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Microsoft ran its graph system against vector RAG on 8k, 120k, and a full million token context window, and the million token giant lost Jonathan Larson from Microsoft Research put up the numbers → LazyGraphRAG won 92, 90, and 91 percent of data-local questions, the exact place plain RAG... show more

NO1ennn

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29,076 次观看 • 16 天前 •via X (Twitter)

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MICROSOFT QUIETLY BUILT ONE MEMORY MODULE THAT PLUGS INTO ANY AI AGENT AND CUTS ITS CONTEXT BY UP TO 100X no retraining. no task-specific redesign. you just plug it in Microsoft's insight: agents don't need to remember what happened. they need the knowledge pulled from it - the facts and the reusable skills, not the raw logs so PlugMem stops storing history. it compiles experience into a knowledge graph, then feeds the agent only what matters the result: > up to 1-2 orders of magnitude fewer tokens > highest decision-utility per token of any system tested > one unchanged module beats task-specific memory on chat QA, multi-hop search, and web agents more memory was never the answer. better memory is - and Microsoft just shipped it as a plug-in bookmark this. the agents that win won't remember more, they'll remember smarter.

MICROSOFT QUIETLY BUILT ONE MEMORY MODULE THAT PLUGS INTO ANY AI AGENT AND CUTS ITS CONTEXT BY UP TO 100X no retraining. no task-specific redesign. you just plug it in Microsoft's insight: agents don't need to remember what happened. they need the knowledge pulled from it - the facts and the reusable skills, not the raw logs so PlugMem stops storing history. it compiles experience into a knowledge graph, then feeds the agent only what matters the result: > up to 1-2 orders of magnitude fewer tokens > highest decision-utility per token of any system tested > one unchanged module beats task-specific memory on chat QA, multi-hop search, and web agents more memory was never the answer. better memory is - and Microsoft just shipped it as a plug-in bookmark this. the agents that win won't remember more, they'll remember smarter.

NO1ennn

225,476 次观看 • 13 天前

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

128,678 次观看 • 1 个月前

There is no better place on the internet to learn about Context Engineering than this repo. It's literally a course with a learning path. It gathers the best resources and covers the theory + code for anything related to context, RAG, memory, and agentic systems and more.

There is no better place on the internet to learn about Context Engineering than this repo. It's literally a course with a learning path. It gathers the best resources and covers the theory + code for anything related to context, RAG, memory, and agentic systems and more.

ℏεsam

48,999 次观看 • 1 年前

Google just showed how AI engineers are moving from simple "RAG" to "graphs", memory and multimodal agents • 15:32 - setting up the production agent stack • 28:00 - turning disconnected data into a knowledge graph • 41:00 - Graph RAG with semantic + hybrid search • 58:00 - extracting graph context from images, text and video • 1:09:00 - orchestrating specialized agents with ADK • 1:20:00 - giving agents persistent memory across sessions 94-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from -> RAG → Graph RAG → Memory → Multimodal Agent Graphs. The workshop uses Spanner Graph, Gemini, ADK and Memory Bank to build that stack. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

Google just showed how AI engineers are moving from simple "RAG" to "graphs", memory and multimodal agents • 15:32 - setting up the production agent stack • 28:00 - turning disconnected data into a knowledge graph • 41:00 - Graph RAG with semantic + hybrid search • 58:00 - extracting graph context from images, text and video • 1:09:00 - orchestrating specialized agents with ADK • 1:20:00 - giving agents persistent memory across sessions 94-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from -> RAG → Graph RAG → Memory → Multimodal Agent Graphs. The workshop uses Spanner Graph, Gemini, ADK and Memory Bank to build that stack. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

Morlex

72,329 次观看 • 7 天前

Rockstar spent 7 years, 2,000+ developers, and $500 million to build Red Dead Redemption 2. Kimi K3 rebuilt the same scene in 10 hours. One prompt. 4 million tokens. It's open source. 2.8 trillion parameters. A 1 million token context window. It beats Claude Opus 4.8 and GPT-5.6. It sits on par with Fable 5. Fable 5 results, at Sonnet pricing. The wall between you and a AAA game used to be a studio. Now it's a prompt.

