting users to give them useful feedback(e.g. “The worst thing about Stripe is…”, “The worst thing about this page is…”, or “I really hate the way Stripe does…”). As Patrick explains: “At that stage, you have to be kind of masochistic. We’d always be waking up to all these emails telling us all the terrible things about Stripe. But that was a helpful to-do list for the day ahead. Video source: Y Combinator (2018)

Startup Archive

45,833 görüntüleme • 7 ay önce

Stripe CEO Patrick Collison shares the tactics he used for finding product/market fit “We tried very hard to understand in granular detail what exactly it was that people were doing, where they were tripping up and so on.” Patrick gives some examples of specific tactics: - A public chat room to provide support to people integrating Stripe - For the first 10 users of Stripe, every API request sent an email to the founders so they could better understand how users were using their product and see if users were doing anything weird - All errors generated a high-priority email to the founders. This created a pleasant user experience where 15 minutes after hitting an error, Patrick could reach out to them and let them know the issue was fixed “These are all kind of examples of a general pattern of trying to be hyper-attentive to all the micro details of what people were doing in the product and iterating rapidly in response to it. Generally speaking, I think pre-product/market fit metrics are actually relatively unhelpful because probably not that many people are using your product. If it’s 20 users, you can in some sense afford to just look at everything they’re doing to understand what’s working and what isn’t.” Another example of this Patrick gives is embedding a text input on each of their web pages with placeholder text prompting users to give them useful feedback(e.g. “The worst thing about Stripe is…”, “The worst thing about this page is…”, or “I really hate the way Stripe does…”). As Patrick explains: “At that stage, you have to be kind of masochistic. We’d always be waking up to all these emails telling us all the terrible things about Stripe. But that was a helpful to-do list for the day ahead." Source: Y Combinator (Oct 2018)

Stripe CEO Patrick Collison shares the tactics he used for finding product/market fit “We tried very hard to understand in granular detail what exactly it was that people were doing, where they were tripping up and so on.” Patrick gives some examples of specific tactics: - A public chat room to provide support to people integrating Stripe - For the first 10 users of Stripe, every API request sent an email to the founders so they could better understand how users were using their product and see if users were doing anything weird - All errors generated a high-priority email to the founders. This created a pleasant user experience where 15 minutes after hitting an error, Patrick could reach out to them and let them know the issue was fixed “These are all kind of examples of a general pattern of trying to be hyper-attentive to all the micro details of what people were doing in the product and iterating rapidly in response to it. Generally speaking, I think pre-product/market fit metrics are actually relatively unhelpful because probably not that many people are using your product. If it’s 20 users, you can in some sense afford to just look at everything they’re doing to understand what’s working and what isn’t.” Another example of this Patrick gives is embedding a text input on each of their web pages with placeholder text prompting users to give them useful feedback(e.g. “The worst thing about Stripe is…”, “The worst thing about this page is…”, or “I really hate the way Stripe does…”). As Patrick explains: “At that stage, you have to be kind of masochistic. We’d always be waking up to all these emails telling us all the terrible things about Stripe. But that was a helpful to-do list for the day ahead." Source: Y Combinator (Oct 2018)

Startup Archive

32,192 görüntüleme • 1 ay önce

your OpenClaw🦞 has NO IDEA what you do all day... what if we give it eyes and make it observe you all day? introducing synapse, a daemon that runs in the background, watches your screen activity through , and uses Claude to detect the workflows you keep repeating! then, it does two things: 1) turns those patterns into ready-to-use automations (e.g. claude code commands and skills you can run immediately) 2) feeds that context directly into your openclaw agent so it can act on what it sees your agent goes from "what do you want me to do" (which seems to be the default mode for almost all ai agents) to "i noticed you've been doing this repeatedly, want me to handle it for you?" let me give you a real example. > UEFA Champions League is coming up. > you want to place a prediction on juventus vs galatasaray > so you start doing what you always do: open a tab for injury reports, another for head-to-head history, another for current form tables, another for the odds across prediction markets > by the time you actually place anything you've spent 45 minutes on research you've done a hundred times before. synapse sees all of that! it automatically detects the pattern: "user repeatedly researches match injuries, form, and h2h stats before placing predictions on polymarket." it doesn't just log it. it proposes a prediction market research skill that pulls injury reports, recent form, historical matchups, and odds from multiple sources, and then gives you a structured breakdown with a confidence score. next champions league match day, you just type one command to get all of this done. beauty! all this happens while everything is locally stored on your machine. get started below ↓

