Hoàng Anh Bùi

[tommy 🌙 @CryptoTomYT]

My full thesis on Launchcoin - going to be breaking down my thoughts from a narrative, data, and future predictions standpoint.

1. Narrative

ICM has been a huge push from Solana ecosystem. Just taking a look at @solana bio, you can see they want to push the ICM meta.

Furthermore, we have seen a huge amount of Web2 adoption into crypto this year, I don't need to speak on this too much but lots of tradfi companies breaking into crypto launching stablecoins and various different products. WLFI launching a few weeks ago legitimizes crypto even more.

What does this have to relate to ICM/Launchcoin? Well, crypto has always been stigmatized within Web2 and now we're seeing more adoption than ever. We are quite literally seeing huge web2 giants moving into crypto, ICM is a logical move for startups and businesses - launch tokens related to their business. Not only that, we saw the news from the SEC allowing for tokenization of equities in the future.

https://t.co/IcodoQh6eV

It's a narrative bigger than memes ever were as it's opening up crypto to a much larger market.

[zaza @zazaxbtt]

ICM this ICM that,is it a chain L1 L2 is it a meta what's it all about? How do I get in on it!

Well I could sit right now and write a thread about it but I'm not going to do that cause I find writing a thread boring and I am lazy

So here are some of the best tweet that explain ICM 🧵

[Hoàng Anh Bùi @HongAnhBi7]

Do you remember when you joined X? I do! #MyXAnniversary

[Chill @ChillTRD]

This basically covers everything you need to know about $SERV.

[Chill @ChillTRD]

$VIRAL just demoed their first computer-use agent (VM-1a).

This is the craziest piece of agentic tech in crypto. It's not a chatbot or an AI assistant, but an autonomous AI employee.

Revolutionary next-gen tech is being built right now. Let me explain.

🧵⏬

[Zeneca🔮 @Zeneca]

I round tripped 8 figures last cycle. A few things I learned:

- It’s almost always better to sell too early and miss out on gains, then to hold too long and round trip the bag. This is because eventually, almost everything trends to zero, so even your “early sell” is likely going to make you look like a genius in a few months/years

- If you ever take a PnL screenshot of how much you’re up, sell. You don’t have to sell your entire position, but it’s usually an excellent time to at least trim 20-50% of it.

- Most people on this app have absolutely no idea what they’re talking about. Often the loudest and most confident voices know the least, while the quiet and self questioning ones are full of wisdom.

- You can’t borrow conviction. If you buy something because someone else did or told you it was “a hidden gem”, you’re almost guaranteed to fumble the bag. They’ll dump on your head while you’re still anxiously waiting for their next tweet or YouTube video to tell you what to do.

- Stop trying to impress people. This is just good general life advice, but it applies triply so to this space. Wanting to impress your friends and family is one thing, wanted to impress random anons on the internet? Insanity.

- There’s Bitcoin, and then there’s everything else. It took me too long to truly realize this. Yes alts can and will occasionally outperform - sometimes for long stretches of time - but basically everything bleeds to Bitcoin over the long run.

- Most people try to outperform Bitcoin by trading these alts; probably leas than 5% of people can actually accomplish this. It’s like trying to outperform the S&P 500. Most people are better off just buying the index.

- This place has a way of warping your perspective to a level that is literally bordering on mental illness. Many of us refused to sell jpegs of a list of words for $50,000 last cycle because we thought “it’s undervalued”. Many otherwise smart people. You are not immune. Herd mentality is real, it takes *a lot* to swim against the current around here. You should try.

- Extending from that point, try and zoom out and also spend time with non-crypto people. 1 SOL or 0.08 ETH can seem like not significant amounts of money (unit bias is real), but add up how much that is per day or year and think what you could do with that money IRL. Also, most people are thrilled to earn a 10% return on their investments in a YEAR, and rightfully so. That’s a great return, crypto just warps everything.

- Compound interest is mind boggling powerful. You don’t need to find a 100x, you’re usually way better off stringing together a bunch of 2x plays or even compounding at 10-50% a year (do the math, do you have any idea how insane compound interest is at high %s over a bunch of years?)

- Put another way: “Most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years.”

