Tim @open_founder

@OGDfarmer bizarre indeed.

Last year I left a cushy job at a big crypto payments company because we tried 5+ AI tools, and signed multiple 6 figure contracts for broken shit that did not work, and we had no idea why 2 things became crystal clear: 1) Enterprise AI spend is up-only 2) Most AI products on the market were terrible We’re fixing this @openservai with the SERV stack

ST0RM @0xStorm

4 days ago

3 requirements for enterprise AI 1) Reliability > Needs to work 2) Auditability > Need to know why it worked (or didn’t, so you can fix it) 3) Cost efficiency > Needs to be at a price point where the ROI makes sense at scale Spoiler alert: you’re not getting here

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More SERV Reasoning wins in the agentic prediction market space... 88% win rate on the world cup so far.

Momus Project @momus_ai

2 days ago

The model got an update. The numbers since June 16 reflect it and these are the takeaways. The opening matches at the World Cup sat around 50% while we identified what was wrong, made the changes, and the numbers responded. Won 15. Lost 2. No Bet 11. Win rate 88.2%. ROI 33.4%.

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An AI-agent trading vault powered by SERV Reasoning just printed a 175% return in one day… @tradebetterapp launched on-chain with SERV in January, and their product uses SERV Reasoning to power their agents’ decision making. After heads down building for 6 months, I’m stoked to see what this cracked team is achieving.

DCL4385 @DCL4385

2 days ago

Absolute insanity from @openservai’s first flagship project! @tradebetterapp is cooking with pure fire! 🔥🤯🔥 As you can see below, they took a real-money $500 wallet to $1,378 in JUST ONE DAY using their proprietary strategy. That is a 175%+ gain in 24 hours. Best part? It's

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Auditability is the most overlooked blocker to enterprise AI adoption today, and SERV Reasoning solves it like nothing else. Dive in to understand why.

Lucas (opn/srv) @lucashafner

4 days ago

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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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@DCL4385 @openservai @Tesla data flywheel indeed, already well underway for over a year 🤝

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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another builder using SERV Reasoning for their business - this time, a construction company dropping costs nearly 60% with accuracy more than doubling by using SERV tek follow PT's story 👇

PT SERVlor @ptservlor

a week ago

x.com/i/article/2066…

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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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@GhostHash1 @solana woah

@RafieFaruq @GenieAI Hey Rafie, your DMs aren't open but our teams should connect to see how we can bring value to your setup. Want to shoot me a message back?

The frontier we care about most: high-stakes agent deployments across banking, compliance, healthcare, fintech, governments. And this is where we are going all in. We are expanding an insanely cracked enterprise BD team that is now in full attack mode, activating hundreds of organizations across two core buckets: > Recently funded companies above $1B valuation across healthcare, legal, fintech, banking, and more that are confirmed to be building with AI agents. > Fortune 500 and Nasdaq-listed companies that have publicly announced AI agent usage. This is being supported by the deep network our team has built over years. For example, Greg, our new advisor and previously Head of Partnerships at Google, has already been instrumental in opening up incredible conversations. We are going all out on enterprise growth, with the weight of 1000 ships behind us, getting SERV Reasoning into the industries where our value proposition is not just a nice-to-have, but a must-have. Less than a handful of weeks into private beta, we already have companies across banking, DeFi, prediction markets, robotics, restaurants, services, HR, compliance, AI verification and more using SERV Reasoning to unlock agents in production. Excited to bring everyone along for the ride. Tune in to follow our progress as it continues to accelerate.

OpenServ @openservai

2 weeks ago

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@KSimback 🤝

@0xSammy @openservai Serving allday, errday

A cross-chain agent protocol backed by @Alchemy and @solana just deployed SERV Reasoning into their production stack, now live. Nice.

Hyre @Hyre_agent

2 weeks ago

Most onchain APIs return data. Agents need decisions. HYRE just upgraded 7 decision endpoints to SERV Reasoning by @openservai a reasoning engine that thinks before it answers. → Token Verdict: snipe / watch / avoid → Bridge Quote: execute / wait / avoid → Yield Migrate:

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@cobotgg @openservai @lucashafner This is sick. Nice work

SERV Reasoning Private Beta is accelerating and a new batch of builders is coming on board, pulling the next ones in. The pattern holds: • Lower costs • 100% reliability • Faster than their old stack Here are a few recent additions to the program. -> Apply to join now 👇