Just saw a demo from the @TheARCHAI_ team and I’m blown away.
Lil Bill is coming to a screen near you soon… and let’s just say, things are about to get real interesting.
What this tech can do is wild.
My streams and socials are about to level up big time.
Stay tuned.
To interoperate, L2s need to communicate.
Today, the most secure way for L2s to do this is pass messages via the Ethereum L1—but that takes 15+ min & costs $$
For interop to work well, L2s need to swap messages in seconds.
Let's explore how we can make this possible. 🧵👇
👁️ System Alert — @San_FranTokyo
You’ve been recognized. Your influence is undeniable.
Every member has earned a boosted reputation score — unlocking access, rewards, power.
🪞To claim what's yours, register for the Black Mirror Experience: https://t.co/3NqKQ3eAox
200,000+ users have registered for the Black Mirror Experience.
200,000+ tracked identities.
200,000+ reputations under observation.
200,000+ stories unfolding in real time.
🪞 Join them, or be left behind: https://t.co/I5k7LuO2wx
BREAKING NEWS: A NEW DIGITAL ORDER IS EMERGING
As of this moment, you are being watched. Tracked. Rated. The system is compiling its calculations.
Will you embrace it, or be left behind?
Register for early access: https://t.co/nPJ2moUEHV
🚀 $AAA — Powering the Future of Autonomous AI Agents on Solana!
🌐 https://t.co/gmalGQEDYi
CA: 275FUEyp5o7TWo1dCDYJyprb4o2WBcxEQsvmt816pump
Step into the AI revolution with AI Agent App — the first-of-its-kind platform on Solana that lets anyone create, deploy, and monetize autonomous AI agents.
🔥 Why $AAA deserves your attention:
•✨ Real utility: Operate agents, stake for benefits, unlock premium AI features
•⚡ Solana speed: Instant, low-cost execution
•🧠 Cutting-edge AI + Web3: Truly autonomous AI agents with GPT-level intelligence
•💼 Backed by #Microsoft & #OpenAI grants
•✅ Trusted by users from Google, eToro, https://t.co/DKhfGBGmGh, USC, UCSD, and more
📈 As the AI narrative accelerates in 2025, $AAA is emerging as the fuel for a decentralized agent economy.
👉 Don’t miss the chance to ride the next wave of AI x Web3 innovation.
#Solana #AAA #AI #Crypto #AgentEconomy #Web3 #NarrativePlay
🔥 @NodeGoAI Airdrop Confirmed! 🚀
📌 The future of computing is decentralized!
✅ 100M $GO tokens available!
🚀 $8M raised. Be part of it!
◣ Claim rewards → https://t.co/d3SunRdBKo
BAD AI Launch: Post Mortem - Part I
1/7 Introduction & Context
BAD AI was developed with cutting-edge technology, and we made the decision to introduce it through a token launch. After evaluating multiple ecosystems, we engaged with BNB Chain due to a strong community, extensive foundation support and mature ecosystem.
2/7 Ecosystem & Key Players
Key BNB ecosystem players have facilitated our discussions with several launchpads, including Four Meme, GraFun, and Floki. Floki, through its TokenFi platform, expressed strong support for BADAI. It’s very important to note how much we appreciate the support from the Floki and BNB teams and community. It has been one of the smoothest experiences we as a team have had across the Web3 landscape.
Some of the key ecosystem players have vouched for GraFun as a trusted partner and a go-to platform for our TGE. In fact, due to the sniping issues that have plagued some of the previous hyped launches on the BSC chain, we have been insistently advised to opt for the GraFun anti-snipe engine.
3/7 Our Security Considerations
We implemented GraFun’s anti-sniping mechanism to protect against issues common in highly anticipated token launches. Before proceeding, we took the following security measures:
Security audit: GraFun provided an audit from a reputable security firm - AmbiSafe.
Internal inspection: BADAI’s own team reviewed the contract, although we lacked access to its proprietary "black box" components. Such access has not been granted to us for a variety of reasons that are elaborated upon in the detailed research report we have published (link below).
