Introducing Google’s File Search Tool | Towards Data Science
The search giant fires its latest salvo against traditional RAG processing.| Towards Data Science
On Wednesday, November 12, 2025, OpenAI unveiled GPT-5.1, a major mid-cycle upgrade to its flagship ChatGPT models. The update introduces two new variants — GPT-5.1 Instant and GPT-5.1 Thinking — both designed to make conversations faster, smarter, and more natural. According to OpenAI, GPT-5.1 is built to feel “warmer” and more human-like in tone, while also improving instruction-following and reasoning. The Instant model […] The post OpenAI just dropped GPT-5.1: faster, sm...| Fello AI
Which is actually how some people do it The post How to Build an Over-Engineered Retrieval System appeared first on Towards Data Science.| Towards Data Science
The search giant fires its latest salvo against traditional RAG processing.| Towards Data Science
(With apologies to Hal Draper) By the time the Office of Epistemic Hygiene was created, nobody actually read anything. This was not, the Ministry constantly insisted, because people had become lazy. It was because they had become efficient. Why spend six months wading through archaic prose about, say, photosynthesis, when you could simply ask the… Continue reading MS Fnd in a Modl (or, The Day the Corpus Collapsed)| Davblog
If GitHub themselves have a native code review bot, why not just use it?| Alex Ellis' Blog
In the previous blog posts of this series, we discussed the user-level and admin-level functions of the Model Context Protocol (MCP) server for MinIO AIStor. In the first blog, we learned how to review the bucket’s contents, analyze objects, and tag them for future processing. In the second blog,| MinIO Blog
Yesterday was the 5th anniversary of the publication of my book Evidence-based Software Engineering. The general research trajectory I was expecting in the 2020s (e.g., more sophisticated statistical analysis and more evidence based studies) has been derailed by the arrival of LLMs three years ago. Almost all software engineering researchers have jumped on the LLM […]| The Shape of Code
The AI said I had to do a database first, not code. Who am I to argue? So, with all the prompts outlining the goals of the project, I’ve gone forward with the project, and step one is creating a PostgreSQL database on Azure. This is part three of a multi-part set of articles. I’ll The post Building dbRosetta Using AI: Part 3, Creating a Database appeared first on Redgate.| Blog | Redgate
This is the next installment of the series on building a database and an application called dbRosetta using AI/LLM. Part 1 introduces the concept. THE AI PICKED DATABASE FIRST! Look, I talk databases at this thing a lot, so it probably knows my own preference, but when I asked it, it chose to build a The post Building dbRosetta Using AI: Part 2, Defining the Project & Prompt Templates appeared first on Redgate.| Blog | Redgate
Learn how to build dbRosetta, an AI-driven translation engine for database concepts across platforms. Part 1 covers design principles, prompt strategies and more.| Redgate
Enough is enough| wok
Millionen Menschen fragen täglich ChatGPT, Claude oder Gemini nach allem Möglichen: Nachrichten, Fakten, Wissen. Das Problem: Die KIs erfinden in bis zu 40 Prozent ihrer Antworten einfach irgendwas. Zeit, dass wir lernen, wie wir damit umgehen.| Jörg Schieb | Digital und KI
Learn how to take a manual process and optimize it using AI The post How to Automate Workflows with AI appeared first on Towards Data Science.| Towards Data Science
A surprising connection between the newest AI models and a 50-year old academic field The post LLMs Are Randomized Algorithms appeared first on Towards Data Science.| Towards Data Science
For those interested in AI and/or kabbalah, I just finished a first draft of a paper from a conference. Comments via PM welcome In the new global age of AI, the world is informational. For some thi…| The Book of Doctrines and Opinions:
Hey folks, if you’ve been keeping an eye on the cloud-native world, you’ve probably noticed how AI is shaking things up big time. As we roll into late 2025, one of the hottest trends in Kubernetes is its tight integration with AI workloads. We’re talking about everything from training massive models to running inference at […]| Collabnix
Large language models are powerful, but they can also be unpredictable. They might generate long explanations when you expect a short summary, skip fields in a JSON output, or change the format completely from one request to another. When you’re buil...| freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
