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We just took 1st place at the OpenAI Codex Hackathon 🏆 Built Model Combat with Rishit Bansal in ~6 hours. It’s a live AI security battleground: Models attack, defend, patch their own apps, and exploit others to steal flags in real CTF rounds. Mortal Kombat-inspired. Pure chaos. Extremely fun.... show more

Ashikka

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58,884 просмотров • 5 месяцев назад •via X (Twitter)

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I’m joining OpenAI Codex to work on the future of agentic development! At Cursor, I got to see the shift from autocomplete to agents. The next step isn’t a better IDE. It’s an Agent Development Environment (ADE): systems and tools for orchestrating agents, reasoning over their outputs, and making them autonomous enough to reliably complete ambitious work. After chatting with Alexander Embiricos and Tibo, it was clear that Codex is the best place to realize this vision. The team has consistently shipped SOTA models for agentic coding (check out gpt-5.3-codex) and I’m pumped for the future that the new Codex App points to. What I’m most excited about is the broader mission: accelerating the knowledge work economy. All agents are coding agents, and we’re already seeing Codex used across every job function within organizations. I’m extremely grateful for my time at Cursor, working with the incredible team, and I’m proud of what we built together. I’m excited to take an even bigger swing with Codex. If you’re curious to get a glimpse of where we are headed, download the Codex App! If you want to work on this mission, please apply or reach out - we are hiring across all functions! You can just build things.

I’m joining OpenAI Codex to work on the future of agentic development! At Cursor, I got to see the shift from autocomplete to agents. The next step isn’t a better IDE. It’s an Agent Development Environment (ADE): systems and tools for orchestrating agents, reasoning over their outputs, and making them autonomous enough to reliably complete ambitious work. After chatting with Alexander Embiricos and Tibo, it was clear that Codex is the best place to realize this vision. The team has consistently shipped SOTA models for agentic coding (check out gpt-5.3-codex) and I’m pumped for the future that the new Codex App points to. What I’m most excited about is the broader mission: accelerating the knowledge work economy. All agents are coding agents, and we’re already seeing Codex used across every job function within organizations. I’m extremely grateful for my time at Cursor, working with the incredible team, and I’m proud of what we built together. I’m excited to take an even bigger swing with Codex. If you’re curious to get a glimpse of where we are headed, download the Codex App! If you want to work on this mission, please apply or reach out - we are hiring across all functions! You can just build things.

Rohan Varma

760,109 просмотров • 7 месяцев назад

What happens next? We took a photo with Lens and asked Nano Banana to generate the next 5 seconds of a scene. While it’s not predicting the future in a literal sense, the model draws upon its vast world knowledge to generate a plausible outcome in each of these examples. It's a fun peek at how our latest AI image editing model can be used for creative forecasting and storytelling. Nano Banana is now live in Search – accessible through Lens and AI Mode – so you can try this out for yourself!

What happens next? We took a photo with Lens and asked Nano Banana to generate the next 5 seconds of a scene. While it’s not predicting the future in a literal sense, the model draws upon its vast world knowledge to generate a plausible outcome in each of these examples. It's a fun peek at how our latest AI image editing model can be used for creative forecasting and storytelling. Nano Banana is now live in Search – accessible through Lens and AI Mode – so you can try this out for yourself!

Rajan Patel

74,826 просмотров • 11 месяцев назад

Every year, the best young engineers in the world compete at the FIRST Robotics Competition. This year, a team coached by one of our own came out on top. Jonathan Mi, an electrical design engineer at Shield AI, mentored Team 4414, HighTide, who took first place across 93,000 students, 3,700 teams, and 35 countries. They designed, programmed, and built their robot from scratch. Watch what it does. The strongest engineers build up everyone around them. That's who we hire. Congrats to HighTide, and when graduation comes, you know where to find us! Autonomy for the world. The greatest victory requires no war.

