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A Practical Guide to Git in MATLAB

You know the folder. Somewhere on your machine there is a directory that looks like this:

Every economist who writes code recognises this. You start a model, iterate on it, share it with a co-author, and within weeks, the naming conventions have collapsed. Which file is the current one? Which one did you send to the referee? Nobody knows — least of all you, six months from now.

This creates four problems that compound over time:

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The MathWorks Cambridge office hosted the Europe 2025 edition of the MathWorks Minidrone Competition, welcoming students from universities across Europe. The 2025 edition drew applications from over 40 student teams.

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The MathWorks Cambridge office hosted the Europe 2025 edition of the MathWorks Minidrone Competition, welcoming students from universities across Europe. The 2025 edition drew applications from over 40 student teams.

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※この投稿は 2026 年 6 月 15 日に The MATLAB Blog へ 投稿されたものの抄訳です。 --

今年の1月に 128GB の NVIDIA DGX Spark 上で 1200億パラメータの LLM を動かし、それを MATLAB に接続する方法を紹介しました。私の同僚である Abhijit Bhattacharjee はすぐに続いて、同じモデルとハードウェアを OpenCode と MATLAB MCP Server

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※この投稿は 2026 年 6 月 15 日に The MATLAB Blog へ 投稿されたものの抄訳です。 --

今年の1月に 128GB の NVIDIA DGX Spark 上で 1200億パラメータの LLM を動かし、それを MATLAB に接続する方法を紹介しました。私の同僚である Abhijit Bhattacharjee はすぐに続いて、同じモデルとハードウェアを OpenCode と MATLAB MCP Server

...

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Back in January, I showed how put a 120 billion parameter LLM on a 128Gb NVIDIA DGX Spark and hook it up to MATLAB. My colleague, Abhijit Bhattacharjee quickly followed up with an article showing how to use the same model and hardware agentically using OpenCode and MATLAB MCP Server.

Back in January, I showed how put a 120 billion parameter LLM on a 128Gb NVIDIA DGX Spark and hook it up to MATLAB. My colleague, Abhijit Bhattacharjee quickly followed up with an article showing how to use the same model and hardware agentically using OpenCode and MATLAB MCP Server.

At multi-gigabit data rates, a clean-looking channel simulation can still leave you with a failing eye. The hard part is not just measuring that jitter exists; it is figuring out which timing effects are closing the eye, how much margin they are consuming, and whether the next design move should be clock cleanup, equalization, channel modeling, or noise reduction. This article shows how to turn total jitter from a single pass/fail number into design guidance. We’ll walk through two practical workflows: one for controlled waveform experiments where impairments can be injected and checked, and one for observing jitter metrics directly inside a SerDes system simulation as the design runs. Along the way, we’ll separate random, deterministic, sinusoidal, duty-cycle, and ISI-related jitter; show where the estimates are reliable or fragile; and close with a checklist for getting numbers you can trust before making design decisions. Interested to learn more? visit our

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At multi-gigabit data rates, a clean-looking channel simulation can still leave you with a failing eye. The hard part is not just measuring that jitter exists; it is figuring out which timing effects are closing the eye, how much margin they are consuming, and whether the next design move should be clock cleanup, equalization, channel modeling, or noise reduction. This article shows how to turn total jitter from a single pass/fail number into design guidance. We’ll walk through two practical workflows: one for controlled waveform experiments where impairments can be injected and checked, and one for observing jitter metrics directly inside a SerDes system simulation as the design runs. Along the way, we’ll separate random, deterministic, sinusoidal, duty-cycle, and ISI-related jitter; show where the estimates are reliable or fragile; and close with a checklist for getting numbers you can trust before making design decisions. Interested to learn more? visit our

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Most engineering teams know how to build a prototype. Not every team feels confident explaining exactly what that prototype is doing, especially early in development. Measurement can lag behind innovation, not because it is unimportant, but because it is complex, time‑consuming, and easy to postpone until the stakes are higher. Leela Pawashe has spent her career working in that gap. Across academia, startups, and large medical device companies, she has focused on measuring complex physical systems and helping teams understand what their data actually means. Today, through her startup Bay Area Sonic Solutions, she is bringing that experience to early-stage teams, starting with ultrasonic devices, but grounded in principles that apply far beyond a single technology. A Broader View of Ultrasound When Pawashe talks about ultrasound, she is not just talking about the imaging system you might see in a doctor’s office. She is...

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Digital engineering is getting a lot of attention right now and for good reason. Teams across aerospace, defense, automotive, and industrial sectors are investing heavily in model‑based approaches, SysML v2, digital threads, and digital twins. The promise is compelling: better traceability, earlier insight, and fewer late‑stage surprises. But in practice, many digital engineering efforts stall at the same point. The system architecture models look great. The executable behavior models validate design requirements and enable early design verification. And then… software gets written. That’s where the digital thread quietly breaks. Where Digital Threads Fail in the Real World In theory, a digital thread connects requirements to architecture, behavior, implementation, and verification. In reality, most threads stop short of implementation. The common pattern looks like this: Requirements are captured and traced to system models. SysML or architecture...

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※この投稿は 2026 年 6 月 8 日に The Artificial Intelligence へ 投稿されたものの抄訳です。 --

Guest blogger: ゲストブロガー: Arkadiy Turevskiy Arkadiy Turevskiy は MathWorks における AI および制御製品のプロダクトマネージャーです。Arkadiy は 20 年以上にわたり MathWorks に在籍しています。MathWorks 入社前は Pratt & Whitney で航空機エンジン制御システムを設計していました。

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※この投稿は 2026 年 6 月 8 日に The Artificial Intelligence へ 投稿されたものの抄訳です。 --

Guest blogger: ゲストブロガー: Arkadiy Turevskiy Arkadiy Turevskiy は MathWorks における AI および制御製品のプロダクトマネージャーです。Arkadiy は 20 年以上にわたり MathWorks に在籍しています。MathWorks 入社前は Pratt & Whitney で航空機エンジン制御システムを設計していました。

...

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I often use sendmail to notify me of the progress of long running scripts, or when they have finished or errored. I have had some trouble recently with our internal mail server, so I wanted to try an alternative method of getting notifications. nfty is an HTTP-based service that allows you to send notifications to your phone or desktop using a REST API. I will try using it from MATLAB. Features covered in this code-along style video include: webwrite Functions, source control and projects [bcvid id="6397978825112"] Play the video in full screen mode for a better viewing...

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Today’s guest writer is Rob O'Gara, Accelerator Program Lead at MathWorks. [video width="1920" height="1080" mp4="http://blogs.mathworks.com/startups/files/2026/06/trouble-verify-fix-animation.mp4"][/video] Discover MathWorks Agentic AI Solutions on MathWorks.com The era of agentic AI workflows has arrived and is here to stay. Each week, a growing number of startups across all industries are incorporating tools like Claude Code and Codex into their existing workflows. At the same time, investors are showing growing interest in startups that utilize agentic AI to improve their products and business models. Despite this momentum, there is not yet a clear, proven method for startups to use agentic AI when building technology based on an engineered system. For engineering-focused startups, the lack of existing resources makes it significantly harder...

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