JSON, or JavaScript Object Notation, was popularized by Douglas Crockford in early 2000. Since then, there’s been no looking back. JSON has become the standardized data exchange format between client and server technologies. JSON was built for humans...| freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
You may have built a Retrieval-Augmented Generation (RAG) pipeline to connect a vector store to a powerful LLM. And RAG pipelines are incredibly effective at grounding models in factual, up-to-date knowledge. But if you've worked with them long enoug...| freeCodeCamp.org
LLMとの対話やAIエージェントのプロセスは、過去の対話履歴やLLMの処理結果が蓄積されていく仕組みになります。こうしたデータはコンテキストと呼ばれ、LLMが処理履歴や文脈を把握し、次のリクエストを適切に解決するために活用されます。こうした概念は2025年半ばから[コンテキストエンジニアリング](https://blog.langchain.com/context-engineering-for-agents/)と命名され、LLMやAI...| LayerX エンジニアブログ
LayerX バクラク事業部で AI/MLOpsエンジニアをしている中村(@po3rin)です。そしてこの記事はLayerX AI Agentブログリレー42日目の記事です。2ヶ月以上続いています。そろそろ狂気じみてきましたね。 今回はLLMによる麻雀点数計算問題生成タスクをRepeated Samplingでタスク成功率を上げた話をします。また、今回Repeated Samplingを実装した経験から、Repeated Samplingを実装する際の重要な...| LayerX エンジニアブログ
I’ve now heard from several early-career folks some variant on this statement regarding manuscripts they intend to publish as papers: “I give the AI all my notes and it gives me a draft…| Sauropod Vertebra Picture of the Week
With logits processing, you can force open source LLM to comply with a schema you provide. Grammar Constrained Decoding saves you the process of resubmitting malformed LLM responses. Here we show the process with Bumblebee, and explain the background. Want a specific JSON format reliably?| bitcrowd blog Blog
Ever tried to get a poem from an LLM doesn't contain the letter `e`? Large Language Models are kind of amazing and surprisingly unreliable at the same time. Using Elixir's Bumblebee and open source LLMs, you can get much better control over the generation. In this article, we introduce you to logits processing, and how it can be used to achieve what ChatGPT can not accomplish.| bitcrowd blog Blog
Deep technical comparison of Claude and GPT-4 for developers. Includes API benchmarks, code examples, pricing analysis, and integration patterns.| Collabnix
Master LLM fine-tuning infrastructure with Kubernetes, GPU optimization, and distributed training. Includes YAML configs, troubleshooting, and cost optimization.| Collabnix
Learn to build production-grade LLM evaluation pipelines on Kubernetes with practical YAML configs, code examples, and best practices for scalable AI/ML workflows.| Collabnix
Learn to build production-ready LLM applications with Ollama API. Complete guide with Python examples, Kubernetes deployment, and performance optimization tips.| Collabnix
Vodafone handelt maandelijks 60 miljoen klantcontacten af met kunstmatige intelligentie, zo blijkt uit de halfjaarcijfers. Van die contacten wordt 70 procent positief afgesloten zonder menselijke tussenkomst. De telecomgroep ziet de klanttevredenheid stijgen met acht procentpunten sinds de AI-invoering. De resultaten komen voort uit een samenwerking die Vodafone vorig jaar aanging met Microsoft. De AI-chatbot TOBi, […]| ICTMagazine.nl
Anthropic, het bedrijf wat bekend is van de Claude LLM-modellen, opent nog eens twee kantoren in Europa, naast de huidige drie. Met de nieuwe kantoren in Parijs en München wil het bedrijf zorgen dat ze nog meer mensen kunnen inhuren. Er waren al Europese kantoren te vinden in Zürich, Dublin en Londen en in Europa […]| ICTMagazine.nl
On March 4, 2024, a researcher named Alex Albert posted what he referred to as a “fun fact” deriving from his testing of Claude 3 Opus, the most advanced large language model chatbot released to date by Anthropic, one of … The post ARE AI’S SELF-AWARE? CONVERSATIONS WITH CLAUDE 3 appeared first on ConsortiumInfo.org.| ConsortiumInfo.org
RVIA's 2025 Holiday Travel Intention Survey reveals key travel trends, generational shifts, and how RVs are reshaping holiday plans. The post RVIA: 28 Million to Travel by RV for the Holidays appeared first on Camper Report.| Camper Report
October 2025 brought more than a dozen RV recalls affecting an estimated 23,000-plus vehicles. Is your RV affected? The post NHTSA RV Recalls for October 2025: Is Your RV on the List? appeared first on Camper Report.| Camper Report
The RV Industry Association’s September 2025 report shows a 4.4% rise in total RV shipments, with strong gains in motorhomes and park models.| Camper Report