Rockstar spent 7 years, 2,000+ developers, and $500 million to build Red Dead Redemption 2. Kimi K3 rebuilt the same scene in 10 hours. One prompt. 4 million tokens. It's open source. 2.8 trillion parameters. A 1 million token context window. It beats Claude Opus 4.8 and GPT-5.6. It sits on par with Fable 5. Fable 5 results, at Sonnet pricing. The wall between you and a AAA game used to be a studio. Now it's a prompt.

Dep

99,083 次观看 • 1 个月前

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,950 次观看 • 1 年前

🚨TRUMP SENDS A STRONG MESSAGE: BIG THINGS ARE COMING FOR #XRP AS THE U.S. BUSINESS SYSTEM FACES A MAJOR RESTRUCTURING! THE FUTURE OF DECENTRALIZED MEDIA IS ALSO SET TO UNFOLD ON THE XRPL! BXE TOKEN IS LEADING THE WAY. IT WAS THE FIRST TO INTRODUCE INSTITUTIONAL TOKENIZATION ON THE XRP LEDGER! WITH ONLY 500 MILLION TOKENS IN TOTAL SUPPLY AND A CURRENT PRICE OF JUST $0.03, BXE HAS MASSIVE UPSIDE — POTENTIALLY REACHING $19–$24! DYOR/NFA BXE TOKEN TRADE LINK:

🚨TRUMP SENDS A STRONG MESSAGE: BIG THINGS ARE COMING FOR #XRP AS THE U.S. BUSINESS SYSTEM FACES A MAJOR RESTRUCTURING! THE FUTURE OF DECENTRALIZED MEDIA IS ALSO SET TO UNFOLD ON THE XRPL! BXE TOKEN IS LEADING THE WAY. IT WAS THE FIRST TO INTRODUCE INSTITUTIONAL TOKENIZATION ON THE XRP LEDGER! WITH ONLY 500 MILLION TOKENS IN TOTAL SUPPLY AND A CURRENT PRICE OF JUST $0.03, BXE HAS MASSIVE UPSIDE — POTENTIALLY REACHING $19–$24! DYOR/NFA BXE TOKEN TRADE LINK:

JackTheRippler ©️

50,737 次观看 • 11 个月前

With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

vLLM

102,101 次观看 • 22 天前

🚨#XRP HIT $327,000 ON THE PRIVATE LEDGER according to analyst! THE XRPL IS GEARING UP TO HANDLE TRILLIONS IN TRANSACTIONS!! CTF TOKEN, THE TOP DEFI PROJECT ON THE XRP LEDGER WITH PARTNERSHIPS INCLUDING AMAZON AND WALMART, IS POISED FOR A MASSIVE LEAP!! CTF TOKEN COULD SKYROCKET FROM $1.09 TO $748.50 PER TOKEN WITH A MARKET CAP OF ONLY $20 BILLION!! THE FUTURE IS HERE! DIRECT PURCHASES THROUGH THE XRP LEDGER ARE COMING, AND CTF TOKEN WILL BE KEY IN MAKING IT HAPPEN! WITH JUST 119 MILLION TOKENS IN CIRCULATION, A SUPPLY CRUNCH IS IMMINENT FOR CTF!! CTF TOKEN TRADE LINK: CTF TRADE LINK ON MEXC: Official Website:

🚨#XRP HIT $327,000 ON THE PRIVATE LEDGER according to analyst! THE XRPL IS GEARING UP TO HANDLE TRILLIONS IN TRANSACTIONS!! CTF TOKEN, THE TOP DEFI PROJECT ON THE XRP LEDGER WITH PARTNERSHIPS INCLUDING AMAZON AND WALMART, IS POISED FOR A MASSIVE LEAP!! CTF TOKEN COULD SKYROCKET FROM $1.09 TO $748.50 PER TOKEN WITH A MARKET CAP OF ONLY $20 BILLION!! THE FUTURE IS HERE! DIRECT PURCHASES THROUGH THE XRP LEDGER ARE COMING, AND CTF TOKEN WILL BE KEY IN MAKING IT HAPPEN! WITH JUST 119 MILLION TOKENS IN CIRCULATION, A SUPPLY CRUNCH IS IMMINENT FOR CTF!! CTF TOKEN TRADE LINK: CTF TRADE LINK ON MEXC: Official Website:

JackTheRippler ©️

117,800 次观看 • 1 年前

$Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.$

Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.

