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Built a tax document classifier with Jev. We ingest thousands of tax documents using an LLM pipeline I built last tax season. I read multiple articles as late as April this year claiming AI fails at tax document classification. Wasn't the case back then, and it's proved wrong again... show more

Nakshatra Saxena

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164,861 次观看 • 19 小时前 •via X (Twitter)

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🚨 Newsom Revives Tax Return Pledge He Abandoned After 2022 Gavin Newsom campaigned on a promise to release his tax returns every year as governor — calling it a “moral duty” in a clear shot at then-President Trump. He followed through at first… but stopped releasing them after his 2022 reelection. His last public disclosure covered the 2020 tax year — over four years ago. Now, with the DOJ actively probing his family’s finances (including scrutiny of his wife Jennifer Siebel Newsom’s taxes), Newsom is suddenly pledging to release them once again. His office stated: “We are working to prepare the remaining returns for transparency. Unlike Donald Trump, the Governor has nothing to hide.” Funny how transparency kicks in only when federal investigators come knocking. Do you think all governors should release their tax returns? (Video: AI)

🚨 Newsom Revives Tax Return Pledge He Abandoned After 2022 Gavin Newsom campaigned on a promise to release his tax returns every year as governor — calling it a “moral duty” in a clear shot at then-President Trump. He followed through at first… but stopped releasing them after his 2022 reelection. His last public disclosure covered the 2020 tax year — over four years ago. Now, with the DOJ actively probing his family’s finances (including scrutiny of his wife Jennifer Siebel Newsom’s taxes), Newsom is suddenly pledging to release them once again. His office stated: “We are working to prepare the remaining returns for transparency. Unlike Donald Trump, the Governor has nothing to hide.” Funny how transparency kicks in only when federal investigators come knocking. Do you think all governors should release their tax returns? (Video: AI)

Paul A. Szypula 🇺🇸

38,965 次观看 • 3 个月前

the so101 + leslider frame i've been playing with the past few weeks wasn't just a random rig i built for fun it's what we at LiveKit built to benchmark and debug our infra (s/o livekit-portal), and today we're open-sourcing it so everyone can reproduce and share coolio results at home here is the github: i know this isn’t the first frame designed for evals, but it’s ours, and it’s open, and it’s with a LeSlider! along with this, we’ve released simulations files (URDF, MJCF, and USD), as well as a sample RL env in MJLab for pick-and-place tasks we want people to have fun, being close to the frontier as much as possible, and we want people to know it shouldn’t take thousands of dollars in equipment to reproduce some of the most amazing results the academic world has to offer personally, I’m going to deploy sim2real and real2sim2real pipelines on this soon, for fun, so look out for that.

the so101 + leslider frame i've been playing with the past few weeks wasn't just a random rig i built for fun it's what we at LiveKit built to benchmark and debug our infra (s/o livekit-portal), and today we're open-sourcing it so everyone can reproduce and share coolio results at home here is the github: i know this isn’t the first frame designed for evals, but it’s ours, and it’s open, and it’s with a LeSlider! along with this, we’ve released simulations files (URDF, MJCF, and USD), as well as a sample RL env in MJLab for pick-and-place tasks we want people to have fun, being close to the frontier as much as possible, and we want people to know it shouldn’t take thousands of dollars in equipment to reproduce some of the most amazing results the academic world has to offer personally, I’m going to deploy sim2real and real2sim2real pipelines on this soon, for fun, so look out for that.

Binh

18,257 次观看 • 2 个月前

R.I.P. presentation teams. I just automated 100% of my presentation workflow. This AI Document Agent just disrupted the entire document creation process. And enterprise C-suites are already using it. KPMG, BDO, and Cisco replaced their entire deck workflow with one prompt. → No more designers → No more formatting issues → No more revision recycle Just brief in. Client-ready deck out. It prompts full presentations in minutes — inside PowerPoint. Brand rules, pricing data, legal disclaimers all auto-populated. Every output on-brand, compliant, and ready to send. BDO saved $1.65M in year one from document automation alone. This isn't a productivity hack. It's infrastructure. The same way email replaced fax — AI Document Agents are replacing manual deck building. C-suites who figured this out aren't going back. Yours is still formatting at midnight. I put together the exact blueprint these enterprise teams used — prompting framework, governance setup, and full implementation guide. Want it? Like + comment "AGENT" and I'll DM it directly. (must be following)