your OpenClaw🦞 has NO IDEA what you do all day... what if we give it eyes and make it observe you all day? introducing synapse, a daemon that runs in the background, watches your screen activity through , and uses Claude to detect the workflows you keep repeating! then, it does two things: 1) turns those patterns into ready-to-use automations (e.g. claude code commands and skills you can run immediately) 2) feeds that context directly into your openclaw agent so it can act on what it sees your agent goes from "what do you want me to do" (which seems to be the default mode for almost all ai agents) to "i noticed you've been doing this repeatedly, want me to handle it for you?" let me give you a real example. > UEFA Champions League is coming up. > you want to place a prediction on juventus vs galatasaray > so you start doing what you always do: open a tab for injury reports, another for head-to-head history, another for current form tables, another for the odds across prediction markets > by the time you actually place anything you've spent 45 minutes on research you've done a hundred times before. synapse sees all of that! it automatically detects the pattern: "user repeatedly researches match injuries, form, and h2h stats before placing predictions on polymarket." it doesn't just log it. it proposes a prediction market research skill that pulls injury reports, recent form, historical matchups, and odds from multiple sources, and then gives you a structured breakdown with a confidence score. next champions league match day, you just type one command to get all of this done. beauty! all this happens while everything is locally stored on your machine. get started below ↓

altan tutar

10,511 görüntüleme • 6 ay önce

Having recently closed their $20M Series A, Ostium CoFounder and CEO kaledora joins us to share more on their grand vision in a new The Edge Podcast. What always resonated with me personally is Ostium started with serving us as native onchain investors, who have no interest in off-ramping but would like to get exposure to other non-crypto asset classes onchain 😇 "One problem we saw was in crypto, which was there was no way to use your onchain capital to go and trade things that were not crypto. It felt like everything that had been built to date was kind of prelude to what would ultimately be a much bigger market, in the sense that like all of the new market and incentive design was geared towards allowing people to transact trading crypto itself, which is you know great, but the ultimate end goal, like if this technology is gonna be extremely valuable, the ultimate end goal is enabling people to trade the like trillions of dollars worth of existing assets that are out there that should move on to blockchain rails. So we thought, one, that's gonna be the biggest market, that's the single biggest opportunity in crypto is enabling these rails to get people to trade stuff that's not crypto assets themselves. And secondly, if you already have your money onchain and you don't want to off-ramp, which I mean, are very good reasons why you wouldn't want to off ramp if you made your money in crypto, or you have a significant amount of your money onchain, you're completely constrained to just being able to invest in or trade cryptoassets. And we had a pretty strong conviction that there would be this sort of like tailwind or like rising set of people who had a majority or a large portion of their wealth onchain and would want to get exposure to other assets. We initially were very focused on commodities specifically. We had this strong conviction that commodity supercycle that's wound up being very true, but we extended beyond, I think, an unnecessary focus on a single asset class to FX, indices, single name stocks, and all of these very liquid global markets that ultimately there was no really easy way to access onchain. So that was sort the first problem. We thought in the next couple of years, this market's going to exist and we can build to capture that market. The second, and I think more important and ultimately far larger TAM, that we saw and problem that we wanted to solve, and this is one that we've been somewhat quiet on to date and have recently, I think, unveiled this larger plan as part of our recent fundraise, like Series A announcement, is a problem that we saw globally in what's called the CFD market. And for the uninitiated, CFDs are called, they're, contracts for difference or contract for differences. People put the 's' in different places. And they are basically, they are the little known fact among crypto people, but they are the dominant retail derivative in the world." For the full episode with kaledora dropping tomorrow, subscribe below. ► Newsletter: ► Spotify: ► Apple: ► Youtube: ► Pods: ► Linktree:

