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This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. OpenAI CEO Sam Altman’s oft-discussed promise that Americans will share in the wealth AI creates was in the news again last week. On Thursday, the Financial Times reported that Altman is in…
Microsoft is cutting an additional 3,200 jobs beyond those in the gaming division.
Its issues with current nuclear safety standards are termed semantic, not physical.
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Take control of your AI models with our newest skill, designed to make local fine-tuning efficient.
A visual walkthrough of Linux eBPF. It goes through the basic pipeline: write a small C program, compile it with clang, load it with bpf(), get it past the verifier, JIT it, attach it to a kernel event, and read data back through maps/ring buffers. The second half gets into why people use it for tracing, XDP packet filtering, security hooks, and sched_ext, then ends with the less fun part: the verifier is the safety boundary, and eBPF gets a lot scarier when the person loading the program already has access. submitted by /u/Ok_Marionberry8922 [link] [留言]
It will take several weeks for the Link spacecraft to rendezvous with NASA's Swift observatory.
It will take several weeks for the Link spacecraft to rendezvous with NASA's Swift observatory.
Get excited for a feature film starring an AI character that doesn't exist in any real way.
Microsoft is spinning off four of its Xbox game studios - Compulsion Games, Double Fine Productions, Ninja Theory, and Undead Labs - as part of the restructuring announced today. However, two that are going independent, Double Fine and Compulsion, will get to keep their franchises and games catalogs, according to Xbox CEO Asha Sharma. "Compulsion […]
Apple has started a phased rollout of card payments for Apple Account purchases in India after adapting to the country's payments framework.
Move affects ~20% of the gaming division, which will refocus on its biggest franchises.
I've long argued that Hollywood has simultaneously set and ruined our expectations for smart glasses. But after binge-watching two seasons of Netflix's A Man on the Inside, this is perhaps the first time I've seen Hollywood, perhaps inadvertently, illustrate the biggest cultural problem with smart glasses as they stand today. In a nutshell, Ted Danson […]
Orasort, a sorting algorithm from Oracle, came into the public domain in 2024 after its patent expired, providing massive benefits to open-source software. submitted by /u/Ok_Stomach6651 [link] [留言]
In this blog post, we will see how to performance test a RAG (Retrieval-Augmented Generation) application properly, covering both speed and correctness, and how to wire both into a CI/CD pipeline so regressions get caught before they reach production. Performance testing a RAG application requires two separate testing gates: one for speed and one for answer quality. Traditional load testing tools measure response times but cannot detect hallucinations, where a model returns fast but factually incorrect answers grounded in fabricated context rather than retrieved documents. The guide demonstrates using k6 for load testing end-to-end latency and DeepEval for evaluating faithfulness and answer relevancy using an LLM-as-judge approach. Both gates are integrated into a GitHub Actions CI/CD pipeline so regressions in either performance or output quality are caught automatically on every pull request before reaching production. If you've come from a JMeter or k6 background like I have, your first instinct with a RAG endpoint is probably to point a load test at it and check response times. That gets you halfway there. A RAG app can return a fast, confident, completely wrong answer, and a plain load test will never tell you that. You need two testing surfaces, not one: performance and quality. This guide covers both, using a single running example throughout: a documentation assistant that answers "How do I run JMeter in non-GUI mode?" against a small knowledge base. Why RAG breaks traditional load testing assumptions A conventional API returns a complete response and you measure the round trip. A RAG endpoint does two expensive things before it answers: it retrieves context from a vector store or search index, then it streams a generated response token by token. That second part matters a lot. A single request can stream hundreds of tokens over several seconds, so "request duration" as a single number hides two very different problems: how long the model took to start answe
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TL;DR Tech has taught me many things over the years. It taught me how to learn new technologies, build projects, apply for opportunities, and keep growing. What it didn't teach me was something that turned out to be just as important: how to take care of myself while doing all of those things. For a long time, I believed I would slow down later. Later, when life became less busy. Later, after the next project. Later, after the next opportunity. The problem was that "later" never seemed to arrive. It took an unexpected pause in my own life to realize that building a successful career means very little if we forget to take care of the person trying to build it. Looking back, I don't see that experience only as a difficult chapter. It changed the way I think about success, growth, and what it means to build a career that's sustainable. Today, I still love learning, building, writing, and chasing opportunities. None of that has changed. What has changed is the realization that taking care of myself isn't something separate from my career. It's one of the reasons I'll be able to keep building it for years to come. Along the way, I also realized that many of the things that truly support us are easy to overlook. Rest, movement, nourishing ourselves well, meaningful relationships, and simply checking in on the people around us often receive far less attention than the next framework, project, or milestone, even though they make everything else possible. More than anything, I wanted to write this because I care deeply about this community. I hope none of us have to wait until life forces us to slow down before remembering to take care of ourselves. I hope we build careers we're proud of, but I hope we also build lives we're able to enjoy. This isn't an article about productivity or health advice. It's simply a reflection on something I wish I had understood earlier. Your career matters. So does the person building it. I'd also love to hear your story. Has there been a momen
