New
What does your model evaluation pipeline actually look like in production?
Offline evals, golden sets, LLM-as-judge, human review: what are you running before a model or prompt change ships, and what has caught real regressions?
Topic
Generative AI
222 stories in the last 90 days · 4 community threads
In short
Generative AI covers models that produce text, code, images or other content from a prompt, and the services built on them. For buyers the questions are practical: where your data goes, how outputs are checked, what it costs at real usage and how easily you could change model or supplier later.
Written by Techarda editors · Updated · How we work
Latest stories
The newest Generative AI stories from the last 90 days. Each one opens with our summary and a link to the original source.
Observability
AI For DBAs: Internals and Architecture of LLMs
10/19/26 Ask a database a question, and it returns a result. Ask a large language model, and it gives you an answer too. From the outside, that can look like much the same thing. Under the hood, it’s a different story. As a DBA, I’m ...
Containers & Kubernetes
Who judges the judge? Three helpful ways to audit your LLM evaluators
Is your LLM-as-a-judge truly objective? There’s a common misconception in tech that humans are subjective, so we should rely on AI for objective evaluation. But the reality is that LLMs are also subjective. Just like human reviewers, they can carry biases: Verbosity bias : LLM judges usually favor longer, confident-sounding answers , even when the underlying facts are wrong. Position bias :…
Containers & KubernetesHow-to
Developer’s guide to implementing MCP apps in Gemini
Enterprise assistants still respond mostly in text. However, that can break down for visual, multi-step work, where every adjustment costs another slow chat turn. MCP Apps ( SEP-1865 ) solves this. This open Model Context Protocol (MCP) extension pairs a tool with a live UI. In Gemini Enterprise , that UI renders directly in the chat flow inside a sandboxed iframe, and can expand to fullscreen or…
Enterprise Applications
The Gap That No LLM Can Close
In the next wave of generative AI, it's crucial to combine LLMs and tabular foundation models where they provide the greatest benefit.
Containers & Kubernetes
Google named a Leader in the 2026 IDC MarketScape for Agentic AI Platforms
As organizations work to leverage AI to transform their businesses, it is clear that the raw model intelligence is not the key challenge. Consistently, we hear that the challenges lie in how the agents can be deployed across the company while meeting key requirements for quality, integration, security, and governance. Gemini provides that connected foundation across the enterprise , and it’s why…
Hybrid & Multicloud
When AI finds vulnerabilities faster than organizations can fix them
The gap between finding a flaw and using it is closing In April 2026, Anthropic’s Claude Mythos found a bug in OpenBSD that had gone undetected since 1998, in one of the most heavily audited codebases in the world. In a few weeks it found thousands of previously unknown vulnerabilities across major operating systems and browsers, wrote working exploits, and chained them together with little human…
AI Agents & AutomationComparison
OpenCode vs. Claude Code: Which AI coding tool is best? [2026]
Imagine Claude Code is a taxi. You state the objective, and it starts driving. You're impressed with the decisions it's making, until it stops in the middle of the road: "Sorry, you've hit 100% of your usage." Now, you either turn over your wallet or give up and look for coffee while you wait for the reset. This is why OpenCode surged in popularity when it launched: the car is free, and you can…
Security
Ransomware at Machine Speed: How GenAI Shrinks the Path after Initial Access