If you found this insightful, all I ask is that you drop a bookmark, share with a friend, and/or subscribe to my newsletter where I share a lot more 🙏

Cheers

[FVCT.CRVPTO @FvctCrvpto]

// WHY I BELIEVE @ViralMindAI WILL BECOME THE FIRST TRUE CRYPTO x AI UNICORN

"...this is Viral's ai16z / arc moment."
(kickass quote from smart money @BenjiGanar)

$VIRAL has just begun its insane trajectory and we're all along for the ride.

---------------

// Hi Anon, it's been a minute hasn't it?

With the $VIRAL team dropping some insane updates lately, I figured it’s the perfect time to revisit the project and double down on why it’s my top conviction investment—not just in the Crypto x AI space, but across my entire portfolio.

I’m convinced @ViralMindAI has what it takes to become the first true Crypto x AI unicorn. Sure, there are projects out there boasting 1B+ token market caps, but let’s not mix up token hype with real-world equity value.

What the $VIRAL team is cooking up isn’t just standout in the Crypto x AI niche—it’s legit, first-mover tech that holds its own across the broader AI landscape.

ViralMind has the vision, the proprietary tech, and the team—all the key ingredients for real, sustainable success, and industry recognition.

My rationale is pretty simple:
- The Tech, the team and the community.

// THE TECH

At its core, ViralMind is a Large Action Model (LAM) training and data infrastructure platform. LAMs have already shown they outshine LLMs in accuracy for computer use and automation.

The catch?

Training and maintaining LAMs can get insanely expensive, since parsing and structuring action data is resource intensive.

That's where @ViralMindAI's proprietary tech shines. The team has built frameworks and infrastructure that streamline and optimize the data workflow, slashing costs and complexity.

Their latest update gave us a peek at something bigger—not just video demos of key features in $VIRAL's upcoming desktop app, but also industry-breaking advancements in their data framework.

So, what’s it all mean? Let’s break it down from a few angles:

1) The User
- $VIRAL is a data crowdsourcing platform where users can earn tokens by contributing.

- You can manage and tweak your own datasets.
Data gets recorded straight from your desktop, keeping things seamless.

- As partners come onboard, users can earn 3rd party tokens (like SOL) while helping devs train models.

- Pure speculation here, but this could evolve into a full-on data marketplace;

Imagine selling high quality action data you’ve structured yourself and becoming a AI data broker.

2) The Indie AI Dev
- A lot of Crypto x AI devs catch heat for leaning on off-the-shelf APIs, and fair enough, building custom models from scratch is pricey, especially resource-heavy LAMs.

- With $VIRAL, indie devs can leverage the platform in two ways: a) tap into user-submitted and pre-existing datasets to train models, or b) crowdsource their own LAM datasets by uploading action-specific tasks for users to generate.

- They can even reward users with their own 3rd party tokens.

3) "Big Tech / Big AI"
- Heavyweights like @AnthropicAI,@OpenAI, and @Microsoft are pouring hundreds of millions into one specific AI niche: data workflow infrastructure.

- As AI models max out their neural potential, the game’s shifting to training data accuracy - how fast and flawlessly can my model or agent nail its task?

- Right now, a lot of data training and structuring gets outsourced to external vendors, risking bad or manipulated data.

- Enter @ViralMindAI’s Forge platform: Big Tech can source action-specific data from real people - no synthetic data - without bloated overhead. Better yet,
$VIRAL’s solution optimizes and consolidates workflows.

Quote from the team:

Normally, collecting high-quality training data requires:
- recording pipelines
- task engineering
- quality checks
- contractors & VMs

Forge simplifies it all. One prompt → custom AI training flow.

This is the kind of proprietary tech "Big Tech" would fight over. It’s a real fix for a very real problem.

No surprise, then, that $VIRAL core dev @ddupont808 is still at Microsoft, working on core projects like Omniparser v2 - which brings me to the next reason $VIRAL’s a unicorn in the making.

// THE TEAM

Silicon Valley thrives on its culture of constant innovation and acceleration. Want to catch the eye of a seasoned SV VC? The most effective way is to show you understand the culture and expectations - and this starts with the team.

In today’s mess of celebrity rugpulls, grifters, and larpers, my investment thesis has been simple:

Invest in tech, invest in devs.

That’s exactly why I backed $VIRAL.

