# Machine learning Machine learning is the development and use of computer systems that adapt and learn from data to improve accuracy. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Do engineers still need to understand how LLMs work? DevFeed: [Do engineers still need to understand how LLMs work?]() Original publisher: [Read original article]() Author: Dr Milan Milanović Published: 2026-09-17T15:01:44Z Content type: opinion Language: en Sources: [Tech World With Milan Newsletter]() Topics: [Large Language Model](), [Machine learning](), [AI Development](), [Code]() Tags: [agent](), [agents](), [ai](), [code](), [llms](), [machine-learning]() ### AI overview An interview with Sebastian Raschka about why software engineers should understand how large language models work. It discusses the value of learning LLM fundamentals, reasoning models, and AI agents, along with ways engineers can remain effective as AI writes more code. ### Source excerpt With Sebastian Raschka, author of "Build a Large Language Model (From Scratch)" ## How energy teams turn theft detection into governed action with Genie and AI business processes DevFeed: [How energy teams turn theft detection into governed action with Genie and AI business processes]() Original publisher: [Read original article]() Author: Daniel Zoccali; Jack Yallop Published: 2026-09-15T16:50:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Machine learning](), [Artificial Intelligence]() Tags: [databricks](), [energy](), [fraud](), [industries](), [ml](), [model](), [reporting](), [safety](), [speed]() ### AI overview The article explains how energy teams can operationalize energy-theft detection by connecting model-generated risk signals with investigation, field operations, revenue recovery, and reporting in a governed workflow. It presents a Databricks implementation using a Databricks App, Lakebase, and Unity Catalog. ### Source excerpt Energy theft is the deliberate use of gas or electricity without paying for it, typically... ## Children's Hospital of Philadelphia Uses Open Source AI and MONAI to Model Pediatric Hearts DevFeed: [Children's Hospital of Philadelphia Uses Open Source AI and MONAI to Model Pediatric Hearts]() Original publisher: [Read original article]() Author: Isha Salian Published: 2026-09-15T09:00:42Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [MONAI](), [Medical imaging](), [Artificial Intelligence](), [Open Source](), [Machine learning]() Tags: [ai](), [ai-for-good](), [healthcare-and-life-sciences](), [machine-learning](), [medical-imaging](), [monai](), [open-source](), [openusd](), [robotics](), [simulation](), [software]() ### AI overview Children's Hospital of Philadelphia uses open source AI tools built on MONAI to generate anatomically precise pediatric heart models from medical images in seconds. Its teams are applying machine learning to support care for children with congenital heart disease. ### Source excerpt Children's Hospital of Philadelphia is using open source AI tools to model children's hearts in seconds -- with the goal of enabling safer, more precise care for kids with congenital heart disease. ## Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection DevFeed: [Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection]() Original publisher: [Read original article]() Author: Osher Jacob Published: 2026-09-14T10:00:01Z Content type: article Language: en Sources: [Unit 42]() Topics: [AWS CloudTrail](), [Cloud](), [threat detection](), [Threat Research](), [SIEM, Security, Observability](), [Machine learning](), [Algorithms](), [SQL](), [IAM](), [identity and access management](), [Amazon Web Services]() Tags: [algorithms](), [amazon-web-services-aws](), [analysis](), [aws-cloudtrail](), [cloud](), [cloud-cybersecurity-research](), [cloud-detection](), [devops](), [iam](), [identity-and-access-management](), [logs](), [machine-learning](), [post](), [sql](), [threat-detection](), [threat-research]() ### AI overview This article presents a behavioral clustering model for mapping cloud identities to functional roles using activity patterns from audit logs. It applies unsupervised machine learning with UMAP and HDBSCAN to data from more than 40,000 identities across 125 cloud environments, and shows how the resulting map can support automated threat detection. The article also explains how lightweight heuristics extracted from the map can classify identities at scale using standard SQL, reducing the need for continuous resource-intensive machine learning pipelines. ### Source excerpt We designed a behavioral clustering model to map cloud identity roles from audit