David Dorman (@ddorman) header
David Dorman (@ddorman) profile picture

Gam ve gam. marketing/growth @modal. similar stuff before @grafana @digitalocean A-22351

New York, NY
Joined April 2009
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David Dorman retweeted
Kimi K3.1 Pro from @Kimi_Moonshot is now available on Modal. A major post-training update to Kimi K3, with expanded reinforcement learning for stronger coding, financial research, and long-horizon agentic work. Kimi K3.1 Pro supports text and images with 1M context.
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David Dorman retweeted
And we added first-class support for post-training Beam yourself. Get started with Modal Dojo now: dojo.modal.dev/
Reflection Beam is now available on Modal. Beam is a 501B-parameter open-weight MoE (23B active) from @reflection_ai built for coding, agentic workflows, and reasoning. It supports a 1M token context window, with token efficiency a core focus. In early access, the Oura Health Intelligence team found that "Beam had fewer context leaks and was much more grounded in its answers than other frontier models we evaluated.”
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The REAL reasons I joined @modal: - Cool logo in my Twitter handle. - Compatible with my green wardrobe from @nvidia. - Unlimited GPU credits and tokens. - @charles_irl's hair.
📢 ADSP Episode 310 is out! 📢 In this episode, @blelbach and @code_report chat about Bryce leaving @nvidia, where he is headed next and more! 🥳 adspthepodcast.com/2026/10/3…
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Great to be back on Unsupervised Learning with @jacobeffron and @patrickachase! We talked about the day-to-day knife fighting of getting GPUs, why we contribute a lot to open source, and how our most experienced engineers are the biggest agent-maxxers.
Is the compute market still the Wild West? What will happen to GPU prices over the next few years? And how will the inference market evolve? @bernhardsson, co-founder and CEO of @Redpoint portfolio company @modal, was the perfect person to discuss this and more. Modal has crossed a $1B revenue run rate and recently raised $750M at a $15.75B valuation. A few takeaways from my conversation with Erik, alongside @patrickachase: ▪️ Why compute won’t become a commodity GPU capacity varies in disk, networking, location, and especially reliability. That variation makes a true exchange hard to build, so much of the market still runs on late-night WhatsApp groups and hallway deals at events like GTC. Erik expects it to get somewhat more efficient over time, but to stay structurally messy. ▪️ NeoClouds, NeoLabs, and the cost of capital Erik disagrees with the idea that NeoClouds are going to die, and he sees them as sound businesses built on leasing hard assets against locked-in contracts. Their real disadvantage is cost of capital, which is why the successful hyperscalers all sit inside cash-rich public companies. NeoLabs carry a different risk. GPU deals often require only about 30% upfront, so a lab that raises $1B can commit to far more compute than it can pay for today. If funding tightens, Erik expects some NeoLabs to resell capacity and start to look more like NeoClouds. ▪️ Where durable advantage lives in inference Erik sees pure token serving as a race to the bottom, with routers already commoditizing the underlying providers. Better kernels and smarter KV caching still matter, but those techniques spread quickly. He believes the more durable edge comes from offering a cluster of adjacent building blocks. ▪️ Who is actually adopting coding agents Erik expected junior engineers to adopt agents first, but he has seen the opposite. For senior engineers, agents are like having 10 interns. Give one a full POSIX test suite and it can write a new file system in three days. He also sees training data creating a new kind of incumbency in software, with developers defaulting to frameworks like React because agents already know them well. ▪️ Why GPU prices may go up Erik changed his mind on this 48 hours before we recorded. After reading Anthropic’s S1, he concluded that the largest buyers of compute are financially strong enough to keep acquiring capacity, which points to higher prices over the next 12 months. Full Unsupervised Learning Episode 👇 0:00 Intro 1:26 The Compute Market Today 5:16 Modal’s Path to LLM Inference 10:02 Building the Hard Infrastructure 12:32 Unsolved Problems in Inference 16:07 Chips & Model Sizes 21:21 What Wins in Inference 24:56 Prioritizing a Multi-Product Roadmap 30:20 Agents as Modal’s Users 33:19 The Next Big Bet After Sandboxes 38:58 Robotics & Multimodal Inference 41:04 Scaling From $2M to $1B 42:51 NeoClouds & NeoLabs 48:15 RL as a Service 51:40 Quickfire YouTube: youtu.be/HJDekXH-h5c Spotify: bit.ly/4dZ0O4e Apple: bit.ly/4inZJnE
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David Dorman retweeted
So a career update: I've joined @modal leading Developer Relations in Europe! To celebrate, I won't do resets like @thsottiaux but I'll do one better, and give out free Modal credits. Tell me what you're building with @modal in Europe and I'll pick a few projects to fund!
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David Dorman retweeted
Students shouldn’t have to scale down their research ideas because they can’t get access to compute. The @generalcatalyst Research Fellowship will support 20 undergraduates with up to $25K in compute from Modal to power their most ambitious ideas. We're excited to see what you build!
Introducing @generalcatalyst Research Fellows - a program for undergraduates ready to pursue their most ambitious ideas. We’re supporting approximately 20 exceptional undergraduate builders from leading schools. In partnership with @Modal, each fellow will receive up to $25K in compute for a 90-day project of their own. For the bold, the curious, and those building what they wish existed: applications are open now. Tell us what you want to build! generalcatalyst.com/research…
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David Dorman retweeted
We recently announced that Modal has crossed $1B in annualized run rate, as well as raised a $750M Series D at a $15.75B valuation. Our CEO and co-founder @bernhardsson stopped by @NYSE to share more about what's being built on Modal today and what's next for the company post-fundraise...