Alexander Whedon

13,276,800 次观看 • 3 个月前

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,403 次观看 • 2 年前

Prime Intellect engineer: "everyone's bragging about a million-token context. here's what they don't tell you. at 256k tokens GPT-5.5 scores 80% on retrieval. push it to a million and it drops to 36%. the model accepts the context, it just can't reason across it. people call it context rot." in a 20-minute talk he explains why bigger context windows won't save your agents. continual learning + training on your own traces + real environments - that's the fix. Watch the talk, then save!

Prime Intellect engineer: "everyone's bragging about a million-token context. here's what they don't tell you. at 256k tokens GPT-5.5 scores 80% on retrieval. push it to a million and it drops to 36%. the model accepts the context, it just can't reason across it. people call it context rot." in a 20-minute talk he explains why bigger context windows won't save your agents. continual learning + training on your own traces + real environments - that's the fix. Watch the talk, then save!

Carnage

745,910 次观看 • 1 个月前

THE $4,000 DGX SPARK BENCHMARKS AT 342 T/S ON PROMPT PROCESSING - THEN DROPS TO 73 WHEN IT ACTUALLY GENERATES. that clip is a dgx spark running llama-bench on qwen3-coder 30b, side by side with a mac. two numbers decide everything here: pp512, prompt processing = 342 tokens/sec tg128, token generation = 73 tokens/sec that gap is the whole lesson. local ai isn't one speed, it's two. prompt processing is reading your input: your files, your long context. the spark is compute-heavy, so it eats that fast. token generation is writing the answer, and it's bound by memory bandwidth, where the spark is modest and unified-memory macs stay close. so "which box is faster" has no single winner. feeding huge context or running agents that read a lot? prompt processing wins, the spark shines. long chat replies, one token at a time? generation matters more, and a mac holds its own. no single benchmark number, no universal "fastest box", no upgrade that wins every workload. the uncomfortable part isn't that the spark is fast. it's that "tokens per second" was always two numbers, and the hype only ever quotes the flattering one. follow RetroChainer for the benchmark truth nobody screenshots, and bookmark this before you buy on one number.

THE $4,000 DGX SPARK BENCHMARKS AT 342 T/S ON PROMPT PROCESSING - THEN DROPS TO 73 WHEN IT ACTUALLY GENERATES. that clip is a dgx spark running llama-bench on qwen3-coder 30b, side by side with a mac. two numbers decide everything here: pp512, prompt processing = 342 tokens/sec tg128, token generation = 73 tokens/sec that gap is the whole lesson. local ai isn't one speed, it's two. prompt processing is reading your input: your files, your long context. the spark is compute-heavy, so it eats that fast. token generation is writing the answer, and it's bound by memory bandwidth, where the spark is modest and unified-memory macs stay close. so "which box is faster" has no single winner. feeding huge context or running agents that read a lot? prompt processing wins, the spark shines. long chat replies, one token at a time? generation matters more, and a mac holds its own. no single benchmark number, no universal "fastest box", no upgrade that wins every workload. the uncomfortable part isn't that the spark is fast. it's that "tokens per second" was always two numbers, and the hype only ever quotes the flattering one. follow RetroChainer for the benchmark truth nobody screenshots, and bookmark this before you buy on one number.

RetroChainer

42,451 次观看 • 10 天前

‼️MASSIVE DEVELOPEMENT: UAE MAKES DIRECT PAYMENT TO CHINA OF 50 MILLION USING MBRIDGE WHICH IS A PRIVATE LEDGER THE CONNECTS TO TE XRP LEDGER!!! ITS HAPPENING!! INSITUTIONAL XRP LEDGER PROGRAM WAS JUST LAUNCHED DAYS AGO POWERED BY YET AGAIN ANOTHER XRP LEDGER TOKEN, BXE TOKEN!! THINGS ARE ABOUT TO GO INSANE!!!‼️ BY THE WAY THIS BXE TOKEN IS THE SAME ONE I TOLD YOU ABOUT AT $0.01!! NOW ITS $0.025!! BUT WE HAVENT EVEN STARTED!! THE TOTAL SUPPLY OF BXE TOKEN IS ONLY 500 MILLION TOKENS!! NOTHING!! WE COULD BE TALKING $19-24 EASILY!!! BXE TOKEN IS GOING TO BE THE BRIDGE FOR INSTITUTIONS ONTO THE XRPL AND CLEARLY THAT IS WHERE EVERYONE IS HEADING!! LOOK INTO IT BEFORE ITS TOO LATE!! DYOR! BXE TOKEN TRADE LINK:

‼️MASSIVE DEVELOPEMENT: UAE MAKES DIRECT PAYMENT TO CHINA OF 50 MILLION USING MBRIDGE WHICH IS A PRIVATE LEDGER THE CONNECTS TO TE XRP LEDGER!!! ITS HAPPENING!! INSITUTIONAL XRP LEDGER PROGRAM WAS JUST LAUNCHED DAYS AGO POWERED BY YET AGAIN ANOTHER XRP LEDGER TOKEN, BXE TOKEN!! THINGS ARE ABOUT TO GO INSANE!!!‼️ BY THE WAY THIS BXE TOKEN IS THE SAME ONE I TOLD YOU ABOUT AT $0.01!! NOW ITS $0.025!! BUT WE HAVENT EVEN STARTED!! THE TOTAL SUPPLY OF BXE TOKEN IS ONLY 500 MILLION TOKENS!! NOTHING!! WE COULD BE TALKING $19-24 EASILY!!! BXE TOKEN IS GOING TO BE THE BRIDGE FOR INSTITUTIONS ONTO THE XRPL AND CLEARLY THAT IS WHERE EVERYONE IS HEADING!! LOOK INTO IT BEFORE ITS TOO LATE!! DYOR! BXE TOKEN TRADE LINK:

FinanceBro

42,181 次观看 • 1 年前

‼️BREAKING: #XRP REPORTED TO BE ON ROUTE TO $3000 EACH ACCORDING TO INFLUENCER!! THIS COULD BE JUST THE BEGINNING!!‼️ MASSIVE ANNOUNCEMENT FOR INSTITUTIONS BY THE TOP #XRPL TOKEN, CTF TOKEN!! THINGS ARE ABOUT TO GO INSANE FOR CTF TOKEN!! IMAGINE JUST $20 BILLION MARLET CAP WOULD SEND CTF TOKEN FROM $0.61 TO $748.50 PER TOKEN!! THIS IS NO JOKE, YOU HAVE A TOKEN WITH ONLY 120 MILLION!! IT WILL SKYROCKET!! 120 MILLION IS ABOUT THE SAME SUPPLY AS #ETH!! DO YOUR RESEARCH BEFORE ITS TOO LATE!! CTF TOKEN IS A GOLD MINE!! CTF TOKEN TRADE LINK: CTF TOKEN TRADE LINK ON MEXC: Website:

‼️BREAKING: #XRP REPORTED TO BE ON ROUTE TO $3000 EACH ACCORDING TO INFLUENCER!! THIS COULD BE JUST THE BEGINNING!!‼️ MASSIVE ANNOUNCEMENT FOR INSTITUTIONS BY THE TOP #XRPL TOKEN, CTF TOKEN!! THINGS ARE ABOUT TO GO INSANE FOR CTF TOKEN!! IMAGINE JUST $20 BILLION MARLET CAP WOULD SEND CTF TOKEN FROM $0.61 TO $748.50 PER TOKEN!! THIS IS NO JOKE, YOU HAVE A TOKEN WITH ONLY 120 MILLION!! IT WILL SKYROCKET!! 120 MILLION IS ABOUT THE SAME SUPPLY AS #ETH!! DO YOUR RESEARCH BEFORE ITS TOO LATE!! CTF TOKEN IS A GOLD MINE!! CTF TOKEN TRADE LINK: CTF TOKEN TRADE LINK ON MEXC: Website:

FinanceBro

54,763 次观看 • 1 年前

A new Robinhood launchpad by print.world is releasing soon and I found the first token launched on the platform. On their docs page here: It says that its standard token list can be found here: And the very first token created on there is $HERO 0x0b9dfa193e0af4e47c2fee5b12f3a7ac78230e32 And the $GME token featured in the video is this one: 0xba11ef8f0d67579e5afe9efed6081abd39b3e607 Order of tokens from newest to oldest confirms that HERO is first:

A new Robinhood launchpad by print.world is releasing soon and I found the first token launched on the platform. On their docs page here: It says that its standard token list can be found here: And the very first token created on there is $HERO 0x0b9dfa193e0af4e47c2fee5b12f3a7ac78230e32 And the $GME token featured in the video is this one: 0xba11ef8f0d67579e5afe9efed6081abd39b3e607 Order of tokens from newest to oldest confirms that HERO is first:

TrenchDiver

22,642 次观看 • 1 个月前