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Stress-tested Perplexity Perplexity Finance Computer on a real equity research workflow: Map a representative AI infrastructure supply chain across 70+ companies across multiple tiers with sourced financials, bottleneck analysis, and company classifications. The kind of deliverable that might take a junior analyst days to weeks. It produced ~2,000 lines... of structured research across 4 phased reports (~5,300 credits burned in ~75 minutes of work). Every data point labeled Actual, Guidance, or Estimate with inline source citations. Then it built an interactive dashboard to make the information digestible with 4 deep research .md files compiled. A cool feature is when it flagged when its own sources conflict. It caught 8 data conflicts and wrote explicit resolution notes. TSMC CoWoS capacity ranges explained as report timing differences. ASML's Q4 FCF anomaly identified as a billing artifact. Intel Foundry's loss methodology change across fiscal years. That is fundamentally different from silently picking a number and requiring an experienced SME to catch it. Other strong points: hyperscaler capex table aligned across 5 different fiscal year-ends. ORCL RPO correctly flagged as potential overstating near-term conversion (90%+ partner-funded). Power identified as the binding constraint with MSFT carrying $80B in power-constrained backlog. Some interesting analytical observations, not headline summaries. Where it fell short: never ran the numerical supply/demand gap calculation I specifically asked for (but it was a very large ask tbf). Spot checked multiple data points I know to be true but some ones that also looked off (to be expected, especially across varying degrees of company size and disclosure). Put ~30 companies into one Three Curves bucket (defeats the framework). Some debatable calls (but wouldn't expect AI to be good at this today). A few blog-tier sources where you'd want filings is always the shortcoming of a finance-focused search. Verdict: Arguably first-draft quality from a mid-tier sell-side initiation. 80% of the work done pretty well. The remaining 20% is exactly what you'd mark up before an experienced analyst or PM sees it. Not the finished product but a remarkable effort in a short amount of time. A research scaffold you can interrogate with actual domain expertise. This was a pretty genuinely cool result and I am intrigued to keep playing around with the tool. cc: Aravind Srinivas RYY Roshanshow more

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42,176 görüntüleme • 2 ay önce •via X (Twitter)

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I asked Perplexity Computer one question: “If Nvidia’s AI data center revenue doubles again, map exactly where every incremental dollar flows across the semiconductor supply chain with a chart” It generated this full supply chain chart and video in minutes. It shows exactly who benefits - memory, foundry, packaging, and more. This level of research used to take hours. Now it takes minutes.

I asked Perplexity Computer one question: “If Nvidia’s AI data center revenue doubles again, map exactly where every incremental dollar flows across the semiconductor supply chain with a chart” It generated this full supply chain chart and video in minutes. It shows exactly who benefits - memory, foundry, packaging, and more. This level of research used to take hours. Now it takes minutes.

Venu

101,207 görüntüleme • 3 ay önce

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

GREG ISENBERG

134,438 görüntüleme • 2 ay önce

Just tried Fellou's Deep Search feature and we’re genuinely impressed. We asked it to analyze competitor pricing across multiple sources and got back a comprehensive visual report in minutes. The fact that this level of research automation is free to start with is pretty remarkable. So many more Fellou features to explore!➡️ #Fellou #FellouAI

Just tried Fellou's Deep Search feature and we’re genuinely impressed. We asked it to analyze competitor pricing across multiple sources and got back a comprehensive visual report in minutes. The fact that this level of research automation is free to start with is pretty remarkable. So many more Fellou features to explore!➡️ #Fellou #FellouAI

AI Frontliner

99,498 görüntüleme • 9 ay önce

Marc Andreessen: Most successful companies started “product first” “There are products that become companies, and then there are companies that come up with a product. One of the interesting things over the years is that many of the most successful technology franchises were products first, way before they ever became companies.” In this talk, Marc gives a few examples: • His team at the University of Illinois worked on the research project that became Netscape for three years before it became a company • Bill Gates and Paul Allen were deep into PCs before there was a software business • Jobs and Wozniak built the first Apple computer as hobbyists • Mark Zuckerberg was running Facebook out of his dorm room before he ever thought of starting a company • Twitter was a side project at the failed podcasting app Odeo Marc believes that this “product becomes a company” template is successful because “it’s a demonstration that the product has to exist. The market needs the product so badly that somebody actually built it and deployed it and you can actually see evidence that people want it before there was an economic motivation to do so.” He contrasts this with the failure cases he often sees when entrepreneurs try to figure out the idea after starting a company. “It’s very easy in that process to fool yourself into believing that there’s a market because you want to find something and you have a very strong motivation to come up with an answer. It’s hard to go through that process for three months and then say, ‘you know what, we can’t come up with any good ideas.’” There are of course there exceptions. Marc gives Hewlett Packard as an example. But that’s more the exception than the rule. As Marc explains: “The moral of the story is it has to be a really good idea. That often will be an idea that is preexisting at the time you decide to start a company. And if it isn’t, be really careful because you’re walking on sharp rocks at that point with a high risk of falling off the cliff into the ocean.” Video source: Stanford eCorner (2010)

