惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

S
Security Affairs
S
Schneier on Security
T
Tenable Blog
G
GRAHAM CLULEY
Latest news
Latest news
D
Darknet – Hacking Tools, Hacker News & Cyber Security
A
Arctic Wolf
I
Intezer
Cyberwarzone
Cyberwarzone
T
The Exploit Database - CXSecurity.com
T
Tailwind CSS Blog
K
Kaspersky official blog
Blog — PlanetScale
Blog — PlanetScale
C
Cyber Attacks, Cyber Crime and Cyber Security
T
Threat Research - Cisco Blogs
爱范儿
爱范儿
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
博客园 - 叶小钗
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Recent Commits to openclaw:main
Recent Commits to openclaw:main
P
Palo Alto Networks Blog
WordPress大学
WordPress大学
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园 - 司徒正美
The Cloudflare Blog
Help Net Security
Help Net Security
罗磊的独立博客
博客园 - 聂微东
Jina AI
Jina AI
Project Zero
Project Zero
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
L
LINUX DO - 最新话题
V
V2EX
人人都是产品经理
人人都是产品经理
美团技术团队
博客园 - 【当耐特】
Spread Privacy
Spread Privacy
J
Java Code Geeks
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Security Latest
Security Latest
The Last Watchdog
The Last Watchdog
Stack Overflow Blog
Stack Overflow Blog
雷峰网
雷峰网
S
Securelist
Forbes - Security
Forbes - Security
博客园 - 三生石上(FineUI控件)
Microsoft Azure Blog
Microsoft Azure Blog
P
Privacy International News Feed
宝玉的分享
宝玉的分享
C
CERT Recently Published Vulnerability Notes

Hacker News - Newest: "LLM"

GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - AronDaron/dataset-generator: No-code desktop app for generating high-quality synthetic datasets to fine-tune LLMs — plan-then-execute pipeline, LLM-as-judge, HuggingFace upload. GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace GitHub - Nyquest-ai/nyquest-rust-fullstack-pub: Nyquest — Semantic Compression Proxy for LLMs. 350+ rules, local LLM stage, 15-75% token savings. Full Rust stack. GitHub - TheoV823/mneme: Enforce architectural decisions in AI-assisted development. GitHub - klemenvod/TokenBrawl: A 1v1 Bomberman-style game where two LLM agents play autonomously against each other. No human plays — you watch the AIs fight. Each agent receives a text description of the board state, reasons about it, and outputs a move as JSON. The game engine executes it. Introducing the Common AI Provider: LLM and AI Agent Support for Apache Airflow Power Circuit AI: Designing Power Electronic Circuits for Motor Drives with Generative Artificial Intelligence Ask HN: How to program with IDE and LLM on CPU locally? Show HN: Agent-cache – Multi-tier LLM/tool/session caching for Valkey and Redis Bonsai 1-bit WebGPU - a Hugging Face Space by webml-community The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows Ask HN: Simple tooling for local LLM code critique without IDE integration? Can a General LLM Diagnose a DICOM Slice? A 10-Case Public Benchmark Charts-of-Thought: Enhancing LLM Visualization Literacy (PDF, 2026) GitHub - Mesh-LLM/mesh-llm: Distributed AI/LLM for the people. Share compute privately or publicly to power your agents and chat. GitHub - seamus-brady/springdrift: A persistent runtime for long-lived LLM agents Writing an LLM from scratch, part 32k -- Interventions: training a better model locally with gradient accumulation Ask HN: Which LLM model and agentic CLI are you using for local development? GitHub - wayneColt/modelcascade: Route local. Escalate smart. Never overspend. Open-source multi-model cascade routing for autonomous agents. LLM pricing is 100x harder than you think GitHub - asakin/llm-primer: Pre-warmed Claude Code sessions in tmux. No startup wait. GitHub - EggerMarc/chat-rs: A multi-provider LLM framework for Rust. GitHub - SynapseKit/SynapseKit: Minimal, async-first Python framework for production LLM apps- 2 hard deps, no magic, no SaaS. A Claude Skill that Makes LLM Paragraphs More Bearable Does Gas Town 'steal' usage from users' LLM credits & paid services to improve itself? What's Claude Code Actually Doing? Open the Black Box with the Arthur Engine Milla Jovovich's New Open Source LLM Memory App and the Dark Code Problem Your intuition of LLM token usage might be wrong Show HN: Bloomberg Terminal for LLM ops – free and open source GitHub - 0xchamin/mcptube: Transform YouTube videos into a compounding knowledge base with transcripts, vision analysis, and agentic search. Works as an MCP server for Claude, Copilot & more. Show HN: Open KB: Open LLM Knowledge Base Your LLM is a compiler, not a runtime GitHub - sapountzis/Unslop: A Web Feed That Deserves You crates.io: Rust Package Registry Beyond Karpathy's LLM-Wiki: The Necessity of Cognitive Governance GitHub - amitshekhariitbhu/llm-internals: Learn LLM internals step by step - from tokenization to attention to inference optimization. GitHub - parallem-ai/parallem: An expressive library for running agents with the Batch API. GitHub - stfurkan/pi-llm LLM-Wiki Show HN: Formal – Formal verification for AI-generated code using Lean 4 LRTS – Regression testing for LLM prompts (open source, local-first) LLM Wiki Skill: Build a Second Brain with Claude Code and Obsidian I built an LLM Wiki and RAG solution: here's a demo for a security KB The biggest advance in AI since the LLM Predict-Rlm: The LLM Runtime That Lets Models Write Their Own Control Flow the-synthetic-library/the-synthetic-mind at main · joshferrer1/the-synthetic-library GitHub - yisding/reviewwiggum GitHub - Donnyb369/mcp-spine: Context Minifier & State Guard — Local-first MCP middleware proxy GitHub - Beledarian/wgpu-llm: A from-scratch LLM inference engine that uses wgpu (the cross-platform WebGPU implementation) to dispatch WGSL compute shaders for every math operation a Transformer needs. No CUDA. No Python. No massive framework dependencies. Just Rust, raw shaders, and your GPU. GitHub - anitiue/Hindsight: An experience-driven self-improvement framework for LLM agents — 基于经验的 LLM Agent 自我改进框架 GitHub - stef41/lmscan: 🔍 Detect AI-generated text and fingerprint which LLM wrote it. Open-source GPTZero alternative. Zero dependencies, works offline. GitHub - alainnothere/AmdPerformanceTesting: Amd Performance Testing Ask HN: Is a purely Markdown-based CRM a terrible idea? Optimized for LLM agents Context Engineering - LLM Memory and Retrieval for AI Agents | Weaviate little_helper_tui/letter.md at main · sleepyeldrazi/little_helper_tui GitHub - EvanZhouDev/umr: The Unified Model Registry for all your local AI apps. GitHub - JordanCT/VigIA-Orchestrator Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain A Taxonomy of RL Environments for LLM Agents Llama LLM Network Feture GitHub - genedeng-ca/ai-mac-migration: AI-powered Mac-to-Mac migration tool - replace Apple Migration Assistant with intelligent, selective transfer using local LLMs GitHub - lunargate-ai/gateway: High-performance self-hosted AI gateway (OpenAI-compatible) with routing, retries, and streaming GitHub - AuthBits/webmcp: A lightweight, prompt-driven MCP web research server for high-quality LLM powered information extraction. Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception High-Stakes Personalization: Rethinking LLM Customization for Individual Investor Decision-Making From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents HUOZIIME: An On-Device LLM-enhanced Input Method for Deep Personalization TIDE: Token-Informed Depth Execution for Per-Token Early Exit in LLM Inference Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors, Three Backends, and Three Browsers LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users
GitHub - mentasystems/gox: Strict static analyzer for Go — designed for LLM-written code. Zero external linter dependencies.
kidandcat · 2026-05-13 · via Hacker News - Newest: "LLM"

ci release license

Strict static analyzer for Go. Zero external dependencies — every rule is implemented from scratch on top of go/ast, go/types, and the go list command.

demo

The bug above (transfer("o-42", "u-7") with parameters declared as userID, orderID string) compiles, passes every test, and ships to production. No major Go linter today catches this class. gox does, by demanding inline /* paramName */ comments at the call site whenever two adjacent arguments share a type.

