AI/ML News & Innovations Hub

Unit testing Spring MVC applications with JUnit 5

Spring is a reliable and popular framework for building web and enterprise Java applications. In this article, you’ll learn how to unit test each layer of a Spring MVC application, using built-in testing tools from JUnit 5 and Spring to mock each component’s dependencies. In addition to unit testing with MockMvc, Mockito, and Spring’s TestEntityManager , I’ll also briefly introduce slice testing using the @WebMvcTest and @DataJpaTest annotations, used to optimize unit tests on web controllers and databases. Also see: How to test your Java applications with JUnit 5 . Overview of testing Spring MVC applications Spring MVC applications are defined using three technology layers: Controllers accept web requests and return web responses. Services implement the application’s business logic. Repositories persist data to and from your back-end SQL or NoSQL database. When we unit test Spring MVC applications, we test each layer separately from the others. We create mock implementations, typically using Mockito , for each layer’s dependencies, then we simulate the logic we want to test. For example, a controller may call a service to retrieve a list of objects. When testing the controller, we create a mock service that either returns the list of objects, returns an empty list, or throws an exception. This test ensures the controller behaves correctly. We’ll use Spring MVC to build and test a simple web service that manages widgets. The structure of the web service is shown here: Steven…

Build to Last

Chris Lattner on software craftsmanship and AI

What’s the Go language really good for?

Over its more than 15 years in the wild, Google’s Go programming language has evolved from a curiosity for alpha geeks to the battle-tested programming language behind some of the world’s most important cloud-native software projects. If you’ve ever wondered why Go is the language of choice for projects like Docker and Kubernetes , this article is for you. We’ll discuss Go’s defining characteristics and how it differs from other programming languages. You will also learn what kinds of projects Go is best suited for, including the state of Go development for AI-powered tools . We’ll conclude with an overview of Go’s feature set, some limitations of the language, and where it may be going from here. Also see: Golang tutorial: Get started with the Go language . Go is small and simple Go, or Golang as it’s often called, was created by Google employees—chiefly longtime Unix guru and Google distinguished engineer Rob Pike—but it’s not strictly speaking a “Google project.” Rather, Go is a community-developed open source project, spearheaded by leadership with strong opinions about how Go should be used and the direction the language should take. Go is meant to be easy to learn and straightforward to use, with syntax that is simple to read and understand. Go does not have a large feature set, especially when compared to languages like C++ . Go’s syntax is reminiscent of C , making it relatively easy for longtime C developers to learn. That said, many features of Go, especially its c…

High-Efficiency Diffusion Models for On-Device Image Generation and Editing with Hung Bui - #753

In this episode, Hung Bui, Technology Vice President at Qualcomm, joins us to explore the latest high-efficiency techniques for running generative AI, particularly diffusion models, on-device. We dive deep into the technical challenges of deploying these models, which are powerful but computationally expensive due to their iterative sampling process. Hung details his team's work on SwiftBrush and SwiftEdit, which enable high-quality text-to-image generation and editing in a single inference step. He explains their novel distillation framework, where a multi-step teacher model guides the training of an efficient, single-step student model. We explore the architecture and training, including the use of a secondary 'coach' network that aligns the student's denoising function with the teacher's, allowing the model to bypass the iterative process entirely. Finally, we discuss how these efficiency breakthroughs pave the way for personalized on-device agents and the challenges of running reasoning models with techniques like inference-time scaling under a fixed compute budget. The complete show notes for this episode can be found at https://twimlai.com/go/753.

LATAM Digital Media Festival 2025 to discuss AI, influencers, migration and more in Mexico City

From Nov. 5 to 7, the LATAM Digital Media and Journalism Festival will bring together more than 55 journalists and creators from 14 countries to Mexico City to explore the challenges of journalism in an era full of contrasts. This year, the Festival —organized by the organization Factual — will revolve around six major themes, […] The post LATAM Digital Media Festival 2025 to discuss AI, influencers, migration and more in Mexico City appeared first on LatAm Journalism Review by the Knight Center .

LATAM Digital Media Festival 2025 to discuss AI, influencers, migration and more in Mexico City

From Nov. 5 to 7, the LATAM Digital Media and Journalism Festival will bring together more than 55 journalists and creators from 14 countries to Mexico City to explore the challenges of journalism in an era full of contrasts. This year, the Festival —organized by the organization Factual — will revolve around six major themes, […] The post LATAM Digital Media Festival 2025 to discuss AI, influencers, migration and more in Mexico City appeared first on LatAm Journalism Review by the Knight Center .

