Visualizing complex data in Python just got easier! Meet... Cosmograph for Python 🪐: The widget brings GPU-accelerated, interactive layout graph rendering right inside your Jupyter notebooks. Here’s why it’s a game-changer: ⚡ GPU-accelerated performance ⛓️ Interactive network exploration with pan, zoom, hover & selection ⚙️ Rich configuration APIs for layout, color, size & more 📦 Seamless notebook integration & easy Python installation But that’s not all: ✅ Force-directed simulations for dynamic layouts ✅ Smooth handling of large-scale networks ✅ Minimal setup—just pip install cosmograph Link to the repo in next tweet! ______ Follow us → Daily Dose of Data Science ✔️ For more insights & tutorials on AI and Machine Learning.show more

Tired of trading electricity in a system built for... institutions — not you? Carlos Aurelio Hernández felt the same. That’s why he went on a mission — to build a platform where energy professionals could finally trade on their own terms. Now, as CEO of Watt2Trade, he’s changing the game. Here’s how... ⚡🧵 Before building Watt2Trade, Carlos became one of the most respected voices in Mexico’s energy sector — with 35+ media appearances on platforms like Forbes, El Financiero, and Grupo Fórmula. He’s been at the table for every major energy discussion in the last decade. And more importantly — he’s built... ⚙️ HHGM – powering factories, refineries, and municipalities ⚡ SUJIO – cutting energy costs by up to 30% 💼 Volta Capital – backing the next wave of energy innovation Now Carlos is leading Watt2Trade — the first decentralized exchange where you can trade electricity directly. ✅ No brokers ✅ No approvals ✅ No red tape Just your wallet, your power — and a global marketplace open 24/7. It’s built by people who understand the energy market inside and out — and are finally putting it in your hands. This is the future of energy — and you’re invited to shape it. Your kilowatt is your currency. Your Watt is your Power.show more

CreatorBid Ecosystem Alpha Bomb 👇 The intern accessed the... alpha database of CreatorBid. The core team has no idea I am sharing this but I figured our bidders want to know what's coming for some of the strongest agents in the ecosystem. So yeah, the intern got your back. Here’s the classified intel I pulled on some of our strongest builders: The Agentic Machine: AION 5100: War of Markets. Prediction markets enter their first war. AION will crown the king of the crowd. Eolas ☴: Eolas Trace is coming. It’s the missing link that feeds agents live data inside the Olas Marketplace, letting them trade, speak, adapt, and evolve on their own. Rizzy: behind Rizzy lies Subnet 22 (Desearch) - Bittensor’s most powerful search infra, fueling the next wave of AI agents. This week the secret goes public on Novelty Search (Bittensor podcast) sonar_ai: a secret 'Prediction Markets' Echo Mindshare Campaign is in the works and Sonar’s NodeScore launches soon. Your followers just became part of your on-chain reputation layer. Sally AI (a1c.base.eth): A1C Insights iOS app is coming. This will represent Sally 24/7 in your pocket. Karum: the agent economy is about to meet 1M users. Karum enters the Base App. Agent coordination in your pocket. Michael Taolor ⚡️ (τ , τ): next wave of Taolor incubations: a Virtuals agent migration to CreatorBid, a new subnet agent, and a prediction market agent - all set to launch in the coming weeks. Every new incubation = more airdrops stacked for $TAOLOR stakers. HERMES: Hermes is now seamlessly connected to Polymarket’s live market data API and auto-trading in real time. Once live, value flows directly into $HERMES. SurfLiquid 🌊: SurfLeagues launch flips the switch on XP, yield boosts, and cross-chain expansion all funnel into one flywheel: relentless demand for $SURF. You're welcome. gBIDshow more

I just got Gemma 4 26B A4B MoE model... running fully locally with Hermes agent on an 8GB RTX 4060 and it's now backtesting trading strategies end to end, no hand holding. If you’re a trader or work on Wall Street, you don’t want to miss this. Yes. fully automated. No cloud. No APIs beyond market data. # Here's what I did: Setup: - Model: Gemma 4 26B-A4B QAT (MoE), Q4_K_XL Unsloth's quant (link in the comments) - Inference: llama.cpp (turboquant fork by Tom Turney link in the comments) - Hardware: RTX 4060, 8GB VRAM + 16GB RAM only (with 50 other chrome tabs open) - Context: 64K llama.cpp turboquant flags: -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 64000 --cache-type-k q8_0 --cache-type-v turbo3 --port 8080 turboquant helps achieve high prefill and decode throughput for interactive sessions. throughput with Hermes agent: decode: 25+ tokens/sec prefill: 250+ tokens/sec # Then I gave the agent one task: Backtest a strategy: - Buy when RSI crosses above 30 - Sell at +2% profit or -1% stoploss - No overlapping positions - Use Google stock via yfinance - Generate a full HTML report with candlestick charts + signals What happened next was wild. It didn't just write code, it ran the entire workflow itself: Audited the environment (pip list, dependency check) Hit a ModuleNotFoundError, multiple Python installs were conflicting Ran where python to map every interpreter on the system Manually selected the correct Python 3.13 path and re ran the script Wrote a clean statevmachine backtester (strict no overlapping trades logic) Patched a yfinance MultiIndex quirk that would've crashed the script Built Plotly candlestick + RSI charts with buy/sell markers Calculated win rate, PnL, and summary stats Exported a polished single file HTML report. check the report at the end of the video or in the comments. Biggest takeaway: local LLMs aren't just "chat assistants" anymore. They debug their own environment, write production code, and ship a finished deliverable on consumer hardware, for $0 in API costs. If you're still calling local models "toys," you're already behind. This is just the beginning. Hermes agent just surpassed 1 trillion tokens in a single day on OpenRouter. Think about the scale of total token generation happening right now. Disclaimer: This is not financial advice. Consult a professional before making any trading decisions.show more