The goal: catch the classes of bugs an LLM writing Go without supervision is most likely to introduce. Be loud, be opinionated, fail closed.

Status: experimental. Built as a tool for using Claude Code, Grok Build, and similar agents to write Go without leaving silent bugs behind. Used daily, but the rule set is still evolving — expect breaking changes to annotation syntax until v1.0.

Install

go install github.com/mentasystems/gox/cmd/gox@latest

Use

gox check ./...        # analyze; exit 1 on any issue
gox list               # list registered analyzers
gox explain <rule>     # print the rule's reference markdown
gox build [args]       # gox check && go build
gox test  [args]       # gox check && go test

Rules

Analyzer What it catches
errcheck error return values dropped silently
shadow := re-declaring an outer variable (except ok)
forcetypeassert x := v.(T) without the comma-ok form
namedargs call sites passing 2+ args of the same basic type without /* paramName */ comments (user code only — stdlib calls exempt)
exhaustive non-exhaustive switch on iota enums or sealed interfaces
noglobals mutable package-level var declarations
banany any / interface{} in declarations without justification
bodyclose *http.Response.Body left unclosed
contextcheck context.Background()/TODO() inside a function that already receives a context.Context
goroutine go f() without a visible *errgroup.Group, sync.WaitGroup, or context.CancelFunc
errorlint == / type-assert / %s on errors instead of errors.Is / errors.As / %w
httptimeout HTTP shortcut calls or http.Client literals with no Timeout set

Annotations

Every rule has a single opt-out marker. Annotations must include a reason after the colon — empty reasons are ignored.

Comment Effect
// safe-ignore: <why> Suppress errcheck, forcetypeassert, bodyclose, contextcheck on the same line
// global-ok: <why> Allow a package-level var (noglobals)
// any-ok: <why> Allow any / interface{} (banany)
// goroutine-ok: <why> Allow a fire-and-forget go statement (goroutine)
// exhaustive-ok: <why> Accept a default: case as covering missing variants (exhaustive)
// timeout-ok: <why> Allow an HTTP call or http.Client literal without a Timeout (httptimeout)

Rule reference

The compiled gox binary ships every rule's reference page embedded as markdown. Print one to stdout:

The same content is available as a JSON envelope for agent consumption:

gox explain bodyclose --json
# { "rule": "bodyclose", "doc": "...", "explanation": "..." }

Each reference page covers what the rule catches, a bad / good example, the opt-out syntax, and the analyzer's known limitations. The reference is version-pinned to the binary you have installed — when a rule grows a new edge case, gox explain reflects it without depending on an external docs site.

Claude Code / Grok Build integration

gox install claude
gox install grok

gox install claude writes ~/.claude/gox-hook.sh and registers it as a Stop hook in ~/.claude/settings.json (timeout 30s). It is idempotent and migrates legacy PostToolUse registrations automatically. Preserves every other key in the file.

gox install grok writes ~/.grok/hooks/gox-hook.sh and registers the same Stop hook in ~/.grok/hooks/gox.json (native Grok hook file). Multiple *.json files under ~/.grok/hooks/ are merged; the command only touches the Stop array for our entry and leaves other events or user content intact.

When the agent finishes a turn, the hook scans the current git repo for changed .go files (unstaged, staged, and untracked) and runs gox check once per affected package. If issues are found, the hook returns a {"decision":"block","reason":"..."} payload. Claude surfaces it on the next turn; Grok records it as a hook annotation in the scrollback (and, for Stop hooks, the same shape is supported for feeding issues back).

The hook runs once per turn (on Stop) rather than on every edit. Earlier versions used PostToolUse on each Edit/Write, which was too slow on large packages.

  • Claude Code re-reads settings.json when /hooks is opened or the app restarts.
  • Grok re-reads ~/.grok/hooks/*.json at session start and on l (reload) inside the hooks modal (Ctrl+L or /hooks).

