Steven

No one is ready for Rei Core 0.3. ​ It will start to change the way we view the use of large language models altogether. ​ @ReiNetwork0x is a beast in the making. As many of you know, Rei is taking a novel approach when it comes to the use of LLMs and how we should interact with them. ​ Today, we simply assume LLMs can't be trusted and that we must verify each bit of data to confirm their output. ​ While we treat LLMs as 'human', it's just predicting which words need to be put after the first and providing an answer; there is no 'thinking'. ​ This is unacceptable if we want LLMs and AI to play a larger role in our lives, as it has started doing for so many of us. ​ We need to give LLMs both cognition and the ability to remember. ​ There is still a debate on how to approach these issues, but I believe the more we mimic biology, the better the output. ​ We should strive for an agentic system, specialized agents working together in harmony. ​ Rei shares this vision and built their bowtie, which changes how queries are approached compared to models like ChatGPT, for example. Rei is already showcasing its reasoning skills and has proven to be a strong competitor with their Core model. ​ Since its inception, Rei Core has proven to work as intended, being able to tackle many standard tests and closely compete with models 10-12 times larger than itself while scoring within the same range of ChatGPT. ​ We will need to see an updated benchmark test for Core 0.3 and see how much better it has gotten, but it's safe to say we'll see some major improvement, most notably in the reasoning tasks, which make up 3/5 of the test. Besides reasoning, memory is a core aspect of building a proper language model. Without it, models tend to hallucinate, and conversations become less reliable with every query you send. ​ In order for a model to think, it must be able to remember. ​ Core 0.3 will allow units to gain access to their own memory structure, also known as metacognition, which will also allow us to do the same. ​ This gives us real-time insights into which components of Core are working on your query, and means we can go far more in-depth to verify where an output came from. ​ We can view this using hypergraphs, making things more visually sensible, which is a great addition for us to better understand how Core operates and which components are more active when handling a specific query. ​ Core 0.3 is a huge improvement compared to the LLMs we've experienced so far, and it will show. There is still a long way to go, but Rei is making fast moves that allow them to compete with the strongest models we know on a far more efficient scale when it comes to cost and compute. ​ All the while, funds are taking a keen interest, and Core is being used for Ecliptica with a clear revenue model in place. ​ Name one project with a market cap of around $115M that has numerous advantages, continuously ships, and is already developing a real-world use case. ​ At some point, Rei isn't a beast in the making; it becomes one. ​ $REI remains one of my top conviction investments, and I see it as one of the easiest picks this cycle. ​ $1B is much closer than you think. ​ I hope you enjoyed this post 🫡 ​ These posts take a long time, so I'd appreciate it if you could like and retweet it.

Rei continues to push huge updates. ​ Even in closed beta, they already locked in a crucial partnership to prove their product fit. ​ I can't seem to stop writing about @ReiNetwork0x, let's explore how. 📃👇 Rei recently partnered with @EclipticaFund, which is trying to take a slice of the analysis markets like Nansen. ​ They offer three subscription services to clients, from small traders to funds, powered by Rei Network. ​ Not only do they use Rei for their services, but they have also invested in $REI, making this an even bigger win and showing Ecliptica is putting their money where their mouth is on all fronts. This partnership finally allows Rei to showcase their model at a larger scale, and all this while they are still in beta. ​ You might think this is too soon, but Rei Core is already a strong competitor; their bowtie infrastructure is starting to pay off in a major way, especially if we compare its performance to other models. ​ Some stats that were also highlighted in Ecliptica's deep dive article into Rei Network: ​ 🔹 GSM8K: 89.16% accuracy (mathematical reasoning benchmark) 🔹 MMLU: 86.03% (massive multitask language understanding) 🔹 AGIEval: 62.78% (advanced reasoning benchmark) 🔹 ARC-C: 72.61% (complex reasoning tasks) 🔹 ARC-E: 85.35% (elementary reasoning tasks) ​ These numbers are impressive on their own, but there's a far larger catch. ​ At 32 billion parameters, Rei_Logic_32B achieves comparable results to models with 10-12x more parameters (400B+) ​ Read that again, these results are comparable to models over 10X Rei's size. ​ Add this to the fact that Rei is a growing innovation team that is shipping incredibly fast, and all of a sudden, this partnership makes a lot of sense for both parties; everybody wins. ​ Secondly, I wanted to talk about a great article written by @0xreitern, I highly recommend you give it a read as well. ​ To sum their article up very quickly: ​ AI shows clear reasoning limits, supported by Apple's "The Illusion of Thinking" paper, showing a 30-40% performance drop with structural changes. While we tend to say AI, specifically Large Language Models (LLMs), can 'think,' they can't. ​ They are just really good at guessing what words should come when presented with a query, but these things have no clue if they are right or wrong, meaning we just have to accept nothing presented is fact. ​ Reitern asks a fair question: Why do we accept this? ​ "Imagine a doctor delivering every diagnosis with supreme confidence, regardless of available evidence. We'd call that malpractice. Yet we've normalized this in AI, accepting that we must fact-check everything." ​ I think this is a great question, as AI has lost its training wheels; the grind of building AGI has only just begun, and like Rei's infrastructure is showing, we can still scale a lot. ​ AI needs to be better for it to take its position in our day-to-day lives, and in order for that to happen, we need to look at it differently. We can't just assume it to be human because it is able to talk to us like a human might be able to. ​ These models need to learn to remember, but only the details that matter, and they must be able to 'learn'. ​ This ties back perfectly with what Rei is building as we speak. ​ They outperform huge models, even in fields that aren't the main focus point, and their model excels at it because it's simply built differently. ​ I don't know how I can make this more clear, but it should make you very bullish on Rei no matter how you look at it. ​ Don't forget, it won't be long until we see Rei's economy kick off, with more projects being incubated, $REI staking, and $REI buybacks. ​ It might not feel like it when $REI is around a $100M market cap, but we have so much more room to grow. I've stated 1B is only a matter of time but the more I research Rei the more I feel this estimate is far too low, this could be a top coin in general if they keep this up. ​ Rei is set to be an absolute beast, and people won't know what hit them when they start seeing Rei everywhere. ​ I hope you enjoyed this article, they take a lot of time to write. ​ I'd love it if you could bookmark and retweet it so you never miss any Rei alpha again, see you next time!