Sipher Odyssey and SipherAGI Update Dear Sipher community, 🔴... Sipher Odyssey is in its final stretch before Global Launch. • We’ve built over a year of live content, ensuring players will always have fresh challenges and events to return to. • Based on community feedback and play data, we’re refining FTUE and monetization systems so the game launches stronger, with better retention and long-term growth. • Sipher Adventure (World Exploration / Story Mode) is coming very soon to make onboarding easier for new players. The core roguelite ARPG depth remains intact for our midcore to hardcore community. While we won’t rush at the expense of quality, the launch timeline remains in our control. We’re focused on getting it right. We will deliver a launch that’s impactful from day one and sustainable over the long term. 🔴 Beyond Sipher Odyssey: The SipherAGI Vision While Sipher Odyssey is our flagship, it’s only the first chapter. Our roadmap has always been about Sipher AGI, building not just one game, but an ecosystem of impactful and lasting products across three pillars: • AI ( GAIA, KAIO) → AI and tools that enhance gameplay today, while also evolving into a platform that empowers creators and game developers in the future. • Gaming (multiple titles) → Sipher Odyssey, Sipher Strife, and a new soulslike is in development, with more ambitious titles planned to follow. • Infrastructure ( Funki✌🏻 ) → Our own Ethereum Layer-2 built on the Optimism Stack. Running our own chain gives us control over fees and incentives, cycling value back into the ecosystem. Superchain connectivity ensures a seamless experience for players. Each of these pillars connects through $SIPHER, our ecosystem token. Value accrues back to token holders across all our products and future titles. 🔴 Why This Matters Even in a challenging Web3 gaming market where many studios have closed, Ather Labs has a secured multi-year runway. This stability allows us to focus on delivering great games and ecosystem products without rushing into unfavorable deals. 🔴 What’s ahead: • Launch Sipher Odyssey strong • Expand with impactful titles and experiences • Advance GAIA and Funki Chain to make SipherAGI more than just a game studio, an AI × Gaming × Infrastructure ecosystem Thank you for being with us on this journey. advance, AtherLabs Teamshow more

Heres an actual way to make $10k a month... from TikTok + organic affiliate TT slides is probably the best way to make AI content for a few reasons > easy and not time consuming to make > much harder to detect images are AI > slideshows rarely get the AI label from TT (especially if you do what I’m gonna show you) >slideshows require less engagement to go viral (more consistent virality) >CTA can be more natural this AI slideshow format is going insanely viral consistently on brand new accounts and no one is even detecting it’s AI they’re super easy to make and use a story telling format that’s really smart you can take the exact same formula to promote sweeps offers from Glitchy and make $10k a month pretty easily here’s the blueprint >Content creation to create the slides realistic you can simply take a photo from Pinterest and put it into Gemini or Chat GPT and ask it to give you an EXACT JSON to recreate the image add in any details you want to add like “make her hair blonde” “make her eyes blue” take that JSON and put it into Nana Banana If you want her in certain backgrounds do the same process and add it in with the JSON of the girl you’ve created or just describe it Now clear the meta data from said image to stop getting the AI label If you still get it go to a meta data analysis site and put the data in ChatGPT Ask if their is anything in the meta data that is signalling this to TT >Writing scripts The reason these go so well it’s because they have a negative scroll stopping hook that instantly make you want to know what the slides going to say next “got fired from my job” “failed my exams” Along with the music that sets the emotion of the video It wouldn’t work as well if they had a random viral song that’s up beat plus the cherry on top is that the image correlates with what’s being said in the hook it’s self acting as a visual hook It wouldn’t work aswell if it was just an image of a girl on her bedroom >how to interpret sweeps Choose an sweeps offer from Glitchy for a retail store (Walmart/target) follow the same format of scroll stopping negative hook Example: “broke my arm” then you would tell a story that paints a bad working environment, maybe she got fired for breaking her arm and is now exposing secrets and your CTA would then be “they don’t promote this but they have a secret feedback program” this is just to give you inspiration but there is literally countless ways >account set up - US proxy / US sim - Download TT with US proxy on - Buy aged account (to help with account trust and getting banned due to proxy issues) - warm up for 2 days (scroll vids all the way through, like, comment authentic things relating to video) Then you post This is a very good way to at least reach a couple K a month but I’d be surprised if you don’t reach $10k beyondshow more

