llama.cpp has released an update to its Ling 3.0 parser to correctly honor json_schema constraints in chat interactions. Previously, the parser built grammars only for tool calls, ignoring input schemas and leaving response formats unconstrained. The new implementation adds an eager response-format grammar path with higher precedence than tools, requiring a think block before JSON when thinking is enabled and disallowing trailing prose. This fix addresses issue #29652 and improves reliability for structured data extraction in local inference pipelines.
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llama.cpp Releases · June 12, 2026 · Same story
llama.cpp Releases · September 20, 2026 · Same story
This release tackles the notorious memory hunger of long-context inference for Qwen4-exp models by halving indexer score memory. The optimization works by computing head scores in place rather than materializing separate tensors, a change that significantly reduces VRAM pressure during heavy workloads. Beyond memory efficiency, b11372 expands hardware coverage with CUDA 13 support and Vulkan tiling for the lightning indexer. It also adds ROCm 10.0 binaries, keeping AMD users in step with NVIDIA's latest driver ecosystem. The result is a leaner runtime that handles extended contexts without hitting out-of-memory errors as quickly.
This release significantly tightens llama.cpp’s integration with Intel’s OpenVINO backend, specifically targeting Mixture of Experts (MoE) models like Qwen3.5 and Gemma-4. By fusing MoE routing and GDN normalization operations, prefill throughput on Arc GPUs jumps from 66 to over 1,600 tokens per second, effectively removing a major bottleneck for local inference on Intel hardware. The update also fixes critical stateful execution bugs that previously caused crashes or incorrect axis handling during decoding. This makes OpenVINO a far more viable option for running complex MoE architectures on consumer-grade Intel GPUs without relying on NVIDIA CUDA.
This release resolves a critical crash in Mamba SSM inference when batch cells aren't contiguous. By gathering recurrent states into a single reserve that covers every split, the engine avoids illegal graph reallocations under strict scheduling modes. It’s a quiet but essential fix for anyone running non-standard sequence lengths or complex batching logic with stateful models.
This release stabilizes the core session management of Claude Code, specifically targeting the fragile state of resumed conversations where context or thinking traces were previously lost. It also patches critical reliability issues in the Model Context Protocol (MCP) integration, ensuring tool calls don't duplicate or hang indefinitely when remote servers misbehave. The addition of $.ui.selection() for mods and better GitHub CLI handling in cloud sessions shows a focus on developer workflow friction rather than new capabilities. These are necessary maintenance updates that make the tool more robust for heavy daily use.
This release is a classic maintenance patch for Claude Code, focusing on stabilizing the terminal interface and tightening security rules. It fixes critical bugs where deny/ask rules were bypassed in nested shell commands or via symlinks, ensuring sandbox policies actually hold. The update also resolves numerous UI freezes caused by malformed HTML tags and plugin rendering errors, making the agent feel less brittle during complex coding sessions.
© GitHub ChangelogGitHub finally exposes Copilot code review to external automation via REST and GraphQL APIs, moving it from a manual UI action to an integrable pipeline step. This allows developers to trigger reviews directly from scripts or internal tools rather than relying on the web interface. Simultaneously, the default effort level shifts to Balanced, striking a middle ground between speed and depth for most repositories. While Lite remains available for those prioritizing raw throughput, the API access is the real win here, enabling true CI/CD integration for automated code quality checks.