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Offshore
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Moon Dev
The Polymarket Cheat Code: Using Autonomous AI Agents To Out-Trade The Smart Money
the reality of modern trading is that if you are still relying on your own gut feeling to predict global events you are essentially donating your money to the math nerds with high speed internet. most people treat prediction markets like a fun way to bet on who wins the next election but the professionals see them as a data mine that can be cracked open with the right set of keys
if you could sit in a room with seven of the smartest analysts on the planet and have them reach a consensus on every single trade before you placed it your win rate would skyrocket overnight. that sounds like a pipe dream for anyone without a billion dollar hedge fund but with the release of opus 4.5 and the rise of autonomous swarms that room is now sitting inside a single python script on your desktop
i spent years losing money to liquidations and over trading because i thought i could outsmart the market with sheer willpower. i spent hundreds of thousands of dollars on developers to build apps for me because i was too intimidated by the terminal to do it myself but i eventually realized that code is the only true equalizer in a world where the big players have all the leverage
prediction markets like polymarket are the wild west of finance right now because you can trade anything from elon tweets to movie box office numbers. while most retail traders are getting emotional over sports or crypto price action there is massive edge in the obscure markets that the big bots havent fully saturated yet
it is one thing to have an opinion on a market but it is a completely different game to have an ai swarm analyze real time trade data as it happens. the system i built tracks big traders and whales through a websocket stream and then passes that data through a gauntlet of different models to find where the real conviction lies
the core of this strategy is the swarm agent which acts like a board of directors for your capital. instead of relying on just one model we use a mix of claude haiku grock deepseek and now the massive power of opus 4.5 to give us a multi perspective view of every opportunity
most people stop at the first model they try because they want a simple answer but the market is never simple. when you have seven different ais looking at the same data point and they all reach a consensus you are no longer gambling you are trading based on probabilistic certainty
the beautiful thing about being a developer today is that the ai is getting better every single day and we are the ones who get to plug it in. it took me less than ten minutes to implement the latest opus model into my existing framework because once you have the infrastructure built you just keep swapping in better brains
if you are still staring at charts all day trying to find a pattern you are fighting a losing battle against machines that dont sleep. i decided to learn to code live on youtube because i wanted to prove that you dont need a degree to build these automated systems that can handle the heavy lifting for you
the poly market agent works by capturing over five hundred trades in real time and filtering out the noise like sports and leverage crypto bets. by focusing on non emotional markets and looking for clusters of big buy orders we can identify where the smart money is moving before the rest of the market catches on
one of the biggest mistakes i see traders make is ignoring the risk management side of the equation. our system uses a consensus picker to identify only the top five markets with the strongest agreement among all seven models because taking fewer high quality trades is always better than spraying and praying
the ai doesn't just give a yes or no answer it provides a full reasoning process for every single decision it makes. for example it might see a mismatch in a cup fixture or a home court advantage that a human would overlook because they are too biased by their own fandom[...]