Revolutionizing Content Automation: Building Multi-Agent Pipelines with LangGraph TL;DR : LangGraph transforms AI content automation by enabling sophisticated multi-agent systems. It orchestrates specialized agents for complex tasks, integrates seamlessly with Celery for asynchronous task management, and uses Redis for efficient state tracking. This framework surpasses traditional workflows by supporting dynamic decision-making and complex agent interactions. Introduction Imagine content automation systems that are intelligent and adaptive, capable of understanding context and making decisions autonomously. LangGraph, a cutting-edge framework, is making this vision a reality by empowering developers to build dynamic, multi-agent content pipelines. As AI engineers and system architects strive to automate intricate content processes, LangGraph offers a robust alternative to traditional linear workflows, promising enhanced efficiency and adaptability. LangGraph's Orchestration Capabilities LangGraph excels in orchestrating multiple specialized agents within a single pipeline. Unlike traditional systems, which often rely on linear processes, LangGraph enables the simultaneous operation of various agents, each with specific roles and expertise. Key Features Agent Specialization : Engineers can design agents specialized in tasks such as research, writing, editing, and publishing. Each agent functions independently yet collaboratively within the pipeline. Dynamic Interactions : Agents interact in real-time, sharing data and insights to refine content outputs collectively. Complex Task Handling : The architecture supports complex task management, ensuring each agent contributes effectively to the overall goal. Multi-Agent Collaboration and Specialization The core of LangGraph is its multi-agent collaboration mechanism. This shift from linear workflows to collaborative systems enables specialization, significantly improving the quality and efficiency of content automation. B
While LLMs are great, there are some limitations in using LLMs: LLMs can hallucinate, presenting factually incorrect information when they don't know the answers, and their knowledge gets frozen at the time of training. That's when Retrieval Augmented Generation (RAG) addresses both of these problems. It is the process of optimizing the output of the LLM. This article walks through what RAG is, why it matters, and how to build a working RAG pipeline using two of the most popular tools in the space: LangChain , a framework for building LLM-powered applications, and Pinecone , a managed vector database designed for fast similarity search at scale. A typical RAG pipeline has three core steps: Retrieve : When a query is entered, the system searches an external data source (like a vector database) for the most relevant documents. Augment : The system attaches those relevant retrieved documents to the original user prompt. Generate : The LLM reads the appended context and formulates a highly accurate, grounded answer. RAG is popular because it solves practical problems that pure fine-tuning or prompting can't easily solve: Freshness — You can update the knowledge base without retraining the model. Domain specificity — You can ground responses in your company's internal documents, product manuals, or proprietary data. Traceability — Because answers are based on retrieved documents, you can cite sources and reduce hallucination. Cost — Retrieval is far cheaper than fine-tuning a model every time your data changes. Why LangChain and Pinecone? LangChain drastically speeds up AI development. It is an open-source orchestration framework that provides pre-built components to connect Large Language Models (LLMs) to external data, manage memory, and create multi-step workflows. It abstracts away the complex boilerplate usually required to build production-ready AI applications. Pinecone is a purpose-built vector database. Once your documents are converted into embeddings (numerica
Originally published on DevToolHub . Helm 4 shipped in November 2025. Eight months later, most teams are still running Helm 3 in production CI/CD because it works. But Helm 3's final feature release lands September 9, 2026, and security patches stop completely on February 10, 2027. This helm 4 migration is simpler than it looks. Your charts don't need rewriting — Helm 3 Chart API v2 charts are fully compatible with Helm 4. But the automation around Helm has four real breaking points that fail silently if you don't know where to look. [IMAGE: articles/images/2026-07-05-helm-4-migration-guide-featured.png | alt: "helm 4 migration flow from Helm 3 to Helm 4 upgrade path"] Why This Helm 4 Migration Matters Now The EOL timeline has three stages, and they matter differently based on your situation: September 9, 2026 — Final Helm 3 feature release (limited to Kubernetes client library updates only after this date) February 10, 2027 — All security patches stop If your organization runs regulated workloads with requirements around supported software, February 2027 is your hard deadline. But waiting until then means doing this migration under pressure, after 14 months of Helm 4 fixes shipped without you tracking them. The better path: upgrade now, before September, so you're on supported software when new Kubernetes releases land and need updated client libraries. What Actually Broke: The Four Real Changes 1. Post-renderers require plugin registration In Helm 3, you could pass any executable directly to --post-renderer : helm install myapp ./chart --post-renderer ./scripts/mutate.sh Helm 4 drops this. Post-renderers must now be registered as named Helm plugins and referenced by plugin name: helm install myapp ./chart --post-renderer my-post-renderer If your pipeline calls --post-renderer ./path/to/script.sh , it fails on Helm 4. The error message doesn't say "plugin required," so this is easy to miss in a quick smoke test. To wrap an existing script as a plugin, create a plug