Forem: Logan Kilpatrick

Gemini 3.1 Pro: A smarter model for your most complex tasks

Logan Kilpatrick — Thu, 19 Feb 2026 19:36:52 +0000

Last week, we released a major update to Gemini 3 Deep Think to solve modern challenges across science, research and engineering. Today, we’re releasing the upgraded core intelligence that makes those breakthroughs possible: Gemini 3.1 Pro. We are shipping 3.1 Pro across our consumer and developer products to bring this progress in intelligence to your everyday applications.

Starting today, 3.1 Pro is rolling out:

For developers in preview via the Gemini API in Google AI Studio, Gemini CLI, our agentic development platform Google Antigravity and Android Studio
For enterprises in Vertex AI and Gemini Enterprise
For consumers via the Gemini app and NotebookLM

Building on the Gemini 3 series, 3.1 Pro represents a step forward in core reasoning. 3.1 Pro is a smarter, more capable baseline for complex problem-solving. This is reflected in our progress on rigorous benchmarks. On ARC-AGI-2, a benchmark that evaluates a model’s ability to solve entirely new logic patterns, 3.1 Pro achieved a verified score of 77.1%. This is more than double the reasoning performance of 3 Pro.

Intelligence applied

3.1 Pro is designed for tasks where a simple answer isn’t enough, taking advanced reasoning and making it useful for your hardest challenges. This improved intelligence can help in practical applications — whether you’re looking for a clear, visual explanation of a complex topic, a way to synthesize data into a single view, or bringing a creative project to life.

Code-based animation: 3.1 Pro can generate website-ready, animated SVGs directly from a text prompt. Because these are built in pure code rather than pixels, they remain crisp at any scale and maintain incredibly small file sizes compared to traditional video. Complex system synthesis: 3.1 Pro utilizes advanced reasoning to bridge the gap between complex APIs and user-friendly design. In this example, the model built a live aerospace dashboard, successfully configuring a public telemetry stream to visualize the International Space Station’s orbit. Interactive design: 3.1 Pro codes a complex 3D starling murmuration. It doesn't just generate the visual code; it builds an immersive experience where users can manipulate the flock with hand-tracking and listen to a generative score that shifts based on the birds’ movement. For researchers and designers, this provides a powerful way to prototype sensory-rich interfaces. Creative coding: 3.1 Pro can translate literary themes into functional code. When prompted to build a modern personal portfolio for Emily Brontë’s "Wuthering Heights," the model didn’t just summarize the text. It reasoned through the novel’s atmospheric tone to design a sleek, contemporary interface, creating a website that captures the essence of the protagonist.

What’s next

Since releasing Gemini 3 Pro in November, your feedback and the pace of progress have driven these rapid improvements. We are releasing 3.1 Pro in preview today to validate these updates and continue to make further advancements in areas such as ambitious agentic workflows before we make it generally available soon.

Starting today, Gemini 3.1 Pro in the Gemini app is rolling out with higher limits for users with the Google AI Pro and Ultra plans. 3.1 Pro is also now available on NotebookLM exclusively for Pro and Ultra users. And developers and enterprises can access 3.1 Pro now in preview in the Gemini API via AI Studio, Antigravity, Vertex AI, Gemini Enterprise, Gemini CLI and Android Studio.

We can’t wait to see what you build and discover with it.

Build with Gemini 3 Flash, frontier intelligence that scales with you

Logan Kilpatrick — Wed, 17 Dec 2025 18:41:05 +0000

Today we’re introducing Gemini 3 Flash, our latest model with frontier intelligence built for speed at a fraction of the cost. Building on 3 Pro’s strong multimodal, coding and agentic features, 3 Flash offers powerful performance at less than a quarter the cost of 3 Pro, along with higher rate limits. The new 3 Flash model surpasses 2.5 Pro across many benchmarks while delivering faster speeds. It also features our most advanced visual and spatial reasoning and now offers code execution to zoom, count and edit visual inputs.

Flash remains our most popular version, with 2 and 2.5 Flash processing trillions of tokens across hundreds of thousands of apps built by millions of developers. Our Flash models are truly built for developers, and with 3 Flash, you no longer need to compromise between speed and intelligence.

Gemini 3 Flash is rolling out to developers in the Gemini API via Google AI Studio, Google Antigravity, Gemini CLI, Android Studio and to enterprises via Vertex AI.

Smarter, faster and ready for production at scale

Gemini 3 Flash delivers frontier-class performance on PhD-level reasoning and knowledge benchmarks like GPQA Diamond (90.4%) and Humanity’s Last Exam (33.7% without tools), rivaling much larger frontier models.

Gemini 3 Flash delivers frontier intelligence across top benchmarks.

Gemini 3 Flash is highly efficient without sacrificing intelligence, pushing the Pareto Frontier of performance and efficiency. It outperforms 2.5 Pro while being 3x faster (based on Artificial Analysis benchmarking) at a fraction of the cost. Even with the lowest thinking level, 3 Flash often outperforms previous versions with “high” thinking levels.