Artificial Intelligence technologies have made a huge impact on the security industry. But as with any public technology, adversaries have access to AI as well. They’re wasting no time incorporating AI into their malicious workflows and ransomware operators have been able to leverage it - unfortunately, this has led to some of their successes.Fortunately, there is plenty of potential for…
Earlier in the last 90 days
- How Itaú is building generative AI for its entire customer baseOpenAI · 17 Oct 2026
- Nemotron 3.5 Super VL model now available on Amazon SageMaker JumpStartAmazon Web Services · 17 Oct 2026
- Day 0 support for Reflection’s Beam on Google Cloud: 5x faster perf with llm-d and NVIDIA DynamoGoogle Cloud · 17 Oct 2026
- Closing the Binary Blind Spot: Domain-Knowledge Injection for LLM Malware Triage in npm PackagesDatabricks · 17 Oct 2026
- Introducing the Three-Layer Model for Intelligent ProductsThoughtSpot · 17 Oct 2026
- Securing AI Agents from Discovery to Runtime: Check Point on Google Cloud’s Gemini EnterpriseCheck Point Software · 17 Oct 2026
- Muse Spark Open model now available on Amazon SageMaker JumpStartAmazon Web Services · 16 Oct 2026
- Bringing the borderless Lakehouse into the agentic era with innovations for GeminiGoogle Cloud · 16 Oct 2026
- How Visko streams real-time world models on Google Cloud TPUsGoogle Cloud · 16 Oct 2026
- What the Gemini agent means for startupsGoogle Cloud · 16 Oct 2026
- How Netflix Made AI Coding Agents More Efficient with JetBrains IDE IntelligenceJetBrains · 15 Oct 2026
- The Most Common Questions About Agent Evals, AnsweredLangChain · 15 Oct 2026
- Threat Actor Abuses Agentic AI Pentesting Framework to Target South Korean Financial InstitutionsZscaler · 15 Oct 2026
- Harbor + Docker Sandboxes: Run Safe, Reproducible Agent EvalsDocker · 15 Oct 2026
- Connected Intelligence: Operationalizing secure, cost-optimized graph architectures across multi-cloud AINeo4j · 15 Oct 2026
- The AI Security Paradox: How Shadow AI Became the Enterprise’s Biggest Blind SpotCisco · 15 Oct 2026
- Predictable AI spend in BigQuery: Introducing cost management for generative AI functionsGoogle Cloud · 15 Oct 2026
- Cloud CISO Perspectives: New Gemini Notebook can help CISOs and policymakers prepare for PQCGoogle Cloud · 15 Oct 2026
- Introducing Claude Platform Tracing in New Relic AI MonitoringNew Relic · 14 Oct 2026
- Amp puts AI agents to work beyond code with OpenAI modelsOpenAI · 14 Oct 2026
- Reducing Qwen training time by 6x on AI RuntimeDatabricks · 14 Oct 2026
- How Third Moment Research runs 10,000 agents for 36+ hoursOpenAI · 14 Oct 2026
- Claude vs. ChatGPT: Which is best? [2026]Zapier · 14 Oct 2026
- The best AI chatbots in 2026Zapier · 14 Oct 2026
- Gemini 4 Argon on GitLab: Built for long-running agentic workGitLab · 14 Oct 2026
- Gemini 4 Argon in Public Preview on Snowflake Cortex AISnowflake · 14 Oct 2026
- Deep Agents Deploy: an open alternative to Claude Managed AgentsLangChain · 14 Oct 2026
- Gemini 4 Argon available on Google Cloud, built to help solve your business’ hardest problemsGoogle Cloud · 14 Oct 2026
- Check Point Research Discovers High Risk Vulnerabilities in LiteLLM, One of the Most Widely Used LLM GatewaysCheck Point Software · 13 Oct 2026
- Higgsfield takes AI video from 1 minute to over 10OpenAI · 13 Oct 2026
- What are ChatGPT dots? Zapier · 13 Oct 2026
- Gemini 4 Argon now available on AI GatewayVercel · 13 Oct 2026
- Google Gemini 4 Argon now available in AI Gateway and Agent RunnersNetlify · 13 Oct 2026
- Agentgateway Joins AAIF as an Open Gateway for Agentic AI Infrastructure | Solo.ioSolo.io · 13 Oct 2026
- Introducing TimesFM 3.0 with multivariate time-series forecasting in BigQueryGoogle Cloud · 13 Oct 2026
- How Flo Health built safer, more useful open models with Databricks AI RuntimeDatabricks · 13 Oct 2026
- Why future AI data centers need to be open and heterogeneousHPE · 13 Oct 2026