This blog post focuses on optimizing the performance of a real model using the QuickTune script, illustrated with an example of offline GEMM tuning for the Qwen model on an AMD MI308 GPU. Developed by the AMD Quark Team, the QuickTune script delivers significant GEMM performance improvements with minimal time overhead. QuickTune is an advanced tool for hipBLASLt offline GEMM tuning. It allows users to complete offline tuning with one click, instead of using hipblaslt-bench to tune the model m...| AMD ROCm Blogs
Low-bit quantization has become increasingly important for large language models (LLMs), as model sizes reach hundreds of billions of parameters, where balancing efficiency and accuracy is critical. AMD Quark, the model optimization toolkit from AMD, offers cross-platform optimized models for accurate low-bit model deployment. Building on the concepts we introduced in our previous blog, this blog focuses on MXFP4 and MXFP6 low-precision quantization techniques on large language models and dem...| AMD ROCm Blogs
Applications using LLMs open to a huge number of vulnerabilities and potential attacks. Learn why and what to do to enhance the security of your products. Links:| Mozaic Works
I feel like I understand computers pretty well, but when I do some maintenance on my tankless water heater, this is the best understanding I have: My tankless water heater is a Navien NPE-240A natural gas water heater. That's not an endorsement, since I didn't…| mattsayar.com
If you have built a retrieval pipeline or a recommendation feature lately, you have already touched embeddings. They are the trick that turns messy real-world inputs such as text, images, audio, logs, or user actions into points in a mathematical space where similar things are literally near each other. Once your data lives as vectors, […]| Collabnix
Learn how to implement A/B testing for LLM models using Kubernetes, Istio, and modern MLOps practices. Includes code examples and production strategies.| Collabnix
Build a production-ready LLM-powered code review system with GitHub Actions. Automate PR analysis, catch bugs, and improve code quality with AI.| Collabnix
この記事では、LLMに何回か答えを出させて多数決をとるときの、計算コストを減らす方法を紹介します。 LLMは1回の回答だけでは不安なことがあり、何度か答えを出して多数決をとる方法がよく使われます。ただし、この方法は計算に […] The post LLMに何度も答えさせるコストを10分の1に削減する手法 first appeared on AIDB.| AIDB
ブックマーク 本記事では、要件変更の影響範囲をLLMで特定する手法を紹介します。システム開発では、途中で仕様変更が入るのは珍しくありませんが、その影響がどこにまで及ぶのかを見極めるのは難しく、手間もかかります。そこでLL […] The post 要件変更の影響はどこまで広がる？LLMで影響範囲を特定する手法の検証 first appeared on AIDB.| AIDB
本記事では、創薬の現場で実際に使われはじめたエージェント型AIの実例を紹介します。 新薬の開発には、多くの時間と費用がかかります。そのため最近では、AIを使って効率化しようとする動きが進んでいます。実際にどう使われ、どん […] The post 創薬におけるAIエージェント実例8選 他分野での応用も考察 first appeared on AIDB.| AIDB
本記事では、LLMがコードを生成する際に、プロンプトの質がセキュリティにどう影響するかを取り上げます。10種類のLLMで大規模な実験が行われています。 問題を指摘するだけでなく、この問題の緩和にプロンプト手法が有効かどう […] The post ユーザーによる「曖昧な指示」や「不十分な依頼」、コード生成にどう影響する first appeared on AIDB.| AIDB
AIエージェントを業務に使う企業が増えています。ただし、どのLLMをベースに選べばよいか、とくにセキュリティ面での判断は大事です。にもかかわらずそうした調査はとても難しいとされてきました。 そうした中で、31種類もの主要 […] The post LLMエージェントのベースモデルに何を使う？安全性ランキング調査結果 first appeared on AIDB.| AIDB
Find out how to scrape video transcripts for any Youtube video. This is a powerful way to unlock deeper content analysis like searching for specific words, summarizing video content, and analyzing sentiment across videos.| SerpApi
Learn about the new commands for Model Runner and how they can help you publish and share your own AI models on Docker Hub.| Docker
LLMs, Kubernetes, and cool stuff what not| gruchalski.com
Who is AI really for? The creatives? Or the people that want you to create something for them?| The Networking Nerd
Master enterprise RAG system security with practical examples for authentication, data governance, and compliance. Includes Kubernetes configs and Python code.| Collabnix
Master LLM model versioning with practical examples, DVC, MLflow, and Kubernetes integration. Complete guide for production AI/ML deployments.| Collabnix
Learn to scale LLM applications from prototype to production with Kubernetes, vLLM, and best practices for GPU resource management and cost optimization.| Collabnix
A containerized approach to natural language database queries with built-in safety and auditability| kubetools.io
Master Kubernetes autoscaling for LLM inference workloads. Learn HPA, KEDA, VPA configuration with practical examples for efficient GPU utilization.| Collabnix
What is MCP? To set the stage, we’ll begin with a quick overview of MCP: What it is, why it matters and how it can be used with Tableau. At its core, the Model Context Protocol (MCP) is a standard that gives large language models... The post Connecting LLMs to Tableau: A Practical Guide for Using Tableau MCP appeared first on InterWorks.| InterWorks