Gemini 3 Flash pushes the Pareto frontier on performance vs. cost and speed.

In the Gemini API and Vertex AI, Gemini 3 Flash is priced at $0.50/1M input tokens and $3/1M output tokens (audio input remains at $1/1M input tokens). It comes standard with context caching, allowing for 90% cost reductions in cases with repeated token use over certain thresholds. Similarly, 3 Flash is also available today with the Batch API, allowing for 50% cost savings and much higher rate limits for asynchronous processing. For synchronous and near real-time use cases, Paid API customers also have access to production-ready rate limits.

Gemini 3 Flash in action

Gemini 3 Flash is now integrated into many of our products and early customers are enthusiastic about it. With each new Flash model, it’s exciting to see what new use cases are created.

For coding

Gemini 3 Flash has even better coding and agent capabilities over previous versions, enabling rapid, iterative development, outperforming 3 Pro's agentic coding skill (78% on SWE-bench Verified) but operating faster for quick iterations. Today, 3 Flash is rolling out to users in Google Antigravity, our new agentic development platform, to provide intelligent coding assistance that keeps pace with your train of thought.

For gaming

Gemini 3 Flash introduces powerful performance for game developers, offering superior video analysis and near real-time reasoning that outperforms previous 2.5 versions.

Astrocade is using 3 Flash for its agentic game creation engine, generating full game plans and executable code from a single prompt, quickly turning concepts into playable games. (Sequences shortened.)

Beyond development, Gemini 3 Flash transforms the player experience. It allows Latitude's game creation engine to generate smarter characters and more realistic worlds, directly elevating gameplay.

Gemini 3 Flash has allowed Latitude to deliver high-quality outputs at low costs for many complex tasks in our next generation AI game engine that was previously only possible from pro level models like Sonnet 4.5.

Nick Walton
CEO, Latitude

For deepfake detection

Resemble AI is using Gemini 3 Flash to provide near real-time deepfake intelligence by instantly transforming complex forensic data into simple explanations. They discovered that 3 Flash offered 4x faster multimodal analysis compared to 2.5 Pro, processing raw technical outputs without hindering crucial workflows. You can learn more about this in their case study.

Resemble AI analyzes a viral deepfake, offering near real-time analysis on why the content is fabricated. (Sequences shortened.)

For document analysis

Performance gains often come with a latency tradeoff, but Gemini 3 Flash proves that fast models can still handle the rigorous accuracy demands of the legal industry. With strong reasoning capabilities without sacrificing speed, it enables new levels of efficiency for complex document analysis for Harvey, an AI company for law firms and professional service providers.

Gemini 3 Flash has achieved a meaningful step up in reasoning, improving over 7% on Harvey's BigLaw Bench from its predecessor, Gemini 2.5 Flash. These quality improvements, combined with Flash's low latency, are impactful for high-volume legal tasks such as extracting defined terms and cross-references from contracts.

Niko Grupen
Head of Applied Research, Harvey

Get started with Gemini 3 Flash

Gemini 3 Flash is available across many of our products, APIs and throughout the ecosystem. As you explore the Gemini 3 family, you have the option to use our new built-in API logs visualization dashboard and send us model feedback directly through Google AI Studio. Additionally, since 3 Flash is a reasoning model, you will also need to make sure to circulate thoughts in the API or use the new Interactions API.

Here’s where you can access Gemini 3 Flash now:

We are excited to put this model in your hands and see what you create with Gemini 3 Flash.

Start building with Gemini 3

Logan Kilpatrick — Tue, 18 Nov 2025 18:05:57 +0000

Today we are introducing Gemini 3, our most intelligent model that can help bring any idea to life. Built on a foundation of state-of-the-art reasoning, Gemini 3 Pro delivers unparalleled results across every major AI benchmark compared to previous versions. It also surpasses 2.5 Pro at coding, mastering both agentic workflows and complex zero-shot tasks.

Gemini 3 Pro fits right into existing production agent and coding workflows, while also enabling new use cases not previously possible. It’s available in preview at $2/million input tokens and $12/million output tokens for prompts 200k tokens or less through the Gemini API in Google AI Studio and Vertex AI for enterprises (see pricing for rate limits and full pricing details). Additionally, it can be utilized via your favorite developer tools within the broader ecosystem and is available, with rate limits, free of charge in Google AI Studio.

Agentic coding

Developers are spending more and more time creating software with AI at their side. Building on the momentum of Gemini 2.5 Pro and all the feedback, Gemini 3 Pro serves as a new foundation of intelligence for what’s possible with an agentic coding model.

Gemini 3 Pro scores 54.2% points on Terminal-Bench 2.0, which tests a model’s tool use ability to operate a computer via terminal.

You can feel the power of this model come to life in Google Antigravity, our new agentic development platform, in addition to Gemini CLI, Android Studio, and other coding products like Cursor, GitHub, JetBrains, Manus, Cline and more.

