GitHub cuts AI deals with Google, Anthropic

Original Article ↗ Hacker News Discussion ↗

GitHub Copilot is adding support for Anthropic’s Claude and Google’s Gemini models alongside OpenAI, signaling a shift toward multi-model AI tooling and giving Microsoft leverage and regulatory cover in its deep partnership with OpenAI. Commenters weigh whether this will meaningfully improve code quality and developer productivity, sharing mixed experiences with LLM-assisted coding—from game-changing boilerplate generation and code exploration to time-wasting hallucinations and subtle bugs that demand close review. The move also rekindles concerns about training on open source code, the commoditization of foundation models, and whether current LLM architectures can truly progress beyond “smart autocomplete” for more complex software engineering tasks.

New Copilot capabilities

  • GitHub Copilot will let users choose between multiple LLMs (OpenAI, Anthropic/Claude via AWS Bedrock, Google/Gemini; Llama/Mistral mentioned as future/partial options).
  • Multi‑model support is mostly for chat / code editing; impact on inline autocomplete speed is unclear.
  • Copilot is expanding IDE support (e.g., Xcode) and integrating with external sources like Stack Overflow.

Motives and strategy

  • Many see this as Microsoft:
    • Hedging against over‑dependence on OpenAI after governance drama.
    • Turning Copilot into a model‑agnostic platform and “commoditizing the complement” (models) to keep strategic power at the IDE/DevOps layer.
    • Potentially helping antitrust optics by not being tied to a single provider.

Model comparisons and tool ecosystem

  • Several commenters prefer Claude 3.5 Sonnet for code quality and reasoning; others find GPT‑4o/o1 better for some tasks, especially with web tools.
  • ChatGPT app is praised for polish (code interpreter, search, voice, custom GPTs), while Claude is praised for raw coding ability and artifacts.
  • Many alternative frontends and IDE tools mentioned (Cursor, Aider, Cody, Continue, local LLM frontends), often valued for multi‑model support and deep project context.

Productivity vs. reliability

  • Strong split:
    • Some report 2–5× productivity gains, using LLMs for boilerplate, one‑off scripts, refactors, and cross‑lib “glue”.
    • Others see little or negative net gain due to hallucinated APIs, subtle bugs, repetitive error cycles, and time spent verifying.
  • Common “sweet spots”: bash/scripts, SQL, poorly documented libs, initial scaffolding, and test boilerplate.
  • Common failure modes: short prompts, complex or novel problems, large refactors, domain‑specific logic, and over‑trusting generated code.

Open source, licensing, and GitHub data

  • Strong concern that Copilot and other tools are trained on OSS (including copyleft like GPL/AGPL) without attribution or compensation; some call this IP “laundering”.
  • Others argue it’s analogous to humans learning from code; legality and “derivative work” status are seen as unsettled.
  • Some developers are considering or executing migrations away from GitHub, though network effects and convenience are high.

Perceptions of AI progress

  • Many see rapid capability gains; others perceive diminishing returns and predict an eventual “AI winter” or bubble correction.
  • Debate over whether LLMs show “intelligence” or only powerful pattern prediction; standardized test performance is contested as a metric.
  • Consensus that LLMs are already changing how people search, learn APIs, and approach coding—even if they’re far from trustworthy autonomous programmers.