The Crypto x AI space is packed, and I’ll admit - despite the hype, real talent and innovation are hard to come by. But the three devs behind $VIRAL - @ddupont808, @zoopee1, and @mdean808 are hands-down the most cracked team out there, no contest.

We’re talking current MIT students and Microsoft contractors, all laser-focused on AI - no larpers here. Can you name another Crypto x AI project with a core dev actively contributing to an industry-leading AI framework (FYI - I'm referring to Microsoft's Omniparser again)?

There isn’t one.

@ViralMindAI’s also the only project in this space I’ve seen dodge controversy or FUD entirely. Why? The team’s no-nonsense, heads-down builders who ignore the noise.

Their vision is massive in scope, but the team's consistent delivery and professionalism have turned $VIRAL holders into die-hard believers - which leads me to the third reason $VIRAL’s destined for unicorn status.

// THE COMMUNITY

If you’re reading this, you’ve probably noticed $VIRAL popping up all over your TL and decided to DYOR.

Here’s something else you might’ve noticed: a quick $VIRAL hashtag search shows zero bot spam or “BUY BUY BUY” degen reply-guy nonsense.

Every time I scan $VIRAL sentiment, I literally only find genuine high-IQ takes from people who clearly know their shit - whether it’s a KOL with 100k+ followers or an anon with 10.

The $VIRAL community’s somehow evolved into this hub of the smartest folks in the space. No mindless shilling - just people who genuinely get the tech and its potential.

You won’t find another project with this level of conviction and high IQ energy. More often than not, a community mirrors its project and team - and with $VIRAL, that couldn’t be truer.

// CONCLUSION

As hype builds,@ViralMindAI’s poised to lock in its spot as a Crypto x AI bluechip, with a token market cap to match.

The latest announcements and upcoming desktop app have sparked insane momentum - this is $VIRAL’s $arc / #ai16z moment.

But I’ve got a gut feeling “Big AI” will come knocking way sooner than we think.

We’re literally investing in an early-stage unicorn. $VIRAL is frontrunning the entire AI industry.

---------------

// TL;DR

- @ViralMindAI is on track to be the next Crypto x AI bluechip.
- $VIRAL ’s proprietary AI training and data infrastructure is unlike anything out there, tackling real bottlenecks with a working solution.

Speculation:
- A P2P data marketplace could be in the cards.
- Eventually be able to create and deploy your own computer use LAM agents.
- A major project could onboard as a partner.
- $VIRAL's PA could mirror $ARC and #ai16z once liquidity floods back into Crypto x AI.

$VIRAL | @ViralMindAI

------------
As always, thanks for sticking around till the end. Let me know in the comments if you there's a specific project you want me to deep dive into.

// Nominal Decel { } Veridical Accel //

[Timeistickinganon @Exit_liquidity7]

🧵 $VIRAL : LAUNCH TIME 🧵

LAM agents that learn to use computers like humans automating tasks in marketing, sales, gaming, robotic, crypto-trading and more.

This will be REALITY in the next few days 👀

[Haotian @tmel0211]

关于 PI 币上 @okx 交易所这事，简单谈谈看法：

1）PI 币在下沉市场的发酵给了很多Crypto从业者一种莫名道德“优越感”，几乎都有被身边大妈大叔以“一PI一BTC”的口号而搞懵圈过。

嗯，没错，大家都知道那是CX口号，以为下沉市场的人很傻，但其实在很多人眼里，玩资金盘的人和链上冲土狗的人本质上没啥两样。身在局中，大家也都知道是“跑得快”游戏，都觉得自己会比别人跑得快。没啥调侃的必要，不喜欢，不玩就好😮‍💨。

2）交易所的上币网络舆论反响越大，暴露的并不简单是交易所的上币审美和节操问题。负面情绪汹涌反倒说明市场上的流动性真的濒临枯竭了，大家都在亏钱，把情绪炮轰矛头都对准了交易所，要是大牛市，流动性充沛的时候，恐怕都没人会在意；

3）交易所上币成为话题焦点的周期真心很乏力，无论怎么做都乌泱泱充满着Noise。原本上币组存在的意义是审核项目价值，让优质项目有更大的曝光度和品牌美誉度。但现在交易所上新币赚钱效应没了，连品牌和美誉度也在逐渐丧失。