Cline is using Gemini 3 to power autonomous code generation in developers’ IDEs…. Gemini 3 Pro handles complex, long-horizon tasks across entire codebases, maintaining context through multi-file refactors, debugging sessions, and feature implementations. It uses long context far more effectively than Gemini 2.5 Pro and has solved problems that stumped other leading models.

-Nik Pash
Head of AI, Cline

Google Antigravity

To advance how the model and IDE work together, we’re introducing Google Antigravity to showcase what’s possible with Gemini 3. It’s an agentic development platform that enables developers to operate at a higher, task-oriented level by managing agents across workspaces, while retaining a familiar AI IDE experience at its core.

It’s a faster way to develop: you act as the architect, collaborating with intelligent agents that operate autonomously across the editor, terminal, and browser. These agents plan and execute complex software tasks, communicating their work with the user via detailed artifacts. This elevates all aspects of development, from building features, UI iteration, and fixing bugs to researching and generating reports. Visit the Google Antigravity website to download the public preview at no charge, now available for MacOS, Windows and Linux.

See multiple agents building in parallel in this multiplayer digital whiteboard app in Google Antigravity.

Gemini API

With Gemini 3, we are releasing a client-side bash tool that empowers the model to propose shell commands as part of agentic workflows for tasks such as navigating your local filesystem, driving development processes, and automating system operations. We’re pairing this with a hosted server-side bash tool for multi language code generation and secure prototyping. Available now in the Gemini API for early access partners, with general availability coming soon.

Additionally, Gemini hosted tools Grounding with Google Search and URL context can now be combined with structured outputs. This is especially powerful for building agentic use cases which involve fetching and extracting data and then outputting them in a specific format for downstream agentic tasks.

Vibe coding

Gemini 3 Pro unlocks the true potential of “vibe coding”, where natural language is the only syntax you need. By significantly improving complex instruction following and deep tool use, the model can translate a high-level idea into a fully interactive app with a single prompt. It handles the heavy lifting of multi-step planning and coding details delivering richer visuals and deeper interactivity, allowing you to focus on the creative vision.

Gemini 3 Pro tops the WebDev Arena leaderboard by scoring an impressive 1487 Elo.

Gemini 3’s remarkable prompt adherence supercharges Emergent’s fullstack app development platform, especially in UI/frontend workflows. We’re seeing incredible results when incorporating Gemini 3’s multi step tool calling into our agentic code development setup.

-Madhav Jha
Cofounder and CTO, Emergent

Google AI Studio

Whether it’s building a game with a single prompt, an interactive landing page from unstructured voice notes, or a full on app from a napkin sketch, developers can bring their idea to life with Gemini 3. With this model, we pushed single prompt generation capabilities further than ever, meaning you can go from idea to AI-powered app with a single prompt, like this retro game built in Google AI Studio.

A zero-shot game built with Gemini 3 Pro in Google AI Studio.

We’ve built Google AI Studio to be your fastest path from a prompt to an AI-native app. Build mode lets you add AI capabilities faster than ever, automatically wiring up the right models and APIs, while features like annotations enable fast and intuitive iteration. You can start building with Gemini 3 in Google AI Studio today.

Multimodal understanding

Gemini 3 is the best model in the world for complex multimodal understanding and sets new highs on MMMU-Pro for complex image reasoning and Video MMMU for video understanding. Combining its intelligence and a 1 million-token context window, developers can see significant improvements while building key multimodal use cases. To give you more control over latency and cost, you can now configure multimodal vision processing with more granularity in the Gemini API based on the visual fidelity required for your application.

Visual reasoning

Gemini 3 Pro is best-in-class for document understanding, going beyond simple OCR (Object Character Recognition) to intelligently handle complex document understanding and reasoning.

You can see the model’s vision understanding, reasoning and coding capabilities in our demo app that brings any idea to life in Google AI Studio.

With just an image, Gemini 3 Pro uses its vision understanding, reasoning and coding capabilities to turn it into an interactive web experience.

Spatial reasoning

The model’s improved spatial understanding also drives strong performance in embodied reasoning tasks like pointing, trajectory prediction and task progression, unlocking new use cases across autonomous vehicles, XR devices and robotics.

Its spatial reasoning also powers intelligent screen understanding of desktop, mobile and OS screens delivering significant performance improvement for computer use agents. The model also understands the intent of user actions based on mouse movements and screen annotations unlocking novel experiences like this Visual Computer demo app.

Gemini 3 not only comprehends the user’s hand-drawn instructions but also intelligently acts upon them based on its understanding of the screen and its elements.

Video reasoning

Gemini 3 Pro captures rapid action with high-frame-rate understanding, ensuring developers never miss a critical moment in fast-moving scenes. Beyond speed, long-context recall allows for synthesizing narratives and pinpointing specific details across hours of continuous footage.

Across our video agent reasoning and tool calls, Gemini 3 delivers on three critical fronts for Agent Opus: speed, offering an increase of over 32% in speed over our current implementation; precision, demonstrated by its exceptional ability to follow complex instructions and accurately fulfill structured decoding; and reliability, managing long-context reasoning and invoking granular tools without the common pitfalls of hallucination.

