Just Lock In🔮 (@Cryptopuss) header
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$serv will lead the way, eventually. @openservai

your head
Joined January 2018
Photo by Just Lock In🔮 (@Cryptopuss)Photo by Just Lock In🔮 (@Cryptopuss)Photo by Just Lock In🔮 (@Cryptopuss)Photo by Just Lock In🔮 (@Cryptopuss)Photo by Just Lock In🔮 (@Cryptopuss)Photo by Just Lock In🔮 (@Cryptopuss)Photo by Just Lock In🔮 (@Cryptopuss)Photo by Just Lock In🔮 (@Cryptopuss)Photo by Just Lock In🔮 (@Cryptopuss)Photo by Just Lock In🔮 (@Cryptopuss)
Nobody was ready, except for me. Not selling the green days, only adding more and more. Conviction will always win if you are brave and patient enough. Soon taking over the entire world with $serv @openservai
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Just Lock In🔮 retweeted
Deze Française legt haarfijn uit waarom deze 'Franse studenten' zo ontzettend boos zijn:
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Gm $NEARKAT bottom is in. Trust the pump 😺 prrrrrrrr prrrrrrrrt
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Ben Healy winning this would be fucking crazy #Montreal2026
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Seixas not making friends here #Montreal2026
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Fucking told you all... And guess what, still going x20 coming weeks. We just keep on pumping boys and girls, brrrrrrrrrt thank you boss I'm fat af 😋😋😋 $NEARKAT
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We are so fucking back. Never left actually but who cares, selling gold at these prices makes no sense. More printing, more $NEAR . Prrrrrrt prrrrrrt that's right boss, give me my near for free thank you very much boss $NEARKAT 😺🔥🔥🔥🔥🔥
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Omg I'm going to choke in all the near I'm getting from just holding this pussy $nearkat
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I just love $near and it's lil cat ❤️
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Bought the $NEARKAT dip, thanks for selling 👌🏻
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Duitsland tegen negerland, altijd lachen #NEDGER
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$serv $ondo $nearkat $near what else? 300 iq portfolio making moves 🔥🔥🔥
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Just Lock In🔮 retweeted
Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.
Jev is seriously impressive. SERV makes it better. We put Jev through the same benchmark we use to evaluate leading AI models, testing it both standalone and armed with SERV Reasoning. Paired with SERV, Jev moves into the top tier of our benchmark, beating Claude Fable 5 at 30x lower cost, with cost similar advantages over GPT-5.5 (~15–20x), Gemini 3.5 Flash (~15x), and Grok 4.3 (~6–7x). The result is clear: Jev + SERV materially outperformed Jev on its own. Jev is extremely fast, well designed, and great at decision-making. Instead of relying on open-ended responses, it produces structured decisions with probability scores attached. SERV pushes that performance further, adding a stronger reasoning layer to improve the quality of those decisions for high volume agentic work. The SERV Reasoning API is live, so any developer can test and deploy SERV Reasoning today in just a couple minutes. Get access at: openserv.ai The agentic economy won’t scale on better models alone. It needs better reasoning infrastructure that are finetuned to the reliability, auditability, and affordability requirements of enterprise agents. SERV is building that layer.
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$nearkat is on a godrun but nobody knows yet, it's feeling like 2016 again 🤯🤯
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So buying $nearkat is better than buying $near?
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Think $SERV will be one of those who run quik back to ath Ai agents are the future.
Nothing has changed with my conviction with $SERV. At this stage in the market, you just need some patience. With things popping off left right and centre, it can be tempting to chase. Everyone wants the fast gains. But projects like $SERV are the type of plays that one day just moon. They build for so long, creating such a bullish backdrop, sellers run out, and it begins to reprice to the level it should be at. It's my highest conviction low cap AI bet. We did get a little lower than I expected, but this is the game. You just average lower and have patience. With the 1D RSI most oversold over, it wont be long imo. Comfy in this.
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3 absolute gems, $serv $bstonk and #m87 are on fire. X100 coming to my portfolio 😋
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$BSTONK is what every other coin wants to be. Free money, literally.
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Just Lock In🔮 retweeted
excited to share that i’ve joined @openservai to lead marketing ai is moving ridiculously fast, but running agents reliably at scale is still a massive problem. costs and errors compound and in a lot of cases you need to know exactly how a decision was made SERV is building the reasoning layer between apps and the models powering them, bringing production-grade ai reasoning to a world increasingly run by agents if you’re building across ai x crypto or want to learn more about SERV, hit me up. lots cooking behind the scenes. billions will serv
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