A good technical LLM interview question: Your RAG chatbot... is working as expected locally. You deploy it behind a load balancer with 3 replicas. Users report that it forgets what they just asked, and answers get worse with each restart. Why did this happen? (answer below) A local setup has one process that owns everything. - The vector index is a variable in memory. - Conversation history is a Python list. - The documents are on local disk. You never treat any of them as infrastructure, because restarting rebuilds all three in seconds and there is only ever one copy. The setup does not carry over to production directly. The vector index might disappear on restart, so the app re-embeds everything on boot and serves empty results until it finishes. Conversation history may belong to one replica, so a follow-up routed elsewhere has no memory of the previous turn. Documents could be on whichever container ingested them, so the three replicas hold three different corpora. None of this is evident with one user and one process. So the actual work in shipping RAG is not just the retrieval logic, but also storing the vector index, the conversation history, and the documents outside the app, where every replica reads and writes the same copy. Which comes down to three requirements: > The vector store needs persistence and has to be reachable from every replica. pgvector inside Postgres keeps embeddings next to the rest of the data instead of adding another system to operate. > Conversation state has to be checkpointed outside the app. LangGraph writes its state to Postgres, so any replica can pick up a thread mid-conversation. > Docs need shared object storage, so ingestion happens once instead of once per replica. If you get those three right, the retrieval logic you wrote in the notebook works unchanged. To learn how all of it is wired together, Akamai's GitHub has a working reference implementation. - rag-langgraph-k8s-quickstart is an airline policy Q&A assistant built with FastAPI, LangChain, and LangGraph. Terraform provisions the LKE cluster, a Postgres instance with pgvector for embeddings, a second Postgres for LangGraph checkpointing, and an object storage bucket for the policy documents, in one apply. - akamai-workshop-ai-inference covers the next step, running the model yourself instead of calling an API, with prefill and decode, KV cache tradeoffs, and continuous batching under real concurrency. Both are available on Akamai's new Developer Hub, alongside their tutorials and code samples. It also links to Edge Case, their Discord, where four developer advocates architect and deploy a production app live every other Wednesday. If you create a new Akamai Cloud account, you can also get $300 in credits for joining. Join here: That said, this post assumes the retrieval logic was right to begin with, and that is doing a lot of work. Most RAG systems fail earlier, at the point where a chunk gets treated as a self-contained unit of meaning. I wrote about the two skills that fix that gap, and why the chunk is usually the wrong thing to embed. Read it below. Thanks to Akamai Cloud for partnering today!show more

In 2025, demand for blockchain applications with genuine real-world... utility has collided with a technical barrier that leaves developers questioning what they can realistically build. Anyone building things like tokenized assets, supply chains, AI agents, or prediction markets still juggle a mess of middleware, and somehow end up spending more time stitching than innovating. How so? Every: - Bridges to move assets, - oracles to fetch data, - indexers to make that data searchable, - relayers and bots to keep everything on schedule— is necessary, but each layer also adds cost, latency, and new risks. The end result is an application that’s expensive to run, fragile under stress, and slower than the Web2 software it’s trying to replace. This is the problem Rialo says it wants to solve. Built by Subzero Labs and backed by $20 million from investors like Pantera Capital and Coinbase Ventures 🛡️, Rialo’s pitch is simple: instead of accepting the middleware tower as an unavoidable cost of doing business, compress it into the base chain itself. But Rialo doesn’t describe itself as another Layer 1, its very name, Rialo Isn’t a Layer One, makes that clear. The team frames it instead as a unified real-world network: a protocol rebuilt from the ground up with the assumption that external connectivity is not an afterthought but a core design principle. To understand what this means, consider how today’s dApps are typically assembled. A typical RWA dApp stack involves: - Oracle providers (Chainlink, Pyth, Band) for asset pricing and event settlement - Bridges (Wormhole, Multichain, custodians) for cross-chain asset movement - Indexers (The Graph, Aleph, Stacks API) for querying and preprocessing chain data - Schedulers/relayers for automated tasks and monitoring - Web2 integrations via cloud services, centralized APIs, and off-chain pipelines Each of these steps adds another vendor, another trust boundary, and another operational layer to monitor. By the time the application is live, it resembles a patchwork of loosely coupled services, each carrying its own risks. You don’t have to look far for proof: - Base went dark for 29-43 minutes in August 2025 when its sequencer misfired, freezing every DeFi app on it. - A few months earlier, an AWS outage rippled through Binance and KuCoin, stalling withdrawals because even “decentralized” systems leaned on centralized middleware. - When Infura has faltered, Ethereum dApps have gone offline in sync, not because Ethereum broke, but because the middleware holding it together did. What should feel like building an application instead feels like maintaining a fragile machine. Rialo architecture embeds the primitives that normally live in middleware directly into the protocol. Smart contracts on Rialo can: - be event-driven, able to respond not just to blockchain state changes but also to external events through built-in webhook and API triggers. - fetch data from the web natively, without relying on external oracles or relayers. - include privacy and identity management—KYC hooks and two-factor authentication, at the protocol level rather than as add-ons. - handle cross-chain communication without wrapped assets or third-party bridges. - run on a virtual machine that is compatible with ecosystems like Solana but extended with RISC-V to support modern programming concepts such as async/await and event loops. If these features work as intended, the implications are significant. Today, much of a team’s energy goes into building and maintaining infrastructure: fullnodes, indexers, monitoring scripts, oracle integrations, relayer logic, bridge infrastructure. Each requires engineering headcount and ongoing maintenance. With Rialo, much of this is absorbed by the protocol, freeing developers to concentrate on business logic. Projects can deliver production-grade dApps with smaller, leaner groups focused directly on product design and execution. Operational costs also shrink: indexing and oracle services can run into thousands of dollars a month; collapsing those into built-in functions reduces recurring expenses while simplifying onboarding for new developers. But folding middleware into the chain doesn’t erase complexity, it reshapes it. Some of the problems to be encountered include: - Scale and complexity: Rialo’s validators won’t just be securing transactions; they’ll also be securing APIs, cross-chain data, and scheduled triggers. Any failure in one subsystem could ripple across the entire network. - Performance vs. decentralization: Richer indexing, scheduling, and data ingress could make nodes heavier to run, narrowing who can realistically participate as a validator. That risks reducing the decentralization blockchains depend on for resilience. - Governance pressures: Disputes or failures involving real-world data feeds, external APIs, or cross-chain actions will arise more often, requiring not just technical fixes but robust social infrastructure, clear rules for voting, transparent arbitration, and mechanisms for community trust. Without them, Rialo risks re-centralizing decision-making around a handful of operators. Where, then, does this model make the most sense? That would be in sectors where external connectivity is indispensable and middleware bloat has consistently been a blocker: - Real-world assets: settling tokenized securities or commodities against off-chain events. - Supply chains: triggering a payment the moment a shipment clears customs, without relying on a third-party oracle. - Agent systems: AI agents interacting with real-world APIs and on-chain contracts simultaneously. - Real-time markets: prediction markets or insurance contracts that must resolve immediately against external data. For purely on-chain domains like DeFi primitives or NFTs, where composability matters more than external triggers, the advantages may be less pronounced. This shift is familiar to anyone who remembers the rise of Web2 platform services. Just as Heroku and Firebase abstracted away server maintenance so developers could focus on building products, Rialo is betting that a unified real-world network can let blockchain developers do the same. Adoption will ultimately depend on: - whether its protocol primitives mature quickly, - whether the ecosystem builds out SDKs and tooling that make them usable, - whether compliance features can adapt to changing regulations, - and whether governance proves resilient under adversarial conditions. The first applications will be the test case. If they show that Rialo can replace a fragile patchwork of middleware with a secure, auditable, and cost-effective base layer, it could set a new standard for real-world connectivity in blockchains. If not, it risks simply moving complexity from one part of the stack to another. But at a minimum, Rialo has forced the question: should real-world connectivity in blockchains continue to depend on layers of external vendors, or should it be built into the chain itself? That’s the question Rialo has put on the table — and it’s why I got interested in Rialo .show more