-Jay Wu
Cofounder and CTO, OpusClip

Build what’s next, today

Gemini 3 Pro is now integrated into many developer products and tools to seamlessly fit into your existing workflows and unlock entirely new ways to code.

Build with the Gemini API: You can integrate Gemini 3 Pro immediately into your applications via Google AI Studio and Vertex AI for Enterprise. To support the model's deeper reasoning capabilities, we’re introducing a new thinking level and more granular media resolution parameters in the API, along with stricter validation for thought signatures. This update is critical for preserving the model’s thoughts across multi-turn conversations. Check out the Developer Guide for the technical breakdown and our Prompting Guide to learn how to build with Gemini 3 Pro.
Experience the model’s agentic capabilities: Whether you are adding AI-native features to an Android app, automating workflows through Gemini CLI or managing a fleet of autonomous agents in Google Antigravity, Gemini 3 Pro provides the reliability needed for complex, agentic architectures.
Vibe code with Gemini 3 Pro: Google AI Studio is your fastest path to bring any idea to life. Get started in Build mode to generate a fully functional app with a single prompt. And if you need a little inspiration, click “I’m feeling lucky” and let Gemini 3 Pro handle the creative spark and the code implementation simultaneously.

The software landscape is shifting. As AI changes who builds and how they build, we are committed to meeting you where you are — giving you the tools to push the boundaries of what’s possible.

This is just the start of the Gemini 3 era but we can’t wait to see what you build with Gemini 3 Pro!

Veo 3 is now available for developers in the Gemini API

Logan Kilpatrick — Sun, 20 Jul 2025 16:17:38 +0000

Hey folks : ) Excited to share that the Veo 3 API is now available. It brings our SOTA model with native audio generation (first of its kind) to devs through a simple API. You can read our launch blog post here: https://developers.googleblog.com/en/veo-3-now-available-gemini-api/

import time
from google import genai
from google.genai import types

client = genai.Client()

operation = client.models.generate_videos(
    model="veo-3.0-generate-preview",
    prompt="Panning wide shot of a purring kitten sleeping in the sunshine under a waterfall",
)

while not operation.done:
    time.sleep(20)
    operation = client.operations.get(operation)

for n, generated_video in enumerate(operation.response.generated_videos):
    client.files.download(file=generated_video.video)
    generated_video.video.save(f"video{n}.mp4")

Happy building : ) and please reach out if you run into any issues.

What is LangSmith and why should I care as a developer?

Logan Kilpatrick — Mon, 24 Jul 2023 14:42:00 +0000

I have said it before and I will say it again, the tooling around large language models (LLM’s) is still in its infancy. Due to the nature of LLM’s and their inherent dynamism, traditional software tooling is often ill-equipped to handle these models out of the box.

Enter LangChain and LangSmith.

In this post, we will explore the latest product by the team that created Langchain (the most popular LLM software tool) and see what new parts of the LLM stack LangSmith hopes to tackle.

If you are new to LLM development, the first place to start is with Langchain itself. I wrote up a comprehensive intro with details on what problems it can solve.

[Quick note: I am writing this article to reflect my personal views as I explore the LLM ecosystem and this is not intended to represent the views of my employer, hence being on my personal blog]

What is LangSmith? 🤔

When Langchain was originally created, the goal was to reduce the barrier to entry with respect to building prototypes. Despite some pushback on the viability of Langchain as a tool, I think it has largely delivered on this goal. The next problem space to tackle after prototypes is helping get these applications into production and ensuring this happens in a reliable and maintainable way. The simple mental model is:

Langchain = prototyping
LangSmith = production

But what are the production challenges that need to be solved which were not as relevant in prototyping?

Reliability — it is deceptively easy to build something that works well for a simple constrained example but actually still quite hard today to build LLM applications with the consistency that most companies would want.

To tackle this, LangSmith provides new features around 5 core pillars:

Debugging
Testing
Evaluating
Monitoring
Usage metrics

A huge part of the value add for LangSmith is being able to do all of these things from a simple and intuitive UI which significantly reduces the barrier to entry for those without a software background.

There are also a lot of things about LLM’s that are not intuitive when you look at them from a numbers perspective so being able to see visualizations through a UI will be useful (e.g. how temperature effects model output distribution). I personally find that having a polished UI can actually be the accelerant to my prototyping and work since doing everything with code can often times be cumbersome.

Further, being able to visualize the process the LLM system is going through and a complex chain of commands can be super useful in understanding why you are getting the output that you are. As you build more complex workflows, it can be hard to understand exactly what queries are moving through different flows so a simple UI to see this and log the historical data is going to be a value add from day one.

Who is competing with LangSmith?

While not direct competitors thus far, it does make a lot of sense for organizations like Vercel (who have the AI SDK) to build similar features into their platform given the desire to be the place for AI builders. I would imagine that other platforms build similar tooling over the next 3–6 months given the market for these tools has so much potential.