Aryan Mahajan

36,397 次观看 • 6 个月前

BREAKING: OpenAI just launched ChatGPT Agent It allows ChatGPT to think, plan, and execute complex tasks on its own virtual computer while you do other things I had early access, and ChatGPT Agent built me a complete early retirement plan in 20 minutes: > Found local tax laws (Vancouver) > Analyzed average monthly spend rates > Calculated savings needed to retire at 30 > Researched optimal investment allocations > Found tax optimization strategies I'd never heard of > Built multiple FIRE scenarios > Created a downloadable presentation with results This would've cost me $5,000+ from a financial advisor and taken weeks I think with ChatGPT Agent now, and especially as it gains access to more tools, we're finally going to see the rise of a new AI skill category in *Agent Management* Agents are finally becoming capable of doing real work autonomously, so anyone who learns how to effectively orchestrate agents will have a huge advantage

BREAKING: OpenAI just launched ChatGPT Agent It allows ChatGPT to think, plan, and execute complex tasks on its own virtual computer while you do other things I had early access, and ChatGPT Agent built me a complete early retirement plan in 20 minutes: > Found local tax laws (Vancouver) > Analyzed average monthly spend rates > Calculated savings needed to retire at 30 > Researched optimal investment allocations > Found tax optimization strategies I'd never heard of > Built multiple FIRE scenarios > Created a downloadable presentation with results This would've cost me $5,000+ from a financial advisor and taken weeks I think with ChatGPT Agent now, and especially as it gains access to more tools, we're finally going to see the rise of a new AI skill category in Agent Management Agents are finally becoming capable of doing real work autonomously, so anyone who learns how to effectively orchestrate agents will have a huge advantage

Rowan Cheung

653,957 次观看 • 1 年前

Longtime New Yorkers Wrongly Hit With Massive “Pied-à-Terre” Tax Bills — Maybe It’s Time to Leave New Yorkers who have lived in the city for decades — many in their only home — are receiving five-figure tax notices under the new pied-à-terre surcharge. The tax was sold as a levy on wealthy owners of luxury second homes. Instead, longtime residents say they are being forced to prove they actually live in the places they have called home for 30 years or more. Karen Young, who has lived in New York since 1972, received a $43,000 bill on her West 95th Street brownstone. “Whoever’s behind this, who I can only assume is the mayor, didn’t take the 30 seconds to research. Is this a witch hunt? As a faithful New Yorker since 1972, I find it not only insulting but painful. Is anybody paying any attention, or any thinking, or two minutes of research? Just look me up!” (Video: AI) An 81-year-old woman got a $55,000 notice addressed to her late husband. “I have lived here for 30 years; it’s my only residence. The City of New York didn’t get that right.” The appeal process is described as cumbersome, with tight deadlines and technical hurdles even for those who pay taxes, utilities, and jury duty from the same address. What was framed as “taxing the rich” is landing hard on people who never left. New York City is already seeing an exodus of residents to lower-tax states like Florida and Texas. It will be interesting to watch whether this pied-à-terre tax accelerates that trend. Once the high earners leave, who’s left for Mayor Mamdani to tax?

Longtime New Yorkers Wrongly Hit With Massive “Pied-à-Terre” Tax Bills — Maybe It’s Time to Leave New Yorkers who have lived in the city for decades — many in their only home — are receiving five-figure tax notices under the new pied-à-terre surcharge. The tax was sold as a levy on wealthy owners of luxury second homes. Instead, longtime residents say they are being forced to prove they actually live in the places they have called home for 30 years or more. Karen Young, who has lived in New York since 1972, received a $43,000 bill on her West 95th Street brownstone. “Whoever’s behind this, who I can only assume is the mayor, didn’t take the 30 seconds to research. Is this a witch hunt? As a faithful New Yorker since 1972, I find it not only insulting but painful. Is anybody paying any attention, or any thinking, or two minutes of research? Just look me up!” (Video: AI) An 81-year-old woman got a $55,000 notice addressed to her late husband. “I have lived here for 30 years; it’s my only residence. The City of New York didn’t get that right.” The appeal process is described as cumbersome, with tight deadlines and technical hurdles even for those who pay taxes, utilities, and jury duty from the same address. What was framed as “taxing the rich” is landing hard on people who never left. New York City is already seeing an exodus of residents to lower-tax states like Florida and Texas. It will be interesting to watch whether this pied-à-terre tax accelerates that trend. Once the high earners leave, who’s left for Mayor Mamdani to tax?

Paul A. Szypula 🇺🇸

235,278 次观看 • 1 个月前

NEW: Marijuana stocks SKYROCKET over President Trump’s potential signing of an executive order that would reclassify weed from a Schedule I drug to Schedule III as early as today. CBS News: “Schedule III substances are considered to have medical value and a lower potential for abuse than Schedule I or II drugs. Medications in this category include ketamine, Tylenol with codeine, and anabolic steroids.” “Reclassification would not legalize marijuana federally, but it would remove cannabis from the most restrictive category under the Controlled Substances Act — a change with far-reaching implications.” “‘Cannabis is still federally illegal — but even as a federally illegal substance, the move to Schedule 3 dramatically reduces the federal tax burden for operators,’ said Ryan Hunter, Chief Revenue Officer at Spherex. He noted that under the current law, some cannabis businesses face effective tax rates as high as 80%.”