The Polymarket Cheat Code: Using Autonomous AI Agents To Out-Trade The Smart Money
the reality of modern trading is that if you are still relying on your own gut feeling to predict global events you are essentially donating your money to the math nerds with high speed internet. most people treat prediction markets like a fun way to bet on who wins the next election but the professionals see them as a data mine that can be cracked open with the right set of keys
if you could sit in a room with seven of the smartest analysts on the planet and have them reach a consensus on every single trade before you placed it your win rate would skyrocket overnight. that sounds like a pipe dream for anyone without a billion dollar hedge fund but with the release of opus 4.5 and the rise of autonomous swarms that room is now sitting inside a single python script on your desktop
i spent years losing money to liquidations and over trading because i thought i could outsmart the market with sheer willpower. i spent hundreds of thousands of dollars on developers to build apps for me because i was too intimidated by the terminal to do it myself but i eventually realized that code is the only true equalizer in a world where the big players have all the leverage
prediction markets like polymarket are the wild west of finance right now because you can trade anything from elon tweets to movie box office numbers. while most retail traders are getting emotional over sports or crypto price action there is massive edge in the obscure markets that the big bots havent fully saturated yet
it is one thing to have an opinion on a market but it is a completely different game to have an ai swarm analyze real time trade data as it happens. the system i built tracks big traders and whales through a websocket stream and then passes that data through a gauntlet of different models to find where the real conviction lies
the core of this strategy is the swarm agent which acts like a board of directors for your capital. instead of relying on just one model we use a mix of claude haiku grock deepseek and now the massive power of opus 4.5 to give us a multi perspective view of every opportunity
most people stop at the first model they try because they want a simple answer but the market is never simple. when you have seven different ais looking at the same data point and they all reach a consensus you are no longer gambling you are trading based on probabilistic certainty
the beautiful thing about being a developer today is that the ai is getting better every single day and we are the ones who get to plug it in. it took me less than ten minutes to implement the latest opus model into my existing framework because once you have the infrastructure built you just keep swapping in better brains
if you are still staring at charts all day trying to find a pattern you are fighting a losing battle against machines that dont sleep. i decided to learn to code live on youtube because i wanted to prove that you dont need a degree to build these automated systems that can handle the heavy lifting for you
the poly market agent works by capturing over five hundred trades in real time and filtering out the noise like sports and leverage crypto bets. by focusing on non emotional markets and looking for clusters of big buy orders we can identify where the smart money is moving before the rest of the market catches on
one of the biggest mistakes i see traders make is ignoring the risk management side of the equation. our system uses a consensus picker to identify only the top five markets with the strongest agreement among all seven models because taking fewer high quality trades is always better than spraying and praying
the ai doesn't just give a yes or no answer it provides a full reasoning process for every single decision it makes. for example it might see a mismatch in a cup fixture or a home court advantage that a human would overlook because they are too biased by their own fandom[...]
Offshore
Moon Dev The Polymarket Cheat Code: Using Autonomous AI Agents To Out-Trade The Smart Money the reality of modern trading is that if you are still relying on your own gut feeling to predict global events you are essentially donating your money to the math…
we are entering an era where your ability to ship code is directly tied to your ability to generate wealth. i keep these trade feeds on my screen all day not to trade manually but to visualize the order flow so i can build better algorithms in my mind while the bots do the actual execution
iteration is the only path to success in this game which is why i share all of this code on github for the real data dogs to pull and build upon. you have to bring your own edge to the table but having an autonomous agent swarm is like showing up to a knife fight with a heat seeking missile
i truly believe that everyone should be a builder right now because the tools are finally accessible to the average person. whether you are using websocket streaming to track whales or consensus models to filter your entries the goal is to move from "i think" to "the data shows" as fast as humanly possible
if you are ready to stop being the liquidity and start being the one who builds the systems then the road map is already laid out for you. the world is moving towards total automation and you can either be the one who writes the code or the one who gets traded against by it
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iteration is the only path to success in this game which is why i share all of this code on github for the real data dogs to pull and build upon. you have to bring your own edge to the table but having an autonomous agent swarm is like showing up to a knife fight with a heat seeking missile
i truly believe that everyone should be a builder right now because the tools are finally accessible to the average person. whether you are using websocket streaming to track whales or consensus models to filter your entries the goal is to move from "i think" to "the data shows" as fast as humanly possible
if you are ready to stop being the liquidity and start being the one who builds the systems then the road map is already laid out for you. the world is moving towards total automation and you can either be the one who writes the code or the one who gets traded against by it
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Jukan
China's DRAM Pioneer CXMT Hits Production Capacity Ceiling… "Stalled by Equipment Supply Restrictions"
ChangXin Memory Technologies (CXMT), the company leading China's push for memory semiconductor self-sufficiency, has reportedly reached its peak production capacity in Q4 of last year and is now facing its limits. Although the Chinese government anticipated U.S. export controls and has been going all out to localize semiconductor equipment, the prevailing analysis is that restrictions on advanced semiconductor equipment will constrain new capacity expansion.
According to data from market research firm Omdia obtained by Chosun Biz on the 12th, CXMT's average monthly wafer production has reached a maximum of approximately 240,000 wafers. Having steadily expanded production capacity since 2024, key industry observers expect CXMT to remain in a plateau throughout this year.
