Fetching from the wire…
Infra2026-09-17 · source-backed
llama.cpp PR #29008, merged September 17, adds <|User|> and <|Assistant|> as message delimiters to the DeepSeek V3.2 parser, which an earlier PR had skipped when adding them everywhere else. Because V3.2/V4/V4.1 use SWA layers and the KV cache can't roll back to a position without a checkpoint, llama-server only placed checkpoints at end - 4 and end - 4 - n_ubatch, so any divergence before the tail re-prefilled from token 0. On DeepSeek-V4.1-Flash on one RTX A6000 with a 13,167-token agent prompt, a new user question went from 510 prefilled tokens in 13.8s to 16 tokens in 0.8s; editing the second of three user turns went from a full re-prefill to 79 tokens in 5.0s. Two missing strings, a 17x latency difference.
Each link below shares sources, entities, or timing with this story.
RTK has almost 80,000 GitHub stars and a simple promise. It sits between your coding agent and the shell, trims noisy command output before the model reads it, and claims 60-90% savings. Quesma ran it on Terminal-Bench 2.1 and found costs went up. With RTK on, average cost per...
DeepSeek posted a community notice: once V4.1 Flash launches around September 10 Beijing time, and until a V4.1 Pro exists, every V4 Pro request routes to V4.1 Flash and bills at Flash unit pricing. The stated reason is that Flash has surpassed Pro on performance, cost, speed...
DeepSeek released V4 on April 24 and the numbers demand attention. V4-Pro is 1.6 trillion parameters total with 49 billion active, MIT-licensed, native 1M-token context. It scores 80.6% on SWE-bench Verified, putting it within 0.2 points of Claude Opus 4.6. On Terminal-Bench 2...
Enclave published results September 16 in which the model gained code execution on all 11 targets including Grafana, Jenkins and Nextcloud, while all four correctly patched targets held. It found the six planned attack routes plus five unexpected ones. Total spend was $5.14 in...
DeepSeek dropped V4 in mid-June as an open-weight model with a 1-million-token context window, priced at $1.74 per million input tokens, posting near-parity with GPT-5.4 on math and Q&A benchmarks (MindStudio). That's the headline number. The architecture underneath is more in...
Latent Space's September 12 roundup collects reaction to the causal encoder-decoder release: 763B total parameters, 8B active at prefill and 16B at decode, 1M context, KV cache cut to about 890 bytes per token, roughly one eighth of V4 Pro. Sebastian Raschka argues the archite...
MindPattern daily
One email a day at 7 AM. Sources and a take on every story. Unsubscribe anytime.