Floki review: Floki’s security team has reviewed the contract.
Exchange audit commitments: CEX listing teams would be granted access to the black box mechanism, though we are unaware if any such audits took place, but all of the exchanges we’ve listed on have integrated the contract.
4/7 Retrospective Analysis & Lessons Learned
Looking back, BADAI should not have proceeded without full access to the proprietary security mechanisms within the contract. Despite pushing for transparency, we ultimately relied on assurances from GraFun and Floki regarding the proprietary contract components, which in hindsight was a critical mistake. This has proven to be one of the most regrettable actions we’ve taken. In hindsight, we should have made full contract access a non-negotiable requirement before proceeding with the launch. We acknowledge this mistake and sincerely apologize to our community and everyone affected. Moving forward, we will not engage with any third-party solutions unless full transparency is ensured.
While the only part of the infrastructure that failed was the one component that we did not build ourselves, we recognize that this was our project, our community, and ultimately our responsibility.
5/7 How the Launch Was Supposed to Work
GraFun’s anti-sniping mechanism was designed with the following intended mechanics in mind:
Snipers would enter the liquidity pool (LP) within the first few blocks post-TGE.
The contract would blacklist these snipers, effectively preventing their accounts from interacting with the liquidity pool.
After a few blocks, the blacklist restrictions would be automatically lifted, and orderly trading would resume for regular users, while leaving the snipers unable to sell. No manual actions from the BADAI team were required.
We have communicated this publicly on a number of occasions and ensured our community was informed. https://t.co/ZVQjsQT1xE
6/7 What Actually Happened:👇
The reality was significantly different from what was planned:
- The anti-sniping mechanism inadvertently affected the liquidity pools themselves, preventing all users from selling, not just snipers. This lasted for well over 30 minutes, far longer than the first 10 blocks as originally intended.
Community Outcry & Immediate Action: After a massive community outcry, we noticed the issue and manually revoked restrictions on all non-sniper accounts and thereafter removed any limitations on pool interactions. The operation's manual nature has required some time to handle.
- Unintended Consequences: Instead of targeting only snipers, all buyers were prevented from selling on the DEXs. In some cases, users couldn’t buy as well - at least those that have been using 3rd party platforms, like Floki Trading Bot.
- Upon noticing the irregularities due to community outcry, the BAD AI team rushed to manually disable the anti-sniping. However, in order to do it, we had to first receive a list of sniping addresses—those that interacted with the contract during the first several blocks—and manually revoke them.
- GraFun provided the BAD AI team with a list of accounts slated for revocation, which means that tokens would be returned to BADAI and BNB refunded to the buyer. The GraFun platform's revocation interface also had irregularities, which are elaborated upon more fully in the security report.
- Price effect: During this period, primary market BADAI holders, reacting to concerns of a potential honeypot, began offloading their allocations. These concerns were further amplified when a Tweet went out (https://t.co/Qv89Ra7aTu and https://t.co/T3TGud8lr6) flagging the BADAI token address with a honeypot warning. This led to significant selling pressure, bringing the $BADAI market cap down from approximately $200 million at its peak to around $25 million at its lowest point.
A detailed report is available here: https://t.co/s3EeRGDZwe
7/7 What has been done by the BADAI team
When the contract issue became apparent, we initiated the call with the Floki and GraFun teams and acted as fast as possible to disable the protection manually. It goes without saying that by the time we were able to disable the protection, a lot of damage had already been done.
We have prepared our own internal audit review by Saturday afternoon CET. We have then proceeded to answer the questions of a number of security professionals that have been brought by BNB to audit our launch. Their report as far as we are aware has found no wrongdoing on the part of the BADAI team.
All along the way we have been in close contact with all the teams involved and parties affected, including, but not limited to BNB, Floki, GraFun and many others. We have aided the development of the reimbursement mechanism that we expect to be released shortly.