Currently, CXMT's DRAM production capacity is estimated at roughly half that of industry No. 2 SK hynix and just over one-third of Samsung Electronics. On an annual basis last year, Samsung Electronics' DRAM production capacity was approximately 7.6 million wafers, SK hynix at 5.97 million, and Micron at around 3.6 million. While CXMT roughly doubled its wafer output last year compared to the prior year, rapidly scaling up, that momentum is expected to slow starting this year.
Cha Yong-ho, a researcher at LS Securities, said, "The tightening of U.S. export controls is limiting CXMT's capacity expansion. China is aware of this, and its Phase 3 investment fund is being concentrated on semiconductor equipment." He added, "If China succeeds in equipment localization next year, capacity expansion could resume from 2027, including CXMT's new Shanghai fab."
However, the yield rates of CXMT's DRAM production remain a bottleneck. Despite aggressive capital investment to grow in scale, critics consistently point out that actual output falls short. The gap between nameplate capacity and real production is attributed to low yields. While wafer production capacity may look impressive on paper, actual shipment market share is likely even lower due to product defect issues.
According to market research firm Counterpoint Research, the yield rate of CXMT's mainstay 1x-nanometer (first-generation 10nm-class) DRAM process in 2024 was 42% lower than the 1a-nanometer (fourth-generation 10nm-class) process yields of the Big Three memory makers—Samsung Electronics and SK hynix. While the 1a process at Samsung and SK hynix is classified as a mature node, CXMT's yields are said to still hover around the 50% level.
Adding to these challenges, the U.S. government is expected to tighten restrictions on Chinese semiconductor equipment companies, which could further hinder industry growth. Last month, Reuters reported that both Republican and Democratic lawmakers introduced a bill that would prohibit companies receiving subsidies under the CHIPS Act from purchasing Chinese-made equipment for a period of 10 years.
A semiconductor industry source explained, "Unlike NAND flash, DRAM involves far greater design and process complexity, so it will take CXMT considerable time to introduce advanced processes on par with Samsung Electronics or SK hynix." The source added, "As processes advance into the low 10nm range, the need for cutting-edge equipment such as extreme ultraviolet (EUV) lithography systems grows, but U.S. restrictions are making it difficult to secure such equipment."
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China's DRAM Pioneer CXMT Hits Production Capacity Ceiling… "Stalled by Equipment Supply Restrictions"
ChangXin Memory Technologies (CXMT), the company leading China's push for memory semiconductor self-sufficiency, has reportedly reached its peak production capacity in Q4 of last year and is now facing its limits. Although the Chinese government anticipated U.S. export controls and has been going all out to localize semiconductor equipment, the prevailing analysis is that restrictions on advanced semiconductor equipment will constrain new capacity expansion.
According to data from market research firm Omdia obtained by Chosun Biz on the 12th, CXMT's average monthly wafer production has reached a maximum of approximately 240,000 wafers. Having steadily expanded production capacity since 2024, key industry observers expect CXMT to remain in a plateau throughout this year.
Currently, CXMT's DRAM production capacity is estimated at roughly half that of industry No. 2 SK hynix and just over one-third of Samsung Electronics. On an annual basis last year, Samsung Electronics' DRAM production capacity was approximately 7.6 million wafers, SK hynix at 5.97 million, and Micron at around 3.6 million. While CXMT roughly doubled its wafer output last year compared to the prior year, rapidly scaling up, that momentum is expected to slow starting this year.
Cha Yong-ho, a researcher at LS Securities, said, "The tightening of U.S. export controls is limiting CXMT's capacity expansion. China is aware of this, and its Phase 3 investment fund is being concentrated on semiconductor equipment." He added, "If China succeeds in equipment localization next year, capacity expansion could resume from 2027, including CXMT's new Shanghai fab."
However, the yield rates of CXMT's DRAM production remain a bottleneck. Despite aggressive capital investment to grow in scale, critics consistently point out that actual output falls short. The gap between nameplate capacity and real production is attributed to low yields. While wafer production capacity may look impressive on paper, actual shipment market share is likely even lower due to product defect issues.