logs, enabling continuous threat detection using standard SQL queries. The post Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection appeared first on Unit 42. ## iPhone Duo Seemingly Can't Capture Spatial Photos Or Video DevFeed: [iPhone Duo Seemingly Can't Capture Spatial Photos Or Video]() Original publisher: [Read original article]() Author: Craig Storm Published: 2026-09-11T21:15:11Z Content type: news Language: en Sources: [UploadVR]() Topics: [iphone](), [Artificial Intelligence](), [Machine learning]() Tags: [3d-media](), [ai](), [cameras](), [iphone](), [iphone-duo](), [machine-learning](), [photos](), [video](), [vision]() ### AI overview Apple's $1,999 iPhone Duo appears not to support native spatial photo or spatial video capture, despite having cameras and processing hardware comparable to supported iPhone Pro models. Apple has not explained the omission, and the device was not independently tested. ### Source excerpt Apple's $2000 iPhone Duo has two rear cameras, but seemingly can't capture spatial photos or video for viewing on Apple Vision Pro. ## How AI Is Changing Malware Detection: From Traditional Antivirus to Next-Gen Protection DevFeed: [How AI Is Changing Malware Detection: From Traditional Antivirus to Next-Gen Protection]() Original publisher: [Read original article]() Author: Manish Shivanandhan Published: 2026-09-11T15:22:46Z Content type: article Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [Malware](), [Machine learning](), [ransomware](), [cloud-infrastructure](), [PowerShell](), [Microsoft]() Tags: [ai](), [ai-agents](), [attacks](), [cloud](), [machine-learning](), [malware](), [microsoft](), [powershell](), [ransomware](), [security]() ### AI overview An overview of how malware detection is shifting beyond signature-based antivirus toward machine learning, behaviour tracking, and cloud threat data. It also describes how malware evades traditional detection and notes limitations of AI-based approaches. ### Source excerpt Malware used to be simple to describe. A virus attached itself to a file, and antivirus software removed it. That world is gone. Today, a single attack can steal your passwords, lock up your photos, w ## Britain's technology brief is now everyone's job and nobody's responsibility DevFeed: [Britain's technology brief is now everyone's job and nobody's responsibility]() Original publisher: [Read original article]() Author: Lindsay Clark Published: 2026-09-11T13:12:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Machine learning](), [Architecture & Design](), [Security & Privacy](), [Vibe coding](), [NVLink](), [Aeternum]() Tags: [ai](), [cybersecurity](), [government](), [government-of-the-united-kingdom](), [open-source](), [opinion](), [public-sector](), [science](), [space](), [spacex](), [systems](), [technology](), [whitehall]() ### AI overview This technology news roundup examines how Britain's science, AI, and digital-government responsibilities are spread across competing ministerial portfolios. It also covers security incidents, AI companies, semiconductor infrastructure, open-source software, operating systems, and developer tools. ### Source excerpt Whitehall has scattered science, AI, and digital government across a thicket of competing ministerial portfolios ## Building a reliable cloud native foundation for distributed AI training DevFeed: [Building a reliable cloud native foundation for distributed AI training]() Original publisher: [Read original article]() Author: Abhi Kulkarni and Shishir Jindal, Atlassian Published: 2026-09-11T11:00:00Z Content type: article Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Training AI Models](), [Machine learning](), [distributed-systems](), [GPU](), [cloud-infrastructure](), [Network design](), [Inference]() Tags: [ai-training](), [blog](), [distributed-training](), [gpu](), [infrastructure](), [storage]() ### AI overview The article explains how to make multi-node AI training reliable by treating inter-node communication, shared storage, hardware placement, network topology, and validation as platform concerns. It identifies RDMA for GPU-node communication and Lustre for concurrent training-data and checkpoint access. ### Source excerpt AI workloads are changing what platform teams need from infrastructure. Provisioning GPUs and standing up a cluster no longer makes a platform "AI-ready." Once training spans more than one node, the bottlenecks show up in places... ## CERN PGDay 2027: Announcement and CfP DevFeed: [CERN PGDay 2027: Announcement and CfP]() Original publisher: [Read original article]() Author: Swiss PostgreSQL Users Group Published: 