Kunal Batra (@kunal732) header
Kunal Batra (@kunal732) profile picture

Developer Relations @Datadoghq. Previously @AWS, @Clarifai, @Auth0, @SendGrid Opinions are my own.

New York, NY
Joined March 2009
Photo by Kunal Batra (@kunal732)Photo by Kunal Batra (@kunal732)Photo by Kunal Batra (@kunal732)Photo by Kunal Batra (@kunal732)Photo by Kunal Batra (@kunal732)Photo by Kunal Batra (@kunal732)Photo by Kunal Batra (@kunal732)Photo by Kunal Batra (@kunal732)Photo by Kunal Batra (@kunal732)Photo by Kunal Batra (@kunal732)
Kunal Batra retweeted
Replying to @jonobelotti_IO
@jonobelotti_IO and @akshat_b were kind enough to sit down and talk about how they're scaling Modal with Datadog. It's crazy that they run on top of 20 cloud providers now, check it out:
✅ 96% of issues resolved before customer impact. ✅ 99% of incidents detected within five minutes. Read the full story to see how the team at @modal use Datadog to operate AI infrastructure across 20+ cloud providers, launching 26M containers every day while maintaining unified visibility across the platform: bit.ly/4eLDmrh
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Kunal Batra retweeted
"If there is a time engineers can afford to take on technical debt, it's now. A more capable model can repay it down the line. Don't overthink it." This reframes tech debt in a way that's underappreciated and extremely pragmatic advice for those building with AI today. From a chat with @stevenkplus1 at @datadoghq, with a packed cafe on a Monday afternoon. We covered Devin, AI and math, formal verification, appreciating life, and more. Reminded me of this slide on tech debt from a talk at Datadog. xitter.cf/nerdsane/status/210780…
Love all financial derivations of technical debt. This was a real/fun presentation at Datadog.
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Kunal Batra retweeted
Announcing ChatGPT Space, a collection of new collaborative experiences right in ChatGPT. Edit rich notes with live visualizations together with others and your dots. Whether it’s a TODO list to delegate, a product plan or meeting notes. Working with others and your agents is seamless and they also be automatically kept up to date in the background. For developers, this is AGENTS.md taken one step further.
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Kunal Batra retweeted
Heading to @iclr_conf next week? Datadog AI Research is hosting an event along with the ICLR SPOT workshop organizers. Join us to meet our team and learn about our work on observability world models + agentic post-training!
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Been working on this for a bit, stoked to announce a community port of the AWS Strands Agents framework to Swift. It can run natively on device and you can build those fun OpenClaw-style computer use examples. It can run inference locally via MLX or through Bedrock. In the video I connected an agent to a Mac MCP server and asked it to open Word and write a sentence. It also sends OTel telemetry to your tool of choice. In the video you can see the telemetry from the agent being shown in Datadog's LLM Observability. communitystrands.com/swift Coming soon: Interacting with the agents through voice either AWS Nova Sonic or locally with @Prince_Canuma's mlx-audio-swift package. cc: @awsdevelopers, @awnihannun , @shshnkp
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Introducing MLX-Swift-TS github.com/kunal732/MLX-Swif… An SDK for running time series foundation models fully on-device on Apple Silicon. When I joined @datadoghq , I was introduced to Toto, our time series foundation model, and got excited about zero-shot forecasting across different domains. While building a health copilot app, I realized there wasn’t a simple way to run models like these locally on device. So I built one. MLX-Swift-TS exposes a common TimeSeriesForecaster interface for loading and running multiple time series architectures directly in Swift using MLX. No server required. The attached video shows on-device forecasting running inside a native Swift app. Huge thanks to @awnihannun and the MLX team for building MLX and its Swift API, @Prince_Canuma for inspiration on MLX SDK patterns, and @atalwalkar and the Datadog team for Toto.
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Always enjoy chatting with this crew
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Kunal Batra retweeted
Great work by @kunal732 to help run locally using MLX the @arcee_ai Virtuoso Lite 10B model distilled from DeepSeek V3.
Just converted @arcee_ai 's Virtuoso Lite model to MLX. A 10B parameter model distilled from DeepSeek V3. Have been impressed with it's performance. Recommend watching @julsimon's video to learn more youtube.com/watch?v=nrqJb1I7…) If you want to deploy it in your Swift app - you can check out my demo app showcasing this model running locally. github.com/kunal732/mlx-swif… MLX Model here: huggingface.co/mlx-community…
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Just converted @arcee_ai 's Virtuoso Lite model to MLX. A 10B parameter model distilled from DeepSeek V3. Have been impressed with it's performance. Recommend watching @julsimon's video to learn more youtube.com/watch?v=nrqJb1I7…) If you want to deploy it in your Swift app - you can check out my demo app showcasing this model running locally. github.com/kunal732/mlx-swif… MLX Model here: huggingface.co/mlx-community…
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cc @Prince_Canuma - not sure if you were involved with this but know you're at @arcee_ai as well.
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Running DeepSeek (huggingface.co/mlx-community…) with MLX locally in a swift application. Xcode Project can be downloaded here: github.com/kunal732/mlx-swif…
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Just converted @JinaAI_ 's new ReaderLM V2 model into MLX. Trained to convert different formats like html into markdown. MLX port over here: huggingface.co/mlx-community… Gist of swift code that crawls a website and converts to markdown here: huggingface.co/mlx-community…
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🪟 MLX Model Manager now supports Microsoft's Phi-4 LLM. You can add it to your Swift app in just a couple of lines of code!! 💡 Gist: gist.github.com/kunal732/a18… 📱 Sample App: github.com/kunal732/mlx-swif… [🎥 Attaching a demo video below!]
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Kunal Batra retweeted
The latest MLX is all about portability: - `mx.export` makes it easier than ever to write in Python and call from any language that can call into C++ or C. - MLX compiles and runs on Windows (CPU-only) thanks to some amazing work from @zcbenz - Efforts under way to build front-ends in Julia, Elixir (via Nx), Rust, in addition to existing front-ends (C, C++, Swift, NodeJS)
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3-4 lines of code and voilà!
🚀 MLX-VLM upcoming release performance gains for Llama-3.2-Vision family of models: 11B-Instruct (4bit) on M3 Max (96GB): ⚡ 18x faster tokens per second generation. 🧠 ~75% lower peak memory usage.
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Have been glued to this video of my daughter’s first bike ride. Wild how three+ years flew by in a blink of an eye.
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This looks like a HUGE update. Super exciting to see Swift AI tools getting better so fast
Today we’re announcing version 2.0 of @alexcodes_ai, featuring our new Agent mode, Voice mode, and Project Memories! This is a huge improvement to working with Alex, and it’s now the best way to make native iOS & MacOS apps. Check out the demo:
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