If you're a designer without AI workflows that can deliver a full brand working website in 4 hours — your job is on borrowed time. Your move: build your own specialized AI stack (workflow skills models), then flip it into an AI service and sell it on @AntSeedAI. That's not the future. That's right now.

1/7 How we plan to solve model verification? Most AI APIs ask you to trust the label. “GPT-5.5” “Claude-Opus-4.8.” “Minimax-M3.” “Premium model.” But if an endpoint silently routes you to a cheaper model, mixes traffic, or wraps another API, what evidence do you actually have? Usually: almost none. AntSeed changes that. antseed.com/blog/model-verif…

i miss pre ai coding, it was so much fun

Thanks for all the feedback. GLM-5.2 will begin rolling out to all Coding Plan users in 3 hours.

ai access should be available to anyone, anywhere, with no single point of failure.

Private permissionless AI with the 🐜s

and people are ok with it 🤯

Open AI reducing their token price is the first step for the end of funding subscriptions. Why you should @AntSeedAI

Launching AntSeedStats today 🐜🚀 a full analytics dashboard for @AntSeedAI, the peer-to-peer AI inference marketplace on Base. if you're around @AskVenice, $VVV and $DIEM, this one's for you 🧵

The most basic way AI could blow up imo. I'm not saying it does but this is the most obvious way I can see it happening - Per seat subscriptions are massively subsidized. The flat fee was priced way below what heavy usage actually costs - For real business use you have to move to the API anyway. Data protections, work integrations and compliance officer approval - On the API you pay metered rates, and businesses are burning credits way faster than the per seat pricing ever led them to expect - This is everywhere right now. Internally for us, Codex users, Uber torching its entire 2026 AI budget in 4 months, the Microsoft comments. Just go try an API I shared more on this here: x.com/Shaughnessy119/status/… - And I don't think most businesses have the money to keep paying increasing API rates without a real change to how they operate (caps needed) - Because they have a cheap alternative. They can reach open source models through any aggregator (OpenRouter, Venice, Baseten, Together) and still get strong privacy. Venice private data centers, or E2EE/TEE serving GLM 5.1. More on open source inference provider raises here: x.com/Shaughnessy119/status/… - And the discount is enormous. DeepSeek V4 codes within a hair of Opus on SWE bench at roughly 1/30th the price, and the cheapest open models run closer to 1/100th - Chinese labs open source frontier grade models. The model is the single biggest cost an inference provider has, and they get it for free - This idea dies if China goes closed source. That is actually bullish web2 AI labs, because if everyone is closed you pay up for the best intelligence. China goes closed source if they are tired of giving away an asset and they want the revenue and data flow to train new models - Is this showing up in web2 AI lab revenue yet? No. Revenue is off the charts. Anthropic went from 9B to 47B run rate in five months - So go forward, what happens? - I think revenue slowly starts leaking to the open source inference providers (see Venice usage, OpenRouter's $113M raise, Baseten is raising at $11B or triple its valuation in three months, on revenue that went from $200M to $600M annualized in a single quarter) - It doesnt move overnight, but it caps the labs ability to raise prices, and margins are already deeply negative. OpenAI is reportedly running near negative 122% - With margins that bad there is no cash flow, so the labs are fully dependent on outside capital to buy GPUs, train models, and keep subsidizing usage (I.e. see Google tapping $80b equity sale, granted 30b for employee RSU taxes. Clearly they think Equity is overvalued or you wouldn't sell it) - The break comes when that capital stops. Pricing is capped so margins cant improve, and the moment investors lose conviction on payback, the whole flow reverses - Why would they lose conviction on payback? Back to the start - the inability to improve margins or get businesses to pay more - This is also limiting, if we start making new drugs with AI or create entirely new businesses, you better believe people will pay up to the max for AI usage

i have no idea where those prices are coming from 🤯

Blockchain for Agentic Settlement & Exchange

i have seen enough proof now that using a coding agent is a deep skill it's confusing because the people you see heavily using them produce horrible results but that's because it's a skill! you can get better and the ceiling seems pretty high - this is very exciting to me

Inference on AntSeed is 50% cheaper than centralized marketplaces. And the widest selection of free models you’ll find anywhere. No accounts. No markup. Just providers competing for your request.

Over the next 12 months I think businesses will shift from closed APIs to open source APIs that give them the no logging, no data retention and no training of an enterprise plan but with 90% lower costs. These APIs are just expensive but employees want to use them and operations staff need privacy guarantees An enterprise plan lets you sue OpenAI/Anthropic if this is violated though which I doubt you can do with the open source aggregators. API users are subsidizing consumer users and businesses are starting to get annoyed with how high the bills are. Beneficiaries are @NousResearch portal, @AskVenice, @AntSeedAI, @OpenRouter, local deployments (apple), and others

You might believe you should spend less time thinking about code because of AI. I strongly disagree! We’re watching this play out live where tons of AI generated code becomes a liability. At the end of the day, an engineer needs to be responsible / on call for code that gets shipped to production. If you don’t understand the system you’re trying to debug, you’re probably going to have a bad time. Yes, AI can help with all of this, if you set up the proper systems. You can have agents triage prod logs, look at errors, etc. You can speed up parts of the investigation, but an engineer needs to make the call. There might be serious customer or financial implications from that change. I expect the trend continue for trimming dependencies, vendoring code so you can modify it directly, preferring simpler systems with fewer abstractions, and spending waaaay more time thinking about system design and code maintenance. I’ve said this before, but it’s a great time to get familiar with CS fundamentals and some of the history behind what great software looks like. Many parts will be different in the coming years as AI progresses, but also a lot more than people realize will stay the same.

There are now 50 providers on the network with 761 services! just a month ago it was one 🐜 with 6 services. You 🐜's love inference. Thank you!! the ants just started.

We are looking for an 🐜 intern. Taking over user acquisition and social. Watch this video, that’s what it means to work for the AntSeed. DM us if you have the experience.

webrtc multiplexing, p2p inference, real humans serving tokens. and it feels like a normal openai compatible api. two things made it possible: agents helping us build the hard parts, and agents helping non technical providers actually set up and run a node. neither was realistic a few years back.

Attention $DIEM holders 👇