Do programming certifications still matter?

If you’re a software developer or architect, you might wonder if programming certifications are still worth the effort, especially in the era of rapid AI-driven evolution . The short answer is, it depends. “Certifications are shifting from a checkbox to a compass. They’re less about proving you memorized syntax and more about proving you can architect systems, instruct AI coding assistants, and solve problems end-to-end,” says Faizel Khan, lead AI engineer at Landing Point , an executive search and recruiting firm. “In the AI era, fewer students will get trained on the job, which means they have to train themselves,” Khan says. “Certifications—especially architectural ones like AWS, Kubernetes, Terraform—are still the clearest path to do that.” Pros and cons of programming certifications It’s not all black and white when it comes to deciding whether to pursue programming certifications. The effort involves both pros and cons. “In terms of pros, certifications concretely demonstrate that you have a skillset at a documented level,” says Chris Riccio, vice president of engineering at Uplevel , an engineering optimization system provider. “They also show that you’ve put in the time and effort to learn, study, and prepare.” Programming certifications are “a useful way to validate foundational skills and show that someone understands core concepts,” says Greg Fuller, vice president of Skillsoft’s training provider, Codecademy . “They’re especially helpful for people entering the f…

Are there superefficient statistics that shrink toward the true parameter value, in probability?

Stein (1964) defined a superefficient estimator for the univariate normal variance with population mean unknown. (It's unrelated to James-Stein; see here for a summary.) He gave an indicator variable of when, in probability, the sample variance is an overestimate. When it is, one shrinks the estimate toward 0, by dividing by n + 1. The procedure shrinks the estimate toward its parameter value, in probability, but only incidentally: it's really shrinking toward 0, and the parameter happens to lie between the two points. But suppose the indicator variable implied whether the estimate is more probably an underestimate or an overestimate, with the procedure applying a correction in the indicated direction. This version would shrink the estimate toward the latent parameter's value, without specifying a manifest point. For all superefficient statistics of which I'm aware, the shrinkage point is fixed, either by the researcher (e.g., James-Stein) or by design (often at 0, as with the LASSO ). Are there examples that shrink toward a non-fixed point? Say, via empirical Bayes? The closest I've found is a case where the endpoints of the CI for the t -statistic are shifted toward "the experimenter's best prior estimate" (p. 1512).

Vibe Coding's Uncanny Valley with Alexandre Pesant - #752

Today, we're joined by Alexandre Pesant, AI lead at Lovable, who joins us to discuss the evolution and practice of vibe coding. Alex shares his take on how AI is enabling a shift in software development from typing characters to expressing intent, creating a new layer of abstraction similar to how high-level code compiles to machine code. We explore the current capabilities and limitations of coding agents, the importance of context engineering, and the practices that separate successful vibe coders from frustrated ones. Alex also shares Lovable’s technical journey, from an early, complex agent architecture that failed, to a simpler workflow-based system, and back again to an agentic approach as foundation models improved. He also details the company's massive scaling challenges—like accidentally taking down GitHub—and makes the case for why robust evaluations and more expressive user interfaces are the most critical components for AI-native development tools to succeed in the near future. The complete show notes for this episode can be found at https://twimlai.com/go/752.

How to use Python dataclasses

Everything in Python is an object, or so the saying goes. If you want to create your own custom objects, with their own properties and methods, you use Python’s class object to do it. But creating classes in Python sometimes means writing loads of repetitive, boilerplate code; for example, to set up the class instance from the parameters passed to it or to create common functions like comparison operators. Dataclasses, introduced in Python 3.7 ( and backported to Python 3.6 ), provide a handy, less-verbose way to create classes. Many of the common things you do in a class, like instantiating properties from the arguments passed to the class, can be reduced to a few basic instructions by using dataclasses. The backstage power of Python dataclasses Consider this example of a conventional class in Python: class Book: '''Object for tracking physical books in a collection.''' def __init__(self, name: str, weight: float, shelf_id:int = 0): self.name = name self.weight = weight # in grams, for calculating shipping self.shelf_id = shelf_id def __repr__(self): return(f"Book(name={self.name!r}, weight={self.weight!r}, shelf_id={self.shelf_id!r})") The biggest headache here is that you must copy each of the arguments passed to __init__ to the object’s properties. This isn’t so bad if you’re only dealing with Book , but what if you have additional classes—say, a Bookshelf , Library , Warehouse , and so on? Plus, typing all that code by hand increases your chances of making a mistake. He…

Ray is Joining The PyTorch Foundation

Dubai left lane rules: Fines, speed limits, and restrictions explained

UAE: 6 traffic rules to follow when driving in the left lane in Dubai

The U.S. Public Wants Regulation (or Prohibition) of Expert‑Level and Superhuman AI

Three‑quarters of U.S. adults want strong regulations on AI development, preferring oversight akin to pharmaceuticals rather than industry "self‑regulation."