If you’ve built a “Naive” RAG pipeline, you’ve probably hit a wall. You’ve indexed your documents, but the answers are… mediocre. They’re out of context, they miss the point, or they just feel wrong. Here’s the truth: Your RAG system is only as good as its chunks. Chunking—the process of breaking your documents into searchable pieces—is one of the most important decision you will make in your RAG pipeline. It’s not just “preprocessing”; it is the foundation of your A...| Analytics Yogi
If you’re starting with large language models, you must have heard of RAG (Retrieval-Augmented Generation). It’s the magic that lets AI chatbots talk about your data—your company’s PDFs, your private notes, or any new information—without “hallucinating.” It might sound complex, but the core logic of a simple RAG pipeline can be boiled down to six simple steps. We’re going to walk through the “conductor” script that runs this pipeline, showing you how data flows from a raw ...| Analytics Yogi
Learn how to deploy scalable LLM inference services using Knative on Kubernetes. Complete guide with code examples, GPU support, and production best practices.| Collabnix
Learn how to build a production-ready multi-tenant LLM platform on Kubernetes with isolation, resource management, and scaling. Includes YAML configs and code.| Collabnix
When working with organizations on key data and knowledge management initiatives, we’ve often noticed that a roadblock is the lack of quality (relevant, meaningful, or up-to-date) existing content an organization has. Stakeholders may be excited to get started with advanced … Continue reading The post How to Leverage LLMs for Auto-tagging & Content Enrichment appeared first on Enterprise Knowledge.| Enterprise Knowledge
Master LLM gateway patterns with practical rate limiting and load balancing strategies. Includes code examples, Kubernetes configs, and troubleshooting tips.| Collabnix
Master load balancing strategies for scaling Ollama deployments in production. Complete guide with Kubernetes configs, HAProxy setup, and troubleshooting tips.| Collabnix
Master document processing for RAG systems with practical examples, code snippets, and best practices. Learn chunking strategies, embedding optimization, and production deployment.| Collabnix
Unlocking Claude AI Skills for Enhanced Performance What Are Claude Skills? A Game-Changer for AI Productivity Claude Skills represent a revolutionary approach to customizing and extending Claude’s capabilities through specialized knowledge modules. Think of Skills as expert plugins that transform Claude from a general-purpose AI assistant into a domain-specific powerhouse tailored to your exact needs. […]| Collabnix
When testing trading algorithms, jumping straight into live markets is risky. AI trading sandboxes provide a controlled environment to explore strategies without real financial consequences. These spaces offer a glimpse into market behavior, helping traders experiment and refine their tools safely. So, what makes these environments so helpful, and what other advantages do they offer? […]| Collabnix
A client intake workflow streamlines how businesses collect, organize, and manage essential information. When powered by AI, it becomes smarter—automating tasks like sorting responses or identifying priorities. By combining secure tools with integrations like CRM systems, workflows simplify operations while ensuring data privacy. This guide explains how to build a seamless system that’s efficient and […]| Collabnix
Discover the Key Cursor AI Benefits for 2025 Introduction: Why Developers Are Making the Switch Over 1 million developers have already made the switch to Cursor AI, including engineers at OpenAI, Perplexity, Samsung, Shopify, and Midjourney. The question isn’t whether AI will transform coding—it’s whether you’re ready to leverage it before your competition does. If […]| Collabnix
Understanding AI Text Detection in 2025 I have tried more than a dozen AI detectors within the last six months, and I have to say something uncomfortable to you: YES, AI-generated text can be identified, but the process of identifying it is messy, inconsistent and, more often than not, erroneous. When you are writing essays […]| Collabnix
Exploring Cursor AI: Features and Best Practices Cursor AI has rapidly emerged as one of the most powerful AI-assisted development environments in 2025, serving billions of code completions daily to developers at Fortune 500 companies. Unlike traditional IDEs with bolt-on AI features, Cursor was architected from the ground up to integrate artificial intelligence into every […]| Collabnix