Rei is making massive moves! ​ They just released a huge update on their ecosystem, operations, and business model, and it changes everything. ​ Let's update you on everything that @ReiNetwork0x has been up to 📃👇 REI has always been positioned as more than just an AI agent platform. They’re building a full-stack research and deployment engine. ​ This will set them alongside the likes of Google, moving from a speculative project and transitioning it to a true competitor with real value accrual for $REI. ​ REI is now built around 4 integrated revenue streams: ​ 🔹 Subscription Products 🔹 Enterprise Solutions 🔹 Agent Marketplace 🔹 Incubation Program Each stream creates its value but also reinforces the others, feeding into a long-term flywheel that ties all growth back into REI demand and utility. ​ Users get access to REI Units (agents) and API services, with flexible tiers ranging from standard creators all the way to enterprise clients. ​ I'm a big fan of this model as it not only allows them to scale but also grow along with companies that work with them. Keeping a client is far easier than acquiring one, and preparing for this is crucial to remain profitable over time. ​ Including fair payment options for users also allows REI to take a more web2-based approach, which is where they are more likely to find a larger target base as a highly innovative research AI project. What stands out? ​ The Enterprise arm goes even deeper than what we've seen so far. ​ This isn’t some “white glove” service wrapper. Rei actively partners with companies to deploy agents, integrate systems, and build long-term collaborations. ​ We're talking full-stack integrations, implementation support, and aligned incentive structures. ​ This opens the door to real-world use cases across verticals and anchors REI into the enterprise AI landscape. ​ The Agent marketplace will also supercharge the growth rate of new agents that can be utilized for their agent swarm. ​ Here’s how it works: ​ 🔹 Creators list agents (Units) 🔹 Users rent or purchase access 🔹 Listing fees + commissions feed platform revenue ​ The best part? Every transaction builds deeper network effects, more agents, more users, more volume, and more value for $REI. ​ REI is setting up to incubate early-stage AI projects, with a truly founder-first approach. No forced token launches. No hand-wavy funding promises. ​ Two distinct paths: ​ 1️⃣ Onchain Bootstrapping → for founders ready to build with tokens. 2️⃣ Product Backing → for those who just want to ship the product. ​ The Onchain path is for teams that want community funding + token mechanics from Day 1. ​ ✅ Raise via $REI + $ETH ✅ Liquidity support + fee buffers ✅ Token pairing with $REI ✅ Milestone-based support ​ Heavily vetted. High-impact only. And fully aligned with the long-term value of $REI. ​ Not every founder wants to tokenize. Some just want to build. ​ REI backs these teams with infrastructure, grants, and technical support, while setting clear API fee models + revenue agreements that all route back into the $REI economy. ​ Everything together creates an incredibly strong flywheel that is just getting started. This update brings real value to $REI and gives users a reason to hold with the promise of getting something back for their support. ​ Not only will they launch staking, which allows you to earn airdrops and access to incubation sales, but this update also brings consistent buybacks to $REI and $REI liquidity pools, which will lower the circulating supply. ​ This will increase price stability and make this one of the comfiest holds I've had this cycle. ​ I believe Rei is making all the right moves. They're focused on enhancing their model while accelerating both internal and external growth, all within just six months of the token launch. ​ I've never been more optimistic about Rei's potential. A $1B market cap seems entirely achievable, especially considering the rapid progress the team delivers in such short timeframes. I hope you found this update helpful. 😊 ​ If you enjoy these breakdowns, I’d appreciate a like and RT, it helps a lot and keeps these posts coming.