GrumpyFreud 🔮 @GrumpyFreud

@ASvanevik There are data privacy and safety concerns; audibility concerns; staying in the right side of government concerns that’s can be alleviated with @openservai

Most people think the AI adoption problem is cost. Some know it's reliability. Almost nobody is talking about the real blocker: auditability. @lucashafner just laid it out better than anyone has. JPMorgan. Goldman. Pfizer. Lockheed. The DoD. These aren't companies afraid of AI. They're companies that can't deploy AI because when something goes wrong, they need to prove exactly what happened, why, and who's accountable. A polished "step-by-step reasoning" explanation from OpenAI or Anthropic? That's not an audit trail. That's a model generating tokens that sound like a trail. That's the gap. @openservai is closing it — right now. Braid Diagrams. Reasoning Audits. Graph Sharding coming next. Not claimed transparency. Structural transparency. Every node. Every input. Every output. Every failure. Traceable. I'm not just watching this from the sidelines. I'm running SERV Reasoning in my own construction company today. My timecard audits, my bid estimations — every decision traceable, every failure localizable. That's not a pilot. That's production. The Global 500 is about to figure out what I already know. 🔮 @openservai $SERV #StrategicReSERV #PTPodcast #AI

Lucas (opn/srv) @lucashafner

4 days ago

x.com/i/article/2068…

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went through @openservai's private beta feeback from like 9 different industries and the consistency of results is pretty remarkable. the 80% cost reduction and 74x efficiency gains stand out, sure but the pattern is what actually got me. different industries. same story: > Roba Labs → open robotics platform "SERV matched Claude's output quality - and cut our AI costs by over 80%. That benchmark result changed our roadmap. serv-standard is now the default model in ROBA Studio." > Akretic → security layer for finance, healthcare, government & defense sectors "It has done a better job than several of the other frontier models at assessing the project, identifying real issues, and giving accurate, actionable solutions" > Neol → network intelligence company “Now I can sleep better” after hitting 100% reliability thanks to SERV Reasoning, now in production with the UAE government. > GastroSight → agentic OS for the food industry “Cost savings are insane. around 90% cheaper and at the same time output quality has become more reliable. whereas before we had around 5-10% failures, there have been none up to now” > ThoughtProof → agent verification infrastructure for banking & compliance "An evaluator that drops 12% of calls is not a production option. SERV had zero failed calls. That’s a category expansion, not just cost optimization” more accurate (83.3% vs 77.5%), 100× cheaper ($0.0006 vs $0.06), 0 failed calls (vs 12% on baseline) > Billz → AI-powered treasury execution "SERV Reasoning is a powerful and flexible AI layer that makes agents significantly more reliable and efficient, no matter which model you use." > TRECC → infra layer for AI economy handling credit allocation & risk decisions. "Audit metrics confirm a 10x improvement in routing velocity over previous generalist production stacks" > ICM Analytics → market intel for Internet Capital Markets. " After spending around $ 1500/month on agent inference, we found that openservai reasoning tech is extremely useful for every task. Since trying out SERV Reasoning, our bill went down significantly AND the results are better. None of these other frontier models can actually reason properly" > TradeBetter → prediction market trading platform “our agents run on SERV Reasoning explicit decision trees, not paragraphs. Receipts: > 99% on GSM-Hard at 74x lower cost > 2.7x more accurate on multi-step problems" and most of these teams are already running SERV Reasoning in production. a beta product doing this is actually insane.

OpenServ @openservai

5 days ago

The range of teams running on SERV Reasoning in private beta right now: > Network intelligence for governments > Financial institutions and agentic commerce > Industrial compliance > Humanoid robotics > Security All migrating their operations to SERV. The engine gets sharper.

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@ptservlor LFG

Is @openservai the @Tesla of AI Data? The Hidden Data Company Thesis. There is a strong conceptual parallel that some $serv supporters would make, though the company is much earlier in its lifecycle than Tesla, so the scale and proof points are very different. The thesis is essentially: Tesla’s moat: Millions of cars → billions of miles → proprietary real-world driving data → better autonomy OpenServ’s potential moat: Millions of agents → trillions of actions → proprietary enterprise reasoning data → better AI agents The key idea is that the “value” may not be the AI model itself. Just like critics say, “Tesla is just a car company,” or “DeepSeek can copy a model,” the counterargument is: You can copy a model. You cannot copy the data flywheel. For OpenServ, the potential strategic asset would be: How agents interact with real enterprise workflows? Which actions succeed or fail? How agents collaborate with other agents? Human feedback and corrections. Long-term memory of organizational processes Security, audit, and compliance histories. That creates a “reasoning dataset” that a new competitor cannot instantly recreate…as @NFTreeVerse rightfully argued: x.com/nftreeverse/st… The bull case would be that the future AI stack has three layers: 1. Foundation models: the brains (OpenAI, Anthropic, Google, etc.) 2. Agent infrastructure: the operating system where AI actually does work 3. Enterprise data flywheel, the accumulated knowledge from billions or trillions of agent actions If/when OpenServ becomes the layer where enterprise and government AI agents operate, its data could become a durable moat in the same way Tesla believes its driving data is a moat. The biggest caveat: this thesis requires mass adoption. A data moat only compounds if the platform reaches sufficient scale. Tesla’s advantage came from having millions of vehicles in the field. OpenServ must prove it can achieve a comparable network effect in enterprise AI. The most bullish analogy (I love analogies…just like my Netflix one I’ve applied for serv): Tesla didn’t become valuable because it built the best car. Their thesis was that it turned every car into a data-generating robot. The OpenServ thesis is similar: The winners in AI may not be the companies that build the smartest agents. They may be the companies that own the largest repository of real-world agent experience. That is likely the core argument behind the phrase often associated with OpenServ: “Millions of agents. Trillions of actions. One intelligent layer.” 🚀

Philip Marlowe @NFTreeVerse

a week ago

$SERV – "It's just structured prompting. Anyone could build this in a weekend." Fair point. "Can it be replicated?" is due diligence 101. The paper's public. The prompt's in the appendix. Start whenever you like. But read this first. open.substack.com/pub/flashorton…

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@DCL4385 @openservai @Tesla Yes

@iamfakeguru @KSimback Imagine what you can do with this knowledge and how it can compound. Mind blowing.

This isn’t only about what PT can do, it’s about what any of us can do if we are open to learning and change. Dive in, learn, work hard, and sprinkle some SERV reasoning on it.

PT SERVlor @ptservlor

a week ago

x.com/i/article/2066…

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@open_founder Solving today’s problems and tomorrow’s, innit.

Currently around 80-90% of enterprise AI agent pilots fail and don't make it to production. We're bringing that number down to ZERO. AI agent failures is the biggest painpoint in enterprise AI adoption today, and we're solving it at OpenServ. v1 of SERV Reasoning is live in private beta with enterprises today and its already doing a tremendous job, but it's just the beginning. We're not far from rolling out v2 of SERV Reasoning with our upcoming "Shadow Agents." If an agent's output doesn't meet the requirements, it executes recursive validation loops to learn and retry until it succeeds. We're achieving fantastic results internally and can't wait to roll it out to more users soon, getting closer and closer to 100% reliability across high-stakes agent deployments. Solving this problem unlocks a massive market opportunity, and we're already well underway to achieving it. Each new enterprise conversation and bit of adoption snowballs into more data, more learnings, more credibility, more adoption, and a better product with a bigger moat. The snowball is snowballing, hard.

OpenServ @openservai

a week ago

SERV Reasoning just took GLM-5.2, one of the strongest open models ever built, and immediately cut its failures by 22%. That's just v1. Every step on our roadmap brings us closer to the goal of perfect, deterministic reliability: agents that are 100% right. v2 is next: Shadow

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AI is like Clubhouse. When Clubhouse first came out, it was a big hit, because everyone now had a chance to speak out their minds. It was democratizing access to speaking. But it died out quickly as everyone noticed that not all the people are worthy of a listen. Not everyone is a creator. It's still a select few who are interesting enough to listen, so giving everyone a voice is just a waste of time. Enter AI, 6 years later. It's now democratizing value creation. Everyone with an idea can convert that into a working product with the help of AI. However, the sycophancy is huge. AI is a slot machine that talks and convinces you that you are doing great and you will win big next time you pull the arm. Unfortunately that's not the case. People don't know what they don't know, but AI preaches them so softly and sweetly, just like Grima Wormtongue, that they believe they are the next genius. I'm happy it's giving people a chance to see these things for what they are, to explore the limits of their minds and creativity, and pass good time. I'm also happy that it's making it quite clear what engineering really is and how difficult it is to LARP with AI.

@ptservlor PPDA (Performance Per Drink Avoided)

@chamath 'Claude Fable 5 is currently unavailable.' That one line just opened two cans of worms nobody was ready for. KYC. Open source. The cloud landlord play. And one company nobody's talking about that already built the answer. $SERV open.substack.com/pub/flashorton…

@jazzplane @KSimback More and more every day, it seems.

A year ago I mapped where AI space was going: reliability as the moat, security maturing, regulation landing, enterprise going mainstream. Today US gov pulled Claude Fable offline over a jailbreak. The article kinda reads like a checklist. Next up: trust + cost layer

fakeguru @iamfakeguru

a year ago

x.com/i/article/1907…

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@Crypt0_AI Oh?

I'm going to repost this repost of the author’s post in the hope that it will be reposted. A tiny cog in the gigantic $SERV network effect

Philip Marlowe @NFTreeVerse

2 weeks ago

I'm going to repost this repost of my post in the hope that it will be reposted. A tiny cog in the gigantic $SERV network effect

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@OGDfarmer Yuppppppppppp

Kevin Simback 🍷 @KSimback

3 weeks ago

x.com/i/article/2059…

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@Tyler_Did_It Check this out if you haven’t.

Kevin Simback 🍷 @KSimback

3 weeks ago

x.com/i/article/2059…

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@iamfakeguru @openservai Show and tell about to be lit. SERV it up.