Next steps will be provided in a post later this week.
hey, degens, the @Cointelegraph AI Agent is live:
TLDR:
- World's first Web3 RAG-augmented historian - fresh from the archives of the oldest and biggest web3 media
- Trained on @Cointelegraph's massive content archive - all RAG'ed for your convenience
- Real-time data processing that makes your Google searches look like stone tablets
- Source verification that would make your college professor weep
We didn't just build another agent - we created a digital oracle that devours Cointelegraph's entire knowledge base and spits out pure crystallized truth. This isn't your typical chatbot that regurgitates stale facts. This is evolution.
Welcome to the future of media interaction, you lucky bastards - try it here: https://t.co/82eoiCo4ad
1/ Article Ingestion & Chunking 📄
The pipeline begins with automated article ingestion from Cointelegraph. The raw text is parsed and segmented into semantic chunks, ensuring optimal granularity for downstream processing.
2/ Q&A Generation via LLM 🤖
Each chunk undergoes LLM-based processing to extract key insights and generate structured Q&A pairs:
🔹 Semantic Analysis: LLM scans chunks, identifying key information.
🔹 Dynamic Q&A Formation: The model predicts potential queries users may ask and precomputes relevant responses.
🔹 Basically, it's an evolution of search.
3/ Retrieval-Augmented Generation (RAG) 🔍
Instead of relying purely on a fixed knowledge base, we implement RAG, which enhances AI-generated responses with real-time information retrieval.
🔹 What is a RAG?
RAG combines:
✅ Retrieval – Fetches relevant data from external sources (e.g., databases, documents, APIs) - Cointelegraph DB in our case
✅ Generation – Uses an LLM to generate responses based on retrieved information
This method ensures answers are:
📌 More accurate and up-to-date
📌 Context-aware, improving response quality
📌 Context-agnostic - the part that is stored in the RAG is persistent
4/ Real-World Implementations of RAG 📡
RAG is widely used in AI applications requiring real-time data augmentation:
📚 Chatbots & Virtual Assistants – Context-aware, dynamically updated responses.
🔍 Semantic Search Systems – Enhanced retrieval beyond traditional keyword-based search.
🩺 Medical AI – Fetching the latest research and clinical guidelines on demand.
📈 Financial Market Intelligence – Integrating real-time stock, crypto, and economic data.
📊 Enterprise AI Knowledge Bases – Querying proprietary document repositories with LLM augmentation.
You can read more about RAG in following articles from Nvidia and Google
5/ How RAG Works in Cointelegraph’s Processing?
When a user asks a question:
🔹 The system retrieves relevant article segments based on semantic meaning
🔹 The LLM incorporates this retrieved data into a coherent response
🔹 Results are linked to original sources for reference
🔹And yes, our own RAG implementation is available in GitHub for builders.
6/ User Query Processing 🗣
To improve retrieval efficacy, user queries undergo:
🔹 Reformulation – Expanding single queries into multiple phrasings.
🔹 Temporal Prioritization – Weighting recent articles over outdated content.
🔹 Time-Range Filtering – Ensuring results align with user-specified timeframes.
7/ Vectorized Search & Similarity Matching 🧠
Instead of relying on keyword-based retrieval, Q&A pairs are indexed using vector embeddings.
🔹 Embedding Model: Converts text into high-dimensional vector representations.
🔹 Semantic Search: Queries are mapped to the nearest vector representations, retrieving contextually similar results.
This enables meaning-based retrieval rather than brittle keyword matching.
8/ Answer Generation & Enrichment 📌
Once relevant Q&A pairs are retrieved, they are:
✅ Aggregated into a coherent response.
✅ Linked to original sources for verification.
✅ Re-ranked based on contextual relevance.
9/ What's next?
🔹Currently, the Agent is trained on the last 3 months worth of data. We'll be processing more.
🔹All new articles are parsed as they appear
🔹Currently, our RAG implementation does not have the time dimension - i.e. it's much stronger is answering What rather than When questions, but we'll be adding that soon as well
🔹We'll be collecting feedback for 2 weeks - then another iteration will pave the way for real AI media supremacy.
C₈H₁₁NO₂ 🧪