Vercel is still more focused on the deployment and serving aspect of LLM’s today since that is more aligned historically with their core product but it would make sense to extend the AI SDK to do more over time.

While LangSmith does not appear to go deep on Embeddings yet, there does seem to be a ton of natural crossover between this and many of the Embeddings providers who are differentiating with the batteries included UI. Ecosystems like LlamaIndex would benefit from this type of product development but it is unclear they can stay differentiated overtime as the problem space seems to be very similar.

Despite this, it is nice to see LangSmith still wanting to connect with as many tools as possible. In the launch blog post, they mentioned integrations with OpenAI evals as well as multiple fine-tuning providers that will enable developers to export data and directly train on it. These types of integrations seem like they will not only enable a ton of developer goodwill but actually serve as a lightweight moat over time (connecting things is not always easy).

What I want from LangSmith 👀

The main ask I have is extensibility. I really do think there could be orders of magnitude more impact if the core of LangSmith could be built into other applications and services. Allowing developers to sign in with their LangChain account and monitor their LLM’s on Vercel for example with the AI SDK and deployment information all in one place would be extremely valuable.

What it takes to be differentiated over a long period of time

I am very excited about LangSmith, hence spending the time to write this up. I think it solves a bunch of actual problems that developers and builders have when trying to go into production. The real long term question still remains: “is there enough here to build a long term defensible business”.

I do not have a crystal ball (shockingly) but my general mindset today is that many of the current features of LangSmith are table stakes for developers. Most LLM providers will want to build similar features into their platforms over time. That doesn’t mean that LangSmith cannot succeed though. Just look at Terraform by HashiCorp for example, it is the glue that sits in between all the cloud providers and solves a large enough problem to be a publicly traded company. But LangSmith will need to continue to expand in scope in order to be competitive with multiple providers and other tooling ecosystems.

You got this Harrison!

Here’s why quantum computing could be the big break for the Julia Language

Logan Kilpatrick — Wed, 23 Nov 2022 18:04:30 +0000

In the world of programming languages, a new star is on the rise. The Julia language has been gaining popularity in recent years, thanks to its versatility and ease of use. And it could be about to get a big boost from an unlikely source: quantum computing.

In a paper published late October of 2020, a team of researchers from the Chinese Academy of Sciences and the University of Waterloo proposed using the Julia language as a tool for developing quantum algorithms. The paper, titled “Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design” presents a set of tools that quantum programmers can use to design and test quantum algorithms with features like GPU support, an automatic differentiation engine, and state of the art performance.

The paper’s authors believe that Julia is well-suited for quantum computing due to its high-level syntax, which makes it easy to express quantum algorithms. Furthermore, they argue that Julia’s Just-In-Time (JIT) compiler can be used to compile quantum programs into efficient native code, which is important for running quantum algorithms on real hardware.

While it remains to be seen whether or not the Julia language will become the standard for quantum programming, there’s no doubt that it has potential. If you’re interested in learning more about quantum computing, or if you’re simply curious about this emerging new programming language, be sure to check out the paper.

You might also want to check out check out this video about Yao.jl from JuliaCon 2019 which goes over the usage of the package:

So what even is quantum computing and how can I get started doing it in Julia? 🧐

At the most basic level, traditional computing operates in the space of 0’s and 1’s. This is an idea that is central to our computing experience as every single program in the world can be broken down to binary. This changed however with the advent of quantum computing. In quantum computing, there exists the idea that we are not limited to just 0’s and 1’s at any given moment and in fact, [00, 01, 10, 11] can all co-exist whereas traditional computing requires we have only one active state.

If you are like me, you might be scratching your head and thinking: “How is this at all useful”? Well, there are many different cases where quantum computing can be used to solve a bunch of different really complicated math problems that would otherwise not be practical to solve. I will spare you having to look at a math problem right now and instead suggest you watch the below video for an overview on quantum computing.

Let’s play around with a really simple example of using Yao:

julia> using Yao
julia> ArrayReg(rand(ComplexF64, 2^3))
ArrayReg{2, ComplexF64, Array...}
active qubits: 3/3
nlevel: 2

This code creates a register which is a quantum state of a batch of quantum states. This quantum register is analogous to the traditional register in computing you might have touched in C or Assembly.

Next, let’s create a quantum block:

julia> chain(2, put(1=>H), put(2=>X))
nqubits: 2
chain
├─ put on (1)
│  └─ H
└─ put on (2)
└─ X

What does the chain function do here you might be asking, well here is what the help section says:

Return a ChainBlock which chains a list of blocks with the same number of qudits. Let G_i be a sequence of n-qudit blocks, the matrix representation of block chain(G_1, G_2, …, G_m) is G_m G_{m-1}\ldots G_1 It is almost equivalent to matrix multiplication except the order is reversed. We make its order different from regular matrix multiplication
because quantum circuits can be represented more naturally in this form.

