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You can now run LLMs for software development on consumer-grade PCs. But we’re still a ways off from having Claude at home.
Success with agents starts with embedding them in workflows, not letting them run amok. Context, skills, models, and tools are key. There’s more.
Why the rush to replace developers with LLMs is leaving companies with brittle systems, runaway cloud bills, and a painful rebuild.
Plan mode restricts Gemini CLI to working with read-only tools and prevents it from modifying any files except its own internal plans.
Tracy, an open-source Kotlin library, helps developers trace, monitor, and evaluate AI-powered features directly from their Kotlin or Java projects, JetBrains said.
If we want AI-assisted development to actually scale, we need to confront the barriers to deploying AI-generated code safely and reliably.
AgentCore delivers an enterprise-grade infrastructure and operations layer for deploying and managing AI agents at scale, with a few wrinkles.
The 120B parameter model aims to improve compute efficiency and accuracy for complex multi-agent workloads such as software development and cybersecurity triage.
By integrating Quotient’s evaluation and reinforcement‑learning tech, Databricks hopes to address a growing CIO challenge: ensuring AI agents behave consistently, explainably, and safely in real‑world operations.
So-called ‘safety’ guardrails in AI models are not making us safer. In fact they’re downright dangerous.