NEW: Marijuana stocks SKYROCKET over President Trump’s potential signing of an executive order that would reclassify weed from a Schedule I drug to Schedule III as early as today. CBS News: “Schedule III substances are considered to have medical value and a lower potential for abuse than Schedule I or II drugs. Medications in this category include ketamine, Tylenol with codeine, and anabolic steroids.” “Reclassification would not legalize marijuana federally, but it would remove cannabis from the most restrictive category under the Controlled Substances Act — a change with far-reaching implications.” “‘Cannabis is still federally illegal — but even as a federally illegal substance, the move to Schedule 3 dramatically reduces the federal tax burden for operators,’ said Ryan Hunter, Chief Revenue Officer at Spherex. He noted that under the current law, some cannabis businesses face effective tax rates as high as 80%.”

RedWave Press

226,620 次观看 • 9 个月前

NVIDIA just made AI detect objects 10x faster by deleting one step. It's called LocateAnything, and it removes the biggest bottleneck no one else was fixing in vision-language models. Normally a model builds each bounding box one coordinate token at a time. 100 objects means thousands of tokens before an answer. NVIDIA scrapped that: their Parallel Box Decoding predicts the whole box in a single forward pass, as one atomic unit. → 12.7 boxes/sec on one H100 → 10x faster than Qwen3-VL → +3.8% F1 on LVIS, accuracy up, not down → 3B params, runs on one consumer GPU Treating the box as one unit keeps its coordinates tied together, which is why accuracy climbed instead of falling. One model handles detection, GUI grounding, OCR, and document understanding, ready for computer-use agents, robotics, and document pipelines. 100% open source, weights, code, demo, and paper all live.

NVIDIA just made AI detect objects 10x faster by deleting one step. It's called LocateAnything, and it removes the biggest bottleneck no one else was fixing in vision-language models. Normally a model builds each bounding box one coordinate token at a time. 100 objects means thousands of tokens before an answer. NVIDIA scrapped that: their Parallel Box Decoding predicts the whole box in a single forward pass, as one atomic unit. → 12.7 boxes/sec on one H100 → 10x faster than Qwen3-VL → +3.8% F1 on LVIS, accuracy up, not down → 3B params, runs on one consumer GPU Treating the box as one unit keeps its coordinates tied together, which is why accuracy climbed instead of falling. One model handles detection, GUI grounding, OCR, and document understanding, ready for computer-use agents, robotics, and document pipelines. 100% open source, weights, code, demo, and paper all live.

Alvaro Cintas

202,075 次观看 • 2 个月前

China's government built this with 1 trillion dollars. It now has more bullet trains than the rest of the world combined. They didn't build it for profit, but to provide a public service. Here, our government is experimenting with everything just to figure out How to make a profit from it. How to collect tolls from roads. How to collect tax from every new item. And yes, I have seen them spending millions on airports that no one uses.

China's government built this with 1 trillion dollars. It now has more bullet trains than the rest of the world combined. They didn't build it for profit, but to provide a public service. Here, our government is experimenting with everything just to figure out How to make a profit from it. How to collect tolls from roads. How to collect tax from every new item. And yes, I have seen them spending millions on airports that no one uses.

🚨Indian Gems

88,520 次观看 • 4 个月前

Two weeks apart. Same person. Different choices. This isn’t a “fell off” post. This isn’t shame. This is reality. This is part of the story. After the show, I went from 168.4 lbs to 182.2 lbs. And at this point… it’s a choice. I’ve realized something about myself: When I hit a peak, I tend to sabotage it so I have something to chase again. But the truth is.. Greatness is built by staying disciplined after the high, not restarting from the low. And as someone who’s lived both extremes more times than I can count, I’ll say this with full honesty: The pain of discipline is ALWAYS better than the pain of regret. So here’s the good news… I get to use this moment as fuel. I get to document the entire climb back and show you exactly how I’m going to do it using the same systems I teach in G7 Coaching and the G7 Fitness App. No waiting for New Year’s. No “Diet Starts Tomorrow.” We start today. It’s Game Time. 💪🏾😤