Marc Andreessen: Most successful companies started “product first” “There are products that become companies, and then there are companies that come up with a product. One of the interesting things over the years is that many of the most successful technology franchises were products first, way before they ever became companies.” In this talk, Marc gives a few examples: • His team at the University of Illinois worked on the research project that became Netscape for three years before it became a company • Bill Gates and Paul Allen were deep into PCs before there was a software business • Jobs and Wozniak built the first Apple computer as hobbyists • Mark Zuckerberg was running Facebook out of his dorm room before he ever thought of starting a company • Twitter was a side project at the failed podcasting app Odeo Marc believes that this “product becomes a company” template is successful because “it’s a demonstration that the product has to exist. The market needs the product so badly that somebody actually built it and deployed it and you can actually see evidence that people want it before there was an economic motivation to do so.” He contrasts this with the failure cases he often sees when entrepreneurs try to figure out the idea after starting a company. “It’s very easy in that process to fool yourself into believing that there’s a market because you want to find something and you have a very strong motivation to come up with an answer. It’s hard to go through that process for three months and then say, ‘you know what, we can’t come up with any good ideas.’” There are of course there exceptions. Marc gives Hewlett Packard as an example. But that’s more the exception than the rule. As Marc explains: “The moral of the story is it has to be a really good idea. That often will be an idea that is preexisting at the time you decide to start a company. And if it isn’t, be really careful because you’re walking on sharp rocks at that point with a high risk of falling off the cliff into the ocean.” Video source: Stanford eCorner (2010)

Startup Archive

35,823 görüntüleme • 10 ay önce

Scott on why conviction in an idea is dangerous as an investor but required as a founder: "From the investor seat, I don't think you want to be in love with an idea. That's a risky thing because you'll find a way to express that by investing and make some compromises on the team because you know this idea is just so good, its time has come. But if the team isn't there, 90% of the time it doesn't work. Then there's a whole thing can you swap out the team? But the whole Founders Fund thesis was always no, you need the founder to run the company. But on the company side, you have to be in love with the idea. It's not that rational to start a company. There's a lot easier, more comfortable ways to make money if that's the goal. So it's got to be about what the company is doing specifically. If you want to actually start a company, it better be something that you're really passionate about, or you think that the problem is really important."

Scott on why conviction in an idea is dangerous as an investor but required as a founder: "From the investor seat, I don't think you want to be in love with an idea. That's a risky thing because you'll find a way to express that by investing and make some compromises on the team because you know this idea is just so good, its time has come. But if the team isn't there, 90% of the time it doesn't work. Then there's a whole thing can you swap out the team? But the whole Founders Fund thesis was always no, you need the founder to run the company. But on the company side, you have to be in love with the idea. It's not that rational to start a company. There's a lot easier, more comfortable ways to make money if that's the goal. So it's got to be about what the company is doing specifically. If you want to actually start a company, it better be something that you're really passionate about, or you think that the problem is really important."

Patrick OShaughnessy

33,016 görüntüleme • 2 ay önce

IFPI has released an interview with its Deputy Director of Global Markets, Tom Bendall, explaining how their data is collected as of March 20, 2026, this confirms that JENNIE’s Ruby sales were not included in this year’s report due to the label not submitting the data. “(…) So if I give an example, if the reporting companies submitted revenues of 70 million, and they contributed 70% of consumption in a market for a reporting format, then we'd extrapolate up to get a total market size of 100 million. What this means is that ultimately we have accurate and consistent reporting across every single market globally.” In example: they collect data from companies/labels that actually submit it, then they use that submitted data to estimate the full market Key point: if something isn’t submitted at all, it can’t be counted in the first place. it took them long but they’ve finally addressed the matter. Source of everything in the post: IFPI

IFPI has released an interview with its Deputy Director of Global Markets, Tom Bendall, explaining how their data is collected as of March 20, 2026, this confirms that JENNIE’s Ruby sales were not included in this year’s report due to the label not submitting the data. “(…) So if I give an example, if the reporting companies submitted revenues of 70 million, and they contributed 70% of consumption in a market for a reporting format, then we'd extrapolate up to get a total market size of 100 million. What this means is that ultimately we have accurate and consistent reporting across every single market globally.” In example: they collect data from companies/labels that actually submit it, then they use that submitted data to estimate the full market Key point: if something isn’t submitted at all, it can’t be counted in the first place. it took them long but they’ve finally addressed the matter. Source of everything in the post: IFPI

Cia.

