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· Past edition · 6 stories
Retrospective edition reconstructed from later captures. Paper versions may include later updates; coverage labels describe the evidence used.
Infrastructure & deployment
AgSpec targets two weak points in retrieval-based speculative decoding for coding agents: what text can be retrieved and how long each draft should be.
Why it matters: For coding-agent pipelines, retrieval performance depends not just on finding prior text, but on whether the corpus matches what the agent emits and whether draft length responds to verification feedback.
Infrastructure & deployment
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Magnitude’s founders describe an inference engine that tunes kernels on the user’s hardware and targets local agents, reporting speed and memory gains over llama.cpp in two specified tests.
Why it matters: For local-agent users, decode speed is only one part of the decision: captured commenters also raise large-context performance, KV-cache use, concurrent sessions, hardware detection, and comparisons against MLX. The reported results are a starting point, not a universal performance verdict.