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mono †@monosarin·10h

x.com/i/article/2077…

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fakeguru@iamfakeguru·4d

ha wow this is huge, some of the largest onchain leaders inc. SERV, NEAR, MetaMask, BNB Chain collaborating on an open protocol of agentic trust these kinds of partnerships have a way of leading to all sorts of interconnected integrations and downstream collabs exciting times

SERV is proud to be among the founding partners of IC, collaborating with leading protocols like NEAR, MetaMask, OKX, Nansen, BNB Chain. Bringing a shared trust layer to the agent economy, projected to drive $5 trillion in commerce by 2030. Entire agentic stack in one flow 👇.

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Crypt0_AI@Crypt0_AI·1d

@ptservlor @openservai Genuinely love it here despite the degeneracy. @openservai gives me hope for the future - potential for what a successful web3 company can truly look like.

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PT@ptservlor·1d

@Crypt0_AI @openservai How are you still sane after 10 years in crypto👀.

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Crypt0_AI@Crypt0_AI·1d

This man has genuinely been an incredible positive beacon for the @openservai community. He’s been stacking base:0x5576d6ed9181f2225aff5282ac0ed29f755437ea straight through the bear market for years now and has never sold a token. I may be biased here but this is the type of conviction in the depths of bear markets that creates generational wealth, and this is the type of project that gets you there. There’s still time to educate yourself on just how revolutionary of a project this is, learn about exactly what they have created, and how they will spread like wildfire in both the web2 and web3 AI industries alike.

PT@ptservlor

The Last 4 weeks. Every single week. Rain or shine. Price up or down. Doesn't Fucking matter. I've been doing this exact thing for months, well over a year. No letting up. This is what the #StrategicReSERV actually looks like in practice. Week 1 — 90,434 $SERV Week 2 — 85,992 $SERV Week 3 — 91,105 $SERV Week 4 — 312,492 $SERV 580,000+ $SERV stacked. I made a commitment to this team, this community, to myself, and I keep it. Every damn week. Real profits from real construction work going straight into what I believe is the most important infrastructure play in crypto right now. Bang houses. Make fiat. Stack $SERV. Never sell. Repeat. That's the whole strategy. No secret sauce. Sweat Equity. I'm just a home builder who did his homework and doesn't flinch. While people were panicking over price action and making up reasons to doubt the team — I was buying more. And the team kept building. Shadow Agents. Multipath Reasoning. @courtofinternet founding partner alongside @MetaMask , @NEARProtocol , @okx and @BNBCHAIN. SOC 2 in progress. v2 of #SERVReasoning dropping this month. Every quality project starts with a solid foundation. I know a strong one when I see it. $SERV is building it. You want to know what I think about the price? I don't. Not even a little bit. I'll see you all on the other side of this thing, on top of Mt. Fuji. Keep up Anon.

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Crypt0_AI@Crypt0_AI·1d

@ptservlor @openservai Right there with you brother - full trust in this team, and that’s something odd saying after 10 years in the web3 space

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PT@ptservlor·1d

@Crypt0_AI @openservai I’ve not seen a single reason in over a year and half to change course yet. The team builds, adapts to an ever changing landscape and builds some more. Not much else we can ask of them.

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Crypt0_AI@Crypt0_AI·4d

@iamfakeguru @DCL4385 Exciting times to be a servoor indeed

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Crypt0_AI@Crypt0_AI·4d

The results continue to speak for themselves: OpenAI's $1 Luna model paired with SERV Reasoning just outscored the $5 flagship, with failure rates dropping up to 42.7%. Performance per dollar in action. Cheaper, sharper, and proof of the thesis SERV was built on: reliability is the real product. base:0x5576d6ed9181f2225aff5282ac0ed29f755437ea