87,919 görüntüleme • 3 ay önce

One distinct memory I have from playing games in the 80s was that every once in a while I would stumble across a game that looked epic but I had exactly zero clue what to do. Wizball was such a game. Looked and sounded pretty cool, but... "where to go, what to do, and what's the goal here?" - something along those lines went through my mind at the time. I stuck with it an eventually (much later) figured it out. Do you remember a game that you played but didn't really know what the goal was or how to get there?

One distinct memory I have from playing games in the 80s was that every once in a while I would stumble across a game that looked epic but I had exactly zero clue what to do. Wizball was such a game. Looked and sounded pretty cool, but... "where to go, what to do, and what's the goal here?" - something along those lines went through my mind at the time. I stuck with it an eventually (much later) figured it out. Do you remember a game that you played but didn't really know what the goal was or how to get there?

exQUIZitely 🕹️

15,998 görüntüleme • 3 ay önce

I just verified this with Grok and it’s actually true In America when something is labeled something like “Made with 100% beef” it’s not actually 100% “I work advertisement and a long time ago, I was working on a project with McDonald's, and I learned something that now I can never unsee. It says right there, made with 100% beef, which is such a funny twist of words because it's made with 100% beef, not made of 100% beef. With the McDonald's thing, it was like their chicken nuggets are made with 100% chicken. You know, there's a lot of other stuff in there, and some of it might not be 100% chicken, but it's made with 100%, still tastes good.” When a product is labeled “made with 100% beef,” (or anything) it does not mean the product is 100% beef. Instead, it indicates that the beef portion of the product is 100% beef, but the product as a whole may contain other ingredients Insane.

I just verified this with Grok and it’s actually true In America when something is labeled something like “Made with 100% beef” it’s not actually 100% “I work advertisement and a long time ago, I was working on a project with McDonald's, and I learned something that now I can never unsee. It says right there, made with 100% beef, which is such a funny twist of words because it's made with 100% beef, not made of 100% beef. With the McDonald's thing, it was like their chicken nuggets are made with 100% chicken. You know, there's a lot of other stuff in there, and some of it might not be 100% chicken, but it's made with 100%, still tastes good.” When a product is labeled “made with 100% beef,” (or anything) it does not mean the product is 100% beef. Instead, it indicates that the beef portion of the product is 100% beef, but the product as a whole may contain other ingredients Insane.

Wall Street Apes

295,186 görüntüleme • 1 yıl önce

After Ancient Apocalypse aired, the critical response from mainstream media and academic archaeology was swift and coordinated. Randall went through ten to twelve independent criticisms of the show - from journalists, from archaeologists, from sources presenting themselves as separate and unaffiliated - and found nothing of substance in any of them. What he found instead was something that struck him as far more significant than the criticism itself. The same phraseology. The same arguments. The same framing, repeated across supposedly independent sources with a consistency that pointed not to genuine independent analysis but to a shared origin. Randall is direct about what that pattern implies. Someone assembled that script and distributed it. The coordination required to produce identical talking points across multiple mainstream outlets and academic voices does not happen organically - it requires organization, intent, and a decision made somewhere by someone with both the motivation and the reach to make it happen. Randall names Graham Hancock as a friend and makes clear this is personal as well as principled. The erasure and rewriting of history he has been documenting throughout his research is not an abstract institutional tendency. It has an author - and Randall states plainly that in the interest of disclosure, he would very much like to know who that is.

After Ancient Apocalypse aired, the critical response from mainstream media and academic archaeology was swift and coordinated. Randall went through ten to twelve independent criticisms of the show - from journalists, from archaeologists, from sources presenting themselves as separate and unaffiliated - and found nothing of substance in any of them. What he found instead was something that struck him as far more significant than the criticism itself. The same phraseology. The same arguments. The same framing, repeated across supposedly independent sources with a consistency that pointed not to genuine independent analysis but to a shared origin. Randall is direct about what that pattern implies. Someone assembled that script and distributed it. The coordination required to produce identical talking points across multiple mainstream outlets and academic voices does not happen organically - it requires organization, intent, and a decision made somewhere by someone with both the motivation and the reach to make it happen. Randall names Graham Hancock as a friend and makes clear this is personal as well as principled. The erasure and rewriting of history he has been documenting throughout his research is not an abstract institutional tendency. It has an author - and Randall states plainly that in the interest of disclosure, he would very much like to know who that is.