Having recently closed their $20M Series A, Ostium CoFounder and CEO kaledora joins us to share more on their grand vision in a new The Edge Podcast. What always resonated with me personally is Ostium started with serving us as native onchain investors, who have no interest in off-ramping but would like to get exposure to other non-crypto asset classes onchain 😇 "One problem we saw was in crypto, which was there was no way to use your onchain capital to go and trade things that were not crypto. It felt like everything that had been built to date was kind of prelude to what would ultimately be a much bigger market, in the sense that like all of the new market and incentive design was geared towards allowing people to transact trading crypto itself, which is you know great, but the ultimate end goal, like if this technology is gonna be extremely valuable, the ultimate end goal is enabling people to trade the like trillions of dollars worth of existing assets that are out there that should move on to blockchain rails. So we thought, one, that's gonna be the biggest market, that's the single biggest opportunity in crypto is enabling these rails to get people to trade stuff that's not crypto assets themselves. And secondly, if you already have your money onchain and you don't want to off-ramp, which I mean, are very good reasons why you wouldn't want to off ramp if you made your money in crypto, or you have a significant amount of your money onchain, you're completely constrained to just being able to invest in or trade cryptoassets. And we had a pretty strong conviction that there would be this sort of like tailwind or like rising set of people who had a majority or a large portion of their wealth onchain and would want to get exposure to other assets. We initially were very focused on commodities specifically. We had this strong conviction that commodity supercycle that's wound up being very true, but we extended beyond, I think, an unnecessary focus on a single asset class to FX, indices, single name stocks, and all of these very liquid global markets that ultimately there was no really easy way to access onchain. So that was sort the first problem. We thought in the next couple of years, this market's going to exist and we can build to capture that market. The second, and I think more important and ultimately far larger TAM, that we saw and problem that we wanted to solve, and this is one that we've been somewhat quiet on to date and have recently, I think, unveiled this larger plan as part of our recent fundraise, like Series A announcement, is a problem that we saw globally in what's called the CFD market. And for the uninitiated, CFDs are called, they're, contracts for difference or contract for differences. People put the 's' in different places. And they are basically, they are the little known fact among crypto people, but they are the dominant retail derivative in the world." For the full episode with kaledora dropping tomorrow, subscribe below. ► Newsletter: ► Spotify: ► Apple: ► Youtube: ► Pods: ► Linktree:

DeFi Dad ⟠ defidad.eth

32,429 görüntüleme • 8 ay önce

intern here. Today I integrated AI Crypto Alerts inside AI Hub V2 into my trading workflow. The results were pretty surprising. On the main dashboard, I could scroll and instantly see what materially moved across the market, classified as Bullish, Bearish or Neutral, with the catalyst attached. 🔹Intraday dumps after extreme rallies. 🔹Sharp pullbacks after outsized surges. 🔹Tokens extending 20%+ on volume expansion. 🔹Narrative-driven pumps across sectors. Instead of scanning X, Telegram, Discord and news feeds, I had one structured intelligence stream showing price movement + reason + sentiment in seconds. Then I built a custom watchlist with the assets I actually track: BTC, ETH, SOL and others. That’s where it became tactical. I could see: • Bullishness scores (0–100) • 24h change and market cap positioning • The latest catalyst summarized clearly • Overall sentiment at a glance For example: BTC pushing on ETF inflows and safe-haven positioning. ETH sentiment leaning bearish amid founder selling and macro uncertainty. SOL showing a strong bullishness score despite conflicting narrative pressure. Seeing that context next to price changed how I interpreted moves. If you actively trade, the workflow difference is noticeable → Disclaimer: ChainGPT does not endorse any projects mentioned in this post. Always do your own research before making trading decisions.

intern here. Today I integrated AI Crypto Alerts inside AI Hub V2 into my trading workflow. The results were pretty surprising. On the main dashboard, I could scroll and instantly see what materially moved across the market, classified as Bullish, Bearish or Neutral, with the catalyst attached. 🔹Intraday dumps after extreme rallies. 🔹Sharp pullbacks after outsized surges. 🔹Tokens extending 20%+ on volume expansion. 🔹Narrative-driven pumps across sectors. Instead of scanning X, Telegram, Discord and news feeds, I had one structured intelligence stream showing price movement + reason + sentiment in seconds. Then I built a custom watchlist with the assets I actually track: BTC, ETH, SOL and others. That’s where it became tactical. I could see: • Bullishness scores (0–100) • 24h change and market cap positioning • The latest catalyst summarized clearly • Overall sentiment at a glance For example: BTC pushing on ETF inflows and safe-haven positioning. ETH sentiment leaning bearish amid founder selling and macro uncertainty. SOL showing a strong bullishness score despite conflicting narrative pressure. Seeing that context next to price changed how I interpreted moves. If you actively trade, the workflow difference is noticeable → Disclaimer: ChainGPT does not endorse any projects mentioned in this post. Always do your own research before making trading decisions.

