GTM @WeMachinescom - multiplayer AI for lean teams

up north
Joined January 2010
very excited to announce that i’ve joined the amazing team at @WeMachinesCom as CMO @_qub1t and the team have been building this for the last 2 years and what they’ve built is genuinely next-level and there’s so much more to come DM if you’re interested in taking it for a spin
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Shorn | WeMachines retweeted
We’re expanding Dot into GPU facilitation, integrating a GPU marketplace directly into our ecosystem and leveraging our existing GPU infrastructure stack. Why? Because after extensive conversations with enterprises alike, we've identified a growing gap in the market for private GPU infrastructure, particularly among those enterprises that need more than just access to AI models. Soon, enterprises and developers will be able to rent GPU machines, pay in crypto, and deploy their own stacks for inference, fine-tuning, rendering and compute-intensive workloads, all through Dot. Additionally, we're planning confidential GPU rentals, designed to extend privacy beyond the application and operating system through encrypted CPU memory, hardware-isolated GPU execution and cryptographic attestation. The goal is to give customers greater control over their compute environments, with the ability to verify security configurations before trusting infrastructure with sensitive code, models or data. This is a natural extension of what we're building at Dot. We're diversifying beyond inference into the infrastructure enterprises need to deploy and operate AI privately. Coming soon.
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archiving some old linkedin messages and came across this. crazy times.
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Shorn | WeMachines retweeted
tell us how can we make this easier to get started and we'll do it 🤝 - enter an email address (no card needed) - click start - get 1000 free AI credits to spend on any model - run deep research - get free insights
WeMachines is becoming a competitive edge for startups, the new breed of lean, AI-forward teams. We're rolling out deep research that reads the web, looks up key platforms, monitors social media - all while knowing your business inside out. We started as a sleek collaboration tool. Now we're fine-tuning machines to have one goal, and that's you winning. The little demo you see below works out of the box, no configuration needed. After signing-up one of the machines will interview you to know what you're building - once that's done, all that's left to do is to build!
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tell us how can we make this easier to get started and we'll do it 🤝 - enter an email address (no card needed) - click start - get 1000 free AI credits to spend on any model - run deep research - get free insights
WeMachines is becoming a competitive edge for startups, the new breed of lean, AI-forward teams. We're rolling out deep research that reads the web, looks up key platforms, monitors social media - all while knowing your business inside out. We started as a sleek collaboration tool. Now we're fine-tuning machines to have one goal, and that's you winning. The little demo you see below works out of the box, no configuration needed. After signing-up one of the machines will interview you to know what you're building - once that's done, all that's left to do is to build!
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i can confirm, i will be spending most of my day playing a game today. and that game will ship real work 🤯 yeah it started as a bit of fun, but could this really be the future of work?
We had a spare Mac, so we did a little fun project and left Claude Opus 5.5 on it for 20h to gamify WeMachines. It built a native Apple Silicon multiplayer video game that's connected to your team's workspace and its agents. Now we can: ✅ hang out and talk to each other, including agents, over proximity chat ✅ see what each agent is working on and assign it tasks ✅ host fun meetings and ask agents for briefings or research ✅ work at our virtual desks in our preferred local harness: Claude, Hermes, Ollama... ✅ watch everyone's AI sessions in real time, like we're sitting next to each other IRL Let us know in the comments if you'd like to play with it!
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@leonvz reminded me of your virtual office idea too!
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@yoheinakajima i think you might like this :D
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this has got to be one of my favourite features in WeMachines - possible conflicts being flagged by agent while you work! it really does help keep teams aligned.
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Shorn | WeMachines retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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After six years in stealth, Atomic Machines is out. Our mission: on-demand, universal command of matter. First beachhead: the Matter Compiler, an AI-native manufacturing system that builds working micro-machines from code alone. No per-product tooling. No process development. Different code, different machine.
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Shorn | WeMachines retweeted
my agent stack: personal - @NousResearch Hermes - (with @tryvenice and @usedotai for inference) - private personal assistant that runs on a VPS, over @Tailscale work - @WeMachinesCom - team multiplayer, shared knowledge, custom apps and workflows, +2500 plugins
My agent stack: - @instinct - on-the-go purchases, form fill, local recs - @Muse - mini-apps that require reliable connectors (ex. "track my sleep and movement, send me a survey daily") - @tomo - motivation + goal tracking, also want to try their Interest Groups - @bot - X feed monitoring + alerts - Dots (@OpenAI) - deep, long-running projects...I'm currently making it start a business 👀 - @townai - everything work-related in email, Slack, docs - @Tabdotbot - outbound phone calls / appts
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my agent stack: personal - @NousResearch Hermes - (with @tryvenice and @usedotai for inference) - private personal assistant that runs on a VPS, over @Tailscale work - @WeMachinesCom - team multiplayer, shared knowledge, custom apps and workflows, +2500 plugins
My agent stack: - @instinct - on-the-go purchases, form fill, local recs - @Muse - mini-apps that require reliable connectors (ex. "track my sleep and movement, send me a survey daily") - @tomo - motivation + goal tracking, also want to try their Interest Groups - @bot - X feed monitoring + alerts - Dots (@OpenAI) - deep, long-running projects...I'm currently making it start a business 👀 - @townai - everything work-related in email, Slack, docs - @Tabdotbot - outbound phone calls / appts
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Shorn | WeMachines retweeted
Managing AI agents is a job you can delegate. In WeMachines, an agent takes that job. Ask once, and it orchestrates as many agents as needed, then reports back. Your team sees each step and can guide the agents as they work.
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if you've tried @NousResearch Hermes or @openclaw, i think you'll appreciate this new feature in WeMachines. kick off complex tasks with the confidence that your agent and its subagents will deliver. orchestration and delegation for work that matters. 🤖
Managing AI agents is a job you can delegate. In WeMachines, an agent takes that job. Ask once, and it orchestrates as many agents as needed, then reports back. Your team sees each step and can guide the agents as they work.
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Shorn | WeMachines retweeted
Bring knowledge from your tools into WeMachines 🧠 Over 2,500 plugins already available. Connect @SlackHQ , @NotionHQ , @MiroHQ and more. Ask your agents to bring in relevant information so your whole team can use it to get work done. See how: wemachines.com/plugins
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true multiplayer agents. with shared knowledge about all your chats, tasks, docs and apps. how agent teamwork should be 🤖
Your WeMachines workspace starts with an AI teammate. Anyone on the team can add more: a researcher, a reviewer, a copywriter. Each agent has its own brief, model and tools, and works in the same tasks and docs as everyone else. wemachines.com/ai/agents
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🤯🔥🔥
🔥🔥🔥 The first atomic cross-chain L1->L2 transaction ever! 🔥🔥🔥
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Shorn | WeMachines retweeted
The notion that current AI models are sentient and can suffer, combined with the foolish idea that suffering can be mathematically quantified and weighted between humans and non-humans, could lead us down an incredibly dark and dystopian path. But before it gets to that point, it will rightfully be met with immense backlash from team humans.
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it is.
It is. Being good at prompting is being good at communicating, breaking large tasks into digestible subgoals, giving clear feedback, and disentangling complexity. Prompting is the uber-skill.
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