Randall Carlson

34,148 görüntüleme • 3 ay önce

Designed this site in Fable 5 with just a few prompts. And no, this is not a lame post where I attach my actual (not AI) work on an AI post to get views by making fun of a launch. This is actually Fable 5, as you can clearly see (with some of the elements that scream AI generated). But man is it doing a great job!

Designed this site in Fable 5 with just a few prompts. And no, this is not a lame post where I attach my actual (not AI) work on an AI post to get views by making fun of a launch. This is actually Fable 5, as you can clearly see (with some of the elements that scream AI generated). But man is it doing a great job!

Asaad Mahmood - The Small Square

106,426 görüntüleme • 11 gün önce

It’s hard to believe that 3 years ago, none of this was possible. Now, it’s pretty much going to change everything. Last month, Fish Audio reached out to me to try their voice AI software. They just launched their new S1 model today, and I was curious about the state of AI and voice (and everything else), so I gave it a spin. I liked it enough that when I was asked about a partnership, I said yes. I’m free to talk about what I like and don’t like about the software, and I’m going to share with you a few voices I cloned as well as some of the workflow. It was much easier than I thought. To give you an example, here is a private (research only) voice I cloned as a test. I grabbed a clip of Rutger Hauer’s famous speech from Blade Runner and uploaded it to the voice cloner on the Fish Audio Website as a private voice (no one else can use it, as it is for research only). I didn’t think it would work. The audio sample is very short. But Fish Audio was able to clone the voice extremely well and very fast. I didn’t have to upload any more than that to produce these results. I used Grok to write the new dialog, and I added the rain and background effects and the result is pretty impressive. It only took just a few minutes once I had the audio uploaded for everything from cloning to generating multiple takes. I’ll give some tips and pointers on how to get the best results at the conclusion of this thread and show you some surprising things it can do. (con’t) #Promotion

It’s hard to believe that 3 years ago, none of this was possible. Now, it’s pretty much going to change everything. Last month, Fish Audio reached out to me to try their voice AI software. They just launched their new S1 model today, and I was curious about the state of AI and voice (and everything else), so I gave it a spin. I liked it enough that when I was asked about a partnership, I said yes. I’m free to talk about what I like and don’t like about the software, and I’m going to share with you a few voices I cloned as well as some of the workflow. It was much easier than I thought. To give you an example, here is a private (research only) voice I cloned as a test. I grabbed a clip of Rutger Hauer’s famous speech from Blade Runner and uploaded it to the voice cloner on the Fish Audio Website as a private voice (no one else can use it, as it is for research only). I didn’t think it would work. The audio sample is very short. But Fish Audio was able to clone the voice extremely well and very fast. I didn’t have to upload any more than that to produce these results. I used Grok to write the new dialog, and I added the rain and background effects and the result is pretty impressive. It only took just a few minutes once I had the audio uploaded for everything from cloning to generating multiple takes. I’ll give some tips and pointers on how to get the best results at the conclusion of this thread and show you some surprising things it can do. (con’t) #Promotion

Grummz

69,859 görüntüleme • 1 yıl önce

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

Santiago

142,696 görüntüleme • 1 yıl önce

Guys, I think this is a very important bug to do research into with the rest of the cast. I've tried already with other char's 1,2 , but can't replicate the same as it shows here. It very well may be that a lot of moves have this bug in the game. We just never had an explanation

Guys, I think this is a very important bug to do research into with the rest of the cast. I've tried already with other char's 1,2 , but can't replicate the same as it shows here. It very well may be that a lot of moves have this bug in the game. We just never had an explanation

FOX | Sin

134,828 görüntüleme • 1 ay önce

"You can look at it from a lot of points of view. Obviously the gap is smaller now to Lando than it was before the disqualification but Max is also closer in the fight as well. It goes both ways but I think we've taken the lessons, analysed what happened in Vegas. Here is obviously very different to Vegas and a lot of other circuits we go to so hopefully it can be a good weekend! 😁☺️"