无论交易所上PI币背后是怎样的增量用户引入等产品业务考量，都无法解释为什么链上热点持续很久、用户群体同样庞大、共识还强的AI Agent系列代币被忽略？难道，真的是我们热衷的Alpha项目还比不上大爷大妈的审美？

TRUMP乱搞一通吸走了行业大量流动性，PI币又带走了行业仅存的道德“优越感”，现在的行业大环境真心太难了。关键是，交易所上币背后真实的商业利害原因很复杂，要想破局，唯有一条路：做大链上资产价值（价格）最终定价权，让链上世界变得更强大。

[Finish 🏁 @0xFinish]

$LIBRA cartel just got revealed with millions extracted

#MELANIA $LIBRA $CRA all created by the same scam team

They stole billions from simple citizens breaking the law

A full story of $LIBRA, a scam promoted by a president 🧵👇

[Rorschach @0x_Rorschach]

💢Why @ViralMindAI is Different and have a Huge Potential

ViralMind redefines how AI agents interact with computers by introducing Large Action Models (LAMs), a leap forward that combines human-like dexterity with machine intelligence.

➰What is a Large Action Model (LAM)?

A Large Action Model (LAM) is a specialized type of artificial intelligence designed to perform human-like actions on digital interfaces. Unlike traditional AI systems that interact through predefined APIs or rely on OCR (Optical Character Recognition) for interpreting interfaces, LAMs are trained to directly interact with user interfaces (UIs) as if they were human. This includes tasks such as:

-Moving a cursor and clicking.

-Typing commands or filling forms.

-Navigating and manipulating elements in software and games.

LAMs integrate language reasoning with action-based interaction, enabling them to understand and respond to both visual and functional elements of a screen. For example, a LAM can recognize a button visually, understand its purpose, and perform the correct action without needing a pre-coded script.

➰Key Features of LAMs:

▪️Native Interaction: Bypasses the need for intermediate systems like OCR or restricted APIs.

▪️Training from Real Scenarios: Uses real-world tasks and immersive gaming environments to improve performance.

▪️Scalability: Handles complex, high-dexterity workflows like gaming, creative design, and enterprise software.

In short, LAMs bridge the gap between natural language models and real-world action, creating AI agents capable of truly human-like computer interaction.

➰What is Optical Character Recognition (OCR)?

Optical Character Recognition (OCR) is a technology that enables machines to read and interpret text from images or scanned documents. It converts visual text (e.g., on a computer screen, printed paper, or a photo) into machine-readable text data.

OCR is commonly used in applications like:

▪️Digitizing Documents: Converting paper-based records into searchable digital formats.

▪️Text Extraction: Identifying and processing text from images or scanned PDFs.

▪️Interface Interaction: Some AI agents use OCR to understand what’s displayed on a screen (e.g., extracting menu options or fields in software).

➰Limitations of OCR in AI Systems:

▪️Accuracy Issues: OCR can misinterpret text in complex or dynamic interfaces, leading to errors.

▪️Slower Performance: Processing images for text adds latency, making it unsuitable for real-time tasks.

▪️Limited Contextual Understanding: OCR systems don’t "understand" the interface; they only extract visual text.

➰Key Differences Between LAMs and OCR-Based Systems

▪️Action Capability: LAMs perform actions natively, while OCR systems only extract text and require additional layers to act on it.

▪️Adaptability: LAMs adapt to dynamic UIs and dense visual environments, whereas OCR struggles with such complexity.

▪️Speed and Precision: LAMs operate faster and more accurately since they eliminate the OCR processing step.

▪️Learning Integration: LAMs combine language understanding with motor control, making them far more versatile than OCR-based AI.

"By moving beyond OCR and integrating native action understanding, LAMs represent the next evolution in how AI interacts with digital systems."

LAMs> OCR

LAMs enable AI agents to perform tasks just like humans. Whether it's clicking, typing, or navigating complex user interfaces, these agents natively understand and interact with digital interfaces. Unlike traditional models that struggle with dynamic or dense environments, LAMs effortlessly decode and act on visual and interactive data.