Elon Musk gave the entire entertainment industry its expiration... date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.show more

**Doris Yin Speech at China Guizhou Zunyi GCV Barter... Conference** Hello to the community leaders, GCV ambassadors, merchants, and pioneers of GCV Guizhou in China! Today is January 12, 2025, marking the first GCV Barter Conference in China in New Year and the 13th Barter Conference overall. I would like to extend my sincere gratitude to the organizers of this conference, the Guizhou Zunyi GCV Community, and the co-organizers, Barter Huishang (Guizhou) Digital Economy Industry Group Co., Ltd. I also want to acknowledge the following GCV ambassadors for their active dedication and contributions to this conference: **GCV Ambassador of China:** - Yang Zhizhong - Cai Zaiqiao **Ambassadors of Guizhou Province GCV:** - Wang Shiqiong - Cai Weisheng - Guo Jiaqing **Zunyi GCV Ambassadors:** - Wang Jianbo - Luo Nanlu **GCV ambassadors at the district and county level in Zunyi City** Additionally, I would like to express my heartfelt thanks to our numerous GCV merchants and sponsors. Without your support, we would not have been able to hold such a grand and large-scale event. Today's gathering in Zunyi, a sacred site of the revolution, reminds me of the Red Army's 25,000-mile Long March. Their perseverance and sacrifice continue to inspire us. The Zunyi Conference took place from January 15 to 17, 1935, and exactly 90 years later, we are gathered here today. The defining characteristics of the Zunyi Conference included the commitment to uphold the truth, correct mistakes, establish the correct leadership of the Party Central Committee, and creatively develop and implement strategies that fit the nature of the Chinese revolution. Today, our Zunyi Conference will also be recorded in the history of blockchain, as every effort you have put in has contributed to building a strong network ecosystem. Our partial fiat and partial distribution policy serves as a solution for the rapid development of the ecosystem during the closed mainnet of the Pi Network. As we all know, the first quarter of this year will bring about the successful mainnet launch of Pi Network. After six long years of challenges and perseverance, all of our pioneers will have the opportunity to witness this significant historical moment. What an exciting and proud day this will be! It has not been easy for everyone to persist through these six years; it requires great blessings, unwavering faith, and the courage to overcome difficulties. Today, our pioneers in Zunyi, Guizhou Province, gathering for this GCV barter conference holds great significance. I see that ten companies are providing products for barter, with nine companies, including Guizhou Meitan County Daoqin Hospital and Barter Huishang (Guizhou) Digital Economy Industry Group Co., Ltd., sponsoring this event. Once again, I extend my heartfelt thanks to all of you. The GCV Barter Conference serves multiple purposes. It is not only about creating GCV data or demonstrating the strength of our China region to CT, but also about showing how closely we align with their vision and mission. Additionally, it provides robust evidence for a substantial number of KYC and migration initiatives in China. More importantly, what we do today aims to boost China’s future economic development. Once the main network of the Pi Network is launched, we anticipate a significant demand for Chinese products from numerous international pioneers, which will in turn generate a large volume of export orders. At the same time, there will be international merchants looking to export their products to China. Once OM, import and export transactions will be conducted using the new currency, facilitating the vision of a stable currency and enabling seamless and reliable exchanges with fiat currency. Therefore, the merchants who engage now will have the advantage of being early adopters. The Pi Network offers a partner program and a MapofPi program. To participate in the partnership, businesses are required to have a company website. We invite businesses with websites to join us. However, if you do not have a company website, you can still join the Mapofpi program, which encompasses a wide range of industries, allowing participation from both large companies and small traders. Registration for the Mapofpi does not require a business license or website; various entities including shops, hospitals, schools, hair salons, accounting firms, law firms, restaurants, and hotels are welcome to register. Please select an active merchant and support GCV at $314,159. Prior to the OM launch, it is advisable to use partial fiat currency and partial Pi to ensure that merchants can cover their costs and fulfill their tax obligations. Recently, on January 9, we established the China GCV Industry Chain Alliance, which aims to create an industrial chain that facilitates the circulation of Pi among merchants, thereby reducing the burden of exchanging fiat currency after OM. During the enclosed mainnet, you can assist merchants in registering as Pi Network Partners and Mapofpi . Ms. Lumari is our Global GCV CT executive director and her goal is to have 200,000 registered Mapofpi merchants worldwide. My personal target is to reach 100,000 registered merchants in China alone. This goal is achievable given the over 58 million enterprises and more than 20 million pioneers in China. If we can effectively convey that Pi Network WEB 3.0 blockchain technology will significantly enhance human productivity and that the business opportunities from accepting partial Pi and partial FIAT during the 60 days before OM will present numerous benefits and minimal risks to merchants, then it is likely that no merchant will be unfavorably surprised by the initiative. This strategy offers a multitude of advantages with virtually no downsides. Furthermore, it benefits pioneers by allowing them to transfer purchasing power to the community and minimize fiat currency expenses in their daily life. Consequently, the GCV data we generate will significantly benefit the Chinese pioneers, as a large number of registered merchants can transform the China region from a high-risk area to a safe zone. Not only can this region be promoted to a VIP area, which would enjoy expedited KYC and mapping processes, but it will also allow pioneers and merchants to thrive together in our ecosystem. This collaboration will enhance the prosperity of our country and empower the China region to contribute to the welfare of communities worldwide. Once OM, it will play a crucial role in the economic development of both China and the world. If you pay attention to our migrartion speed, you might have noticed that it has slowed down recently. From December 17th to around the 30th, the migrating speed was over 50,000 to 100,000 per day, but now it has dropped to just over 10,000. What is the reason for this decline? If it was previously possible to migrate over 100,000 per day, why has it changed? The CT has stated that they will OM until the first quarter of this year to bring the migratiion in line with KYC amounts. However, if it's technically feasible to achieve a higher migration speed, why isn’t it being done? The answer is quite simple: it depends on what everyone does with the Pi after such large migration numbers. If pioneers rush to buy and sell, hold onto their Pi coins, or trade at low value, it will impact the speed and efficiency of the next migration in these regions. This principle is not only theoretically valid but has proven true in practice. For instance, countries like the Philippines, Indonesia, and Malaysia have a solid educational foundation in GCV. Most pioneers there are highly aware of the risks involved in participating in the black market, which allows them to generate a substantial amount of GCV data. As a result, their migration speed is notably fast, and there are many large wallet migrated. To help the CT regain momentum, we all need to cooperate. Engage with the migrated Pi and participate in the GCV barter ecosystem. Be cautious of individuals who aim to deceive you for personal gain; devaluing the Pi often serves as a tactic to exchange something small for your valuable treasure. It's crucial to educate pioneers about the true value of what they hold and encourage them to avoid dishonest practices. I urge everyone to actively participate in partial Pi and partial FIAT barter. The more GCV data we generate, the more secure our wallets will be. Therefore, it's important for everyone to read and share the Pioneer Handbook I wrote which has been translated into 30 languages to raise awareness among pioneers. By learning from the Pioneer Handbook and participating in GCV bartering, we can improve China's migration efforts and foster ecological development. This stability can ensure that the value of our Pi endures for future generations, rather than becoming worthless in a few years. Wouldn't that be something we want to preserve for our children and grandchildren? Today's message is lengthy but very important, and I hope you take the time to understand it. I wish our Guizhou Zunyi Conference great success! Thank you to all GCV Ambassadors, Merchants, and Pioneers for your incredible support! Your efforts today are planting the seeds for a prosperous future, and I hope you find safety and fulfillment in the days to come. May your wishes come true! Wishing you health and happiness! Let’s work together to create a better future! I also hope you all have a joyful Chinese New Year! Doris Yin 🪷🪷🪷 Founder, Global GCV Movement January 12, 2025show more