- Anthropic Claude Sonnet 5.5 and Claude Opus 5.5 are now available on Kiro in AWS GovCloud (US)Amazon Web Services · 10 Oct 2026
- What’s new with Google Data CloudGoogle Cloud · 10 Oct 2026
- Asana cuts model costs 76x in browser tests with GPT-6.1 SolOpenAI · 9 Oct 2026
- OpenAI GPT-6.1 Sol now supports Ultrafast mode on Amazon BedrockAmazon Web Services · 9 Oct 2026
- Agentic SDLC with the Claude Code PluginPostman · 9 Oct 2026
- September 2026 Cyber Threat Landscape: Global Attacks Jump 48% as Phishing and GenAI Data Exposure RiseCheck Point Software · 9 Oct 2026
- Pollo AI turns creative ideas into campaigns with OpenAIOpenAI · 8 Oct 2026
- Empowering SMBs to do more with GeminiGoogle Cloud · 8 Oct 2026
- Innovation in Ireland: How Irish brands scale with Gemini EnterpriseGoogle Cloud · 8 Oct 2026
- Welcome to Gemini at Work 2026: Introducing the Gemini agentGoogle Cloud · 8 Oct 2026
- Cut coding agent costs by 90%: Secure LLM serving with Ray + vLLM on AnyscaleAnyscale · 8 Oct 2026
- Building a Multi-Channel AI Agent with Twilio Conversations and eveTwilio · 8 Oct 2026
- ReLearn at CVPR 2026: can AI still learn from humans?Lambda · 8 Oct 2026
- Gemini Never Left the Sandbox. The Sandbox Had a Door.Tigera · 8 Oct 2026
- Your coding agents got faster. Did your controls?CloudBees · 8 Oct 2026
Showing the newest 60 of 222. The feed archive lists every story by month.
What practitioners are discussing
Stories say what companies announced. These threads are where people who work with Generative AI compare notes.
New
RAG vs fine-tuning vs long context: what is working for your use case?
Share the use case, the approach you chose and the results.
New
How are you securing employee use of generative AI tools?
Blocking, allow-listing, DLP, enterprise licences, policy and training: what combination is working in practice?
New
What does your generative AI cost per useful answer look like?
Token spend, retrieval cost, caching, model routing. How are you measuring and reducing cost without hurting quality?
Companies in these stories
The active companies with the most Generative AI stories in the last 90 days. It counts news coverage, not quality, market share or fit for your needs.
Related categories
Where Generative AI sits among the categories Techarda covers. Each hub shows who is active, and each guide sets out how to evaluate vendors.
Terms to know
Plain definitions of terms that come up in Generative AI stories, from the Techarda glossary.
Frequently asked questions
How is generative AI different from traditional machine learning?
Traditional machine learning usually predicts or classifies, such as scoring a transaction for fraud. Generative AI produces new content, such as a draft reply or a code change. Many products now combine both.
Should we use a hosted model or run our own?
Hosted models are the quickest way to start and need no GPU capacity of your own. Running open-weight models yourself gives more control over data, versioning and cost at scale, but you take on serving, patching and capacity planning. Many teams start hosted and move specific workloads later.
How do we stop staff pasting sensitive data into AI tools?
Give people an approved tool under an enterprise agreement that covers retention and training use, block or monitor unapproved ones, and write a short policy on what may be shared. A sanctioned option works better than a ban on its own.
What should we ask a vendor about its AI features?
Which models it uses and where they run, whether your data is used for training, how prompts and outputs are logged, what happens when the underlying model changes, and how usage is priced.
Stories are filed under topics automatically from their headline and summary, so the odd one may sit in the wrong place. Spotted one, or a topic we should add? Email engage@techarda.com. All topics.