Two weeks apart. Same person. Different choices. This isn’t a “fell off” post. This isn’t shame. This is reality. This is part of the story. After the show, I went from 168.4 lbs to 182.2 lbs. And at this point… it’s a choice. I’ve realized something about myself: When I hit a peak, I tend to sabotage it so I have something to chase again. But the truth is.. Greatness is built by staying disciplined after the high, not restarting from the low. And as someone who’s lived both extremes more times than I can count, I’ll say this with full honesty: The pain of discipline is ALWAYS better than the pain of regret. So here’s the good news… I get to use this moment as fuel. I get to document the entire climb back and show you exactly how I’m going to do it using the same systems I teach in G7 Coaching and the G7 Fitness App. No waiting for New Year’s. No “Diet Starts Tomorrow.” We start today. It’s Game Time. 💪🏾😤

FOUSEY

17,755 次观看 • 9 个月前

Building RAG is easy. Parsing real, unstructured data is the hard part. Most tools fail when documents get complicated. RAGFlow by InfiniFlow makes the entire process visual and flawless 🔥 It is an (open-source!) engine built specifically to find the exact needle in a data haystack, even across literally unlimited tokens. The platform comes packed with: → "Quality in, quality out" parsing for highly complex formats → Multiple recall paired with fused re-ranking → A built-in Python and JavaScript code executor for agents → An orchestrable ingestion pipeline Here's why it stands out: 1️⃣ Structural Understanding Instead of just scraping text, it handles tables across pages, scanned copies, slides, and Excel sheets natively using deep document understanding. 2️⃣ Grounded Citations Every answer is verifiable. The UI highlights the exact chunks used, allowing you to trace any response directly back to the source material. 3️⃣ Enterprise Synchronization Keep your context constantly updated with native data sync from Google Drive, Notion, Discord, and Confluence. Stop letting bad document parsing ruin your RAG systems. Best part? It's 100% Free and open-source. Link to the repo in 🧵↓

Building RAG is easy. Parsing real, unstructured data is the hard part. Most tools fail when documents get complicated. RAGFlow by InfiniFlow makes the entire process visual and flawless 🔥 It is an (open-source!) engine built specifically to find the exact needle in a data haystack, even across literally unlimited tokens. The platform comes packed with: → "Quality in, quality out" parsing for highly complex formats → Multiple recall paired with fused re-ranking → A built-in Python and JavaScript code executor for agents → An orchestrable ingestion pipeline Here's why it stands out: 1️⃣ Structural Understanding Instead of just scraping text, it handles tables across pages, scanned copies, slides, and Excel sheets natively using deep document understanding. 2️⃣ Grounded Citations Every answer is verifiable. The UI highlights the exact chunks used, allowing you to trace any response directly back to the source material. 3️⃣ Enterprise Synchronization Keep your context constantly updated with native data sync from Google Drive, Notion, Discord, and Confluence. Stop letting bad document parsing ruin your RAG systems. Best part? It's 100% Free and open-source. Link to the repo in 🧵↓

Charly Wargnier

19,220 次观看 • 5 个月前

Baidu just open-sourced an OCR model that reads entire 40-page documents in one shot. It's called Unlimited-OCR. 3 billion parameters but only 500 million active during inference. Runs 100% locally on your machine. Why this matters: traditional OCR tools chop documents page by page. Tables that span two pages break. Reading order gets lost. Cross-page context disappears. Unlimited-OCR processes the whole document at once. 32K context window. Text, formulas, tables, reading order all preserved across pages. Output comes out as clean structured Markdown. → 93% accuracy on the standard benchmark. +6 points over the baseline. → Error rate stays below 0.11 even past 40 pages. → Multilingual out of the box. → 2.12 million downloads on Hugging Face last month. 14,600 GitHub stars. For context: Amazon Textract, Google Cloud Vision, and Azure Document Intelligence all charge per page. This runs locally for free.

Baidu just open-sourced an OCR model that reads entire 40-page documents in one shot. It's called Unlimited-OCR. 3 billion parameters but only 500 million active during inference. Runs 100% locally on your machine. Why this matters: traditional OCR tools chop documents page by page. Tables that span two pages break. Reading order gets lost. Cross-page context disappears. Unlimited-OCR processes the whole document at once. 32K context window. Text, formulas, tables, reading order all preserved across pages. Output comes out as clean structured Markdown. → 93% accuracy on the standard benchmark. +6 points over the baseline. → Error rate stays below 0.11 even past 40 pages. → Multilingual out of the box. → 2.12 million downloads on Hugging Face last month. 14,600 GitHub stars. For context: Amazon Textract, Google Cloud Vision, and Azure Document Intelligence all charge per page. This runs locally for free.