Understanding Agentic AI and Its Transformative Business Impact Agentic AI represents the next evolution in artificial intelligence—systems that can autonomously plan, execute, and optimize tasks with minimal human intervention. Unlike traditional AI tools that simply respond to prompts, agentic AI takes initiative, makes decisions, and adapts its approach based on outcomes. For businesses evaluating this […]| Collabnix
Discover the key differences between AI Agents and Agentic AI with practical code examples using LangChain, AutoGen, and CrewAI. Learn architecture patterns, implementation strategies, and real-world applications.| Collabnix
Artificial Intelligence (AI) has become a significant force in transforming various industries. One area where its impact is profound is Technology Assisted Review (TAR). This innovative approach enhances data analysis and document review processes, making them more efficient. By integrating intelligent algorithms, TAR has revolutionized how professionals handle large volumes of information. Understanding Technology Assisted […]| Collabnix
Introduction: The Evolution from Single to Multi-Agent AI Systems The artificial intelligence landscape has dramatically shifted in 2025. While single Large Language Models (LLMs) like GPT-4 and Claude dominated 2023-2024, the future belongs to multi-agent LLM systems where specialized AI agents collaborate to solve complex problems. According to recent research, over 80% of enterprise workloads […]| Collabnix
OpenAI has released GPT-5-Codex, a specialized AI coding model that can work autonomously for hours, revolutionizing software development with advanced agentic capabilities and superior code review features.| Collabnix
As artificial intelligence models continue to grow in size and complexity, the computational and memory requirements for deployment have become increasingly prohibitive. Modern large language models (LLMs) like GPT-4 and Claude contain hundreds of billions of parameters, requiring substantial hardware resources for both training and inference. Quantization has emerged as one of the most effective […]| Collabnix
Cerebras AI has emerged as one of the most innovative challengers to NVIDIA’s dominance in AI infrastructure, pioneering wafer-scale computing technology that delivers 75x faster inference and 10x faster training than traditional GPU clusters. Founded in 2016 by the team behind SeaMicro (acquired by AMD for $334M), the company has raised over $720 million and […]| Collabnix
Docker isn’t just a buzzword anymore—it’s the backbone of modern software development. With over 13 billion container downloads per month and a market projected to reach $993 million by 2025, Docker has become as essential as knowing how to code itself. But here’s the shocking truth: 90% of developers are using Docker wrong. If you’re […]| Collabnix
A comprehensive guide to understanding, implementing, and securing autonomous AI systems in enterprise environments As Agentic AI systems transition from experimental tools to mission-critical business infrastructure, organizations face unprecedented security challenges. Unlike traditional AI that responds to prompts, Agentic AI operates autonomously—planning, executing, and adapting across multiple systems with minimal human oversight. This comprehensive analysis […]| Collabnix
Why the shift from traditional AI to autonomous agents is creating a cybersecurity nightmare that 93% of security leaders aren’t prepared for The Shock That Changed Everything Picture this: You wake up Monday morning to discover your AI assistant has autonomously approved $2.3 million in fraudulent transactions, granted system access to unauthorized users, and leaked […]| Collabnix
Introduction to Qwen-Image-Edit Qwen-Image-Edit represents a breakthrough in AI-powered image editing technology, extending Alibaba’s powerful 20B parameter Qwen-Image foundation model with specialized editing capabilities. Released in August 2025 and featured extensively on Collabnix for its technical innovation, this state-of-the-art model achieves unprecedented performance in semantic image editing, appearance modification, and most notably, precise text rendering […]| Collabnix
Earlier today, I sent this absolutely perfectly crafted piece of slop into GitHub Copilot… Right, but i want thje patche sot be / and /* always And as I already expected, due to using these L…| addshore
I have been exploring the capabilities of structured generation with OpenAI models for a long time, starting from when function calling was introduced. I believe structured generation is a powerful use case for LLMs. The reason is that if we can extract information already present in data and give it a structured format, we can enable significant automation.| Kevin Jivani