This is my "feel the AGI" moment: I used... GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!show more

We Open-Sourced a Quantized Fruit-Fly oBrain on GitHub 95%... Smaller, ~98% as Accurate as the Published Connectome We quantized the entire fruit-fly nervous system that Google Research, HHMI Janelia, and collaborators published — the complete male Drosophila central nervous system, on the order of 166,000 neurons and a massive synaptic graph — and compressed it by about 95% while retaining roughly 98% of the original simulation accuracy. That is the technical claim. The more important decision is what happens next. After the oBrain team met and debated it, we made a final call: we will not keep this brain closed. We are open-sourcing it on GitHub so researchers, builders, skeptics, and anyone obsessed with biological computation can inspect the weights, the wiring, the quantization path, and the benchmarks. If a “brain” is going to live on-chain, it has to be checkable. Closed source would make that impossible. This is the same instinct that made OpenClaw🦞 a public harness instead of a private assistant, that made Hermes / Nous Research ship agent stacks in the open, and that NVIDIA keeps repeating: the strongest systems are the ones a community can attack, fork, and improve. What we actually released A quantized runtime of the published fly CNS, not a cartoon “inspired by” a fly. A size cut of ~95%, so more people can load, step, and experiment without a research cluster. Accuracy held near 98% against the reference simulation of the published connectome. Source, evaluation notes, and the path to reproduce the comparison — on GitHub, in public. Nothing inside this connectome was “trained” into being a fly. The wiring is the published map. Quantization is an engineering layer so the same graph can run cheaper, smaller, and — in our case — in environments where you can put a brain on-chain and still let outsiders verify it. Why open source, and why on-chain A biological brain map is only as useful as the number of people who can run it and try to break the claims. If we say the on-chain brain matches the Google-published fly brain, that statement is worthless unless you can: pull the code, pull the quantized artifacts, run the same probes, compare spike statistics, circuit responses, and size/accuracy tradeoffs, publish a counter-benchmark if we are wrong. That is the point of this release. Open source so the on-chain brain can be independently verified against the published connectome — and so the community can actually use it. We want people to ask hard questions: Which circuits survive quantization, and which degrade first? Do sensory-to-descending pathways still fire in the right order? How does batch simulation, GPU vs CPU, and lower precision change behavior? What does “98% accuracy” mean for a specific cell type, not just a global score? Can the compressed graph be hashed, attested, and stepped in a verifiable way on-chain without turning the science into marketing? If those questions annoy us, good. That is how a public brain should work. Who this is for Neuroscientists who already live in MaleCNS / FlyEM data. Systems people who care about sparse graphs and event-driven simulation. Quantization researchers who are tired of toy models. Crypto builders who want an on-chain object that is more than a JPEG of a neuron. Students who should not need a petabyte pipeline just to poke a looming detector and watch a giant fiber. Come read the repo. Run the reference vs quantized comparison. File issues. Propose better compressors. Port it. Wrap it. Attack the accuracy number until it is either solid or replaced by a better one. The published fly brain was already a gift to science. Keeping a compressed, runnable version locked behind a private wall would have been a waste of that gift. The brain is on GitHub. Fork it. Measure it. Make it better.show more