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David Dorman retweeted
DeepSeek v4.1 Flash 1017 from @deepseek_ai is now available on Modal. An update of DeepSeek v4.1 Flash, it improves substantially on reasoning, coding, agentic tasks, and visual understanding.
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David Dorman retweeted
.@WeAreLegora runs 2,000 agent evals in parallel on Modal Sandboxes, testing a legal agent that works for hours across thousands of documents. Every run boots the entire application, pinned to a specific commit, so engineers can measure the impact of their changes in a realistic environment.
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David Dorman retweeted
We worked with @SakanaAILabs to optimize their Sakana Namazu API down to the kernel level: → Selective NVFP4 quantization (23k+ experts) → DFlash draft model trained on raw token streams (+25% acceptance) → In-house prefix KV routing + serverless autoscaling
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David Dorman retweeted
You can give your @livekit agent a video avatar with open-weight models running on Modal. We used ours to clone @charles_irl, our GPU Enjoyer in Residence.
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David Dorman retweeted
Tinker changed how people do post-training. We're big fans, and so are many of the teams we work with. But as those teams scale up, they keep raising the same two problems: per-token pricing gets expensive quickly, and you’re limited to the models and features the provider decides to support. We think a good API deserves more than one implementation. Today we're releasing Spindle, an open-source, Tinker-compatible training server that runs in your Modal workspace. Your existing Tinker scripts run unchanged, and because you own the server, you pay for GPU time instead of tokens.
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David Dorman retweeted
Performant reinforcement learning is a hard infrastructure and research problem. We’ve recently made on-demand multi-node clusters available and showed how to take advantageous of heterogeneous sampling compute. But there still exists the need for high-fidelity environments, clear and robust reward functions, and tuned training recipes that actually work. Most teams are actually nearly there! With an agent harness deployed in production and evals/agent traces collected from that deployment, you just need an easy way to start training your data. So we built a training library that gives you the building blocks, tuned defaults, and observability you need for easy, production-grade LLM training. It’s available now. All it takes is a prompt.
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If you're using open weight model APIs in prod, I'd like to talk with you about your workflow! Model discovery, evals, traffic shapes & scale reqs, etc. [email protected]
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David Dorman retweeted
Our friends at @modal shipped a new Sticky Sessions feature yesterday that's extremely useful for building very low-latency voice agents and other realtime AI applications. Today, you can build (and serve at scale) voice agents that deliver 500ms voice-to-voice response times. But this is 3-4x faster than most of the voice agents we see in production. To be fast, you need a few things: fast models; good orchestration of turn detection and data streaming between models in your agent pipeline; fast WebRTC or telephony client-server network routing; and optimized placement/hosting/routing for your agent and inference containers. Sticky Sessions makes it easy to route a specific user's requests to the same container every time. If you're an infrastructure person, you might describe this as session-aware serverless routing. The Modal team did a lot of work here to get the details right, so things like load balancing and auto-scaling work nicely in conjunction with Sticky Sessions. The video is a demo clip showing a voice agent running on Modal with a 500ms voice-to-voice latency. Ben Shababo, who built this demo, used: - Modal's Qwen 3 ASR service for transcription - Modal's PhoneLLM Mini service as the agent brain - The brand new @GradiumAI Phonon text-to-speech model for the voice - Pipecat as the agent framework and WebRTC routing layer - Modal Sticky Sessions A few really cool things, here. All the models are running co-located on a mixture of GPUs and CPUs: L4, B200, and the Phonon model is running on CPU. Running Phonon on the CPU means that voice audio starts streaming in <50ms and you don't have to manage a GPU cluster for TTS. There are no extra network hops or network overhead in any of the routing. And the agent container uses Modal memory snapshots so a new agent (orchestration code, Phonon model, and the Smart Turn model) are all ready and responding to new requests in under 2 seconds. If you've ever built out voice agent cluster infrastructure on top of, say, Kubernetes, you know how impressive all of the above attributes are! A link to Ben's repo is in the thread below.
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David Dorman retweeted
Some projects are only done for shareholder value. This one was done for the love of the game, literally! My favorite online game, Curvytron, is now backed by the Sticky Sessions primitive my team recently shipped :) curvytron.modal.dev/#/ Shoutout to @siwonpark_, @ksinpoint1, and @kathirmeyyappan for embodying the @modal spirit and shipping this with speed.
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David Dorman retweeted
the best time to hire a GTM Engineer to scale our data platform was before we hit a $1B revenue run rate the second best time is right now this person will be responsible for building our... - company and people graphs, tracking companies, labs, researchers, and engineers - forecasting inputs for both compute capacity (supply) and sales pipeline (demand) - customer and user journey state machine, ensuring that we're surfacing the right action to the right person at the right time you wouldn't be starting from scratch - we have an incredible team, a cracked engineering culture, the latest in agent infrastructure, and the mandate of Heaven to build the best GTM data platform the world has ever seen apply at the link below and/or DM me a piece of technical writing or presentation about a data system/product you've built jobs.ashbyhq.com/modal/dd6fe…
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This is what packing more sandboxes per host while maintain low latency looks like. Thank you to the @modal team for sharing their benchmarks. More NVIDIA Vera CPU surprises coming!
We got our hands on @nvidia's new Vera CPU and put it to the test. We built a benchmark using real-world agentic workloads to see how it performs under contention. In early tests, Vera delivered up to 2x the throughput of comparable x86 hardware, maintaining low latency even as hundreds of concurrent workloads competed for resources. For Modal Sandboxes, that could mean running more agents in parallel without sacrificing latency.
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