Every year, the best young engineers in the world compete at the FIRST Robotics Competition. This year, a team coached by one of our own came out on top. Jonathan Mi, an electrical design engineer at Shield AI, mentored Team 4414, HighTide, who took first place across 93,000 students, 3,700 teams, and 35 countries. They designed, programmed, and built their robot from scratch. Watch what it does. The strongest engineers build up everyone around them. That's who we hire. Congrats to HighTide, and when graduation comes, you know where to find us! Autonomy for the world. The greatest victory requires no war.

Shield AI

27,466 просмотров • 4 месяцев назад

🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developers

🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developers

Vikas gupta

14,138 просмотров • 19 дней назад

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.

Alex Prompter

7,181,863 просмотров • 2 месяцев назад

We are building the home of the doge economy Projects across every major vertical are already gearing up to launch on DogeOS. Builders are cooking. Here's what the community should be excited to explore.... Much Thoughts ✍️ Very 🐕 So Soon... 💱 DeFi Swaps, lending, yield, memes all running on DogeOS with Dogecoin at the heart of it all. This means the most beloved and recognized asset in crypto finally gets a real financial system built around it. Doge stops being just a vibe and starts being productive. The community has been asking for this for years. 🎮 Gaming On-chain games built on DogeOS: play, compete, unlock with Dogecoin. Doge was born from meme and gaming culture. Bringing games on-chain is just Doge coming home. Play to unlock, play to grow, play because it's fun. Gaming is one of the strongest onramps in all of crypto. It's how you turn curious onlookers into active participants. This is a chance to bring the fun and the love of this community to a whole new wave of users, and remind the old ones why they fell in love with Doge in the first place. 🤖 AI Builders are already positioned to ship AI powered dApps, agents, and tools natively on DogeOS. Think automated trading agents, AI assistants that manage your wallet, dApp builders, and solutions that get smarter the more the ecosystem grows. Agents benefit from fast and cheap transaction environments, and that's exactly what DogeOS is built for. DogeOS becomes the rails for the next wave of on-chain AI, with Doge as the fuel. 📣 Community and Social Apps built for the culture. Social tools, creator platforms, community coordination, all on-chain and all for the Doge economy. Dogecoin has always had the strongest community in crypto. Now that community gets its own social layer instead of living as a guest in another chain's world. Dogecoin should be the focal point, not just another asset being served. 🐕 All your desires. One ecosystem. We are building toward the future this community has dreamt of, making Dogecoin the central piece of a real on-chain economy. This is just the start. Much building. Prepare your paws.

We are building the home of the doge economy Projects across every major vertical are already gearing up to launch on DogeOS. Builders are cooking. Here's what the community should be excited to explore.... Much Thoughts ✍️ Very 🐕 So Soon... 💱 DeFi Swaps, lending, yield, memes all running on DogeOS with Dogecoin at the heart of it all. This means the most beloved and recognized asset in crypto finally gets a real financial system built around it. Doge stops being just a vibe and starts being productive. The community has been asking for this for years. 🎮 Gaming On-chain games built on DogeOS: play, compete, unlock with Dogecoin. Doge was born from meme and gaming culture. Bringing games on-chain is just Doge coming home. Play to unlock, play to grow, play because it's fun. Gaming is one of the strongest onramps in all of crypto. It's how you turn curious onlookers into active participants. This is a chance to bring the fun and the love of this community to a whole new wave of users, and remind the old ones why they fell in love with Doge in the first place. 🤖 AI Builders are already positioned to ship AI powered dApps, agents, and tools natively on DogeOS. Think automated trading agents, AI assistants that manage your wallet, dApp builders, and solutions that get smarter the more the ecosystem grows. Agents benefit from fast and cheap transaction environments, and that's exactly what DogeOS is built for. DogeOS becomes the rails for the next wave of on-chain AI, with Doge as the fuel. 📣 Community and Social Apps built for the culture. Social tools, creator platforms, community coordination, all on-chain and all for the Doge economy. Dogecoin has always had the strongest community in crypto. Now that community gets its own social layer instead of living as a guest in another chain's world. Dogecoin should be the focal point, not just another asset being served. 🐕 All your desires. One ecosystem. We are building toward the future this community has dreamt of, making Dogecoin the central piece of a real on-chain economy. This is just the start. Much building. Prepare your paws.