ChatGPTs web search sometimes embeds images into it's responses. When it does that, rather than serving from an OpenAI run cache, it embeds it directly from the original server, leaving the webmaster| www.bentasker.co.uk
How close is the agreement between the behavior of a compiler and its corresponding language specification?| The Shape of Code
Agent session smuggling is a novel technique where AI agent-to-agent communication is misused. We demonstrate two proof of concept examples. The post When AI Agents Go Rogue: Agent Session Smuggling Attack in A2A Systems appeared first on Unit 42.| Unit 42
こちらはLayerX AI Agentブログリレー36日目の記事です。 LayerX バクラク事業部で AI/MLOpsエンジニアをしている中村(@po3rin)です。今回はAI Agentのビジネス価値を計るバックテスト基盤を構築した話と、そこから学んだAI Agent開発のプラクティスを紹介します。 目次 目次 AI Agent機能の評価の重要性 AI Agent機能のバックテスト バックテスト基盤開発の難しさ バックテスト基盤を...| LayerX エンジニアブログ
Introduction Hello. I’m Mori (@ei01241 ), a security engineer at GMO Flatt Security, Inc. In recent years, the evolution of Large Language Models (LLMs) has accelerated the development of a wide range of AI applications, such as chatbots, data analysis/summarization, and autonomous agents. LLM frameworks like LangChain and LlamaIndex abstract LLM collaboration and external data connections to improve development efficiency, but behind this convenience lie new security risks. In this article...| GMO Flatt Security Research
The internship season is back at Quarkslab! Our internship positions cover a wide range of topics and expertise, and aim at tackling new challenges in various fields.| Quarkslab's blog
L’articolo di Marcello Oberosler, pubblicato su Il Dolomiti e qui riproposto, affronta il tema dell’uso dell’intelligenza artificiale nella scienza e nell’università, oggi sempre più diffuso e pervasivo. Dalle abitudini degli studenti ai comportamenti dei ricercatori, l’Ai è ormai parte integrante d| ROARS
Sandboxed, reviewed parallel agents make sense For coding and software engineering, I’ve used and experimented with various frontends (FOSS and proprietary) to multiple foundation models (mostly pr…| Colin Walters
Introduction Cloud cost management isn’t just about checking invoices once a month — it’s about embedding automation, governance, and insights into your infrastructure so that your engineering teams make cost-aware decisions in real time. With OCI, you have native tools (Cost Analysis, Usage APIs, Budgets, etc.) and infrastructure-as-code (IaC) tooling that can help turn cost … Continue reading Automating Cost-Governance Workflows in Oracle Cloud Infrastructure (OCI) with APIs & Infra...| Technology Geek
Introduction In many modern applications — e-commerce, media platforms, SaaS services — providing real-time personalized recommendations is a key differentiator. With OCI’s streaming, AI/ML and serverless capabilities you can build a recommendation engine that: In this article you’ll learn how to: 1. Architecture Overview Here’s a high-level architecture for our recommendation engine: 2. Setting Up … Continue reading Building a Real-Time Recommendation Engine on Oracle Cloud Infra...| Technology Geek
Lambda Layers are one of AWS Lambda’s most powerful yet underutilized features. While many developers use them for basic dependency sharing, there’s a wealth of optimization opportuniti…| Technology Geek
I’m excited to share something I’ve been working on—my new course, Prompt Engineering for Everyone. Whether you’re a seasoned developer or just curious about AI, this course is designed to change the way you interact with conversational AI. Why Prompt Engineering Matters I remember the first time I used an LLM (Large Language Model). I was amazed but I also quickly realized that my input had a profound effect on the quality and accuracy of the output. It’s not just about asking; it...| Programming Zen
A prompt injection cheat sheet for AI bot integrations| The seclify blog
Last month, I was having dinner with a group and someone at the table was excitedly sharing how they were using agentic AI to create and merge PRs for them, with some review but with a lot of trust and automation. I admitted that I could be comfortable with some limited uses for that, such … Continue reading Schneier on LLM vulnerabilities, agentic AI, and “trusting trust”→| Sutter’s Mill
LayerX のバクラク事業部の AI・機械学習部で機械学習エンジニアをしている島越（@nt_4o54）です。こちらはLayerX AI Agent ブログリレー 31 日目の記事です。 昨日は松村 (@yu__ya4)による「Langfuse の Experiment Runner SDK を利用した AI エージェント機能の性能評価と実験管理」でした。 無事にこのブログリレーも日付換算で一ヶ月を突破しました。過去のブログ記事も知見が溢れて...| LayerX エンジニアブログ
本記事では、LLMを使った消費者調査の新しい手法を紹介します。新製品を開発する際、実際の消費者を集めて製品コンセプトを見せ、購買意欲を尋ねる調査には多額のコストがかかります。| AIDB