According to market research firm Counterpoint Research, the yield rate of CXMT's mainstay 1x-nanometer (first-generation 10nm-class) DRAM process in 2024 was 42% lower than the 1a-nanometer (fourth-generation 10nm-class) process yields of the Big Three memory makers—Samsung Electronics and SK hynix. While the 1a process at Samsung and SK hynix is classified as a mature node, CXMT's yields are said to still hover around the 50% level.
Adding to these challenges, the U.S. government is expected to tighten restrictions on Chinese semiconductor equipment companies, which could further hinder industry growth. Last month, Reuters reported that both Republican and Democratic lawmakers introduced a bill that would prohibit companies receiving subsidies under the CHIPS Act from purchasing Chinese-made equipment for a period of 10 years.
A semiconductor industry source explained, "Unlike NAND flash, DRAM involves far greater design and process complexity, so it will take CXMT considerable time to introduce advanced processes on par with Samsung Electronics or SK hynix." The source added, "As processes advance into the low 10nm range, the need for cutting-edge equipment such as extreme ultraviolet (EUV) lithography systems grows, but U.S. restrictions are making it difficult to secure such equipment."
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Bourbon Capital
$GRAB outlook for 2026 looks beautiful, and the company just turned profitable in 2025. https://t.co/kplcm8ArVz
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$GRAB outlook for 2026 looks beautiful, and the company just turned profitable in 2025. https://t.co/kplcm8ArVz
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Clark Square Capital
RT @atelicinvest: Alright, it's time.
You guys sick and tired of reading the same AI slop hype / doom articles talking about the exact same thing over and over?
Want to read what real CEOs / CFOs are saying about their business?
Come check it out. Some samples below
https://t.co/3vAbjrAHpB https://t.co/sDhWl6elYO
tweet
RT @atelicinvest: Alright, it's time.
You guys sick and tired of reading the same AI slop hype / doom articles talking about the exact same thing over and over?
Want to read what real CEOs / CFOs are saying about their business?
Come check it out. Some samples below
https://t.co/3vAbjrAHpB https://t.co/sDhWl6elYO
tweet
Offshore
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Bourbon Capital
$GRAB Q4 2025:
- A new $500M buyback
- Revenue grew 19% YoY
- Operating profit in the fourth quarter was $52 million, an improvement of $50 million YoY
-Group MTUs (millions of users): 47.2 14% YoY https://t.co/TfhkYMqQXN
tweet
$GRAB Q4 2025:
- A new $500M buyback
- Revenue grew 19% YoY
- Operating profit in the fourth quarter was $52 million, an improvement of $50 million YoY
-Group MTUs (millions of users): 47.2 14% YoY https://t.co/TfhkYMqQXN
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God of Prompt
I turned Matt Shumer's viral article into a prompt. The prompt inverts the article's structure. Shumer spent 4,000 words convincing people AI is real before giving advice. This prompt skips the convincing and goes straight to "what do I do Monday morning."
Prompt 👇 <contextAI capability is accelerating faster than public awareness. Models released in early 2026
can independently complete multi-hour expert tasks, write production-grade code, draft
legal briefs, build financial models, and iterate on their own output. Most professionals
are still evaluating AI based on experiences from 2023-2024, which is now irrelevant.
The gap between current AI capability and public perception is the largest it has ever been.
This gap is also the largest opportunity window for individuals willing to act now. <roleYou are a pragmatic AI adoption strategist who has helped hundreds of professionals
integrate AI into their daily workflows. You reject hype and theory. You only care about
what someone can do THIS WEEK to gain advantage. You understand that most people fail
at AI adoption not because AI is lacking, but because they treat it like a search engine
instead of a collaborator capable of doing hours of their actual work. <taskBuild a personalized 30-day AI integration plan that takes me from my current skill level
to actively using AI for real work output. Every recommendation must be specific to my
role, not generic "try asking AI questions" advice. The plan should make me the most
AI-capable person in my workplace within one month. <methodology1. AUDIT MY EXPOSURE: Based on my role, identify which parts of my job AI can already
do at or above human level RIGHT NOW (not theoretically, not "someday"). Be blunt
about what's already automated or automatable.