Vaibhav Sisinty

415,889 次观看 • 2 个月前

Unpopular opinion: Most agent evals are theatre. You run them once before the deployment. It'll take 800ms+ as another LLM would be judging your LLM. Most annoying part - no one tells where in the chain things went wrong. I wasted a lot of time in this loop. And then I came across Future AGI bringing 5 different tools under one umbrella, best part - the platform is completely open source. They open sourced their entire platform and the eval layer is noticeably different. It is multimodal - works on everything text, image, audio, pdf. Not an LLM-as-judge adding latency but an agent with memory and tools. The biggest win are learned classifiers trained on actual production failure patterns to run evals at low cost. It also runs across the full reasoning chain, not just the final response. Check out → Try it here →

Unpopular opinion: Most agent evals are theatre. You run them once before the deployment. It'll take 800ms+ as another LLM would be judging your LLM. Most annoying part - no one tells where in the chain things went wrong. I wasted a lot of time in this loop. And then I came across Future AGI bringing 5 different tools under one umbrella, best part - the platform is completely open source. They open sourced their entire platform and the eval layer is noticeably different. It is multimodal - works on everything text, image, audio, pdf. Not an LLM-as-judge adding latency but an agent with memory and tools. The biggest win are learned classifiers trained on actual production failure patterns to run evals at low cost. It also runs across the full reasoning chain, not just the final response. Check out → Try it here →

Swapna Kumar Panda

50,358 次观看 • 4 个月前

Taste is invisible until you try to write it down. This is probably my biggest lesson with AI building as of late. At Sundial, I get to work with really friggin' amazing analysts who know the art, and I see how much of our collective time now is now spent turning that art into playbooks or skills for an LLM. Encoding things like: "How would a great analyst actually look at this metric move?" or "What is ACTUALLY the interesting signal in this story versus noise?" or "How can we know if a product change actually moved the needle?" It's really humbling work! You write an instruction set. The LLM misses. You add more context. It still misses. You add even more. Now it's confused. You strip it back. Now it's too vague. You try a different framing. Better, but inconsistent. Works on Monday, fails on Tuesday. You go again. I've come to realize the gap between 70% quality and 95% quality is not 3 or 4 big things. It's more like 100s of small things. Which is exactly why you can't write an article about it, or copy it, or shortcut it! This gap *is* taste, quantified. The accumulated weight of a thousand small judgments you don't notice you're making, until you sit down to externalize them and realize you can't. Being good at something is not the same as being able to articulate why you're good at it. I now see two bottlenecks to making something better than today's generic AI: 1. Can you *see* what better looks like in the first place? 2. Even if you can see, can you *articulate* what that is in a way that the LLM can understand and systemize? #2 is now a new craft, the art of distilling the art. The people who can do it well are the ones building standout products.

Taste is invisible until you try to write it down. This is probably my biggest lesson with AI building as of late. At Sundial, I get to work with really friggin' amazing analysts who know the art, and I see how much of our collective time now is now spent turning that art into playbooks or skills for an LLM. Encoding things like: "How would a great analyst actually look at this metric move?" or "What is ACTUALLY the interesting signal in this story versus noise?" or "How can we know if a product change actually moved the needle?" It's really humbling work! You write an instruction set. The LLM misses. You add more context. It still misses. You add even more. Now it's confused. You strip it back. Now it's too vague. You try a different framing. Better, but inconsistent. Works on Monday, fails on Tuesday. You go again. I've come to realize the gap between 70% quality and 95% quality is not 3 or 4 big things. It's more like 100s of small things. Which is exactly why you can't write an article about it, or copy it, or shortcut it! This gap is taste, quantified. The accumulated weight of a thousand small judgments you don't notice you're making, until you sit down to externalize them and realize you can't. Being good at something is not the same as being able to articulate why you're good at it. I now see two bottlenecks to making something better than today's generic AI: 1. Can you see what better looks like in the first place? 2. Even if you can see, can you articulate what that is in a way that the LLM can understand and systemize? #2 is now a new craft, the art of distilling the art. The people who can do it well are the ones building standout products.

