GTM @WeMachinescom - multiplayer AI for lean teams

up north
Joined January 2010
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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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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Shorn | WeMachines retweeted
Andrej Karpathy
@karpathyOct 11
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.
Andrej Karpathy
@karpathyApr 9
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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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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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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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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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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Shorn | WeMachines retweeted
Come share a joke with us in your WeMachines channel. See if you can make @sh______rn laugh for 100 credits. A new way to say a small thanks between teams 🫶
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Shorn | WeMachines retweeted
Plugins are now live in WeMachines. Plug your agents into @stripe, @supabase, @sentry, @NotionHQ +2500 more. Use your own account, or share plugins with your team. Choose what agents can do with plugins, with fine-grained permissions per plugin. wemachines.com/plugins
You can now connect your WeMachines workspace and agents to over 2500 services. Each connection takes a click or two and comes with additional permission controls 🤗
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Shorn | WeMachines retweeted
Remote team? Build your own virtual office for your team members and AI agents inside your WeMachines workspace with a single prompt. Coffee breaks included ☕️
Our team and AI agents now share a virtual office 😄 we have real biz data + releases + tasks showing on the TVs + voice chat with AI agents (via @ElevenLabs) we built it for fun, but it showcases just how powerful WeMachines apps can be. the best part: anyone can build them.
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Shorn | WeMachines retweeted
Build the tools your team needs, and put your shared knowledge to work @WeMachinesCom
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Shorn | WeMachines retweeted
One thing I don't like about the tools companies use today is that you can't see what the AI consumes. So we added context controls and settings that keep your WeMachines workspace data healthy. That is better AI performance, fewer hallucinations, lower costs. On top of that organizations get an admin layer that manages spend, limits, performance and context health in one place for all your teams.
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Shorn | WeMachines retweeted
koeppelmann
@koeppelmannSep 11
Bermuda is launching! A project I have been advising for a while. It’s the first privacy solution that works fully with @safe. You can have funds in a privacy pool truly controlled by a Safe, meaning that if you can access or recover the Safe, you can access those funds as well!
We are opening beta access to Bermuda's private multisig. Explore private transactions, payroll and other DeFi use cases across the EVM. To get access, join the Bermuda telegram group - link in replies 👇
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Shorn | WeMachines retweeted
We're building WeMachines without ever leaving WeMachines - your team can extend the workspace with anything. How it works: Each app you build exposes private API and outputs its data as context your agents can use. Each app also takes your 3rd-party keys, encrypted, to fetch data or integrate with whatever you want. Now combine apps, schedules, webhooks and one workspace, and prompts like this are today possible: "Build us a Competitors app. Then a schedule that researches our competitors every weekday morning and posts findings into it. Then another schedule for Friday that recaps the best ones in #growth, always tag Filip and Shorn." You can say that out loud with CMD+O. Your agent will walk you through. And all of this happens while agents have the whole workspace: they see all data, read images, do deep research, communicate, access Figma, GitHub, and context from all of your apps. WeMachines comes with desktop app, mobile app, all the essentials, great UX, 200+ models, zero data retention, usage, end to end multiplayer, and much more. You can connect your workspace to other teams, and ours is connected to yours by default, ready to help 24/7 ❤️
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Shorn | WeMachines retweeted
Decisive is now WeMachines We're entering a new chapter. WeMachines is the new way to collaborate, built for top teams that want the most from AI. If your team is ready to take that next step, write or book a call. We'll be happy to get you going! wemachines.com
🤖 Made with AI
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the ultimate end goal of ethereum must be to give every individual the ability to become _ungovernable_.
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Shorn | WeMachines retweeted
The next web is peer to peer. Now is the time to build for it. Freedom browser makes it easy to browse and deploy to the decentralized web without gateways.
In a client-server model, an operator sits at the center and controls access. In Swarm, chunks of data move across peers, and the protocol coordinates storage and communication without a central gatekeeper. To participate, you run the software. You let yourself in. On the topology behind a permissionless network — from Intro to Swarm. 🎬
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Shorn | WeMachines retweeted
GLM 5.3 is now live across DotChat and the Dot API, with beta access in DotCode. • 1M context • Advanced reasoning • Tool calling • Agentic software engineering Dot pricing per 1M tokens: • $1.30 input • $4.09 output • $0.2414 cached input Dot is now 7% cheaper than OpenRouter across input, output and cached input, and roughly 26% cheaper than Venice. The same GLM 5.3. Lower inference costs. Fully private, anonymous access through Dot. Live now: app.usedot.xyz
Introducing GLM-5.3: Built to Code. Ready for Cyber Defense. - Top-tier coding and agentic capabilities, achieved through post-training on the 743B base model - A major leap in cybersecurity, setting a new standard among open models Tech Blog: z.ai/blog/glm-5.3
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Shorn | WeMachines retweeted
States need to work on not depending solely on VISA, Mastercard, Swift, etc. If the US can effectively cause the civil death of your nationals, even Judges, then are you really sovereign?
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Shorn | WeMachines retweeted
GLM-5.3 has released, beating Fable & the new DeepSeek V4-Pro 0813 from just yesterday on Terminal-Bench. It used GLM-5.2 as the base model with gains from more compute spent than ever before on post-training on diverse long-horizon tasks. Open weights have hit escape velocity.
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