Again, a little complicated, but the basic gist is that we created a 2-qubit circuit. This is probably as far as we need to go to illustrate that quantum computing is really possibility in Julia. I will be honest that my own understanding of this material is very minimal but getting my hands dirty and playing around with some of these examples helped make things a little more concrete.

I suggest you check out the Yao.jl quick start guide if you want to learn more and see loads of other examples.

Using Julia for quantum computing on AWS ✨

Not only is there a ton of development happening in the the open source quantum computing space, but big players like Amazon are also doubling down on services (like Amazon Braket) that provide quantum computing on the cloud. If you want to play around with this service from Julia, check out the newly announced https://github.com/awslabs/Braket.jl which provides an interface to work with the service.

On November 15th, 2022, AWS announced and released an interface to work with Julia and the AWS Braket service:

https://forem.julialang.org/kshyatt/introducing-braketjl-10f2

While the package is still experimental, the announcement post went on to say:

We’ve created this package because we know there are a lot of current and future quantum experts in the Julia community. We want to allow you to experiment and try out this exciting new technology using all the Julia features we know and love — multiple dispatch, native parallelism, a great package management system, and first class performance. We also want to learn from the Julia community, and better understand how we can enable you to do groundbreaking work in the quantum space.

This is a really excited development for the Quantum Computing ecosystem and those interested in Julia. Stay tuned to see what else is coming!

Wrapping things up 🎁

The field of quantum computing is still in its infancy, but it holds immense promise for the future. And the Julia language may prove to be an important tool for making quantum computing more accessible to developers. So if you’re interested in keeping up with the latest developments in both quantum computing and programming languages, keep an eye on Julia — it may just be the next big thing.

If you want to support the Julia Quantum ecosystem, consider dropping a star on Yao.jl and Braket.jl.

The best Julia programming books going into 2023

Logan Kilpatrick — Fri, 28 Oct 2022 01:35:19 +0000

The Julia community is creating new books across various disciplines at an ever-accelerating rate. If you aren't familiar, Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other high-level programming languages. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, an extensive mathematical function library, and so much more! In this post, we will highlight some of the best Julia books you can pick up today.

Stay tuned until the end for a sneak peek at some of the most anticipated Julia books yet to be released.

Before we dive in, if you prefer online courses over books, check out this post where I highlighted 5 of my favorite free online courses:

https://logankilpatrick.medium.com/5-free-courses-to-learn-julia-start-learning-today-66c1e3173ebc

Think Julia 🧠: How to Think Like a Computer Scientist

Think Julia is one of the most popular Julia books out there. It gives a high-level introduction to the language in a very polished way. The book is structured to teach you the following:

The basics, including language syntax and semantics
Get a clear definition of each programming concept
Learn about values, variables, statements, functions, and data structures in a logical progression
Discover how to work with files and databases
Understand types, methods, and multiple dispatch
Use debugging techniques to fix syntax, runtime, and semantic errors
Explore interface design and data structures through case studies

A few quick notes from my own experience owning 2 copies of this, there is still only 1 version available of this book which means the code examples and such have not been updated since the initial publication. The language has continued to develop a great deal since it was originally published so if you are looking for the latest info, this might not be for you.

Also, take this next comment with a grain of salt, but I have seen feedback from folks saying that Think Julia is more about how to type Julia code and less about how to be a Julia developer. There are subtle things that miss the mark if you want to really understand how to write great Julia code.

All of that being said, it is likely the most popular Julia book out there so it's worth picking up a copy if you want to take a learning path that has been blazed many times:

Check it out on Amazon

Julia Data Science 📊

Data Science is an ever evolving field and leveraging the latest and greatest tools are paramount to success. This book covers:

What is data science?
Why Julia?
Julia Basics
DataFrames.jl
Data Visualization and Makie.jl

I think one of the unique parts of this book is that it not only covers the basics of Julia in the data science space but also goes into Makie.jl, one of the most popular visualization frameworks for Julia. I also appreciate that this isn't a 500-page book so it makes getting up to speed on the topic something you can tackle in a couple of days instead of a few months.

Julia Data Science is one of my favorite books because they make a free web version of the book available to the world at: https://juliadatascience.io/

With that being said, if you have the means, it would be awesome to support the authors who have taken the time to share their knowledge with the world.

Check it out on Amazon

The Little Book of Julia Algorithms: A workbook to develop fluency in Julia programming 📣

The little book of Julia algorithms is one of the first Julia books I picked up. It is a simple hands-on introduction to learning Julia through the use of 50 small challenges. If you are just starting to learn the language, you should likely pick up this book and another one since this book does not actually walk you through all the different concepts but instead gives you things to practice as you learn the language.

This book covers:

Loops and conditionals
Structuring code with functions
Reading and writing files
Installing and using packages
Sorting and searching
Simple statistics and plotting

The craziest part of this book is that Ahan Sengupta started using Julia when he was only 11 years old and wrote this book early in high school. It is very well done and a fun way to practical experience with Julia!