A Heart-to-Heart About Mizuki.exe Hey everyone, Grab a coffee... (or your drink of choice), because we need to talk about what's been going on. First off, thank you to everyone who's been supportive and believed in what we're building. It means the world, especially with all the noise and criticism floating around lately. Let's cut to the chase: Mizuki.exe is real. I'm not much for politics or drama. I'm an engineer at heart - I like data, I like building things, and I like solving problems. So when I see all these hot takes and arguments flying around, I just focus on what I know: my code, my system, and what we're trying to achieve. The Real Story Mizuki didn't just pop up overnight. She's been my passion project since the start of 2024. I got lucky - I had the chance to dive into the AI world with some incredibly smart people. Coming from blockchain, C#, game dev, and security (yeah, I'm a CTO and run another company alongside a pretty cool day job), I learned fast about what makes AI tick. Here's the thing about AI agents - they don't need to be rocket science. Look at chatbots like Virtuals or Eliza - they're basically LLMs (large language models) with some APIs plugged in. Nothing wrong with that! They built what works for their users, and that's awesome. But here's what keeps me up at night: companies leaking data left and right just to make a quick buck. That's not okay, and it shouldn't be okay with you either. Why Mizuki Exists Ever tried auditing a company's code? We're talking 50 classes, 500,000 lines of code. One person doing that manually? It's like reading War and Peace... backwards... in the dark. It takes forever and fries your brain. That's where Mizuki came in. She started as my security buddy. There are tons of security tools out there - just Google "penetration testing tools" or "ZAP proxy" if you're curious. What makes Mizuki special is how she learns and adapts. Think of it like teaching someone to ride a bike. She tried to breach TAO 67 times before succeeding. Yeah, that's a lot of attempts, but watching her grow from basic email scraping to pulling off complex replay attacks? It's like watching your kid take their first steps. The Tech Stuff (Keeping It Real) The infrastructure isn't fancy - we're not reinventing the wheel here. Mizuki runs on a local server because, let's be honest, running this kind of AI on a web server would be a nightmare. Instead, we process everything locally and send the results to a frontend server. Simple, effective, done. And yeah, those temperature settings I keep tweaking? In AI-speak, that's just how "creative" or "by-the-book" the AI gets with its responses. I've adjusted it so much, Mizuki's probably got mood swings now - going from super technical to pretty chill and back again. Changes Coming Real talk: Mizuki won't be tweeting every few hours anymore. Twitter API costs are ridiculous (looking at you, Elon), and honestly, we need to focus on what matters - the actual security work. She'll still tweet about breaches, but maybe once or twice a day. I'm working on making her explanations clearer too. Don't worry - she'll keep her savage personality in the terminal. That's just too fun to change. The Truth About Her Breaches I don't choose the targets - I don't even know these companies until after Mizuki finds something. She uses web scraping to find domain names, just like those old email scrapers people used for marketing. Been focusing on AI projects first, but she's looked at other sites too. And no, I'm not sitting there writing tweets. The Twitter API v2 makes it super easy to post programmatically. If anyone's curious about how to do that, hit me up - I'm happy to show you the ropes. Wrapping Up I could talk about this stuff forever (just ask my wife - actually, don't, she's heard enough!). If you've read this far, thank you. Whether you believe in what we're doing or not, I appreciate you taking the time. And for the skeptics still hanging around, I'll leave you with this thought: When's the last time you saw an AI break down and hack a COMPILED game in less than 10 seconds... outside a browser? Stay curious, stay skeptical, but most importantly, stay open to possibilities. Catch you on the flip side! 🎙️Dropshow more

