AI Coding Live News

Hangzhou-based AI-for-science company Deep Principle released Hydrogen-1.0 on November 2, its first general-purpose scientific model. It post-trains Zhipu's GLM-5.3 with SFT and RL on chemistry, materials and life-science data plus its own lab records and discovery trajectories, and ships about 753B parameters of BF16 weights on Hugging Face under the MIT license. By the vendor's own evaluation it scores 38.3% on FrontierScience-Research versus 33.3% for GPT-6 Astra; none of the results have been independently reproduced yet.

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In an Oct 26 incident report on its blog, Cloudflare said that between Aug 27 and Sep 9, 155 responses from GLM-5.3 on Workers AI (@cf/zai-org/glm-5.3) to prompts longer than 681,280 tokens contained data from other customers' recent requests, all returned with HTTP 200. The cause was a prefill KV cache pool smaller than the 1,048,576-token context window, combined with an SGLang bug in disaggregated prefill/decode mode. Cloudflare found no evidence of deliberate exploitation, has fixed it, and recommends operators upgrade to SGLang v0.5.22 or later.

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On October 21 Letta released FrontierMemory 1.0. Instead of quizzing agents on facts such as birthdays or hobbies, it gives an agent a set of past trajectories, has it write memory or skills, and scores that memory by how many coding and knowledge-work tasks a task agent then solves. Version 1.0 has 15 tasks adapted from public benchmarks such as DeepSWE, SkillsBench and APEX-Agents. On the model board (memory written in Letta Dream, tasks run in Letta Code) Opus 5.5 and GLM-5.3 both score 73.3% and Grok 4.7 scores 33.3%; on the harness board Letta's own Letta Dream ranks first. All numbers are Letta's own.

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On October 19 Cloudflare released a preview of Clairic, a "System One extraction model". Instead of generating tokens, it uses one parallel forward pass to extract, compute, estimate and forecast typed values from text or up to 4 images, with confidence, character spans and a null probability for each answer, returning null when a value is missing. It is live on Workers AI as @cf/cloudflare/clairic; the docs list a 64K context, vision support and $0.10 per million input tokens with no output charge. In Cloudflare's example, a Clairic+Clef+GLM-5.3 cascade ran about 3.2x faster and about 49% cheaper than GLM-5.3 alone (company figures). Open weights are planned later.

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The SGLang team released v0.5.22 featuring 950 PRs from 245 contributors: DeepSeek-V4.1 delivers up to 2.3x more throughput per GPU on NVIDIA H200 and 1.9x on AMD MI350X/MI355X via DSpark. The new InstantTensor loader cuts model loading time by 10x. B200 decodes Qwen3.8-Flash-Next 12% faster, while B300 speeds up GLM-5.3-Flash by 10% and yields 1.85x decode throughput for MiMo-V2.6-Pro. SGLang also adds native Apple Silicon PyTorch support and ComfyUI integration.

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vLLM and NVIDIA unveiled a unified CuTe DSL decode megakernel (~12,000 LoC authored by NVIDIA CentML's Benjamin Chislett) for Moonshot AI's frontier Kimi K3 model on NVIDIA GB300 NVL72. By fusing attention, MoE, and cross-GPU communication, the optimization cuts per-layer latency from 130 µs (23 kernels) to 69 µs (13 kernels), delivering 842 tokens/s at batch=1 with DSpark speculative decoding (1.73x faster than stock vLLM) and up to 1.8x higher P90 per-user interactivity (403.2–423 tok/s/user) on SemiAnalysis AgentX, with DeepSeek-V4.1-Flash (PR #61868) and GLM-5.3 support coming next.

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Ollama launched US- and EU-hosted Ollama Cloud with strict zero data retention and zero training, adding immediate support for DeepSeek-V4.1-Flash-1017 via one-line launch commands for Claude Code and OpenCode alongside Ollama v0.40.6. Meanwhile, SemiAnalysis revealed that GLM-5.3 was built entirely by post-training the previous GLM-5.2 base model without pretraining from scratch, achieving a 'night and day difference' in cybersecurity and agentic capabilities.

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Addressing KV cache bottlenecks in long-context LLM serving, a new production study on 8xB300 nodes serving GLM-5.3 (arXiv:2610.21500) reveals that unsharded remote KV caches inflate hit-heavy p99 TTFT to 83.32s, and resolves it via rank-sharded asynchronous LMCache loading—boosting GET bandwidth 6.3x to 10.76 GB/s and slashing p99 to 0.67s (+34% throughput). Meanwhile, Baidu PaddlePaddle open-sources Virtual Head Attention (VHA, arXiv:2610.17959), folding linear Q-Premix and Postmix into standard GQA-2 weights at inference to halve KV cache while beating MLA (+0.70%) and GQA-4 (+2.60%) on a 30B-A3B MoE and lifting 8K-128K RULER from 73.0 to 84.4.

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Advancing autonomous AI-for-AI (AI4AI) engineering, researchers from Shanghai AI Lab (InternScience) release MLExpert (arXiv:2610.22344), a 35B Mixture-of-Experts agentic model for autonomous machine learning engineering. Powered by the Kaggle-MLE Factory, hierarchical agentic SFT, and Windowed Interaction RL (WIRL), MLExpert boosts average medal rates on MLE-Bench Lite from 37.9% to 72.7%—matching frontier giants like Kimi-K3 and GLM-5.3 while generalizing to agent scaffold optimization.

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San Francisco lab Paper Instruments launched Ultramarine 1 Flash on 2026-10-14 16:02 UTC: GLM-5.3 Flash post-trained with large-scale non-verifiable RL (per-task rubric rewards) for finance, legal, consulting and operations work. Its report claims +4.1 points on average over the base across six knowledge-work benchmarks with 6.5% fewer output tokens, and 59.3 at $0.13/task on its internal PI Knowledge Work suite vs Claude Fable 5 at 57.8 / $8.19. An OpenAI-compatible API (ultramarine-1-flash) is live; the CEO says weights follow in about two weeks and post-training cost ~$300k. The headline cost comparison rests on an internal benchmark and has not been independently reproduced.

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On 2026-10-12 Z.ai introduced GLM-Legal, built on GLM-5.3-Flash with legal mid-training and RL across tens of thousands of legal environments, co-trained with its agent harness. Vendor-reported: 19.2% Harvey LAB all-pass (base 6.7%); LegalBench and PLawBench claimed at parity without scores. No open weights; API by application; AutoClaw support coming.

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On Oct 9 Prime Intellect shipped a Rust rewrite of Prime Agent, its open-source coding agent harness. A root orchestrator ran 2,209 sub-agents across 10,000+ Prime Sandboxes and ~228.7B GLM-5.3 tokens over two weeks, with differential TUI, harness and protocol parity tests gating every merge. Vendor-measured cold time-to-type dropped from 737.8ms to 55.8ms and post-startup memory from 607MB to 106MB; Windows (beta) and Homebrew installs were added.

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