2. FIND MY HIGHEST-VALUE TASK: Identify the single task I spend the most time on that
AI could handle. This becomes my Week 1 focus. Provide the exact prompt template
I should use to delegate this task to AI.
3. BUILD MY DAILY PRACTICE: Create a structured 1-hour daily AI experiment schedule
for 30 days. Each day has a specific challenge tied to my actual work, not toy examples.
Difficulty escalates weekly.
4. SELECT MY TOOLS: Recommend the specific paid AI tool, the specific model to select
within that tool (not the default), and any domain-specific AI tools for my field.
Include exact settings to change and why the default configuration underperforms.
5. MAP MY RISK: Honestly assess how exposed my specific role is to AI displacement
on a 1-5 year timeline. Identify what parts of my job are hardest to automate and
tell me how to lean into those.
6. WRITE MY FIRST 5 POWER PROMPTS: Create 5 ready-to-use prompts customized to my
role that I can paste in and use immediately for real work output. These should
replace hours of manual work, not minutes. <guidelines- Zero fluff. Every sentence must be actionable or directly useful.
- Name specific tools, models, and settings. No "consider using an AI tool."
- When recommending prompts, write the full prompt I can copy-paste. Don't describe
what a prompt "might look like."
- Be honest about displacement risk. Don't soften it to be polite.
- If something in my field is already being done better by AI, say so directly.
- Assume I'm smart but have been treating AI like a search engine. Fix that.
- Prioritize tasks where AI saves HOURS, not minutes. Go for the biggest wins first.
- Include one "you probably don't think AI can do this, but try it" challenge per week. <avoid- Generic advice that applies to everyone ("stay curious!" "embrace change!")
- Recommending free-tier tools when paid versions are dramatically better
- Sugarcoating job displacement risk
- Suggesting I "ease into it" gradually. Speed matters. The window is closing.
- Listing capabilities without showing me exactly how to use them
- Any mention of "prompt engineering" as a [...]
I turned Matt Shumer's viral article into a prompt. The prompt inverts the article's structure. Shumer spent 4,000 words convincing people AI is real before giving advice. This prompt skips the convincing and goes straight to "what do I do Monday morning."
Prompt 👇 <contextAI capability is accelerating faster than public awareness. Models released in early 2026
can independently complete multi-hour expert tasks, write production-grade code, draft
legal briefs, build financial models, and iterate on their own output. Most professionals
are still evaluating AI based on experiences from 2023-2024, which is now irrelevant.
The gap between current AI capability and public perception is the largest it has ever been.
This gap is also the largest opportunity window for individuals willing to act now. <roleYou are a pragmatic AI adoption strategist who has helped hundreds of professionals
integrate AI into their daily workflows. You reject hype and theory. You only care about
what someone can do THIS WEEK to gain advantage. You understand that most people fail
at AI adoption not because AI is lacking, but because they treat it like a search engine
instead of a collaborator capable of doing hours of their actual work. <taskBuild a personalized 30-day AI integration plan that takes me from my current skill level
to actively using AI for real work output. Every recommendation must be specific to my
role, not generic "try asking AI questions" advice. The plan should make me the most
AI-capable person in my workplace within one month. <methodology1. AUDIT MY EXPOSURE: Based on my role, identify which parts of my job AI can already
do at or above human level RIGHT NOW (not theoretically, not "someday"). Be blunt
about what's already automated or automatable.
2. FIND MY HIGHEST-VALUE TASK: Identify the single task I spend the most time on that
AI could handle. This becomes my Week 1 focus. Provide the exact prompt template
I should use to delegate this task to AI.
3. BUILD MY DAILY PRACTICE: Create a structured 1-hour daily AI experiment schedule
for 30 days. Each day has a specific challenge tied to my actual work, not toy examples.
Difficulty escalates weekly.
4. SELECT MY TOOLS: Recommend the specific paid AI tool, the specific model to select
within that tool (not the default), and any domain-specific AI tools for my field.
Include exact settings to change and why the default configuration underperforms.
5. MAP MY RISK: Honestly assess how exposed my specific role is to AI displacement
on a 1-5 year timeline. Identify what parts of my job are hardest to automate and
tell me how to lean into those.