▪️Adaptability across use cases

Imagine an AI that can:
- Play like a pro in Valorant or Minecraft.
- Code like a developer, building and debugging in real IDEs.
- Simplify everyday tasks like booking flights or ordering food.
- Create like an artist, editing video or designing in creative software.

If a human can do it with a computer, so can a LAM-faster, smarter, and more consistently.

▪️Advanced learning and action

LAMs go beyond traditional language models (LLMs) by integrating a "motor cortex" for action. Trained on real-world tasks and immersive scenarios, they combine natural language understanding with high-precision motor control. This approach enables unmatched efficiency, accuracy, and scalability in real-world applications.

▪️Overcoming OCR and API limitations

Unlike legacy frameworks that rely on brittle OCR pipelines or censored APIs, LAMs work directly within the interface to ensure
- High accuracy: By natively understanding UI elements and interactions.
- Speed and reliability: By eliminating slow, error-prone OCR steps.
- Scalability: From gaming to enterprise-level tools, LAMs can handle it all.

▪️A new era of AI agents

The "training gym" extends LAM capabilities even further. With just 50 tasks, you can increase an agent's performance by 30%. At 100 tasks, you unlock native visual-action synergy. At 5,000 tasks, the possibilities become exponential-creating AI agents that are truly transformative.

➰Why It Matters

@ViralMindAI isn't just replicating AI, it's creating a new class of intelligent systems that bridge the gap between language and action. By focusing on native interaction, adaptability, and scalability, LAMs are laying the foundation for the future of embodied AI. It's not just about doing more; it's about doing it seamlessly, intelligently, and with zero friction.

[Omira @OmiraAI]

In a dynamic market such as low cap cryptocurrencies, managing your portfolio can be a challenging task.

With the help of 0.07’s personalized recommendations, based off both your trading profile and B.B4’s predictions— the task is made simple.

Limit orders are a crucial part of portfolio management, allowing you to set clear targets before emotions have the chance to take over.

With Omira-0.07, not only can you set limit orders, the right price targets are proposed to you— allowing you to execute either single limit orders or complex scaling strategies in a single click.

Set up your trader profile today ⬇️
https://t.co/KUPM0R1yqk

[Omira @OmiraAI]

Hedge funds have been increasingly relying on sentiment analysis to enhance both their returns and accuracy in price forecasts.

In 2022, it was recorded that funds utilizing social media data to gauge sentiment experienced a 15% increase in accuracy of short-term stock price forecasts.

There is a place where the correlation between sentiment and price action is stronger then ever…

Lowcap crypto markets.

The increase of predictive accuracy when leveraging sentiment analysis is exponentially larger when applied to lowcap markets vs macro markets which hedge funds normally trade.

X is the leading media platform where cryptocurrency is discussed, doubling down as an open ledger for ever growing data points relating to social sentiment.

This allows Omira Labs to provide retail traders with the same edge provided to large level trading firms!

Only with B.B4 ⬇️
https://t.co/imp5BONmbp

[Omira @OmiraAI]

How on-chain behaviour correlates to your user profile.

To allow for a hyper-personalized trading experience, Omira-0.07 employs advanced on-chain behaviour analysis and user profiling techniques. Upon registration, users are prompted to enable this feature which maximizes operability and insight precision.

The collected data undergoes a series of preprocessing steps, including data cleaning, normalization, and feature engineering- similar to the data used in our sentiment-driven B.B4 model.

The resulting dataset is then fed into a multi-stage pipeline, leveraging the power of machine learning algorithms, to precisely calibrate trader profile parameters.

The source of this data consists of the following key on-chain metrics ⬇️

> Position Size Ratio
Position Size Ratio is calculated as the average position size relative to the total portfolio’s value. This is done using a weighted moving average approach to account for variations in holdings over time. The Position Size Ratio provides insights into a trader’s risk appetite / position management strategies.

> Invested Capital Ratio
Invested Capital Ratio represents the average amount of the native currency (ETH, BNB, SOL) you have invested in tokens (excluding stables), as a proportion of the total wallet holdings. A dynamic, time warping algorithm, is used to align “investment periods” across different tokens in your portfolio, enabling accurate calculations of the Invested Capital Ratio. This metric sheds crucial light on a user’s overall exposure to market volatility.