An Opinionated Guide to Using AI Right Now

What AI to use in late 2025

[Video Response] What Cloudflare's code mode misses about MCP and tool calling

Theo's Video: https://www.youtube.com/watch?v=bAYZjVAodoo Cloudflare article: https://blog.cloudflare.com/code-mode/ Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Advice for New Principal Tech ICs (i.e., Notes to Myself)

Based on what I've learned from role models and mentors in Amazon

Let’s Build the GPT Tokenizer: A Complete Guide to Tokenization in LLMs

A text and code version of Karpathy’s famous tokenizer video.

7 newer data science tools you should be using with Python

Python’s rich ecosystem of data science tools is a big draw for users. The only downside of such a broad and deep collection is that sometimes the best tools can get overlooked. Here’s a rundown of some of the best newer or less-known data science projects available for Python . Some, like Polars, are getting more attention but still deserve wider notice. Others, like ConnectorX, are hidden gems. ConnectorX Most data sits in a database somewhere, but computation typically happens outside of it. Getting data to and from the database for actual work can be a slowdown. ConnectorX loads data from databases into many common data-wrangling tools in Python, and it keeps things fast by minimizing the work required. Most of the data loading can be done in just a couple of lines of Python code and an SQL query . Like Polars (which I’ll discuss shortly), ConnectorX uses a Rust library at its core. This allows for optimizations like being able to load from a data source in parallel with partitioning. Data in PostgreSQL , for instance, can be loaded this way by specifying a partition column. Aside from PostgreSQL, ConnectorX also supports reading from MySQL/MariaDB, SQLite, Amazon Redshift, Microsoft SQL Server and Azure SQL, and Oracle. The results can be funneled into a Pandas or PyArrow DataFrame, or into Modin or Dask (via Pandas), or Polars (via PyArrow). General support for reading from ODBC is a work in progress. DuckDB Data science folks who use Python ought to be aware of SQLite…

AI Now’s Partnership and Strategy Lead Alli Finn Testifies at the Philadelphia City Council Committee on Technology and Information Services

On October 15, 2025, AI Now’s Partnership and Strategy Lead Alli Finn will provide Testimony to the Philadelphia City Council Committee on Technology and Information Services. Read their full Testimony, Public Policymaking on AI: Invest in People, Not in Corporate Power, below: The post AI Now’s Partnership and Strategy Lead Alli Finn Testifies at the Philadelphia City Council Committee on Technology and Information Services appeared first on AI Now Institute .

Dataflow Computing for AI Inference with Kunle Olukotun - #751

In this episode, we're joined by Kunle Olukotun, professor of electrical engineering and computer science at Stanford University and co-founder and chief technologist at Sambanova Systems, to discuss reconfigurable dataflow architectures for AI inference. Kunle explains the core idea of building computers that are dynamically configured to match the dataflow graph of an AI model, moving beyond the traditional instruction-fetch paradigm of CPUs and GPUs. We explore how this architecture is well-suited for LLM inference, reducing memory bandwidth bottlenecks and improving performance. Kunle reviews how this system also enables efficient multi-model serving and agentic workflows through its large, tiered memory and fast model-switching capabilities. Finally, we discuss his research into future dynamic reconfigurable architectures, and the use of AI agents to build compilers for new hardware. The complete show notes for this episode can be found at https://twimlai.com/go/751.

How to Solve it With Code course now available

An email sent to all fast.ai forum users.

Why do we say that we model the rate instead of counts if offset is included?