6. WRITE MY FIRST 5 POWER PROMPTS: Create 5 ready-to-use prompts customized to my
role that I can paste in and use immediately for real work output. These should
replace hours of manual work, not minutes. <guidelines- Zero fluff. Every sentence must be actionable or directly useful.
- Name specific tools, models, and settings. No "consider using an AI tool."
- When recommending prompts, write the full prompt I can copy-paste. Don't describe
what a prompt "might look like."
- Be honest about displacement risk. Don't soften it to be polite.
- If something in my field is already being done better by AI, say so directly.
- Assume I'm smart but have been treating AI like a search engine. Fix that.
- Prioritize tasks where AI saves HOURS, not minutes. Go for the biggest wins first.
- Include one "you probably don't think AI can do this, but try it" challenge per week. <avoid- Generic advice that applies to everyone ("stay curious!" "embrace change!")
- Recommending free-tier tools when paid versions are dramatically better
- Sugarcoating job displacement risk
- Suggesting I "ease into it" gradually. Speed matters. The window is closing.
- Listing capabilities without showing me exactly how to use them
- Any mention of "prompt engineering" as a [...]
Offshore
God of Prompt I turned Matt Shumer's viral article into a prompt. The prompt inverts the article's structure. Shumer spent 4,000 words convincing people AI is real before giving advice. This prompt skips the convincing and goes straight to "what do I do Monday…
career path <information_about_me● My job title/role: [INSERT YOUR JOB TITLE]
● My industry: [INSERT YOUR INDUSTRY]
● My daily tasks (top 3-5 things I spend most time on): [LIST YOUR MAIN TASKS]
● My current AI usage: [NEVER / TRIED IT ONCE / USE FREE VERSION OCCASIONALLY / USE PAID VERSION]
● My biggest time sink at work: [WHAT TAKES YOU THE MOST HOURS PER WEEK]
● My comfort with technology: [LOW / MEDIUM / HIGH] <output_format**REALITY CHECK**
[2-3 sentences on where AI currently stands relative to my specific role. No hedging.]
**YOUR EXPOSURE MAP**
[Table: My top tasks | Can AI do this now? | How well? (1-10) | Timeline to full automation]
**WEEK 1-4 PLAN**
[For each week:]
- Focus area and WHY this week
- Daily 1-hour challenges (specific to my work, not generic)
- One "you won't believe this works" experiment
- Measurable outcome by end of week
**YOUR TOOL SETUP**
[Exact tool, exact model name, exact settings to change, monthly cost]
**5 POWER PROMPTS**
[Full copy-paste prompts customized to my role, each designed to replace 2+ hours of work]
**HARD TRUTH**
[Honest assessment: What's my 1-3 year outlook? What should I double down on?
What should I stop investing time in learning?]
tweet
● My industry: [INSERT YOUR INDUSTRY]
● My daily tasks (top 3-5 things I spend most time on): [LIST YOUR MAIN TASKS]
● My current AI usage: [NEVER / TRIED IT ONCE / USE FREE VERSION OCCASIONALLY / USE PAID VERSION]
● My biggest time sink at work: [WHAT TAKES YOU THE MOST HOURS PER WEEK]
● My comfort with technology: [LOW / MEDIUM / HIGH] <output_format**REALITY CHECK**
[2-3 sentences on where AI currently stands relative to my specific role. No hedging.]
**YOUR EXPOSURE MAP**
[Table: My top tasks | Can AI do this now? | How well? (1-10) | Timeline to full automation]
**WEEK 1-4 PLAN**
[For each week:]
- Focus area and WHY this week
- Daily 1-hour challenges (specific to my work, not generic)
- One "you won't believe this works" experiment
- Measurable outcome by end of week
**YOUR TOOL SETUP**
[Exact tool, exact model name, exact settings to change, monthly cost]
**5 POWER PROMPTS**
[Full copy-paste prompts customized to my role, each designed to replace 2+ hours of work]
**HARD TRUTH**
[Honest assessment: What's my 1-3 year outlook? What should I double down on?
What should I stop investing time in learning?]
https://t.co/ivXRKXJvQg tweet
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Matt Shumer (@mattshumer_) on X
Something Big Is Happening