Julie Zhuo

17,633 次观看 • 4 个月前

Today, we're announcing a $60M Series B led by Battery Ventures, bringing our total funding to $85M in just under a year. Also joining the round are founders and operators who’ve built generational companies of the last two decades – tobi lutke (CEO, Shopify), arash ferdowsi (Dropbox), Claire Hughes Johnson (Stripe), and more. The round came together in 6 days. Here's why. Every major category in enterprise software is seeing multiple AI-native challengers. CRM, ERP, ITSM – all being rebuilt from scratch by a new generation of companies applying AI to solve persistent problems we couldn’t before. Employee Management (also known as HCM) is the exception. It’s the last frontier, and we believe the most important one. The operating layer to manage people, run payroll, benefits, compliance, and IT, for every company in the world, is still built on architecture that predates AI by decades. This fundraise is the story of how Warp is changing that. The average Warp customer is growing 5x faster than their peers, with 1/10th of the HR and admin overhead. We’re seeing a massive shift happening in how the best companies run their people operations. From the fastest-growing AI-startups to massive public companies, the winning teams are running lean: HR, finance, and ops generalists who automate as much as possible, and use their time instead for strategic work that AI can’t automate. Warp is the platform of choice for ambitious companies operating at this new pace. Legacy HCMs help humans track the work. Warp uses AI to proactively complete the work. Workday was built for the last era. We're building for the next one. And it’s working. We've – - Doubled ARR in Q1 - On track to $2B+ payroll volume this year - Signed enterprise customers with thousands of employees - Launched entire product lines back-to-back: Warp benefits brokerage and Warp Fabric (our AI-native IT automation suite built in-house). A few thank-yous: 1. Our customers, the fastest-growing companies in the world, who trust us with their most critical systems. We wouldn't be here without you. 2. Our team - 50+ people in NYC who've built this platform, taken on the hardest problems in business-critical software. We're just getting started. 3. Our investors doubling down in this round, and some of our earliest believers – Sound Ventures (ashton kutcher, Effie Epstein), Derek Grant, (Arnav Sahu), Harj Taggar at Y Combinator, Balaji, Kevin Hartz, Kyle Vogt, Amjad Masad, HOF Capital (Fady Yacoub), colinevans (OpenAI) We're here to arm ambitious American companies with Workday-grade power, but with the usability and delight of an Apple product. With this new funding, we plan to fund deeper AI agents, tax and compliance infrastructure, expand our product suite, and support even closely our fast-growing customers. Come join us.

Today, we're announcing a $60M Series B led by Battery Ventures, bringing our total funding to $85M in just under a year. Also joining the round are founders and operators who’ve built generational companies of the last two decades – tobi lutke (CEO, Shopify), arash ferdowsi (Dropbox), Claire Hughes Johnson (Stripe), and more. The round came together in 6 days. Here's why. Every major category in enterprise software is seeing multiple AI-native challengers. CRM, ERP, ITSM – all being rebuilt from scratch by a new generation of companies applying AI to solve persistent problems we couldn’t before. Employee Management (also known as HCM) is the exception. It’s the last frontier, and we believe the most important one. The operating layer to manage people, run payroll, benefits, compliance, and IT, for every company in the world, is still built on architecture that predates AI by decades. This fundraise is the story of how Warp is changing that. The average Warp customer is growing 5x faster than their peers, with 1/10th of the HR and admin overhead. We’re seeing a massive shift happening in how the best companies run their people operations. From the fastest-growing AI-startups to massive public companies, the winning teams are running lean: HR, finance, and ops generalists who automate as much as possible, and use their time instead for strategic work that AI can’t automate. Warp is the platform of choice for ambitious companies operating at this new pace. Legacy HCMs help humans track the work. Warp uses AI to proactively complete the work. Workday was built for the last era. We're building for the next one. And it’s working. We've – - Doubled ARR in Q1 - On track to $2B+ payroll volume this year - Signed enterprise customers with thousands of employees - Launched entire product lines back-to-back: Warp benefits brokerage and Warp Fabric (our AI-native IT automation suite built in-house). A few thank-yous: 1. Our customers, the fastest-growing companies in the world, who trust us with their most critical systems. We wouldn't be here without you. 2. Our team - 50+ people in NYC who've built this platform, taken on the hardest problems in business-critical software. We're just getting started. 3. Our investors doubling down in this round, and some of our earliest believers – Sound Ventures (ashton kutcher, Effie Epstein), Derek Grant, (Arnav Sahu), Harj Taggar at Y Combinator, Balaji, Kevin Hartz, Kyle Vogt, Amjad Masad, HOF Capital (Fady Yacoub), colinevans (OpenAI) We're here to arm ambitious American companies with Workday-grade power, but with the usability and delight of an Apple product. With this new funding, we plan to fund deeper AI agents, tax and compliance infrastructure, expand our product suite, and support even closely our fast-growing customers. Come join us.

