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Learn how the AWS Generative AI Innovation Center Partner Agent Factory and Anchor Browser built AI agents that autonomously pay for x402-protected web resources and APIs.
Learn how Orange Business used LangChain and LangSmith to build Live Intelligence, a governed AI platform for agents, evals, observability, and production scale.
Here is Gemini 4 Argon explained in 3 points that get increasingly niche. First is a strong understanding of 3D space and visuals. A lady took pictures of her desk, and Argon turned them into a playable racing game with Blender MCP and Three.js.
GitHub’s context-aware secret classifier improves detection quality, raises weighted acceptance by 11.5%, and reduces median scan execution time by 86%. The post GitHub’s model for secret detection: Finding more secrets with less noise appeared first on The GitHub Blog.
Your best home services content already exists: in repair photos, failed parts, customer reviews, technician notes, weather events, and changes in branch capacity. Any one of those could help a homeowner decide whom to call. Most of it is not ready to publish.
The problem with giving everyone in a company access to AI tools is that people will use them in unexpected ways. And that fact is steadily seeding fears that companies now have new, gaping security holes in their systems or, at least, will soon.
The monthly cloud bill lands and the AI line item has tripled. Finance asks why. Security asks who. The platform team pulls up the usage dashboard they turned on three …
Also, Meta’s Muse Wonder, Claude Haiku 5.5, OpenAI’s Ultrafast tier, Mistral Large 4, Reflection Beam, and new rivals to Jev.
Gemini 4 Argon is now in Roboflow. Use it in Auto Label to label hundreds of images for training a fast, specialized model, or add it to a Workflow as the step that reads text and answers questions about what your cameras capture.
Natural language is collapsing the expertise required to operate professional computing engines, but it does not collapse the expertise required to warrant their results. Education therefore has to move from teaching operation alone toward teaching specification, verification, and recovery.
The big editors all have an agent built in now, and each of them has added a way to look at what the agent did before you keep it. I build Reado, the IDE for reading the code your agent writes, so I have an obvious interest in this question.
Most multi-agent demos I see have the same weakness: the agents agree with each other. One writes, another says "great point", a third summarises. Nothing gets tested. So I built the opposite, as an experiment. It's called Reagent Lab.
Gemini 4 Argon just dropped, and n8n is one of the first companies to use and integrate the model. Check out how you can use it with your existing n8n workflows and how to build new ones. Here are the benchmarks Google published:Benchmarks reported by Google. Source: Gemini 4
Today we’re launching Deep Agents deploy in beta. Deep Agents deploy is the fastest way to deploy a model agnostic, open source agent harness in a production ready way.
We’re giving developers, enterprises, scientists, and consumers access to Gemini 4 Argon, our latest frontier model.
ShipStation Global's engineering team built Deckhand, an AI IT help desk agent for Slack on Amazon Bedrock AgentCore. It answers routine IT questions from a curated knowledge base, reads error-message screenshots, and files Jira tickets.
I started Flama in 2018 to take a trained model to a production API with as little ceremony as possible. With 2.0 LLMs became first-class alongside classic models, and together with Miguel Durán-Olivencia we've now written up the whole architecture in an 83-page paper on arXiv (2608.18733).
Build, secure, and scale your org with the Winter '27 release's most impactful dev and security features.
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