DogeOS

43,197 просмотров • 21 дней назад

🚨 RWA START-UP EXPLODES 1,200% AT LAUNCH — A PROUD MOMENT FOR US Our incubatee T-RIZE’s debut on Kraken was nothing short of extraordinary — peaking at a 1200% — and we’re deeply honored to have been a proud partner from the start. The $RIZE token is now live and trading, marking a major milestone in real-world asset tokenization. With a $300M deal for Canada’s 960-unit Project Champfleury and a $2B pipeline in motion, T-RIZE is setting the new standard for RWA platforms. Built on Rizenet — a proprietary blockchain optimized for decentralized machine learning and AI-driven insights — it’s pushing the space forward in real time. We’re proud to have contributed across strategy, marketing, partnerships, and the incredible shows that brought this to life. Watch the official demo + explore the platform: Disclaimer: We do not provide financial or investment advice of any kind. Always do your own research, as cryptocurrency prices can be extremely volatile. This project is part of our incubation program.

🚨 RWA START-UP EXPLODES 1,200% AT LAUNCH — A PROUD MOMENT FOR US Our incubatee T-RIZE’s debut on Kraken was nothing short of extraordinary — peaking at a 1200% — and we’re deeply honored to have been a proud partner from the start. The $RIZE token is now live and trading, marking a major milestone in real-world asset tokenization. With a $300M deal for Canada’s 960-unit Project Champfleury and a $2B pipeline in motion, T-RIZE is setting the new standard for RWA platforms. Built on Rizenet — a proprietary blockchain optimized for decentralized machine learning and AI-driven insights — it’s pushing the space forward in real time. We’re proud to have contributed across strategy, marketing, partnerships, and the incredible shows that brought this to life. Watch the official demo + explore the platform: Disclaimer: We do not provide financial or investment advice of any kind. Always do your own research, as cryptocurrency prices can be extremely volatile. This project is part of our incubation program.

Mario Nawfal

139,739 просмотров • 1 год назад

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

Arvind Jain

281,328 просмотров • 4 месяцев назад

AI token usage is up 10x in 7 months, compounding 40%/MONTH! There is NO BUBBLE when demand is STILL accelerating And this is just OpenRouter, it doesn't count the labs direct token usage and APIs But here's what's interesting about these numbers, the demand is coming from everywhere at once US models (OpenAI, Anthropic, Google) keep growing, while Chinese open weight models (DeepSeek, Tencent, Xiaomi, Minimax) grew even faster and now drive over 60% of usage on OpenRouter Closed source and open source both compounding at the same time. This is literally the best case scenario for AI Infra investors It means both frontier model tokens and cheaper tokens have product market fit. This means the application layer is finding ways to use both and generate ROI with both types Demand for tokens IS demand for compute. This is why SpaceX is looking to build 10GW of compute by next year, because the demand is clearly here Now combine this demand set up, with NVIDIA yesterday announcing financing platforms with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to mobilize over $500 billion of third party capital for AI infrastructure And Jensen has said publicly he expects $3 to $4 TRILLION of AI infrastructure spend by 2030 The build out will have to continue for a lot longer than the market is expecting, that is very clear to me. Don't let this consolidation period in AI infra stocks shake you out, they will have their moment again and take their next leg higher p.s. if you want to see how im investing in this, you can track my real-time portfolio and the research of all 5 Milk Road PRO analysts with live trade notifications, and it's just $1 to try it out (insane price just to check it out). Learn more here: Good luck out there!