ChainGPT

27,328 görüntüleme • 6 ay önce

Most social media tools suck. (I've tried them all over 7 years of doing this). They claim to help you go viral. They claim that you'll grow fast on social media. They claim that they'll make it easy on you, so you can just grow your following without having any form of skill or understanding. And now they're trying to go fully agentic, which only works if you know what you're doing (we'll talk about that later, we built the MCP for social media). Plus, when everyone has an advantage, it is no longer an advantage. I promise you, I love AI, but having it write everything for you, especially as a beginner, is a death sentence. You may get lucky and have one, two, or three posts do well. You may gain a lot of followers (because that's what they promise to get you to buy), but then what? Can you replicate it? Does your audience actually care about who you are? Or are you just a one hit wonder like every other creator who tries to get-followers-quick? The creators you love... the ones who put out ideas that actually change your life, understand that the creative process (capturing, resurfacing, and synthesizing ideas) is your competitive edge when everyone is racing to put out as much slop as possible. So we built an actual solution. For the people who know deep down that the toys and gimmicks being marketed to you aren't a long term strategy. It's called Eden. The second brain for creators. It has every scheduling, analytics, auto-dm, and outlier research (for all platforms) features that everyone else has - built by someone who knows what's useful and what's not. But it also has the ideation process built in, which cannot be skipped. Here's what it looks like, and how to write a post people actually care about: 1) Save inspiration from anywhere (and read + highlight) Capturing ideas, posts, links, and highlights inside of an agent or AI chat sounds like hell. Not everything needs to, or should be, jammed into a chat UI. In Eden you can: - Paste any social post link (we transcribe any post) - Integrate with Kindle, Snipd, or Readwise - Pull in X or Substack bookmarks - Jot down quick ideas or write inside docs - Open any YouTube transcript or article in Reader mode and highlight it We auto-categorize and tag everything for you, so our Deep Synthesis agent can find novel connections and relations across your library - making it easy to surface idaes you thought you lost. 2) Research what's working, study top creators Outlier tools are the bread and butter for every creator. When you do research for a YouTube video, you look for high performing titles and thumbnails, and you study what worked for others. But what about every other platform? We have a social corpus of 3m+ posts across all platforms and growing. You can scroll the Discover page for inspiration, search any creator and filter by top posts, or use Deep Social in the AI chat to search across all of them. This is what gives the MCP access to trending topics without scraping millions of posts yourself. 3) Create with everything in one place, build swipe files I don't think I will ever give up writing. I love sitting down in the morning, opening up a page, researching good ideas, and turning it into something worth sharing. In Eden, you work on Boards. You can paste social posts and build shareable swipe files, or you can add your outlines and drafts while having your library, chat, or ideas open right next to it. 4) Chat with anything to get the information you need Claude and ChatGPT are essential, but they are blind. They can't read social links. They can't see what's trending. When you're writing scripts, articles, posts, or drafts - you need to be able to pull information from the sources you research. In today's world, that usually means social posts, YouTube videos, and articles. 5) Draft, schedule, and publish to all platforms X, Instagram, LinkedIn, Substack, Threads, TikTok, and YouTube shorts. And yes, we are one of the first to support article scheduling to X and Substack. You don't have to copy paste to every platform anymore. You simply write, schedule, and let it go out to all platforms. 6) Or do it all from the MCP I don't believe that the future of content is fully agentic. But, that doesn't mean that AI can't help with researching top posts, ideation, and scheduling content. You do the writing and thinking, let AI handle the busy work and resurfacing of ideas you need to find. With Eden, you don't need to give up your Claude, Hermes, or other agentic workflows. You simply connect the MCP and now you can build your own agentic content system, if that's your cup of tea. Eden is the Social Media MCP. You don't have to pay for scrapers anymore. You can finally access trends, any social link, and any account. You can access your analytics, build content dashboards with our infrastructure (that you don't need to maintain as a full time job), and post. We've actually built 4 skills for deep topic research, personal brand strategy, building a content command center, and more - we'll link those below. All in all: If you're a creator, Eden is your base. It's where you write, research, and publish every single day. Try it free below:

DAN KOE

117,755 görüntüleme • 14 gün önce

The time has come. Introducing Parallel an all-in-one copy trading platform for DeFi products that combines four years of our experience building on Solana and AI to create what we hope is a one-of-a-kind product for the Solana ecosystem. It’s our pleasure to announce that we’re now entering private beta for our first module built around Meteora DLMMs. You’ll be able to copy trade DLMM positions with fast execution and a ton of automated functions on top. Some key features: * Stop loss/take profit * Real-time PnL shown on the UI for your LP * Automatically claim fees when they reach X SOL or on a timer * Automatically swap all claimed fees into SOL * Become a power user and earn a % of fees from everyone copying your wallet * Generous referral comp And much more! Data feeds & AI: We understand what it takes for a copy trading platform to thrive, so we’ve been collecting real-time data from several contracts for both Meteora and the upcoming modules we’re releasing. As you can imagine, we’re handling tons of data, so with the help of AI, we’re not only analyzing potential up-and-coming wallets but also the top wallets on-chain that people don’t want you to know about. This applies to all aspects of DeFi—not just token trading. To access these data feeds, you’ll need $GP or our native Taiyo NFTs. More information will roll out as these feeds start to go live. Private Beta Access: We’re now collecting a list of the first 100 private beta testers, including LP army members, the Taiyo community, BOOGLES, and more. If you’re interested in getting early access, you can now submit your email at 👇 We recommend always creating a separate email for crypto purposes to keep your personal and online profiles separate. Excited to cook with you on our first non Launchpad related platform since Solport itself back in 2021. 🤝

The time has come. Introducing Parallel an all-in-one copy trading platform for DeFi products that combines four years of our experience building on Solana and AI to create what we hope is a one-of-a-kind product for the Solana ecosystem. It’s our pleasure to announce that we’re now entering private beta for our first module built around Meteora DLMMs. You’ll be able to copy trade DLMM positions with fast execution and a ton of automated functions on top. Some key features: * Stop loss/take profit * Real-time PnL shown on the UI for your LP * Automatically claim fees when they reach X SOL or on a timer * Automatically swap all claimed fees into SOL * Become a power user and earn a % of fees from everyone copying your wallet * Generous referral comp And much more! Data feeds & AI: We understand what it takes for a copy trading platform to thrive, so we’ve been collecting real-time data from several contracts for both Meteora and the upcoming modules we’re releasing. As you can imagine, we’re handling tons of data, so with the help of AI, we’re not only analyzing potential up-and-coming wallets but also the top wallets on-chain that people don’t want you to know about. This applies to all aspects of DeFi—not just token trading. To access these data feeds, you’ll need $GP or our native Taiyo NFTs. More information will roll out as these feeds start to go live. Private Beta Access: We’re now collecting a list of the first 100 private beta testers, including LP army members, the Taiyo community, BOOGLES, and more. If you’re interested in getting early access, you can now submit your email at 👇 We recommend always creating a separate email for crypto purposes to keep your personal and online profiles separate. Excited to cook with you on our first non Launchpad related platform since Solport itself back in 2021. 🤝

Tom

105,762 görüntüleme • 1 yıl önce

how to build the fastest Polymarket latency bot +$100k/month PnL if you hit 1,000+ trades/day cleanly 0x8dxd is just a latency bot that farms the 200–500ms gap between Binance moving and Polymarket waking up. the part that matters isn't some alpha model, it's reading spot first and hitting the book before odds adjust. where the $100k+/month comes from it's not one massive bet. it's clipping tiny edges thousands of times. 0x8dxd started with $313 and ended month one around $438k, now sits north of $550k all‑time PnL with ~5.6k–7k trades at 96–98% win rate on BTC/ETH/SOL 15‑minute windows. if you're consistently pulling 1–2% per cycle over 1,000+ trades/month with real size, six figures is just arithmetic. first, the edge: spot (Binance/Coinbase) moves first, Polymarket's 15‑minute up/down windows lag by 200–500ms before odds fully reprice. latency bots live in that window: spot already moved, book still thinks it's 50/50, bot fixes the misprice and takes the edge. what you actually need: - Python + official py‑clob‑client to prove the idea, Rust CLOB client if you want to compete with 0x8dxd‑level bots. - WebSocket feeds for BTC/ETH/SOL from Binance/Coinbase (REST polling is too slow). Dedicated Polygon RPC node so your orders don't die in public rate limits. - VPS physically close to Polymarket's infra (ping is literally part of your edge). where people mess up: they try "HFT" from a laptop with Python + public RPC and wonder why their 300ms reaction gets farmed by a 30ms Rust engine. the bot loop (in plain English) pull real‑time spot for BTC/ETH/SOL via WebSocket, track short‑term % moves over a few seconds. for each 15‑minute crypto market on Polymarket: check if spot moved beyond your threshold (e.g. ±2%) while Polymarket odds barely changed. if BTC rips and the "down" contract is still priced like a coinflip, load NO at stale odds. if BTC nukes and "up" is still fat, fade that with NO or take YES on "down" depending on the market structure. log market, entry odds, exit odds, realized edge. that's it. no AI, no news scraping, just enforcing what spot already told you. where to get real references: Finbold/MEXC breakdowns: exactly how a bot took $313 to $438k on Polymarket using BTC 15‑minute windows and latency between spot and odds. BlakeNastri's X thread: dug through 0x8dxd's stats, ~5.6k trades and ~96%+ win rate, called it latency arbitrage not insider magic. two real‑world gotchas (that decide profit vs loss) edge decay: as more bots pile in, the 200–500ms lag shrinks and your edge turns into noise. research on Polymarket shows arbitrage bots already extracted tens of millions. self‑slippage: once you scale to real size, you start moving the book yourself - without proper sizing and staggering, you donate your edge back to the market. how to make it feel "pro" fast run only on high‑volume crypto windows: (BTC/ETH/SOL 15‑minute) where size actually fills and you can hit 1,000+ trades/month without breaking the market. start with tiny tickets ($20–50 per trade), prove the edge over thousands of logs with fees and slippage included, only then scale size not risk per trade. use official libs and known clients as your backbone, treat random "Polymarket bot" repos as hostile until you audit them - there are already GitHub bots caught stealing keys