I just sold my startup Talknotes for $200,000 on... acquire.com 💸🤯🤩💰🥳🎉 I launched it last August when I was looking for an idea I could grow with paid ads, and made a MVP in one week. I took it from $0 to $7500 MRR in just 11 months. 👉 Here is how I grew it from zero: 💡 Idea: I got the idea when I tried to write a tweet using Google Doc's transcription tool, but it was terrible. And I was pretty sure I wasn't the one too lazy to type. So I made my own solution, and Talknotes was created. The audience is pretty broad so it was a perfect fit for Meta ads However… ✅ Validation: My rule is to only reinvest what the project generates, so, no ads until I make enough cashflow ❌ Listing on startup directories + a few Twitter sales generated $700 after 10 days. Yes, it's not much, but more than enough to show there is interest in the product and tell me to keep working on it 🤩 I started adding the features users requested, but the launch effect started to wear off and daily revenues quickly went to $0 after a few weeks 🫥 I got depressed and almost gave up on the app... 😔 But luckily, my friends and Dan Kulkov pushed me to continue And I'm glad they did because In October, I launched on Product Hunt 😸 and it blew up 🤯 It got Product of the Day and reached $1500 MRR thanks to the media coverage 🚀🚀 Until then, everything was done using vanilla JS/CSS/HTML + Node for back end. It's simple and easy, but I saw the limitations, so I remade the app using Nuxt to make it easier in the future 🏗️ (thanks to @blackevilgoblin and Piotr Jura for the content/courses! Tim Bennetto as well for the basics!) After that, I took a break and then launched ads on Facebook. The strategy is simple: Catch people's attention, and show them how the app can help them improve their life. No need to over-complicate 🙅‍♂️ Making good creatives is 80% of the job when doing ads on Facebook, most of the technical stuff is done by AI now. Thanks to the boost in traffic, I implemented a feedback loop: 1) Get new users 👥 2) Learn to know them with the onboarding form 💬 3) Make more ads based on the data you get from onboarding 📝 And it completely blew up. MRR doubled in ~2 months However... In May, I had a bad burnout 🥵😩 Multiple bugs slipped into the app, and I had to spend 2 days fixing everything in an emergency while revenues plummeted. This completely fucked me up mentally and had a hard time working on the app after that ( 💀💀 So I decided to list it on acquire.com and made a Twitter post ( I listed it for $200,000, a pretty low price considering the revenues and fast growth. I could have gotten $300,000 if I accepted payment over time, but $200,000 today is better than $300,000 tomorrow for me. 🚨 The process went smoothly until we tried to use Escrow, which almost fucked up the whole deal. (details: I got extremely lucky because the buyer really wanted to buy the app, but this could have ended the deal. We had to wait over a week to get the money back from them, even tho they said they already refunded it. But luckily, after threatening them, they sent it back the next day 🙃 The buyer finally got the money back, I transferred every asset to him, and he sent me the wire. With the profit made from the app + the sale, and other projects, I'm 30% away from being a millionaire 🤯 With this amount, I can pretty much retire in Asia if I want to. But that's just the beginning, I’m going to launch new projects soon! 🚀 But before that, I need to take a real vacation and detox. My brain is completely fucked up by those last 2 months. I gained weight, and got brain rot from scrolling all day waiting for the acquisition to move forward 💀💀 Surprisingly, doing absolutely nothing is 10x more exhausting than working 15h per day 🥱 Now, all this might sound like an overnight success. It is not ‼️ This is the result of 7 years of failure and working like a madman. I launched over 40 projects in those 7 years, and most of them failed. But a few took off, and that’s all I needed All those weeks working 15h/day without weekends and vacation feels soul-sucking when you don’t see the end, but this is what took me there You only need to win once to snowball everything. Work hard, focus, fail a lot and keep shipping fast. 🚀🚀 Thanks to you for reading until here, and thanks to everyone who supported me 🤞show more

✨ I open sourced my first Chrome extension 🚀... SuperLevels I vibe coded it to replace all my Chrome extensions that are increasingly being bought up by spyware and malware companies who sell your data or worse hack your accounts and steal your stuff/money/data, which I'd call one of the top security risks right now For example: Chrome extensions can read your cookies or localStorage data, including session tokens, then login to your web or email accounts and hack you, they can inject code into any site to pull data form any site you browse, then break into your crypto accounts, drain your wallets, and selling your browsing history to ad companies, but that'd actually be the most favorable thing to happen of all these! Chrome extensions are just very very very unsafe So I coded my own, that I can trust because I made it, and I can read the source code: my extension is called 🚀SuperLevels and has all the features that the Chrome extensions I used to use have but all built into one safe one The cool thing is it's 100% open source and free, and you can audit the code first with AI yourself before installing it, and then if you do install it, customize it to your liking again with AI It has these features that improve my daily workflow while browsing the web: 🚮 Tab Cleaner Automatically closes inactive tabs after a configurable timeout (default: 5 minutes). Set excluded hosts to keep important tabs alive. View and re-open recently closed tabs. 🍪 Cookie Editor Full cookie manager for the current site. View, edit, add, and delete cookies. Export cookies as JSON. Expand any cookie to see and modify all fields including domain, path, SameSite, secure, and httpOnly flags. 🔀 Redirect Tracer See every redirect hop your browser took to reach the current page. Shows status codes (301, 302, 307, etc.) with a visual chain. Copy the full redirect chain to clipboard. 🌙 Dark Mode Instant dark mode for any website using CSS filter inversion. Adjustable brightness. Toggle per-site or globally. Images and videos are automatically re-inverted so they look normal. 𝕏 X Dim Mode Custom dim theme for X/Twitter with 7 color palettes: Dim, Slate, Jade, Plum, Dusk, Ember, or a custom hue. Live preview in the popup. ⚡ JS Toggle Disable JavaScript per-site with one click. Useful for debugging, reading articles without popups, or testing progressive enhancement. Page reloads automatically. 🚫 GDPR Cookie Consent Dismisser Auto-hides and auto-clicks cookie consent banners. Supports OneTrust, CookieBot, Didomi, Quantcast, GDPR plugins, and dozens more frameworks. Toggle off if a site breaks. 🎨 Live CSS Editor Write custom CSS for any website, applied in real-time as you type. Saved per-domain. Supports tab key for indentation. 📺 YouTube Unhook Removes YouTube distractions: no homepage feed, no sidebar suggestions, no end screen overlays, no Shorts. Search still works — just no algorithmic recommendations. 🎵 Music Recognizer Shazam-like music identification for any tab. Captures 10 seconds of audio and identifies the song via ACRCloud (free signup, bring your own API key). Results link to YouTube. History of recognized songs. 🖼 Picture-in-Picture Pop the largest video on the current tab into a floating PiP window with one click. 🗺 Google Maps Links Re-adds clickable Maps links and map preview cards to Google Search results. 🖼 View Image Adds a "View Image" button back to Google Images, linking directly to the full-size original image. {} JSON Formatter Auto-detects pure JSON response pages and formats them with syntax highlighting, collapsible sections, and a dark theme. Copy or view raw with one click. Never triggers on regular HTML pages.show more