AI token usage is up 10x in 7 months, compounding 40%/MONTH! There is NO BUBBLE when demand is STILL accelerating And this is just OpenRouter, it doesn't count the labs direct token usage and APIs But here's what's interesting about these numbers, the demand is coming from everywhere at once US models (OpenAI, Anthropic, Google) keep growing, while Chinese open weight models (DeepSeek, Tencent, Xiaomi, Minimax) grew even faster and now drive over 60% of usage on OpenRouter Closed source and open source both compounding at the same time. This is literally the best case scenario for AI Infra investors It means both frontier model tokens and cheaper tokens have product market fit. This means the application layer is finding ways to use both and generate ROI with both types Demand for tokens IS demand for compute. This is why SpaceX is looking to build 10GW of compute by next year, because the demand is clearly here Now combine this demand set up, with NVIDIA yesterday announcing financing platforms with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to mobilize over $500 billion of third party capital for AI infrastructure And Jensen has said publicly he expects $3 to $4 TRILLION of AI infrastructure spend by 2030 The build out will have to continue for a lot longer than the market is expecting, that is very clear to me. Don't let this consolidation period in AI infra stocks shake you out, they will have their moment again and take their next leg higher p.s. if you want to see how im investing in this, you can track my real-time portfolio and the research of all 5 Milk Road PRO analysts with live trade notifications, and it's just $1 to try it out (insane price just to check it out). Learn more here: Good luck out there!

Kyle Reidhead | Milk Road

28,320 просмотров • 1 месяц назад

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

1,183,124 просмотров • 3 месяцев назад

$📍 $20 Trillion in Real-World Assets Is Ready for Tokenization Let me put you on to something big: Decrepit . It’s being called the “Uniswap for RWAs” (Real-World Assets), and for good reason. They’re bridging TradFi and DeFi with institutional-grade tech, opening access to tokenized assets like real estate, commodities, and even carbon credits. This isn’t just another project—it’s the future of RWA 🔑 Why You Should Be Watching IX Swap ✅ Compliance You Can Trust This isn’t just crypto hype. IX Swap is built for institutions and retail investors alike, with security and regulatory compliance at its core. ✅ Scalable Liquidity Their AMM tech is transforming illiquid assets into 24/7 accessible investments. Tokenized real estate, art, carbon credits—you name it. ✅ Diversified Asset Access With IX Swap, you’re not limited to just crypto-native assets. Imagine owning a fraction of a property or even carbon credits. 💡 And here’s what really stands out: They’re backed by big names like Coinbase, LINE, and UOB Bank Ventures. IX Swap is already ahead in the $20 trillion RWA market, and they’re just getting started. 📈 Oh, and it’s live on MEXC. If you’re not paying attention to $IXS, you’re missing out. 🔗 Check out the MEXC listing here 🌐 Learn more about the platform here: This is your chance to get in early on something real. Tokenized assets are the next frontier. Are you ready?$

📍 $20 Trillion in Real-World Assets Is Ready for Tokenization Let me put you on to something big: Decrepit . It’s being called the “Uniswap for RWAs” (Real-World Assets), and for good reason. They’re bridging TradFi and DeFi with institutional-grade tech, opening access to tokenized assets like real estate, commodities, and even carbon credits. This isn’t just another project—it’s the future of RWA 🔑 Why You Should Be Watching IX Swap ✅ Compliance You Can Trust This isn’t just crypto hype. IX Swap is built for institutions and retail investors alike, with security and regulatory compliance at its core. ✅ Scalable Liquidity Their AMM tech is transforming illiquid assets into 24/7 accessible investments. Tokenized real estate, art, carbon credits—you name it. ✅ Diversified Asset Access With IX Swap, you’re not limited to just crypto-native assets. Imagine owning a fraction of a property or even carbon credits. 💡 And here’s what really stands out: They’re backed by big names like Coinbase, LINE, and UOB Bank Ventures. IX Swap is already ahead in the $20 trillion RWA market, and they’re just getting started. 📈 Oh, and it’s live on MEXC. If you’re not paying attention to $IXS, you’re missing out. 🔗 Check out the MEXC listing here 🌐 Learn more about the platform here: This is your chance to get in early on something real. Tokenized assets are the next frontier. Are you ready?