2026-09-11T00:00:00Z Content type: news Language: en Sources: [PostgreSQL news]() Topics: [Databases](), [Machine learning](), [AI search](), [Retrieval Augmented Generation (RAG)]() Tags: [announcement](), [community](), [conference](), [database](), [postgresql]() ### AI overview CERN PGDay 2027 is an announcement and call for papers for a single-day PostgreSQL community conference at CERN in Geneva. Proposed talk themes include large-scale database performance, AI and vector-search workloads, security, and PostgreSQL extensibility. ### Source excerpt CERN PGDay 2027 Date: Friday, February 12, 2027 Location: CERN Campus, Geneva, Switzerland / Suisse Romande Language: English Organizers: Swiss PostgreSQL Users Group (SwissPUG) & CERN Format: Single-track (6-7 sessions) followed by networking social event Homepage: swisspug.org/cern-pgday-2027 About the Event Continuing in the line of work of the past editions, CERN PGDay 2027 returns as the annual gathering for PostgreSQL users, developers, and enthusiasts across Suisse Romande (western Switzerland) and the neighboring French border regions. Co-organized by CERN and SwissPUG, this single-day community conference offers a premier opportunity to network, share practical experiences, and explore the future of the world's most advanced open-source database. The event takes place in the unique, international environment of Geneva--a hub for major scientific institutions, non-governmental organizations, and financial and tech enterprises. Format & Venue Single-Track Schedule: The program consists of a single track featuring 6 to 7 technical sessions presented entirely in English. Social Event & Networking: Following the technical presentations, a social event will take place to facilitate community networking, discussions, and collaboration in the inspiring atmosphere of CERN. CERN Visits: Attendees are encouraged to arrange their schedules before or after the event to take advantage of the conference location and visit CERN, the European Organization for Nuclear Research. Call for Papers (CfP) Speaker proposals for CERN PGDay 2027 can be entered online via Indico. Call for Papers Closes: November 8, 2026 (23:59 CET) Call for Sponsors (CfS) Sponsors proposals for CERN PGDay 2027 are welcome. Please find the contract with all details online. Key Themes We welcome talk proposals covering broader PostgreSQL and community trends and localized enterprise & scientific topics: High-Performance Science & Big Data: Managing massive datasets, extreme write throughput, partitioning ## OpenAI arms devs with AI conversation tool that can talk and listen at the same time DevFeed: [OpenAI arms devs with AI conversation tool that can talk and listen at the same time]() Original publisher: [Read original article]() Author: Thomas Claburn Published: 2026-09-10T22:59:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Machine learning]() Tags: [ai](), [ai-and-ml](), [ai-models](), [api](), [artificial-intelligence](), [audio](), [openai](), [tool](), [voice-ai]() ### AI overview OpenAI's GPT-Live-1 is presented as a conversation tool that lets developers speak with AI models more fluidly. ### Source excerpt GPT-Live-1 makes speaking to AI models more fluid ## ToolGrad: Efficient tool-use dataset generation with textual "gradients" DevFeed: [ToolGrad: Efficient tool-use dataset generation with textual "gradients"]() Original publisher: [Read original article]() Published: 2026-09-10T22:50:22Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [dataset](), [Large Language Model](), [LLM Techniques](), [Machine learning](), [Fine-tuning](), [LLM evaluation / benchmarking]() Tags: [agent](), [cost](), [datasets](), [evaluation](), [fine-tuning](), [generation](), [llm](), [machine-intelligence](), [machine-learning](), [natural-language-processing](), [prompt-engineering](), [tool]() ### AI overview ToolGrad generates tool-use chains before deriving corresponding user queries, aiming to create complex training data for LLM tool use more efficiently and at lower cost than exploration-based approaches. ### Source excerpt Machine Intelligence ## AI job cuts could come with a costly undo button DevFeed: [AI job cuts could come with a costly undo button]() Original publisher: [Read original article]() Author: Lindsay Clark Published: 2026-09-10T16:21:22Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Machine learning]() Tags: [ai](), [ai-and-ml](), [enterprise](), [gartner](), [job-cuts](), [oracle]() ### AI overview Gartner estimates that nearly a third of displaced employees could be rehired by 2029, at a premium. ### Source excerpt Gartner