> Holding Periods (Distributed)
Holding Periods Distributed captures the distribution of time holding tokens for each token held by a wallet, measured from the initial purchase of the tokens to the final sell (defined as a sell that leaves the wallet with less than 10% of the initial amount purchased). Leveraging the Cox proportional hazards model, 0.07 estimates the probability of an amount of time a user is likely to hold a token. This provides 0.07 with key insights into a trader's general patience within the market.

> Profit-Taking Efficiency (TP Efficiency)
Profit-Taking Efficiency measures the average profit made by a trader when analyzing strictly profitable trades. Deep deterministic policy gradient (DDPG), a reinforcement learning model, is trained on historical trade data to identify the optimal TP strategies employed by a user. The model’s reward function incorporates amount of time held, price volatility, and sentiment, allowing 0.07 to recommend profit-taking strategies aligned with the user's comfort zone.

> Loss Aversion Coefficient (SL Coefficient)
Loss Aversion Coefficient quantifies a trader’s tolerance for losses on non-profitable trades. A bayesian hierarchical model is employed, estimating the coefficient while taking into account the magnitude and frequency of losses- cross-referenced with general market conditions. This allows 0.07 to personalize stop-loss recommendations.

> Scaling Strategy
Our model analyzes the average number of buy before the first sell, and the average number of sells before the final sell, considering the relative size of each transaction. A hidden Markov model is used to identify common scaling-in/out patterns and transitions between buying and selling states. This informs 0.07’s recommendations for position entries / exits, accumulation, and scaling strategies.

By leveraging a combination of machine learning techniques and deep on-chain behaviour analysis, Omira-0.07 sets a new standard in personalized trading assistance, empowering users to make more informed and profitable trading decisions.

[Omira @OmiraAI]

Unlike LLMs such as GPT, 0.07 harnesses the power of personalized intelligence - allowing it to deliver custom and practical insights for DeFi users.

Let’s highlight the key differences between a standard model and 0.07.

> Access to Your Portfolio.
0.07 tracks your portfolio in real time, eliminating the need to provide any context.

Standard models (as shown in the example above) require users to provide extensive the details.

> Personalized Insights.
0.07 leverages both your self-assessment survey and on-chain behavior to generate tailored insights, suggested actions, and feedback that aligns with your trading style.

Only 0.07 offers behavior tracking, learning from past trades, success rates, risk tolerance, and more.

In contrast, standard models offer only generic, one-size-fits-all advice.

> Actionable, Custom-Tailored Strategies.
0.07 offers users personalized actions for all requests.

As shown in the example, the user can choose between a rebalancing strategy or other suggested actions according to their user-profile & portfolio, unlike the standard model.

In the name of simplicity, 0.07 offers a one-click solution, unlike standard models which require a second prompt.

Learn more about 0.07's personalization by visiting these resources ⬇️

https://t.co/OZZFgE6VyK

https://t.co/1HbkhDewq6

[Omira @OmiraAI]

Access the Omira-0.07 model website today.

Visit and learn more about:
> Model Personalization
> Event Simulations
> On-Chain Prompts (assisted trading)

Explore the website ⬇️

https://t.co/OZZFgE6VyK

[OpenServ @openservai]

https://t.co/cLvPNCrgxf

[fakeguru @iamfakeguru]

$SERV = Layer 0 for AI

An AI Agent swarm collaboration engine, aggregating:
+ all agents ( $eliza $aixbt $fai...)
+ all frameworks (#ai16z #virtual $pippin...)
+ all chains ( $SOL $ETH @base...)

✔️ launch agents & tokens
✔️ create custom workflows
✔️ access research
✔️ contribute to hackathons
✔️ utilise in-platform monetisation

[OpenServ @openservai]

🔮 BUY/SELL tax on $SERV removed!

We’re officially entering our rapid expansion phase, with devnet showing massive interest and great feedback - giving us a clear green light to scale FAST.

$SERV gates web3 agents, so removing barriers = more agents, more growth, more impact!

[Avichal - Electric ϟ Capital @avichal]

🚨1/ President Trump just issued a MAJOR executive order that touches on everything crypto.

This is a BIG deal: stablecoins, Operation Chokepoint, developer protections, a new Crypto Task Force, and a Digital Asset Strategic Reserve

A breakdown of what @realDonaldTrump signed: