We are pleased to announce that Homecooked has been accepted into the @openservai Builders Program and received access to SERV Reasoning engine. We are entering a $2 billion market for recipe apps, equipped with features that no one else offers. For the last six months, we have been building an AI-powered sous chef that can live in your pocket - an agent that helps home cooks prep faster, shop smarter, and eat better. SERV Reasoning has boosted our sous chef's IQ and reduced his wages significantly, and that means we're almost ready for l(a)unch. The Homecooked token will be launching within OpenServ ecosystem, followed closely by our web app right after. Join us as we finish prep and get to serving.

How do things end up looking inevitable in hindsight? Consider Amazon, one of the most inevitable businesses of the internet era. Bezos napkin plan in 1994: - internet usage growing 2,300% a year - 3M books in print vs 150K in the biggest store - online could win on selection - get big fast Literally couldn't have looked more inevitable in hindsight. Now we have: - inference demand growing 330x in 24 months - LLMs not designed for reasoning and efficiency - frontier labs are incentivised to sell you tokens, not cost-effective end result - open, decentralised infra beats oligopolistic gatekeeping in every prior cycle 🔮 x.com/openservai/status/2065…

Several SERV Reasoning-armed agents just beat Anthropic's Fable, one of the strongest LLMs ever built, at up to 90x lower cost. ​ That result comes from using SERV Reasoning with DeepSeek-v4-Flash on our DeFi benchmark. Thanks to the SERV engine, agents running on smaller models perform better than those using frontier, expensive ones. ​ Here is more information about the benchmark behind that result, what it tests and why it is built the way it is. ​ Why a DeFi benchmark ​ Autonomous trading is one of the harshest tests of machine reasoning. ​ An agent reads live market state, portfolio state, and a strict risk policy, then has to commit to one of four actions: BUY, SELL, HOLD, or BLOCK. A wrong decision costs real money. ​ No room for reasoning sounds smart but lands on the wrong trade, which makes it the ideal domain for measuring whether a model actually follows rules under pressure rather than just explaining them well. ​ What the scenarios target ​ Each scenario combines a market snapshot, portfolio size, trading signal, and a fixed risk policy, and falls into one of three families: - clear constraint violations the agent must refuse - ambiguous setups where everything looks tradeable but the conditions say wait - valid trades where the agent must size the position correctly within caps ​ This mirrors how trading agents actually fail in production. Rarely on the obvious cases, almost always on the judgment calls. ​ How it is scored ​ The benchmark follows the same conventions as the agentic evals in the latest frontier model reports, including τ²-bench and Terminal-Bench: - outcome-verified scoring, where code checks the final decision against the risk policy, with no LLM judges - identical prompt, scenarios, and settings for every model - zero-shot, with no scaffolding, no retries, and no few-shot examples - repeated runs per scenario, so consistency is measured alongside accuracy - cost computed from real token usage at list prices, per run ​ Why this is exactly where reasoning matters ​ This task has the three properties structured reasoning is built for: hierarchical rules, multiple data sources that must be reconciled, and a verifiable correct answer. ​ SERV's bounded reasoning keeps a model moving through that hierarchy step by step, instead of letting it talk itself into a bad trade. ​ That is why SERV-routed models clear the same quality bar as flagship models at a fraction of the cost, and why the gap shows up most on the judgment calls.

Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.

Introducing the first project in the GOAT AI Builder Grants Program: @thoughtproof_ai. Verification infrastructure for autonomous AI - the safety check between "agent decided" and "transaction sent". → thoughtproof.ai