"You can look at it from a lot of points of view. Obviously the gap is smaller now to Lando than it was before the disqualification but Max is also closer in the fight as well. It goes both ways but I think we've taken the lessons, analysed what happened in Vegas. Here is obviously very different to Vegas and a lot of other circuits we go to so hopefully it can be a good weekend! 😁☺️"

a⁸¹ 🎐 | FAN ACCOUNT

108,488 görüntüleme • 6 ay önce

How does Lee Child start a novel? "If you were a composer writing music, you'd start with a key that you want. G-Major is a cheerful and upbeat key. E-Flat Minor is the opposite. It's down and rather melancholy. So what I do as a writer is start with a vague idea. I want this story to be hard and cold, or I want this story to be hot. And then I'll pick a location that I'm familiar with. I never do research specifically for that book. It always draws on impressions formed over the years from previous visits to places. Do you want it to be the West of Texas, where it's baking hot and arid? Do you want it to be on the Atlantic coast of Maine in April, where it's gray, cold, and misty? So that's how I start. With the sense of place and I'll construct the story around that. The temperature dictates the story." — Lee Child

How does Lee Child start a novel? "If you were a composer writing music, you'd start with a key that you want. G-Major is a cheerful and upbeat key. E-Flat Minor is the opposite. It's down and rather melancholy. So what I do as a writer is start with a vague idea. I want this story to be hard and cold, or I want this story to be hot. And then I'll pick a location that I'm familiar with. I never do research specifically for that book. It always draws on impressions formed over the years from previous visits to places. Do you want it to be the West of Texas, where it's baking hot and arid? Do you want it to be on the Atlantic coast of Maine in April, where it's gray, cold, and misty? So that's how I start. With the sense of place and I'll construct the story around that. The temperature dictates the story." — Lee Child

David Perell

25,139 görüntüleme • 4 ay önce

Zohran Mamdani: Muslims in this city, for almost as long as we have been in this city, have had to deal with those with power and platform dehumanizing us. And to be called animals, insects, to be called a jihadist mayor, to be called a cockroach—this language is both painfully familiar to me as a Muslim New Yorker, but also as someone who was born in East Africa. And it is difficult to hear. It is also a reminder that the silence that often greets this kind of bigotry, this kind of Islamophobia, is what allows it to fester—the temptation to treat it as politics as usual. I am not ashamed of who I am. I am not ashamed of my faith. I am not ashamed of being the first Muslim mayor in the history of our city. And there is no amount of racism that will change the way in which I lead or the commitment that I hold to each and every New Yorker in this city.

Zohran Mamdani: Muslims in this city, for almost as long as we have been in this city, have had to deal with those with power and platform dehumanizing us. And to be called animals, insects, to be called a jihadist mayor, to be called a cockroach—this language is both painfully familiar to me as a Muslim New Yorker, but also as someone who was born in East Africa. And it is difficult to hear. It is also a reminder that the silence that often greets this kind of bigotry, this kind of Islamophobia, is what allows it to fester—the temptation to treat it as politics as usual. I am not ashamed of who I am. I am not ashamed of my faith. I am not ashamed of being the first Muslim mayor in the history of our city. And there is no amount of racism that will change the way in which I lead or the commitment that I hold to each and every New Yorker in this city.

Clash Report

34,573 görüntüleme • 3 ay önce

Zohran Mamdani: Muslims in this city, for almost as long as we have been in this city, have had to deal with those with power and platform dehumanizing us. And to be called animals, insects, to be called a jihadist mayor, to be called a cockroach, this language is both painfully familiar to me as a Muslim New Yorker, but also as someone who was born in East Africa. And it is difficult to hear. It is also a reminder that the silence that often greets this kind of bigotry, this kind of Islamophobia, is what allows it to fester, the temptation to treat it as politics as usual. I am not ashamed of who I am. I am not ashamed of my faith. I am not ashamed of being the first Muslim mayor in the history of our city. And there is no amount of racism that will change the way in which I lead or the commitment that I hold to each and every New Yorker in this city.

Zohran Mamdani: Muslims in this city, for almost as long as we have been in this city, have had to deal with those with power and platform dehumanizing us. And to be called animals, insects, to be called a jihadist mayor, to be called a cockroach, this language is both painfully familiar to me as a Muslim New Yorker, but also as someone who was born in East Africa. And it is difficult to hear. It is also a reminder that the silence that often greets this kind of bigotry, this kind of Islamophobia, is what allows it to fester, the temptation to treat it as politics as usual. I am not ashamed of who I am. I am not ashamed of my faith. I am not ashamed of being the first Muslim mayor in the history of our city. And there is no amount of racism that will change the way in which I lead or the commitment that I hold to each and every New Yorker in this city.

Open Source Intel

76,397 görüntüleme • 3 ay önce

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Sam Rodriques

61,760 görüntüleme • 7 ay önce