Evan

27,865 просмотров • 1 год назад

THAT $70 "RUN YOUR OWN LLMS" PI KIT CAN'T RUN A SINGLE LLM. IT'S A VISION CHIP WITH NO RAM. that clip sells a raspberry pi 5 in a slick case with an ai accelerator and the caption "your own llms." clean build, fun kit. the claim is where it breaks. the fine print: the popular $70 pi ai kit uses a hailo-8l, 13 tops. it's built for vision, object detection and image processing, and it has no memory of its own. so it cannot run large language models. full stop the board that actually can is a different one: the newer ai hat+ 2, hailo-10h, 40 tops, with 8gb of dedicated ram. that's $130, not $70 and even that runs only tiny models. llama 3.2 at 1b, qwen 2.5 at 1.5b, deepseek r1 at 1.5b. edge llms live in the 1-7b range, against cloud models at 500b to 2 trillion so the honest pitch: for $130 you can run a very small language model on a pi, slowly, as a fun learning project. that's real and it's cool. "your own llms" on a $70 vision kit is not. why this keeps happening: "ai kit" and a big "tops" number sell. tops sounds like intelligence. but tops measures vision-style math, not whether the chip has the memory to hold a language model. the spec that matters for llms is ram, and the cheap kit has none. the honest caveats, both ways: the $70 kit is genuinely great, just at vision. cameras, object detection, that's its job the $130 hat really does run small llms locally, which a pi couldn't do at all two years ago. that's progress "small" is the load-bearing word. don't expect gpt at home on a pi the takeaway: before you buy a kit because the caption says llm, check two numbers. not the tops. the ram, and the size of the model it can actually load. no 70-dollar miracle, no gpt in a pi case, no tops number that means what you think. save this before you buy the wrong kit for the word on the box.

THAT $70 "RUN YOUR OWN LLMS" PI KIT CAN'T RUN A SINGLE LLM. IT'S A VISION CHIP WITH NO RAM. that clip sells a raspberry pi 5 in a slick case with an ai accelerator and the caption "your own llms." clean build, fun kit. the claim is where it breaks. the fine print: the popular $70 pi ai kit uses a hailo-8l, 13 tops. it's built for vision, object detection and image processing, and it has no memory of its own. so it cannot run large language models. full stop the board that actually can is a different one: the newer ai hat+ 2, hailo-10h, 40 tops, with 8gb of dedicated ram. that's $130, not $70 and even that runs only tiny models. llama 3.2 at 1b, qwen 2.5 at 1.5b, deepseek r1 at 1.5b. edge llms live in the 1-7b range, against cloud models at 500b to 2 trillion so the honest pitch: for $130 you can run a very small language model on a pi, slowly, as a fun learning project. that's real and it's cool. "your own llms" on a $70 vision kit is not. why this keeps happening: "ai kit" and a big "tops" number sell. tops sounds like intelligence. but tops measures vision-style math, not whether the chip has the memory to hold a language model. the spec that matters for llms is ram, and the cheap kit has none. the honest caveats, both ways: the $70 kit is genuinely great, just at vision. cameras, object detection, that's its job the $130 hat really does run small llms locally, which a pi couldn't do at all two years ago. that's progress "small" is the load-bearing word. don't expect gpt at home on a pi the takeaway: before you buy a kit because the caption says llm, check two numbers. not the tops. the ram, and the size of the model it can actually load. no 70-dollar miracle, no gpt in a pi case, no tops number that means what you think. save this before you buy the wrong kit for the word on the box.