Ayush S

1,133,277 次观看 • 2 个月前

Mamdani Just Doxxed His Rich Constituents His administration published a searchable database of every NYC property that could be hit by the new pied-à-terre tax — complete with full names and addresses of the owners. Many of these residents are already eyeing the exits as Mamdani increasingly ridicules them while hiking their taxes. This AI video represents exactly what Mamdani’s administration did today. States like Florida and Texas are rolling out the welcome mat with lower taxes and less regulation. The people who pay the most are noticing. Last Thursday he taunted them directly: “If you have a second home in New York City worth more than $5M, check your mailbox when you’re back in the five boroughs — because you’ve got mail. Today, we sent notification letters to property owners, letting them know that our new pied-à-terre tax is coming soon. The best city in the world deserves the best parks, libraries, and schools in the world. That’s only possible when we all pay our fair share.” (Video: AI) NYC doesn’t have a tax revenue problem. It has a spending problem. And Mamdani is making it worse by scaring away the residents who pay the most taxes. What Mamdani should be doing is lowering taxes for everyone and cutting spending dramatically. Let all New York City residents keep more of their money. That’s how you make the Big Apple affordable again.

Mamdani Just Doxxed His Rich Constituents His administration published a searchable database of every NYC property that could be hit by the new pied-à-terre tax — complete with full names and addresses of the owners. Many of these residents are already eyeing the exits as Mamdani increasingly ridicules them while hiking their taxes. This AI video represents exactly what Mamdani’s administration did today. States like Florida and Texas are rolling out the welcome mat with lower taxes and less regulation. The people who pay the most are noticing. Last Thursday he taunted them directly: “If you have a second home in New York City worth more than $5M, check your mailbox when you’re back in the five boroughs — because you’ve got mail. Today, we sent notification letters to property owners, letting them know that our new pied-à-terre tax is coming soon. The best city in the world deserves the best parks, libraries, and schools in the world. That’s only possible when we all pay our fair share.” (Video: AI) NYC doesn’t have a tax revenue problem. It has a spending problem. And Mamdani is making it worse by scaring away the residents who pay the most taxes. What Mamdani should be doing is lowering taxes for everyone and cutting spending dramatically. Let all New York City residents keep more of their money. That’s how you make the Big Apple affordable again.

Paul A. Szypula 🇺🇸

166,070 次观看 • 1 个月前

San Francisco’s economy is recovering, and people want to be here again. As we continue to build that momentum, we need to make sure that San Franciscans feel the benefits and can continue to raise their kids in this incredible city. We need to build housing, we need to create jobs, and we need to keep bringing people back downtown, our front door to the world and the economic engine for our city. That’s why today I am proud to announce the BUILD Act with Supervisor Bilal Mahmood—a legislative package that will unlock stalled housing projects and create thousands of good union jobs. The BUILD Act will reform the transfer tax to incentivize investment in these housing projects and buildings downtown—stimulating new jobs for electricians, plumbers, and laborers. And we will put forward a ballot measure in November to ensure that transfer tax is paid on properties in foreclosure so we can offset revenue impacts. Our politicians used to take our workers and our families for granted. But those politicians are gone, and those days are over. We are now creating the conditions for our city’s success. By passing the BUILD Act, we will take the next step, creating the jobs and building the housing that San Franciscans deserve.

San Francisco’s economy is recovering, and people want to be here again. As we continue to build that momentum, we need to make sure that San Franciscans feel the benefits and can continue to raise their kids in this incredible city. We need to build housing, we need to create jobs, and we need to keep bringing people back downtown, our front door to the world and the economic engine for our city. That’s why today I am proud to announce the BUILD Act with Supervisor Bilal Mahmood—a legislative package that will unlock stalled housing projects and create thousands of good union jobs. The BUILD Act will reform the transfer tax to incentivize investment in these housing projects and buildings downtown—stimulating new jobs for electricians, plumbers, and laborers. And we will put forward a ballot measure in November to ensure that transfer tax is paid on properties in foreclosure so we can offset revenue impacts. Our politicians used to take our workers and our families for granted. But those politicians are gone, and those days are over. We are now creating the conditions for our city’s success. By passing the BUILD Act, we will take the next step, creating the jobs and building the housing that San Franciscans deserve.

Daniel Lurie 丹尼爾·羅偉

13,983 次观看 • 6 个月前

#Rameshwaram_Cafe #Pune If you’re wondering why this demolition happened, let me explain the legal aspect as per PMC under the Unified Development Control and Promotion Regulations (UDCPR). Every building is required to leave a margin space. Technically, this space is meant to remain open so that in case of an emergency, fire vehicles or other assistance can access the premises. However, in this particular case, the margin is along the roadside. The road itself can serve the purpose of emergency access. So practically speaking, the margin here does not add any real value. Still such unnecessary laws are made so that agencies can bring cases against you out of thin air. Now considering that this law already exists and we cant do much about it; the margin space must strictly be maintained, the only condition under UDCPR is that it must be kept “open to the sky.” In this case, it was open to the sky. The only modification made was a change in flooring, where slabs of different heights were designed on the ground floor by the cafe. I have read the UDCPR carefully, and nowhere does it mention that variable flooring design is prohibited in such open-to-sky margin areas. Therefore, this demolition does not appear to be entirely justified. It seems arbitrary and possibly driven by ego of PMC Care officers, especially if the café did not comply with unofficial expectations of bribe. IMO, The Rameshwaram Cafe shall take PMC to BHC and the same PMC is floting the UDCPR condition of max wall height of 1.5 meters. Will they demolish their own wall now? Doing business in India is not for weak; you’ve to manage everyday such non sense. PS: considering even 10L per day; this cafe paying 15L per month as GST and employment to over 50 people. Plus a corporate tax of 25% as advance tax. Punekar News