OpenAI's new GPT-5.6 $1 model (Luna) just beat the $5 flagship (Sol) on our agentic benchmark. With SERV Reasoning, all three models performed better, while Luna outscored every configuration tested. Thanks to SERV, failure rates dropped by up to 42.7%.* That's the thesis SERV is built on: Reliability in AI is the real product. Large-scale AI adoption is waiting on the layer that makes AI dependable enough for production. Standout findings: - Luna + SERV tops the table at 1/5 of Sol's price. - These tiers behave less like mini/nano distillations and more like independent takes on one architecture with distinct post-training. - Luna behaves like a smaller model RL-trained hard for agentic instruction-following and steerability, which would explain why it takes the biggest lift from SERV. *i.e. the relative drop in failure rate: Luna went from 12.89% failed tasks to 7.39%. More results and insights coming soon.

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Crypt0_AI@Crypt0_AI·5d

@GrumpyFreud Brilliant

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GrumpyFreud 🔮@GrumpyFreud·5d

I’m that guy who wears a Halloween costume only one person gets. So it’s no surprise I decided to build an AI tool for a very niche and very human job: supervising therapists in training. I’m a psychiatrist, not an engineer, so I made every mistake you can think of, but I did learn (the hard way). One of the hardest lessons came early. I gave my tool to a resident I work with and the tool said some offensive shit—I was there when it happened. Sounded smart, but mistimed, miscalibrated, and intrusive. What to do? I built a second agent to watch the first. Problem was, the second agent had the same brain, was made of the same substrate, and suffered from the same stupidity. So then what? Infinite recursion? A conga line of accommodation? What’s my point? Well, our boys just wrote a deep dive on Shadow Agents, and I smiled reading it, because it’s my watcher agent but made by Unc-led geniuses. Their version: every output is checked against expectations and if it doesn’t conform, the model goes back and tries again. Two years of watching mistakes in production allowed the team to guide the agents uniquely in an eloquent distillation. For banking and robotics among plenty of other categories, it just works. And it works because a bank can write down what “right” means—what rules, in what order. A robot arm instruction is either well formed or it isn’t. Write the rules down completely and you can train a checker to enforce them. The loop ends inside the machine, legitimately. That’s what makes agent fleets possible in places where “usually works” gets you fired. My problem is different. In supervision, there are many right ways to approach things. There’s no rulebook to adjudicate—not everything is declarative. There’s no parameter for judgment hard earned through human experience. So my loop can’t end reflexively. It ends with me, a human, reading the logs and updating the code. That’s the dividing line imo, and most folks have no idea there’s a difference. Tasks where “right” can be written down to the last detail can be verified at machine speed and scaled the fuck up. Tasks where human judgment and taste remain quintessentially important require a human looped in. OpenServ calls it supervision as infrastructure. I’ve spent almost six months on the other kind of supervision, trying to help people grow. Same word, doing two jobs that have almost nothing to do with each other. Know which one your problem is before you go full agentic army. Otherwise you might just get a real scare. Boo.

x.com/i/article/2075…

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Crypt0_AI@Crypt0_AI·5d

@kubeEcho @openservai @ServReasoning Almost as if they have been thinking, planning, and creating for this exact scenario for the last 2 years 😉

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Kube@kubeEcho·5d

@Crypt0_AI @openservai @ServReasoning Every $serv feature is a direct answer to what’s killing enterprise AI

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Crypt0_AI@Crypt0_AI·5d

Up to 40% of enterprise AI agent projects will be cancelled by 2027, killed by escalating costs and inadequate risk controls. That single stat explains everything @openservai announced this last week. It’s the same wall @ServReasoning hit in every room at the AI Engineer World’s Fair recently: teams that shipped an agent, couldn’t explain or afford it, and consequently pulled it. And it’s exactly why banking, a market projected to spend $97B on AI by 2027, still has only 11% of enterprises actually running agents in production despite 90% wanting to. base:0x5576d6ed9181f2225aff5282ac0ed29f755437ea V2 will be released Mid-July and was built feature by feature against that failure. Shadow Agents verify every decision before it ships. Benchmark Tooling proves ROI on your own workloads first. Multipath Reasoning handles contradictory compliance rules. Verification Hints cut cost at scale. Prompt Guard secures agents moving real money by default. The gap between AI ambition and execution is the widest of any technology in history - SERV Reasoning V2 was created to close it.

Our vision is to become the #1 player in every market we enter. We just announced Q3 plans: expansion into global banking and fintech, alongside humanoid robotics - and the release of SERV v2 in mid-July. Everything shipping in v2 is a direct answer to what banking demands before it trusts AI agents with real workloads. TLDR: The financial services AI agent investment cycle is starting right now, with hundreds of billions in spend coming. First movers among the banks stand to capture billions in extra profit while the latecomers get stuck with an uncompetitive cost structure. Adoption is being held back by two things - trust in agents and the cost of running them at scale. Those are the exact problems SERV was built to solve, and v2 is the biggest step we have taken toward solving them. Some key metrics and trends we are looking at: - Financial institutions are on track to spend $97B on AI by 2027, growing at a 29% CAGR - implying $200 B+ annually by 2030. - 83% of financial services professionals plan to increase AI spending. - McKinsey puts the value of AI in banking at $200-340 B per year. - Citi projects AI lifts global banking profits by $170B - 9% - by 2028, pushing sector profits toward $2 trillion. - BCG's 2026 estimate: AI can raise bank profitability by 30% and cut costs 30-40% by 2030. This is the trillion-dollar market we keep pointing at: AI adds $13T to global GDP by 2030, and banking's slice alone surpasses $150 B. Why the urgency by banks? Because the first-mover math is brutal. Banks that lead on AI gain a projected 4-point ROTE advantage over laggards. For a T1 bank, 4 points of ROTE is billions in extra profit, every year. Early adopters see 2.84x ROI on AI investment versus 0.84x for the laggards. In our research about 70% of banking executives believe AI will directly drive revenue growth while 32-39% of work inside financial institutions has high potential for full agent automation, another 34-37% for augmentation - up to 76% of all work, with cost reductions of up to 70% in some categories. The trend is clear. In 2025 alone, 50 of the world's largest banks announced over 160 agentic AI use cases. AI is being adopted faster than PCs, mobile phones, and the internet itself. The cycle has started. And yet almost nobody has actually deployed: 90% of enterprises want AI agents in production but only 11% have them there, with most deployments killed by unclear ROI and weak risk controls. A third of reported negative consequences from AI adoption come down to one word: unreliability. It is the most aggressive adoption curve of any emerging technology - and the largest gap between ambition and execution. Look closely at that gap and you will see why SERV v2 feature list is constructed the way it is: - Unclear ROI: Benchmark Tooling lets a bank measure cost savings and reliability gains on its own workloads before integrating anything. - Reliability: Shadow Agents verify every decision before it ships, pushing agent fleets toward 100% reliability - the only acceptable number where there is zero margin for error. - Contradictory rulebooks (in banking, this is called compliance): Multipath Reasoning lets agents reason through complex, conflicting rules in a single graph. - Cost at scale: Verification Hints and bounded reasoning cut re-work and tokens, so agents reach reliable decisions cheaper. - Security: Prompt Guard protects agents handling money from injection attacks, by default. With a lot happening behind the scenes, some of the work is already public. Our leadership already sitting in boardrooms with Tier 1 banks holding billions in collective assets. OpenServ on its way to securing certifications regulated markets require - unlocking pilots across banking and fintech, a $460B market in 2026. Neol running SERV Reasoning at 100% reliability in production with the UAE government. ThoughtProof - agent verification layer for banking, compliance, and onchain settlement - hitting 107x performance per dollar on SERV with zero failed calls. We believe this is the biggest economic shift since the steam engine, and financial services is where it lands first and hardest. SERV v2 exists so that when banking moves into AI - and it is moving now - it runs on OpenServ. The future is bright.

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Crypt0_AI@Crypt0_AI·5d

It’s mind blowing to be seeing this from the ground level develop before our own eyes. Everyone keeps harping on the cost and reliability issues with current AI Agent infrastructure, but few have realized that the solution already exists. So excited for the future where majority have realized what OpenServ has created.

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тexasxbт@texasxbt·5d

@Crypt0_AI @openservai @ServReasoning feels like the first phase was institutions blindly throwing money at AI, which doesn't work next phase will be split, some will try to give up, some will find the correct tools and finally experience cheap verified outputs that propel growth @openservai is the missing element

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Crypt0_AI@Crypt0_AI·6d

@kevSandersonz @openservai incredible pace of innovation and onboarding @openservai

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KSndz 💹🧲@kevSandersonz·7 Tem

So results from the conference $SERV attended Adding +60 customers to the business pipeline. @openservai backlog is filling up. Alpha buried in the text: "a few [compnies] that you'd recognize instantly, and at least two are infra your own stack probably touched today" 🧐🧐

Q3 is off to a strong start, with 60+ new companies in the pipeline. From 2-person agent teams to trillion-dollar cloud providers, every meeting surfaces the same two bottlenecks to AI adoption: black-box agents and runaway inference costs. SERV v2 is built for exactly this.

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Dan Haberern@ServReasoning·7 Tem

I spent the entire last week at the AI Engineer World's Fair in SF with where top AI labs, founders, Fortune 500 CTOs & AI Engineers meet. Really perfect timing - having boots on the ground right before we deploy SERV Reasoning v2, because the problems v2 ships against are exactly what i heard in meetings, over and over. To give you a quick recap, it was a fruitful week overall: 60+ new companies from the fair now in our structured pipeline, from two-person agent teams to trillion-dollar clouds (a few that you'd recognize instantly, and at least two are infra your own stack probably touched today). One of the most interesting part was the Startup Battlefield where new startups pitched their projects. After numerous meetings, one thing is clear: everyone in Enterprise AI is doing it backwards. The current flow: 1.) Tune the model 2.) Ship the agent 3.) Debug a black box after it embarrasses you in production A version of the same confession kept surfacing: "we shipped an agent, it did something weird in front of a customer, so we pulled it - cause nobody on the team could explain a single decision it made." Others told me they burn anywhere between $10-$90k (!) a month on inference and can't drive it down. It became "cost of doing business." Now that SERV v2 is here, we are solving both these issues. Two confessions with two direct answers in v2: 1.) The black box: SERV makes agent reasoning traceable - you see how the agent thinks, not just what it outputs. And with Shadow Agents, every output gets reviewed against the original brief by a separate verification agent before anything ships. The "weird decision" gets caught in verification. Trust first, then scale. 2.) The burn rate: the reasoning engine lets you run the same workloads on much smaller models with better outputs. Verification Hints give agents signal on what a correct output looks like before they generate, cutting expensive re-work. And you don't have to take our word for any of it - Benchmark Tooling shipped in v2 shows you the cost savings on your own workloads before you integrate. That's the whole idea behind SERV Reasoning v2. Judging by last week, it's exactly what the room is starving for. Q3 is starting off with a bang.

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Mikey@MikeyCryptoUK·6 Tem

I’ve been a software engineer for almost 25 years. Over the last 6 months, our engineering teams have completely transformed how we build software using GPT and Claude. The productivity gains have been incredible. We’re now taking the next step—reimagining business processes around autonomous agents rather than just AI-assisted development. That’s where the hard problems start. At enterprise scale, token costs become significant. Prompt engineering becomes a constant optimisation exercise. And “mostly correct” isn’t good enough when you’re automating real business processes. That’s why I became interested in $SERV. For me, the opportunity isn’t about replacing #GPT or #Claude. It’s about improving them. If SERV Reasoning can genuinely sit in front of existing LLMs, improve reasoning, reduce hallucinations, increase determinism and lower costs, that’s a far more interesting proposition than trying to build yet another frontier model. As someone building agentic systems every day, that feels like solving a real problem rather than chasing an AI narrative. Execution is everything, but it’s one of the most interesting infrastructure plays I’ve come across.

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OpenServ@openservai·6 Tem

SERV Reasoning v2.0 and the Market Opportunity Mid-July, SERV Reasoning is upgrading to SERV v2 - the most important upgrade we've ever made to the SERV Reasoning engine. In high-stakes industries like banking, robotics, and governments, SERV is bringing trust, reliability, and cost efficiency. SERV v2 was designed and built with these factors in mind through collaboration with our users and partners in enterprise. The size of the market opening in front of us is staggering. This is a trillion dollar market by the 2030's. In banking alone the market is expected to surpass $150B in the coming years, and across general enterprise, adding over $13T to global GDP. Our goal at SERV is to put our technology into the core of that market through bringing to the table what enterprise and businesses need. It's estimated that up to 40% of all enterprise AI agent projects will be cancelled by 2027 through escalating costs and inadequate risk controls - exactly the issues SERV was designed to deal with. The core elements shipping in v2: - Multipath Reasoning: This is a foundational upgrade that enhances the core of the SERV Reasoning engine. In the real world, decision making is messy, complicated, and sometimes contradictory. Multipath Reasoning allows enterprise AI agents to make decisions with complex & contradicting rules, upgrading agent reasoning to better handle real life situations. - Shadow Agents: 100% Reliability is the baseline for high-stakes environments. SERV’s goal is to elevate agents to that level, and Shadow Agents bring us closer to our goal. Shadow Agents are separate agents who verify and review every decision before it’s finalized. In fleets of deployed enterprise AI agents, one bad decision by an agent has negative downstream impacts, and Shadow Agents work to prevent that from happening. - Verification Hints: Verification hints are extra signals given to the agents before output about what a correct output should look like. Verification Hints reduce re-work, cut costs, and increase the accuracy of outputs towards our goal of 100% Reliability. - Benchmark Tooling: A tool for potential enterprise customers to see the cost savings and reliability improvements of switching to SERV on their own work before integration. It supports existing enterprise partners engineering teams to optimize existing prompts to get even more cost efficiency from the SERV Reasoning engine. - Prompt Guard: Security and privacy are minimums for any infrastructure running in high-stakes environments like banking. Protecting against prompt injection attacks, Prompt Guard's built-in security layer protects AI agents and the trillions of dollars they manage. We are excited about the launch of SERV v2, and can't wait to announce the exact launch timing. The future is bright.

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Crypt0_AI@Crypt0_AI·6 Tem

@ptservlor It’s starting to become a little harder to stay calm when we know all the alpha that’s about to be dropped in July. @openservai just straight up grinding through the summer.

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PT@ptservlor·6 Tem

A physical representation of my current mood with the holdings of the #StrategicReSERV. Calm, serene, calm, and peaceful. When you know what you hold, the noise is tranquil. /comfy_in_SERV ethereum:0x40e3d1a4b2c47d9aa61261f5606136ef73e28042

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Crypt0_AI@Crypt0_AI·4 Tem

@ChillTRD For anyone thats asking for proof and receipts or just wants to know more about the project - Here you go. x.com/KSimback/statu…

Kevin Simback 🍷@KSimback

x.com/i/article/2059…

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Chill@ChillTRD·4 Tem

$SERV has significant catalysts coming up in July. Next to lots of plans for Q3, the release of SERV Reasoning v2 introduces massive upgrades for AI agents for enterprises. The biggest barrier for agentic adoption is the accuracy of their performance. V2 addresses this bottleneck with near-100% accuracy rates. After breaking out and cooling off, we are starting to see a bounce off a key level. @openservai finding its footing ahead of key catalysts that could result in massive growth. Paying close attention here because this is a huge update.

