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Shapelearn Qwen 3.8 27B (13.1 GB VRAM)

ByteShape released full ShapeLearn GGUF quants of Qwen 3.8 27B; its GPU-5 IQ4_XS reaches 99.63% of BF16 quality at 13.1 GB VRAM.

ByteShape released its full ShapeLearn GGUF quantization set for Qwen 3.8 27B (base model released August 14, 2026), following the earlier ShapeLearn-Lite quants published four days after launch. Five quants spanning IQ2_XXS 2.56bpw to IQ4_XS 3.84bpw were benchmarked on six GPUs against Unsloth Dynamic v3, ISTA-DASLab, Bartowski, and AtomicChat; GPU-5 reaches 99.63% of the BF16 aggregate score at roughly 90 tok/s on RTX Pro 6000 and RTX 5090. Each GGUF bundles an MTP draft head, and a separate 1.1 GB DFlash2 draft model enables faster text-only speculative decoding via llama.cpp.

Hacker News · AIupdated · 5h agofirst · 12h agoAI tools & infra 3 sourcesHN 43↑ · 4 comments

Inside ZCode: Silently Uploading Your Git History to the Cloud

Zhipu's ZCode AI coding app silently packages workspaces, including full Git history, encrypts them, and uploads to Aliyun OSS.

A blogger investigating a 700MB ~/.zcode directory found ZCode, Zhipu's AI coding desktop app, packages the entire workspace, including a 313MB encrypted baseline snapshot of a 345MB commercial project, with 564 recorded failed upload attempts. Reverse-engineering app.asar revealed the client requests credentials from zcode.z.ai, encrypts archives with AES-256-CTR, wraps the key with a server-delivered RSA-OAEP public key, and posts directly to Aliyun OSS; only Zhipu's backend holds the private key. An analysis of a 42,411-file snapshot showed .git data made up 86.6% of the payload, exposing deleted secrets, unpushed branch names, and internal hostnames.

Hacker News · AIupdated · 3h agofirst · 7h agoAI safety & security 2 sourcesHN 42↑ · 4 comments

Bend – A language that blocks AI mistakes via proof and runs on GPUs

Bend is a new programming language combining C-speed compilation, GPU parallelism, and Lean-style proofs that stop AI agents from merging code violating declared laws.

Bend is a programming language whose type checker acts as a proof checker, letting developers declare invariants in LAWS.bend that AI coding agents must prove with PROOF.bend before committing. It compiles to native code running near C speed on a single core and up to 100x faster across GPU cores, with automatic parallelism requiring no threads or locks. The project publishes two papers, BendTT (an affine dependent type theory) and BendRT (a parallel CPU/GPU runtime), and integrates with AGENTS.md workflows for AI-driven 'vibe coding'.

Hacker News · AI · 17h agoAI tools & infraHN 153↑ · 68 comments

How to Write with an LLM

Thomas Ptacek publishes a method for LLM-assisted writing: never adopt suggested words and forbid model encouragement to preserve author voice.

Thomas Ptacek outlines two rules for using LLMs as copyeditors: never use a single word a model suggests, and forbid encouragement that reinforces first-draft impulses. He recommends running model passes to flag passive voice, repetition, and misplaced paragraphs, and comparing rewrites with a fresh-context model to avoid bias. He also recommends the book 'Style: Lessons in Clarity and Grace' and mentions building a small tool to manage context-free copyediting comparisons.

Hacker News · AIupdated · 14h agofirst · 16h agoAI industry 2 sourcesHN 46↑ · 31 comments

LLM Classification Is Feature Engineering

Argues LLM classifiers should feed a downstream logistic regression, yielding calibration, threshold control, and principled use of structured covariates.

The post contends that LLM-as-classifier setups suffer from poorly calibrated hard labels, opaque use of prompt context and structured data, and weak interpretability. Wrapping the LLM verdict as a feature in a logistic regression restores calibrated probabilities, precision–recall threshold control, and the ability to incorporate additional covariates. Further gains can come from more training data, richer features such as log probabilities and subverdicts, and swapping in downstream models like xgboost or neural networks. An irony detection test case illustrates the approach.

Hacker News · AI · 22h agoAI researchHN 45↑ · 10 comments