A new form of vendor lock-in is here. And it’s not proprietary languages or rigid enterprise software suites — it’s something more fundamental. It’s the very thing that writes the code.
JetBrains Research found that 74% of developers worldwide use AI tools. Claude Code, available only since May 2025, is now the most popular AI coding tool, followed by Gemini Code Assist and GitHub Copilot, according to Jellyfish’s 2026 State of Engineering Management Report.
The latter study also found that 91% of developers say their productivity has increased in the past 12 months. As coding output expectations are rewritten daily, the engineering world is becoming heavily reliant on paid external AI services.
Gartner predicts that by 2028 spending on AI coding tokens could exceed developer salaries. Yet, tokenmaxxing while vibe coding through a vendor’s cloud-based API feels like a far cry from the open foundations of free programming languages and open models, which many of today’s AI platforms now
Microsoft has been pushing hard to make Visual Studio Code a major way to consume its AI services, mostly in the form of GitHub Copilot. GitHub Copilot’s deep integration with VS Code brings many conveniences — inline autocomplete, for instance — but it’s frustrating for those, like me, who would rather use another model provider, or even a locally hosted LLM, for those functions.
Visual Studio Code 1.122 introduced a new feature, “Use BYOK [Bring Your Own Key] without a GitHub sign-in,” that allows you to “use chat, tools, and MCP servers in air-gapped or restricted environments where GitHub sign-in isn’t possible.” More importantly, it “enables fully offline workflows with local models like Ollama.”
In other words, you can now use locally hosted LLMs for chat, tools, and Model Context Protocol servers inside Visual Studio Code. The one thing you still can’t do is use a local LLM for inline and next-edit suggestions — at least, not without additional tooling.
Choosing a model for BYOK
Learn about the concept of loops to power your coding agents.
The post How to Create Powerful Loops in Claude Code appeared first on Towards Data Science.
Learn how to apply coding agents to verify work in your browser.
The post How to Use Claude Code in Your Browser appeared first on Towards Data Science.
Cisco Foundation AI has open-sourced FAPO (Fully Automated Prompt Optimization), a Claude Code-driven system that autonomously optimizes multi-step LLM pipelines from baseline prompts to target accuracy. FAPO evaluates a chain, attributes failures at the step level, proposes variants across prompt, parameter, and chain-structure levels, and validates each through an independent reviewer. In Cisco's evaluation, it beat GEPA on 15 of 18 model-benchmark comparisons. Here's how the optimization loop works and how to run it.
The post Cisco AI Introduces FAPO: Pipeline-Aware Prompt Optimization With Step-Level Failure Attribution and Claude Code Orchestration appeared first on MarkTechPost.
Claude Code's Artifacts enhance collaborative development, fostering dynamic, secure internal app sharing, potentially boosting enterprise productivity.
The post Claude Code launches Artifacts for sharing interactive apps and dashboards appeared first on Crypto Briefing.
Arbor's superior performance in AI optimization could accelerate advancements in machine learning, influencing future AI development strategies.
The post Arbor framework outperforms Claude Code and Codex by 2.5x in AI optimization benchmarks appeared first on Crypto Briefing.
Nvidia's ENPIRE hands an entire robot fleet to coding agents like Codex and Claude Code, letting them write training code, test it on real hardware, and improve without a human watching.
