I make @modal. Recovering Mathematician.
Brooklyn, NY
Joined September 2009
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If math research were like ML research:
🚨 Introducing NavierStoked 🌊 🏄♂️. The first ever proof of existence and uniqueness* to Navier stokes. Check out our arxiv / blog post / GitHub repo and give us a star!
(*assuming zero viscosity, pressure, gravity, and stress tensor).
Adam Azzam retweeted
hello hello, a little news from me :) i joined @modal to work on all things community 💚
putting hackathons together in college was some of the most fun i’ve had in tech. i’ve worked in a few different corners of tech since then (eng, enablement, all things marketing), but i still love bringing people together around what they’re building.
there’s a craft to creating experiences people want to be part of. community deserves the same care as the product itself. people are giving you their time, and i want it to feel well spent, whether we’re getting together in a room or somewhere on the internet.
if you’re building on modal, have a gathering in mind that could use a home, or are building something cool and want to share, i’d love to meet you :) dms are open!
Adam Azzam retweeted
Introducing Phonic-1, the most advanced speech-to-speech model for agentic tasks — from @phonic_co, the speech-to-speech model company.
We’ve had a breakout year and raised a $27.5M Series A led by @firstround with participation from @Lux_Capital, @felicis, and Definition.
Adam Azzam 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!
Adam Azzam 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.
FrontierCS’s approach is a fresh take on task creation for RSI benchmarks. Excited to see their progress and proud to support them at @modal
Adam Azzam retweeted
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.”
Adam Azzam retweeted
Is this a hot dog?
find out what your favorite LLMs said over at hotdogbench.com
Adam Azzam retweeted
Muse Spark Open, a coding-optimized open weight model from @Meta, is now available on Modal.
Muse Spark Open is the most capable US open weight model to date and is trained specifically for agentic coding and long-horizon tasks; it supports native multimodal understanding and supports a 1M token context window.
Adam Azzam retweeted
Nemotron 3.5 Super VL from @nvidia is live on Modal today with 120B params for visual reasoning and agentic work across images, docs, and UIs.
Hear more about the thinking behind the Nemotron family: At Runtime, @ctnzr explained why “efficiency is also intelligence,” and how NVIDIA pre-trained Nemotron 3 Ultra entirely in FP4.
Move over unit disk, 𝔻 is for 𝔻elighting our users.
Today we're announcing that Modal has crossed $1B in annualized run rate, as well as raised a $750M Series D at a $15.75B valuation, led by @Accel.
Since our Series C, usage has tripled and we’ve grown more than 15x year-over-year.
We are deeply grateful to the developers and companies building on Modal. There's still so much more to build, and we can't think of a better time to be building software.
people talk about "owning your own intelligence" like intelligence is singular. Opus, but your company's Opus.
"owning" IMO is cultural, and starts with driving the marginal effort of customizing your intelligence, per use-case to zero.
Adam Azzam retweeted
You already gave the LLM the answer. So why is it writing it out from scratch?
Introducing a new AI model architecture: Constrained Decoding Models (CDMs)
LLMs were built to write. Most production workloads just need an answer: fast, consistent, and affordable.
CDMs are purpose-built for 1 task each. They read your input once and skip the token-by-token generative loop that makes LLMs slow and expensive.
Not just a decision model, but a family of 3 CDMs live today:
• Classify: define your labels and get back a calibrated probability + reason for each. Used for routing, tagging, and intent detection.
• Extract: describe the entities in natural language and get back well-formed JSON with confidence scores + citations. Used for web scraping, document processing, and PII redaction.
• Summarize: pass in long text, images, or PDFs and get back a consistent summary under 1,000 tokens. Used for context compaction, metadata generation, and transcript digests.
Built for production:
• 1M tokens of multimodal (text + image) input, 31x larger than Jev
• 77.2% on DecisionBench vs Jev's 72%
• 200x faster, 467x cheaper than frontier LLM APIs
• $0.05/M input tokens. Output is free (yes, even for summarize)
Live on your favorite platforms today:
@OpenRouter @vercel_dev @LiteLLM @LangChain @TrustedRouter @itsArthurAI @useRouterPlus
Get 50M input tokens for free (for the first 50 hours) at link below 👀
🤖 Made with AI
Adam Azzam retweeted
Training large models with RL typically runs on a fixed homogeneous cluster of the latest accelerators for numerical stability and faster throughput. This throws away inference optimization work done for other vendors and generations, limits the capacity you can utilize, and is unnecessarily expensive.
So we trained a model on agentic coding tasks using fully asynchronous RL and mixed-precision, mixed-accelerator samplers. With off-policy correction, training stayed stable through convergence, on par with homogeneous samplers, at a lower cost.
Today’s results by @OpenAI are incredible.
I never thought in my lifetime we’d get AI that could decipher Bourgain.
built citedbyagi.com
have you been cited by agi? check the leaderboard of citations from openai's /math release
Adam Azzam retweeted
Runtime is happening in two days.
Join us to hear speakers from @AnthropicAI, @typesafeai, @nvidia, @tryramp, and more.
Adam Azzam retweeted
New on the @modal blog: a write-up by me and Shreya on our work to speed up AI-SQL queries
- why AI-SQL? why high-throughput inference?
- how can we possibly be >10x faster than vLLM?
- what does the future of inference engineering look like?
Read here: modal.com/blog/quail-billion…