reckons nearly a third of displaced employees may be rehired by 2029 - at a premium ## Three principles for building a vector platform at Thumbtack DevFeed: [Three principles for building a vector platform at Thumbtack]() Original publisher: [Read original article]() Author: John Zhu Published: 2026-09-10T15:45:00Z Content type: article Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [Embeddings](), [AI search](), [Database](), [data](), [Machine learning](), [API]() Tags: [api](), [data](), [embeddings](), [engineering](), [etl](), [infrastructure](), [machine-learning](), [ml-platform](), [pgvector](), [retrieval](), [search](), [vector](), [vector-search]() ### AI overview This article explains how Thumbtack built a vector platform that lets ML engineers deploy production vector search without managing database access, custom ETL, or query services. It describes three guiding principles: reuse existing infrastructure, treat embeddings as data, and reduce adoption costs for future teams. ### Source excerpt Reusing what we already had, treating embeddings as data, and lowering the next team's cost Today, an ML engineer at Thumbtack can stand up production vector search without negotiating database access, building a custom ETL, or writing a query service. The team brings their choice of embedding model, the data, and the query; the platform handles what connects them. It took several iterations to get to this point. In this post we'll walk through how we got there and the three principles that shaped what we built. A vector database stores high-dimensional numeric arrays (embeddings) and serves nearest-neighbor queries against them. It's how an ML system asks "what's most similar to this?" instead of "what matches this exact key?" The shift from exact lookup to semantic retrieval is what makes vectors useful: a search can return results that mean the same thing, not just results that spell the same. At Thumbtack, embeddings sit between the models that produce them and the services that consume them: language models for text, multimodal models for images, retrieval models for ranking. The platform we describe here is where those embeddings live and how teams reach for them when they need to. Three principles shaped what we built. Reuse what we have: extend the infrastructure we already run rather than stand up a new system. Treat embeddings as data: flow them through the same pipelines that move every other dataset at the company. Lower the next team's cost: make the platform easier to adopt than to work around. Each principle shaped one layer of the system, and together they took vector search from a one-off project to a platform that any team can build on. Architecture at a glance The platform has four moving parts: where embeddings come from, how they reach the database, where they live, and how consumers query them. Each is a layer, and together they form a pipeline that produces vectors and serves similarity searches as a typed API call. The diagram below traces a ## 47,000 job listings reveal the engineering roles that AI is creating DevFeed: [47,000 job listings reveal the engineering roles that AI is creating]() Original publisher: [Read original article]() Author: Jennifer Riggins Published: 2026-09-10T13:09:28Z Content type: news Language: en Sources: [The New Stack]() Topics: [Machine Learning & Artificial Intelligence](), [AI Development](), [Machine learning]() Tags: [ai](), [ai-engineering](), [ai-strategy](), [andela](), [engineering](), [jobs](), [machine-learning](), [post](), [skills](), [sponsor-andela](), [sponsored](), [sponsored-post](), [tech-careers](), [work]() ### AI overview Andela's analysis of 47,000 Fortune 500 engineering job postings identifies emerging AI-related roles formed by combining established skill sets. The article argues that organizations should use AI to delegate suitable work while retaining human expertise and specialization. ### Source excerpt Every major transformation in tech has led to roles merging, then new ones emerging. Friction between developers and operations drove The post 47,000 job listings reveal the engineering roles that AI is creating appeared first on The New Stack. ## Creating an AI Platform for classic ML online inference DevFeed: [Creating an AI Platform for classic ML online inference]() Original publisher: [Read original article]() Author: Rohith Leeladharan Published: 2026-09-10T07:26:46Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [AI Platform](), [Inference](), [MLOps](), [Machine learning](), [model-deployment]() Tags: [ai](), [ai-platform](), [ai-platform-engineering](), [deploy](), [experiments](), [feature-store](), [inference](), [machine-learning](), [ml](), [mlops](), [monitoring](), [predictions]() ### AI overview This article describes how American Express Global Business Travel built an AI platform for deploying classic machine-learning systems and supporting online inference. It explains the platform's requirements--simplicity, self-service, experimentation, and continuous improvement--and details the pre-process, predict, post-process pattern used by inference engines. ### Source excerpt Introduction In 2021, we were given the mission to have AI Systems running in production. The team, instead of just following a classical MLOps process, that involves transforming a Jupyter notebook into a product running in production, decided to go further by creating a platform to deploy AI systems in production. The team decided the platform should respect these requirements: Simplicity: The code powering AI systems should be simple, readable, and easy to maintain -- less intricacy means fewer bugs in production and greater reliability. Self-service: Anyone should be able to build and deploy AI systems autonomously, without depending on a central team. Experimentation: The platform should make it easy to run and iterate on experiments. Continuous improvement: Data related to events and interactions within AI systems must be captured, enabling monitoring and continuous improvement over time. In this article, we will walk through the work done to build a platform that fulfills these four requirements. Background At American Express Global Business Travel, we use machine learning (ML) models for a variety of user experiences like ranking hotel and flight search results. Our ML models are wrapped in inference engines that handle both pre-processing of input data before we run a prediction with the model, and post-processing of output data before returning the output to the caller. The overall flow looks something like this: Figure 1: Handling an inference request A client service that would like the ML model's predictions provides necessary context about the request like which user the request is for. Then, optionally, the inference engine fetches any necessary features for inference from our feature store [part 1][part 2]. Finally, it pre-processes the data, runs the predictions using the trained ML model, and does any necessary post-processing of the model output before returning the response to the caller. We call this the pre-process, predict, post-process patter ## Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM DevFeed: [Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM]() Original publisher: [Read original article]() Author: Dmitry Soldatkin Published: 2026-09-09T22:26:29Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Deployment](), [Inference](), [Machine learning](), [Language models](), [GPU](), [long-context](), [Mixture of Experts (MoE)]() Tags: [advanced-300](), [agentic](), [amazon-sagemaker](), [amazon-sagemaker-hyperpod](), [deployment](), [gpu](), [how-to](), [inference](), [long-context](), [moe](), [nvfp4](), [quantization](), [qwen](), [reasoning](), [technical-how-to](), [tool](), [vllm]() ### AI overview A deployment walkthrough for serving the open-weight Qwen3.8-2.4T-A95B language model on Amazon SageMaker HyperPod with vLLM and NVIDIA B300 GPUs. It covers provisioning, NVFP4 quantization, an OpenAI-compatible endpoint, reasoning, tool calling, and MTP speculative decoding. ### Source excerpt Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding. ## MIT Schwarzman College of Computing launches pilot to help educators teach AI across disciplines DevFeed: [MIT Schwarzman College of Computing launches pilot to help educators teach AI across disciplines]() Original publisher: [Read original article]() Author: Amanda Diehl | MIT Schwarzman College of Computing Published: 2026-09-09T20:40:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [MIT Schwarzman College of Computing](), [Artificial Intelligence](), [Machine learning](), [Learning](), [Electrical engineering and computer science (EECS)]() Tags: [ai](), [ai-educators-pilot](), [ai-teaching-workshop](), [artificial-intelligence](), [asu-ozdaglar](), [civil-and-environmental-engineering](), [classes-and-programs](), [computer-science-and-technology](), [computing](), [dan-huttenlocher](), [education](), [education-teaching-academics](), [electrical-engineering-and-computer-science-eecs](), [machine-learning](), [mit-class-c01-c51-modeling-with-machine-learning](), [mit-eecs](), [mit-operations-research-center](), [mit-schwarzman-college-of-computing](), [mit-sloan](), [mit-sloan-school-of-management](), [pilot](), [saurabh-amin](), [school-of-engineering](), [shen-shen](), [special-events-and-guest-speakers](), [stem-education](), [teaching](), [teaching-ai]() ### AI overview MIT's inaugural AI Educators Pilot brought higher education faculty to campus for a weeklong workshop on adapting AI and machine learning materials for teaching across disciplines. ### Source excerpt A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms. ## Apple hawks $2k folding fondleslab DevFeed: [Apple hawks $2k folding fondleslab]() Original publisher: [Read original article]() Author: Brandon Vigliarolo Published: 2026-09-09T20:38:18Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [coding](), [Cybersecurity](), [Machine learning](), [Web Development](), [C2]() Tags: [ai](), [apple](), [coding](), [css](), [cybersecurity](), [hardware](), [iphone](), [personal-tech](), [security](), [vibe-coding]() ### AI overview An English-language technology news roundup led by Apple's $2,000 folding device and accompanied by brief items on privacy, AI, cybersecurity, semiconductor infrastructure, open-source software, web development, and operating systems. ### Source excerpt Another new form factor for devs to adapt to ## A C File Runs a 744B Model, Cloudflare Maps Agentic Traffic, and Karpathy's $48 GPT-2 - The Tokenizer Edition #36 DevFeed: [A C File Runs a 744B Model, Cloudflare Maps Agentic Traffic, and Karpathy's $48 GPT-2 - The Tokenizer Edition #36]() Original publisher: [Read original article]() Author: Sairam Sundaresan Published: 2026-09-09T00:00:40Z Content type: article Language: en Sources: [Gradient Ascent]() Topics: [Artificial Intelligence](), [Machine learning](), [C](), [Cloudflare](), [Command-line interface]() Tags: [agentic](), [ai](), [ai-ml](), [c](), [cloudflare](), [terminal]() ### AI overview A curated weekly roundup of AI resources covering research papers, videos, articles, developer tools, and learning materials. Featured items include a C program that streams a 744-billion-parameter model from disk, Cloudflare's analysis of human and agent web sessions, and a low-cost GPT-2-class model training project. ### Source excerpt This week's most valuable AI resources ## Momentum in ML, Explained Visually and Intuitively! DevFeed: [Momentum in ML, Explained Visually and Intuitively!]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-08T21:24:18Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [Machine learning](), [Optimization]() Tags: [ml](), [optimization]() ### AI overview This article explains momentum in machine learning visually and intuitively, presenting it as an optimization technique for speeding model training. The supplied excerpt also previews related coverage of distributed training and hyperparameter optimization. ### Source excerpt (a popular ML interview question) ## Updates on HEIR, the homomorphic encryption compiler project DevFeed: [Updates on HEIR, the homomorphic encryption compiler project]() Original publisher: [Read original article]() Published: 2026-09-04T18:53:40Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [homomorphic encryption](), [Compiler](), [Encryption](), [Cryptography](), [Machine learning](), [Inference](), [GitHub](), [bazel](), [Kaggle]() Tags: [bazel](), [ckks](), [compiler](), [cryptography](), [encryption](), [github](), [homomorphic-encryption](), [inference](), [kaggle](), [mathematics](), [ml](), [programming]() ### AI overview This companion article explains HEIR, a homomorphic encryption compiler that converts programs to operate directly on encrypted data. It discusses compiling pre-trained machine-learning models for private inference, describes the repository and setup, and reports an example involving encrypted credit-card fraud detection. ### Source excerpt On 2026-08-14 I published an article on the Google Security blog with an update on HEIR, our homomorphic encryption (HE) compiler. This is a companion article, in which I have no limits on word count or jargon, and I can feel free to be honest. So strap in. Assuming you won't read the linked corporate blog post, HEIR is a compiler that converts an input program to a program that operates directly on encrypted data. ## Agentic Machine Learning Modeling at Instacart DevFeed: [Agentic Machine Learning Modeling at Instacart]() Original publisher: [Read original article]() Author: Tilman Drerup Published: 2026-09-03T16:05:30Z Content type: article Language: en Sources: [Instacart]() Topics: [AI Agent](), [Model Development](), [AI Development](), [Machine learning](), [Development]() Tags: [agent](), [agentic](), [ai](), [ai-agent](), [artificial-intelligence](), [development](), [instacart](), [machine-learning](), [models](), [research]() ### AI overview Instacart describes how its machine learning engineers are exploring AI-agent-assisted modeling loops. The approach keeps engineers responsible for defining problems and supervising work while agents develop hypotheses, implement experiments, and evaluate them. ### Source excerpt Tilman Drerup, Moe Moazzami, Shih-Ting Lin, Greg Reda (and many more) Introduction At Instacart, artificial intelligence is fundamentally changing the way our machine learning engineers operate. In a prior blog post, we used one of our teams as a case study to illustrate how the emergence of agents has reshaped what machine learning engineers spend their time on. The post below goes a few levels deeper and zooms in on the machine learning modeling process itself, an area where recent developments in AI-assisted research have opened up exciting new frontiers that we are now actively exploring. Based on the combined insights of a small horde of MLEs, we will share some of the big wins, the disappointments, and the surprises we encountered along the way. Let's jump in. Big Picture Machine learning models permeate Instacart's marketplace, powering everything from search results to replacement recommendations and expected delivery times. Each of these models is carefully built, maintained, and iterated upon by our crafty MLEs. And while the hours spent on modeling tend to be extremely impactful for the company, the process itself is quite time-consuming and requires an MLE to make a myriad of both small and large decisions. These decisions include, among other things, the right modeling architecture, the appropriate choice for a large number of hyperparameters, the feature set to include, or the most suitable loss functions. All of these decisions are often grounded in a fairly lengthy review of the associated literature as well as a good dose of MLE intuition. Unfortunately, given the combinatorial complexity of this problem and the constraints on human time, MLEs can typically only explore a small part of the entire universe of modeling options, often leaving substantial value on the table. For a few months now, MLEs across Instacart have been exploring the development of methods and tools to tackle this constraint through AI-agent-assisted modeling loops. What follows ## What Is the Raspberry Pi AI Kit? And What Replaced It? DevFeed: [What Is the Raspberry Pi AI Kit? And What Replaced It?]() Original publisher: [Read original article]() Author: Dhairya Parikh Published: 2026-09-03T01:38:42Z Content type: tutorial Language: en Sources: [RaspberryTips]() Topics: [Hardware](), [Machine learning](), [Person Detection](), [object-detection](), [Linux](), [Generative AI]() Tags: [artificial-intelligence](), [chatgpt](), [guide](), [hardware](), [how-to-tutorials](), [linux](), [models](), [object-detection](), [raspberry-pi](), [raspberry-pi-5](), [tutorial](), [update](), [vision]() ### AI overview A tutorial explaining what the Raspberry Pi AI Kit was, how it enabled AI and computer vision projects on the Raspberry Pi 5, why it was discontinued, and what replaced it. The article also highlights its affordability, Hailo collaboration, software examples, and current availability. ### Source excerpt When I first wrote about the Raspberry Pi AI Kit, it was one of the easiest ways to add AI acceleration to a Raspberry Pi 5. Well, things moved fast: Raspberry Pi has since discontinued it and introduced newer options instead. The official Raspberry Pi AI Kit made it much easier to run AI applications... ## Build Your Own Face Recognition Tool With Python DevFeed: [Build Your Own Face Recognition Tool With Python]() Original publisher: [Read original article]() Author: Kyle Stratis Published: 2026-09-01T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Machine learning](), [object-detection](), [Training AI Models](), [Command-line interface]() Tags: [command-line](), [computer-vision](), [machine-learning](), [python](), [testing](), [train](), [tutorial]() ### AI overview A Python tutorial for building a command-line face-recognition tool that detects faces in images, trains and validates a model, and labels detected faces with bounding boxes. ### Source excerpt In this tutorial, you'll build your own face recognition command-line tool with Python. You'll learn how to use face detection to identify faces in an image and label them using face recognition. With this knowledge, you can create your own face recognition tool from start to finish! [Next page]()