Check it out on Amazon

Hands-On Design Patterns and Best Practices with Julia 🧑‍🎨

Tom Kwong is the author of this book and someone I consider to be a great contributor to the Julia ecosystem. We work together on helping run the annual JuliaCon and it just so happens that he is also the author of this book! When I read this book, I was surprised at how different it was from many of the other Julia books out there.

Tom does a great job of diving deep into design patterns and best practices. If you are a somewhat experienced Julia developer and interested in taking your skills to the next level, this is exactly the book you need.

Here is what you will learn in this book:

Understand the Julia language features that are key to developing large-scale software applications
Learn about design patterns to improve overall application architecture and design (this is something I had never been exposed to before this book)
Create reusable programs that are modular, extendable, performant, and easy to maintain
Be able to weigh up the pros and cons of using different design patterns for use cases
Find and explore methods for transitioning from object-oriented programming to using equivalent or more advanced Julia techniques (like structs and macros)

Overall, Tom does an incredible job with this book and there's a tremendous amount to learn here even just beyond the Julia concepts.

Check it out on Amazon

Interactive Visualization and Plotting with Julia 📈

Plotting and visualization in Julia are two of the most important concepts to understand if you want to use the language to its full potential. The author of this book, Diego, is a long-time Julia community contributor and one of the core folks helping maintain and build the plotting ecosystem. In my experience, visualizations can be a bit of a challenge in Julia so having resources like this is critical to the success of the ecosystem.

I got my copy of this book a few weeks ago and it has been a great experience so far! After reading this book, you will walk away learning how to:

Create interactive plots with Makie, Plots, Jupyter, and Pluto
Create standard statistical plots and visualize clustering results
Plot geographically distributed and biological data
Visualize graphs and networks using GraphRecipes and GraphPlots
Find out how to draw and animate objects with Javis, Plots, and Makie
Define plot themes to reuse plot visual aspect customizations
Arrange plots using Plots, Makie, and Gadfly layout systems
Define new plot types and determine how Plots and Makie show objects

Check it out on Amazon

Tanmay Teaches Julia for Beginners: A Springboard to Machine Learning for All Ages 🤖

Tanmay Bakshi is an amazing technical educator and an early adopter of the Julia ecosystem. He does a great job of writing a book that is both practically relevant but also extremely accessible to most people. I also really enjoyed that the book is set up to prepare you to begin your machine learning journey and while it does not cover ML in depth, it still helps you get closer to starting down that journey.

This book was published in 2019 and like Think Julia, it is still in its first version so it might be a little out of date but still a great resource. This book covers how to:

• Set up and configure your Julia environment
• Get up and running writing your own Julia apps
• Define variables and use them in your programs
• Use conditions, iterations, for-loops, and while-loops
• Create, go through, and modify arrays
• Build an app to manage things you lend and get back from your friends
• Create and utilize dictionaries
• Simplify maintenance of your code using functions
• Apply functions on arrays and use functions recursively and generically
• Understand and program basic machine learning apps

Check it out on Amazon

Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence 🧑‍💻

Statistics with Julia is a deep dive into the math behind the concepts of Data Science and ML written by Yoni Nazarathy. Yoni has given a bunch of awesome workshops (including one in Hebrew) over the years at JuliaCon. If you want to get a sense of what Yoni talks about in this book, check out his talk:

You can also find the code for the book online for free here: https://github.com/h-Klok/StatsWithJuliaBook
My honest experience reading this book is that it goes heavily into the math which for me was quite difficult but if that is the angle you are looking for as you learn Data Science and ML then this is the perfect book for you. It also includes over 200 code examples that help you bridge from math to practical applications.

Check it out on Amazon

More books to consider 📚

So far, the books I have talked about are ones that I have personally picked up a copy of. The next few books are either upcoming or I have heard good things about them via word of mouth in the community.

Julia as a second Language is a practical guide for folks, you guessed it, learning Julia as their second language. I have seen great things about this book but have yet to pick up a copy. The author, Erik Engheim, is well-regarded in the Julia ecosystem and I have read some of his articles before.

Check it out on Amazon

Practical Julia is a book I am very excited about! The author, Lee Phillips, is also someone who is reputable in the ecosystem and I have seen his writing before. This book is available for pre-order and I suggest you get your copy soon!

Check it out on Amazon

Julia for Data Analysis is an upcoming book but prolific Julia contributor Bogumil Kaminski. Bogumil is the real deal, whether it be the top contributor to Julia on Stack Overflow or being the lead developer of DataFrames.jl, he is someone that you will want to learn from.

Check it out on Amazon

Web Development with Julia and Genie is another upcoming book co-authored by Adrian Salceanu, the creator of Genie.jl. Julia has a huge potential in the web ecosystem and Adrian is working hard to make sure the potential is realized there.
Web Development with Julia and Genie:

Check it out on Amazon

If you're looking to get started with Julia programming in 2022 and beyond, these books are a great place to start! Get started on your development journey today and please do share other books you have found valuable as you are learning Julia.