RetroChainer

11,100 просмотров • 2 месяцев назад

The past year has seen me have a renaissance, in the truest sense… I won’t go into details now but will at some point before long. What has brought so much happiness to my life and those around me this past year has been my falling back in love with sport. Cycling has, and always will be, my number one. Yet I’d forgotten that I simply love sport, not for results but for the sheer joy of doing it, I’d completely forgotten that the health of my mind is intrinsically connected to the health of my body. I’ve rediscovered the love I had for sport that existed before the world of professional cycling took over in the way it did. I’ve been pushing myself and trying new things this past year, indifferent to the results, just out having fun and at times going deeper than I thought I was capable of anymore. Last week I got on a TT bike for the first time in a decade, Factor Bikes built me a bike, I’ve been looking at it for two years and decided it was time to get fitted, getting back on it felt like going home. Anyway, the long and the short of this is that it’s inspired me to create a club to inspire and be inspired. A community for us to share our love for getting out there and doing it, because I’ve realized that although I spend most of my sporting life on my own I derive the most pleasure when feeling part of something. It’s in its early days, I’ve called it Sporting Club CHPT3 aka SCC3, I’d love you to check it out and join. It’s still in its infancy, but I hope it’s going to grow into something that will inspire you as much as me.

The past year has seen me have a renaissance, in the truest sense… I won’t go into details now but will at some point before long. What has brought so much happiness to my life and those around me this past year has been my falling back in love with sport. Cycling has, and always will be, my number one. Yet I’d forgotten that I simply love sport, not for results but for the sheer joy of doing it, I’d completely forgotten that the health of my mind is intrinsically connected to the health of my body. I’ve rediscovered the love I had for sport that existed before the world of professional cycling took over in the way it did. I’ve been pushing myself and trying new things this past year, indifferent to the results, just out having fun and at times going deeper than I thought I was capable of anymore. Last week I got on a TT bike for the first time in a decade, Factor Bikes built me a bike, I’ve been looking at it for two years and decided it was time to get fitted, getting back on it felt like going home. Anyway, the long and the short of this is that it’s inspired me to create a club to inspire and be inspired. A community for us to share our love for getting out there and doing it, because I’ve realized that although I spend most of my sporting life on my own I derive the most pleasure when feeling part of something. It’s in its early days, I’ve called it Sporting Club CHPT3 aka SCC3, I’d love you to check it out and join. It’s still in its infancy, but I hope it’s going to grow into something that will inspire you as much as me.

David Millar

111,737 просмотров • 2 лет назад

$SUPERNOVA IS LIVE AND DELIVERED! After all these years, and all the work that went into it, I almost can’t believe I can finally say this out loud, *Supernova is fully live.* (Bears repeating every single day for the next 30 days, and just taking it in) And with it, we introduced the Finance Computer. A bold, categorically different, and remarkably simple product. Built on a radical idea: brands, businesses, agents, and robots don’t want more financial complexity. They simply want a better way to access, move, and program money. One connection. Financial superpowers. This is a B2B and B2M product. And the simpler we can make it, the more powerful it becomes. At first, this may feel like a narrative violation to some people in crypto, but then an important realization comes. This product is not built for crypto people. It is built to strike a chord with the real world. Businesses that want money to move instantly. Brands that want financial capabilities embedded directly into their products. Agents and robots that need to transact on their own. All through one simple interface. Regulated. Secure. Global. A new category of machine-ready finance is here, now. Money, capital, assets, and markets moving around the world in a fraction of a second. Open and live 24/7. Programmable by default. Gradually, becoming accessible to anyone, anywhere. This is what the Finance Computer makes possible.$

SUPERNOVA IS LIVE AND DELIVERED! After all these years, and all the work that went into it, I almost can’t believe I can finally say this out loud, Supernova is fully live. (Bears repeating every single day for the next 30 days, and just taking it in) And with it, we introduced the Finance Computer. A bold, categorically different, and remarkably simple product. Built on a radical idea: brands, businesses, agents, and robots don’t want more financial complexity. They simply want a better way to access, move, and program money. One connection. Financial superpowers. This is a B2B and B2M product. And the simpler we can make it, the more powerful it becomes. At first, this may feel like a narrative violation to some people in crypto, but then an important realization comes. This product is not built for crypto people. It is built to strike a chord with the real world. Businesses that want money to move instantly. Brands that want financial capabilities embedded directly into their products. Agents and robots that need to transact on their own. All through one simple interface. Regulated. Secure. Global. A new category of machine-ready finance is here, now. Money, capital, assets, and markets moving around the world in a fraction of a second. Open and live 24/7. Programmable by default. Gradually, becoming accessible to anyone, anywhere. This is what the Finance Computer makes possible.