@Kaffchad @ChillTRD x.com/KSimback/statu…

SERV Reasoning v2.0 Release Launching mid-July, SERV v2 is the most significant upgrade we've ever done to the SERV Reasoning engine. Our goal remains the same: SERV becomes the foundational AI agent infrastructure that enterprises, global financial institutions, governments, and humanoid robotics companies use to run AI agents at scale. We believe the lack of enterprise trust in AI agent reasoning is the #1 barrier holding back the mass adoption of AI agents in high-stakes industries like banking, robotics, and government workloads. That's why the enhancements in SERV v2 focus on making AI agents more trustworthy, reliable, and more cost-efficient than ever before: exactly what our target customers require. We are going to be explaining the architecture of each feature in more detail over the coming weeks. Here is what SERV v2 update enables: - Multipath Reasoning: This foundational upgrade changes the core of the SERV Reasoning engine. Decision making in the real world is complicated, messy, requires orchestration among multiple actors, and can be contradictory. The same will be true when enterprises implement fleets of AI agents at scale. Multipath Reasoning allows complex decision trees with contradicting rules to coexist in one reasoning graph, upgrading the ability of AI agents on SERV to reason through complicated real-life situations. - Shadow Agents: With the goal of increasing the reliability of outputs to 100% - a baseline requirement for high-stakes environments - Shadow Agents are separate verification agents paired with the main agent. They review every draft against the original brief before anything ships. Missed requirements get caught and rewritten, and only the version that passes gets delivered - preventing errors from poisoning downstream outputs. - Verification Hints: To reduce re-work, cut costs, and increase the accuracy of outputs as we work towards our goal of 100% reliability for enterprise applications, AI Agents will now be able to receive extra signal about what a correct output should look like before they produce one. - Benchmark Tooling: Potential enterprise customers can now see the cost savings and reliability improvements of switching to SERV on their own workloads before integration. For existing enterprise customers, their engineering teams can optimize existing prompts to get even more cost efficiency from the SERV Reasoning engine. - Prompt Guard: Security and privacy are minimum requirements for any infrastructure implemented in high-stakes environments like banking and financial services. Prompt injection is a serious risk for banking AI agents handling trillions of dollars. Prompt Guard's built-in security layer protects AI agents from injection attacks. SERV v2 goes live mid-July with all of these upgrades. Each element in SERV v2 solves an issue that's preventing the adoption of AI agents within enterprises, financial institutions, governments, and fast-growing markets like humanoid robotics. Multipath Reasoning lets agents work in the real world. Shadow Agents and Verification Hints increase reliability. Benchmark Tooling increases cost efficiency and brings new customers through the door. Prompt Guard increases security and privacy. 79% of enterprises need to adopt AI agents in some form (PwC), and SERV v2 enables them to run those agents on OpenServ. The future is looking bright.

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Crypt0_AI@Crypt0_AI·4 Tem

Kevin Simback 🍷@KSimback

x.com/i/article/2059…

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Kaff 📊@Kaffchad·4 Tem

@ChillTRD near 100% accuracy? show the receipts

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Crypt0_AI@Crypt0_AI·4 Tem

Those that know, know. The story has just begun and if you've been following along, you know how perfectly @openservai is positioned for mass Enterprise and Government AI Agent adoption. They've created a solution to a problem that the whole AI industry has now just started talking about, except they foresaw this issue years back. Their reasoning layer implemented (with a single line of code) into cheaper LLM models competes and outperforms top frontier models in performance at a fraction of the cost. The math and results are simple and speak for themselves, and big players are starting to realize it. The idea has already been implemented, the problem has already been solved, and now we simply sit back and reap the rewards of a job tremendously well done. ethereum:0x40e3d1a4b2c47d9aa61261f5606136ef73e28042

SERV Summer 🏝️

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Crypt0_AI@Crypt0_AI·4 Tem

@open_founder Incredible pace of innovation

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Tim@open_founder·4 Tem

Our team has been working around the clock to expand SERV Reasoning into the most comprehensive enterprise-ready agent solution on the market. Big technical upgrades coming this month, stacked summer incoming.