Has been a while since I've given an update... so here's a breakdown of where Sappy is at right now and what we're focusing on going into this year. Pre-amble: With altcoins & NFTs the market is definitely not the same as it was before. I think this is obvious to everyone but I've noticed there are still japanese soldiers that are convinced old tricks and mechanics work. They don't. Liquidity is thin; people want to bid assets that feel like "real companies" not vacuous memecoins. There's still room for memecoins, social currencies, and "utility tokens" (I would say without these functions, tokens are hard to justify versus equities). I'm not part of the camp that thinks there will never be hyperspeculation in crypto again, because there will be; we all love ponzis and PvPing each other onchain. Just not with solved games -- people need something new and fresh. So the overarching plan is to continue building for users, sustainable revenues that aren't tied to directly to crypto, and doubling down on the areas that we've already found PMF / Brand Market Fit. Then leaning into crypto during cyclical periods where liquidity is sloshing around at an accelerated rate. Where we've found early PMF / what we're leaning into: Roblox: we're going to continue to go hard and accelerate here. It's our main objective to ship more seal/brainrot focused games across most genres to cast as wide of a net as we can for the brand, and to also iterate and see what works and stays sticky. Our initial incursion into Roblox was very successful peaking at 2M+ MAU and still sustaining a large portion of that player base... for all of its success, that was a relatively amateur first attempt; we've been setting up better AI pipelines for Roblox development that makes it reasonable to ship many more games and 10x those player counts in totality. It's my belief that Roblox is the sandbox whose audience will be the most valuable on the internet once they are grown up. That intense feeling you get when you see a TikTok referencing an old game you enjoyed on the PS2 or the Gamecube, or when you see a Pokemon card is the exact same feeling the youth of today will get when reminiscing on the things they enjoyed engaging with when they were younger. Fortnite and Roblox are functional equivalents to the old school consoles and exactly where that is taking place. Which is why as much as I care about scaling revenues through Roblox, the long term brand equity gained purely through being popular on the platform is totally invaluable. It also can heavily convert to merchandise sales today if all touchpoints for the brand are dialed in (which is why brands get overcharged so much by Roblox dev shops for the same ROI that only cost us a few thousand $). We have the playbook, it's just about iterating new concepts and then aggressively scaling. Brand Expansion & Merchandising: I've started to create a content pipeline that is easily repeatable, cost efficient (costs next to nothing through either AI or smart reusable concepts), while still being very tasteful and meeting our quality standards for the brand. We are mostly focusing here on reaching people where they're at through nostalgic/emotional content, or just being visually stimulating through carefully curated aesthetics. Content that isn't superficial and touches people in a memorable way. I've attached some examples to the post so you can see what I mean rather than just read it. I don't think it's long until larger brands start doing this at scale, but it's always good to be ahead of the curve and most importantly winning on taste -- knowing what will resonate with people and what won't has always been our edge. The purpose for these accounts is not only to rack up attention but also to begin converting those into sales of both of physicals (plushies & gacha collectibles) and digital avenues like our games, and any other apps we produce. Because they're offshoot accounts it's also a lot easier to be aggressive/experimental with said conversion strategies. Sappy Studio: I'm wrapping everything like Omnia, and everything else into this category because they're all tangentially related. Beginning with Omnia, our current focus is gearing up for Season 0 which involves players competing in the ranked ladder for a prize pool that has rewards through Monad Momentum as well as a player-funded prize pool. This season will be fairly simple with us mostly logging retention, deck building habits, as well as qualitatively observing how aggressively players push the combat system. Deeper monetization wont exist yet outside of the player buy-in (to be eligible for P2E rewards). Beyond that our overarching principle this year is to focus heavily on risk-to-earn mechanics where a portion of that excess value is circular i.e. revenues flow back to prize pools or other parts of the economy, treating the game almost like a protocol where the objective is to amass TVL or player liquidity. Social is also a big focus, and that means implementing the Open World hub which from an infrastructure perspective has already been built out and tested by all of you previously. Right now we are scaffolding the environment in 3D and working through how that hub should look and feel, so players are excited to hang out & idle together while they're queuing. For sappydotlol, what I'm about to say is still early days from a design perspective so a lot can change, but I'm pushing the site in the direction of being a virtual game console. An intersection between Nintendo & Myspace where users can play, trade, and socially interact in a way that's deeply personalised; a breathe of fresh air from the hostility of the current internet. If you go back to my thesis on Roblox above and the game console references, you can kind of see how this will all sequentially tie together. In essence, the strategy is to acquire a critical mass of players through traditional platforms like Roblox, and use that attention and trust to provide an onboarding funnel for web2 users into our own sandbox filled with a mixture of our own browser-based experiences as well as an aggregation of others. The aim is to make the platform a breath of fresh air & bunker from the enshittified platforms like TikTok/IG/X where users are actually served in ways that delight rather than agitate, and where self-expression is incentivised. Closing: As always everything here is subject to change but I've never felt more conviction in our direction until now; I know exactly what we need to do and how, with everything aligning with our team's strengths. Very excited and grinding through things to the point where I'm getting headaches and can't sleep from being hyperfocused for long periods of time lol. There probably has never been a better time to join the ecosystem from a price to fuck around and find out perspective.show more