Beniamin Mincu |🇺🇸/acc

36,362 просмотров • 16 дней назад

I went a little overboard with Codex last week and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.

I went a little overboard with Codex last week and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.

雪踏乌云

23,107 просмотров • 2 месяцев назад

June 4th, 1994 our lives forever changed. We said, “I do!”. With those two words, we said, yes, to all the highs, the lows, and everything in between. God has blessed us with four absolutely amazing children who are now amazing adults, with their own best friends/significant others (that they’re doing life with), we have three incredible grandsons, and a beautiful granddaughter on the way. We’ve lived where we both grew up (on the East Coast), and have now been out here in San Diego for just over 11 years. We’ve gotten jobs (and lost jobs), we’ve had more times than we can count where we couldn’t make ends meet, even though both you and I were working two, and sometimes three jobs at a time, and we’ve been blessed in ways that we could’ve never dreamed of. We’ve watched both my parents pass on, and are now dealing with the overwhelmingly difficult challenge of seeing your parents struggle with their own health in ways that no one should have to go through. Through it all (even in the midst of the chaos), we’ve been blessed to be by each other‘s sides! I thank God for you every day, Jillian! I love our adventures together (the big ones where we fly to somewhere we’ve never been before, and the little ones where we hop in the car with no agenda, and just drive). I love when we find ourselves in deeper conversation, laughter, and tears of joy then ever expected, and in the moments of silence, where no words are even spoken, but when we’re together, just being where our feet are. As the world (as we know it), keeps getting crazier and crazier, let’s continue to keep Christ in the center of all we do, keep leaning on and lifting each other up when it’s needed, and keep living the lives that we have been so incredibly blessed to live together. I love you with all my heart Jillian. Happy 32nd (heading into our 33rd year), Anniversary.

June 4th, 1994 our lives forever changed. We said, “I do!”. With those two words, we said, yes, to all the highs, the lows, and everything in between. God has blessed us with four absolutely amazing children who are now amazing adults, with their own best friends/significant others (that they’re doing life with), we have three incredible grandsons, and a beautiful granddaughter on the way. We’ve lived where we both grew up (on the East Coast), and have now been out here in San Diego for just over 11 years. We’ve gotten jobs (and lost jobs), we’ve had more times than we can count where we couldn’t make ends meet, even though both you and I were working two, and sometimes three jobs at a time, and we’ve been blessed in ways that we could’ve never dreamed of. We’ve watched both my parents pass on, and are now dealing with the overwhelmingly difficult challenge of seeing your parents struggle with their own health in ways that no one should have to go through. Through it all (even in the midst of the chaos), we’ve been blessed to be by each other‘s sides! I thank God for you every day, Jillian! I love our adventures together (the big ones where we fly to somewhere we’ve never been before, and the little ones where we hop in the car with no agenda, and just drive). I love when we find ourselves in deeper conversation, laughter, and tears of joy then ever expected, and in the moments of silence, where no words are even spoken, but when we’re together, just being where our feet are. As the world (as we know it), keeps getting crazier and crazier, let’s continue to keep Christ in the center of all we do, keep leaning on and lifting each other up when it’s needed, and keep living the lives that we have been so incredibly blessed to live together. I love you with all my heart Jillian. Happy 32nd (heading into our 33rd year), Anniversary.

Coach Hines 🇺🇸

10,530 просмотров • 3 месяцев назад