#Rameshwaram_Cafe #Pune If you’re wondering why this demolition happened, let me explain the legal aspect as per PMC under the Unified Development Control and Promotion Regulations (UDCPR). Every building is required to leave a margin space. Technically, this space is meant to remain open so that in case of an emergency, fire vehicles or other assistance can access the premises. However, in this particular case, the margin is along the roadside. The road itself can serve the purpose of emergency access. So practically speaking, the margin here does not add any real value. Still such unnecessary laws are made so that agencies can bring cases against you out of thin air. Now considering that this law already exists and we cant do much about it; the margin space must strictly be maintained, the only condition under UDCPR is that it must be kept “open to the sky.” In this case, it was open to the sky. The only modification made was a change in flooring, where slabs of different heights were designed on the ground floor by the cafe. I have read the UDCPR carefully, and nowhere does it mention that variable flooring design is prohibited in such open-to-sky margin areas. Therefore, this demolition does not appear to be entirely justified. It seems arbitrary and possibly driven by ego of PMC Care officers, especially if the café did not comply with unofficial expectations of bribe. IMO, The Rameshwaram Cafe shall take PMC to BHC and the same PMC is floting the UDCPR condition of max wall height of 1.5 meters. Will they demolish their own wall now? Doing business in India is not for weak; you’ve to manage everyday such non sense. PS: considering even 10L per day; this cafe paying 15L per month as GST and employment to over 50 people. Plus a corporate tax of 25% as advance tax. Punekar News

Defence Brat

248,027 次观看 • 6 个月前

The system we put together with Harberger taxes and auctions linked together seems to really be working Explainer: 1. Creator sells the Orb through an auction 2. Winner (keeper) sets a sales price 3. Keeper pays an ongoing tax—a % of the sales price they set (tax goes to the Creator) 4. Keeper needs to top up the Orb with funds to last longer 5. Anyone new can step in and buy the Orb at any time and become the new Keeper 6. In the meantime, current Keeper can adjust the sales price up or down 7. But if no one buys it and the funds aren't topped up, they will eventually run out of funds 8. Keeper can at any time start a "Keeper auction" in which they are the main beneficiary of the auction proceeds 9. If they run out of funds, a Keeper auction is automatically triggered for them 10. After a Keeper auction finalizes, the Orb has a new Keeper (or if there are 0 bids, Orb goes back to the creator!) 11. Even if the Keeper auction finalizes at some low price, the new Keeper must put a sales price (as usual) so the "auction" never stops going 12. The Orb just keeps trading hands to the highest bidder, always being available, never staying dormant! All through the while, the Creator earns the Harberger tax (except during Keeper auctions, but then they collect royalty fees from the sale), and each week, questions are being asked and answered—all at rates completely decided by the market!

The system we put together with Harberger taxes and auctions linked together seems to really be working Explainer: 1. Creator sells the Orb through an auction 2. Winner (keeper) sets a sales price 3. Keeper pays an ongoing tax—a % of the sales price they set (tax goes to the Creator) 4. Keeper needs to top up the Orb with funds to last longer 5. Anyone new can step in and buy the Orb at any time and become the new Keeper 6. In the meantime, current Keeper can adjust the sales price up or down 7. But if no one buys it and the funds aren't topped up, they will eventually run out of funds 8. Keeper can at any time start a "Keeper auction" in which they are the main beneficiary of the auction proceeds 9. If they run out of funds, a Keeper auction is automatically triggered for them 10. After a Keeper auction finalizes, the Orb has a new Keeper (or if there are 0 bids, Orb goes back to the creator!) 11. Even if the Keeper auction finalizes at some low price, the new Keeper must put a sales price (as usual) so the "auction" never stops going 12. The Orb just keeps trading hands to the highest bidder, always being available, never staying dormant! All through the while, the Creator earns the Harberger tax (except during Keeper auctions, but then they collect royalty fees from the sale), and each week, questions are being asked and answered—all at rates completely decided by the market!

Eric Wall

238,425 次观看 • 3 年前