how to build the fastest Polymarket latency bot +$100k/month PnL if you hit 1,000+ trades/day cleanly 0x8dxd is just a latency bot that farms the 200–500ms gap between Binance moving and Polymarket waking up. the part that matters isn't some alpha model, it's reading spot first and hitting the book before odds adjust. where the $100k+/month comes from it's not one massive bet. it's clipping tiny edges thousands of times. 0x8dxd started with $313 and ended month one around $438k, now sits north of $550k all‑time PnL with ~5.6k–7k trades at 96–98% win rate on BTC/ETH/SOL 15‑minute windows. if you're consistently pulling 1–2% per cycle over 1,000+ trades/month with real size, six figures is just arithmetic. first, the edge: spot (Binance/Coinbase) moves first, Polymarket's 15‑minute up/down windows lag by 200–500ms before odds fully reprice. latency bots live in that window: spot already moved, book still thinks it's 50/50, bot fixes the misprice and takes the edge. what you actually need: - Python + official py‑clob‑client to prove the idea, Rust CLOB client if you want to compete with 0x8dxd‑level bots. - WebSocket feeds for BTC/ETH/SOL from Binance/Coinbase (REST polling is too slow). Dedicated Polygon RPC node so your orders don't die in public rate limits. - VPS physically close to Polymarket's infra (ping is literally part of your edge). where people mess up: they try "HFT" from a laptop with Python + public RPC and wonder why their 300ms reaction gets farmed by a 30ms Rust engine. the bot loop (in plain English) pull real‑time spot for BTC/ETH/SOL via WebSocket, track short‑term % moves over a few seconds. for each 15‑minute crypto market on Polymarket: check if spot moved beyond your threshold (e.g. ±2%) while Polymarket odds barely changed. if BTC rips and the "down" contract is still priced like a coinflip, load NO at stale odds. if BTC nukes and "up" is still fat, fade that with NO or take YES on "down" depending on the market structure. log market, entry odds, exit odds, realized edge. that's it. no AI, no news scraping, just enforcing what spot already told you. where to get real references: Finbold/MEXC breakdowns: exactly how a bot took $313 to $438k on Polymarket using BTC 15‑minute windows and latency between spot and odds. BlakeNastri's X thread: dug through 0x8dxd's stats, ~5.6k trades and ~96%+ win rate, called it latency arbitrage not insider magic. two real‑world gotchas (that decide profit vs loss) edge decay: as more bots pile in, the 200–500ms lag shrinks and your edge turns into noise. research on Polymarket shows arbitrage bots already extracted tens of millions. self‑slippage: once you scale to real size, you start moving the book yourself - without proper sizing and staggering, you donate your edge back to the market. how to make it feel "pro" fast run only on high‑volume crypto windows: (BTC/ETH/SOL 15‑minute) where size actually fills and you can hit 1,000+ trades/month without breaking the market. start with tiny tickets ($20–50 per trade), prove the edge over thousands of logs with fees and slippage included, only then scale size not risk per trade. use official libs and known clients as your backbone, treat random "Polymarket bot" repos as hostile until you audit them - there are already GitHub bots caught stealing keys

0xCryptoGirl

25,534 görüntüleme • 7 ay önce