I am fitting the following GAM model. My response variable is aggregated counts of disease incidence at the county-level. I used log(population) as an offset. If I use it this way, I learned that it models the rate of cumulative cases per unit of population while still treating it as a count GAM. However, I am not very confident in stating this in my manuscript. Could anyone help understand it better? cases ~ α + offset (log(population)) + ƒ(age ) + ƒ(school) + ƒ(associate degree) + ƒ(income) + ƒ(f_over) + ƒ(housing) + ƒ(overcr) + ƒ(smo) + ƒ(drink) + ƒ(dia) + ƒ(lung) + ƒ(obe) + ƒ(uninsured) + ƒ(flu vac) + ƒ(C) + ƒ(N) + ƒ(O) + ƒ(SO) + ƒ(cci) + ƒ(pre) + st + county(random effect) + ε In the partial effects plot, should I write the y-axis as incidence rate or just incidence? Similarly, in the main text, should I use the incidence rate, or just incidence would suffice, when explaining the effect of covariates on the response? As suggested by @Demetri Pananos, I developed the plot with the following code: dg $no), max(lc_data$ no), length.out = 50), pop = 100000) plot_predictions( model = gam_full, newdata = dg, by = 'no', type = 'response' ) Is this fine? I doubt the code above using the plot_predictions function from the marginal effects package. My doubt is whether I should write the code in this way (below) to get the population level of estimates excluding random effects (county is my random effect). Is this the right approach? dg $no), max(lc_data$ no), length.out = 50), po…

[Paper Analysis] On the Theoretical Limitations of Embedding-Based Retrieval (Warning: Rant)

Paper: https://arxiv.org/abs/2508.21038 Abstract: Vector embeddings have been tasked with an ever-increasing set of retrieval tasks over the years, with a nascent rise in using them for reasoning, instruction-following, coding, and more. These new benchmarks push embeddings to work for any query and any notion of relevance that could be given. While prior works have pointed out theoretical limitations of vector embeddings, there is a common assumption that these difficulties are exclusively due to unrealistic queries, and those that are not can be overcome with better training data and larger models. In this work, we demonstrate that we may encounter these theoretical limitations in realistic settings with extremely simple queries. We connect known results in learning theory, showing that the number of top-k subsets of documents capable of being returned as the result of some query is limited by the dimension of the embedding. We empirically show that this holds true even if we restrict to k=2, and directly optimize on the test set with free parameterized embeddings. We then create a realistic dataset called LIMIT that stress tests models based on these theoretical results, and observe that even state-of-the-art models fail on this dataset despite the simple nature of the task. Our work shows the limits of embedding models under the existing single vector paradigm and calls for future research to develop methods that can resolve this fundamental limitation. Authors: Orion We…

Indian rupee, Pakistani rupee and Philippine peso stay soft, lifting dirham value for expats. Should you remit now?

Indian rupee, Pakistani rupee and Philippine peso stay soft, lifting dirham value for expats. Should you remit now?

Announcing the Ray Foundations Certification

When Cristiano Ronaldo became the first billionaire footballer

What are the privacy and transparency advantages of open-source voice-to-AI tools like Ito compared to closed systems?

I’ve been exploring open-source projects that connect speech recognition with large language models for intelligent voice input. Recently I came across Ito , an open-source “voice-to-AI” interface that combines speech-to-text (via Groq/Whisper) and LLM intent processing — allowing users to dictate or give natural-language instructions directly into any app. From a design perspective, this open-source approach seems to emphasize transparency and auditability — users (or security teams) can inspect the code that handles microphone access, hotkeys, and text insertion. In contrast, most similar tools (e.g., Whisperflow, Willow, Aqua Voice) are closed commercial products, so users must trust the vendor regarding data handling. My questions are: What are the real technical and security advantages of using an open-source architecture in this domain? Are there known best practices or frameworks for building auditable, privacy-preserving voice-to-LLM pipelines? For applications that still rely on cloud-based transcription (no full offline mode yet), how can open-source transparency mitigate privacy risks compared to closed-source systems? I’m asking from both a research and developer perspective — trying to understand whether open-source transparency meaningfully improves trust and security for voice-AI applications.

Reward Hacking Resarch Update

Interim report on ongoing work on reward hacking

Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)

Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code Examples

Michael Kleinman reacts to breakthrough AI safety legislation

FLI celebrates a landmark moment for the AI safety movement and highlights its growing momentum