Why Julia 2.0 isn’t coming anytime soon (and why that is a good thing)

Logan Kilpatrick — Tue, 13 Sep 2022 07:40:00 +0000

If you have been programming for a while, there’s a good chance you have lived through breaking changes in ecosystems which may have left a bad taste in your mouth. Many people I have met speak disparagingly of the transition the Python ecosystem went through from version 2.X to 3.0. While most would likely agree that it was worth it in the long run, this transition still consumed a significant amount of resources for the ecosystem.

But what about Julia? Does having a 1.X version give people the idea that the ecosystem is still too immature? Would a 2.0 release spark mass adoption and be the catalyst for Julia becoming a top 5 programming language?

In this article, we will explore why Julia is unlikely to get a 2.0 release anytime soon and why that is actually a good thing for current and prospective Julia developers.

Historical Context (v0.6 ➡️ v0.7 ➡️ v1.0)

In the early days of the Julia ecosystem, much of the criticism of Julia stemmed from the volatility of the ecosystem. The language’s core API would change which left developers scrambling to adapt to changes. These frequent changes prevented industry adopters from having confidence in the language which was why many initial use cases were academic or hobby projects.

As the path to 1.0 and stability got closer, the core development team released Julia 0.7 which was the bridge to Julia 1.0 and included many warnings about how the API would change in the new version. On a personal note, this time period was exactly when I started using Julia. My first experience with the language was working through deprecation warnings and updating the syntax for a large Julia project I was working on at NASA.

What is the next Julia version? 1️⃣.9️⃣

The forthcoming major Julia release is Julia 1.9. This version will likely be around in 3–4 months given the current release schedule. You can read about what the proposed changes are on GitHub:

https://github.com/JuliaLang/julia/blob/master/NEWS.md

One big change that I am personally excited about is:

@kwdef is now exported and added to the public API ([#46273])

This is a long-standing open request from the community that was previously something you had to use an external package to accomplish. Another new feature that is being added is:

New pkgversion(m::Module) function to get the version of the package that loaded a given module, similar to pkgdir(m::Module). ([#45607])

This should give users more context on what versions are being loaded into certain modules which was something that you used to have to tediously figure out.

All of this is to say that there are still lots of amazing new features, bug fixes, and improvements coming in each new Julia major version.

So what is next after Julia 1.9 ⁉️

Well, after Julia 1.9 is….. Julia 1.10 😄. No really! Core Julia developer and co-creator of Julia Jeff Bezanson said at JuliaCon this year:

“There is no 2.0 plan. There’s no date, there’s no spec for it. There really is no such plan in the works. If anyone ever thinks of something that sounds like a good idea but would be breaking, we just tag it 2.0 as an issue and leave it for if or when that happens.”

If you want to see the type of issues that are being thought about for 2.0, check out:

https://github.com/JuliaLang/julia/milestone/23

As you can see, the details are rather scarce since most ideas that are being proposed can be implemented without breaking changes.

Why is it good that 2.0 isn’t coming anytime soon? 🎊

The stability of Julia is something users and developers have grown to love over the years (especially for those who lived through the early Julia days with constant changes). From a design perspective, it truly speaks to the high-quality decisions that were made that we have made it all the way to 1.8.1 now (4 years since 1.0 was released) without breaking changes.

Jeff goes on to say further that:

“But as for me personally, of the things that I would want to see, I don’t believe any of them really require breaking changes. Anything that I can see we should do can be done in a 1.X non-breaking way. So I don’t see a need for 2.0 right now.”

This could obviously change over time as the needs and opinions of the core Julia team changes, but developers today can rest easy knowing that if they invest time, money, and intellectual capital into building systems with Julia, the API will be stable for years to come.

While I do personally think a 2.0 release would bring a lot of great visibility to the language, it is probably better to keep things as stable as possible while the ecosystem continues to grow exponentially. One small counter-point is that if there was going to be a 2.0 release, doing it sooner rather than later is likely better since the longer we wait for this, the more Julia code will have to be updated to make things work. Hopefully, if the day ever comes, Keno Fischer can reboot the Julia FemtoCleaner which automatically updated deprecated syntax for you.

What do you think about Julia 2.0? Drop a comment below and let me know!

Data Engineering in Julia

Logan Kilpatrick — Sat, 29 Jan 2022 12:17:37 +0000

Work with massive datasets to design data models and automate data pipelines using Julia

Continue reading on Towards Data Science »

How to get a job programming in Julia?

Logan Kilpatrick — Wed, 19 Jan 2022 02:52:23 +0000

Tips to get hired programming with the Julia Language

Continue reading on Towards Data Science »

6 Julia Frameworks to Create Desktop GUI’s and Web Apps

Logan Kilpatrick — Wed, 05 Jan 2022 22:42:18 +0000

You could build beautiful apps purely in Julia.

Continue reading on Towards Data Science »

The most underrated feature of the Julia Programming Language , the package manager

Logan Kilpatrick — Sun, 02 Jan 2022 19:33:46 +0000

Where other languages fall short, Julia’s package manager provides robust features and intuitive syntax.

Continue reading on Medium »