Comparing multiple groups to a reference group

I would like to compare several groups to a reference group, with the main idea being to show that the other groups are not inferior to the reference. Ideally, I would also like to test for superiority if not inferior. The sample size is very small: around 20 participants per group. The study was designed without any sample size calculation, and no non-inferiority margin was pre-specified. The investigators, who had no prior experience, concluded non-inferiority simply because the superiority test p-value was >0.05. I should remake the design as not publishable The context is a study in medically assisted procreation (MAP), comparing the number of oocytes retrieved across 5 groups (corresponding to 5 phases of the menstrual cycle). I have several questions: Could such a paper be publishable, even though non-inferiority margins were only defined a posteriori? The working hypothesis is that treatment could begin at any menstrual phase (not necessarily phase 1) without losing efficacy. Therefore, comparisons are only needed versus the first group. Should I run four separate tests? A global test? Should I correct p-values for multiple comparisons? (These should technically be independent tests, right? So no correction required?) For curiosity: how should I calculate the sample size needed for such a hypothesis? Should I compute the required N for each comparison independently and then retain the largest? From the observed confidence intervals of the difference between group (too…

How UAE expats can get an Oman visa on arrival or eVisa

Benchmarking Multimodal AI Workloads on Ray Data

Vector Institute names 13 new Faculty Members, expanding core research leadership across Ontario

The Vector Institute has strengthened Ontario’s AI research ecosystem by elevating 13 exceptional researchers to Faculty Member status. These former Faculty Affiliates will now take on expanded roles, driving impactful […] The post Vector Institute names 13 new Faculty Members, expanding core research leadership across Ontario appeared first on Vector Institute for Artificial Intelligence .

Synapse Magazine 3rd Quarter 2025 Issue 26

Synapse Magazine Africa’s 4IR Trade & Innovation Magazine - 3rd Quarter 2025 Issue 26 AI Expo Africa show Edition goes live

Real AI Agents and Real Work

The race between human-centered work and infinite PowerPoints

HELM Long Context

How can I measure the similarity between distance matrices during embedding training?

I am studying how the distances between embeddings evolve during training of a language model. One way to describe this "evolution" is that the k -nearest neighbours of a particular embedding may change after some training, and eventually "converge." Problem I want to compute some metric of similarity between distance distance matrices at different training steps. For example, I have a distance matrix at the beginning of training and another after some iterations, and I want to quantify how similar they are. What I’ve tried/considered Converting the upper-triangular part of the matrix into a vector and computing correlations (Pearson or Spearman) between different training steps. Looking at averages and standard deviations of the distances over time, though this feels too coarse. Question Are there established metrics or approaches for comparing distance (or similarity) matrices in this way? I am looking for methods that can capture whether the overall structure of the embedding space is stabilizing or converging during training.

Comment on Diffusion Beats Autoregressive in Data-Constrained Settings by Nano Banana AI

Blog comment creationReally interesting takeaway that diffusion models shine in data-constrained settings while autoregressive models are stronger when compute is the bottleneck. It makes me wonder how this trade-off might shape the design of future foundation models, especially as synthetic data generation becomes more common. Do you think diffusion’s advantage in low-data regimes could make it a natural fit for domains like medicine or law where data is scarce and costly to obtain?

Learning

Apprentissage emilie.germain… ven, 09/19/2025 - 15:12

Song Joong-ki turns 40: Fighting all odds, why he's Korea's leading man

Mila's Community of Practice: AI Explainability

Communauté de pratique de Mila : Explicabilité en IA emilie.germain… jeu, 09/18/2025 - 09:42

Daron Acemoğlu, Diane Coyle, and Joseph Stiglitz on COVID-19 and the Economics of AI | GovAI Blog

This event focussed on questions such as: Will COVID-19 cause automation to increase? A decline in labour share of income? A rise of superstar companies? What does COVID-19 teach us about policy re...

Joseph Stiglitz & Anton Korinek on AI and Inequality | GovAI Blog

Over the next decades, AI will dramatically change the economic landscape. It may also magnify inequality, both within and across countries. Joseph E. Stiglitz, Nobel Laureate in Economics, joined ...

David Autor, Katya Klinova & Ioana Marinescu on the Work of the Future: Building Better Jobs in an Age of Intelligent Machines | GovAI Blog

In the spring of 2018, MIT President L. Rafael Reif commissioned the MIT Task Force on the Work of the Future. He tasked them with understanding the relationships between emerging technologies and ...

Announcing the GovAI Policy Team | GovAI Blog

The AI governance space needs more rigorous work on what influential actors (e.g. governments and AI labs) should do in the next few years to prepare the world for advanced AI. We're setting up a...

Announcing the GovAI Policy Program (GAPP) | GovAI Blog

The GovAI Policy Program (GAPP) is a part-time program that allows talented graduate students and professionals to deepen their expertise, expand their network, and build a technically informed...

Annual Report 2022 | GovAI Blog

GovAI's Annual Report 2022.