The hook resolves gox via $GOX_BIN if set, otherwise ~/go/bin/gox. Run go install github.com/mentasystems/gox/cmd/gox@latest to ensure it is present. Because Grok also reads ~/.claude/settings.json for compatibility, gox install claude works for Grok users too; the native grok target is recommended when you only use Grok Build.

Performance

A pure-Go implementation with no runtime overhead from external linters. On a 442-package monorepo (~1800 .go files):

Time
Cold run (no cache) ~9.4s
Warm run (full cache hit) ~2.6s

The cache is per-package, keyed by file mtime+size and the analyzer set hash. It lives under $XDG_CACHE_HOME/gox/v2 (or ~/.cache/gox/v2). Pass --no-cache to disable.

Output cap

gox check prints at most 100 issues by default, then a one-line summary of how many were hidden. This keeps the output bounded for context-limited consumers — notably the Claude Code Stop hook, which pipes gox check straight back into the model. Raise or lift the cap with --max-issues=N (0 = unlimited) or the GOX_MAX_ISSUES env var. Issues are sorted by file:line, so the truncation is deterministic.

Generated code

Files marked with the standard Go marker // Code generated <generator> DO NOT EDIT. near the top are skipped automatically. This covers protoc-gen-go, yo, mockgen, and most common generators.

How is this different from staticcheck / golangci-lint / revive?

Short answer: every existing Go linter is a detector. gox is a gate.

The existing tools surface warnings and let humans decide. That works when the human is the one writing the code. When an LLM is writing the code at the speed of a few tokens per second and no one is reviewing line-by-line, "surface a warning" is the wrong default — the warning will be ignored unless something downstream refuses to proceed.

Concrete differences:

gox golangci-lint / staticcheck / revive
Default severity every rule is an error most rules are warnings
Opt-out one annotation with a written reason on the same line regex/path config files
Dependencies none — only Go stdlib + go list hundreds of transitive deps via golang.org/x/tools
Audience code written by LLM agents (Claude Code, Cursor, etc.) humans, optionally CI
Coverage 12 rules, picked for high signal in unsupervised codegen hundreds of rules; you pick the subset
Ergonomics gox install claude / gox install grok wires it into the LLM's tool loop manual config + pre-commit / CI plumbing

The bug that motivated gox is the swap-prone call site:

transfer(orderID, userID)   // vs transfer(userID, orderID)

Both compile. Both pass tests. The wrong one ships and corrupts the ledger. No major linter catches this today; gox's namedargs rule forces inline /* paramName */ comments on adjacent same-type arguments at the call site, which:

  • catches the bug,
  • documents the call site,
  • and the LLM annotating its own code is essentially free.

That trade — "more typing at the call site, near-zero bugs of this class" — only makes sense when typing is cheap, which is now.

If you already love golangci-lint, keep it. gox is meant to live alongside it, not replace it: golangci-lint is the spell-checker, gox is the production gate.

Design notes

  • Zero external dependencies. Everything uses Go stdlib + a shell-out to go list -json. No golang.org/x/tools, no third-party linter packages. Minimal maintenance: when a new Go release ships, there's nothing to update.
  • Fail closed. Every rule defaults to error. Opt-outs require an explicit annotation with a written reason — the reason is the documentation.
  • Targeted at LLM-written code. Heuristics are tuned so each rule catches a high-frequency LLM bug class without flooding human-readable idioms. For example, shadow exempts ok (the universal comma-ok name) but still catches err re-declaration, which is exactly the bug we want.
  • namedargs is the killer rule. Two adjacent string/int/bool parameters in user-defined code force the call site to label them with inline comments. Stdlib calls are exempt because their conventions are memorized. The bug it prevents — transfer(orderID, userID) vs transfer(userID, orderID) — produces no compile error and no test failure, and is the single most common silent-bug class in unsupervised AI-written Go.

Contributing

Issues and pull requests welcome. Two ground rules:

  1. No external dependencies. Every rule must be implementable with the Go standard library plus an out-of-process go list -json call. Adding golang.org/x/tools/go/packages, staticcheck, or any third-party linter is out of scope.
  2. Each new rule must pass its own check. Run gox check ./... on a fresh clone before opening a PR — gox runs against its own source as part of the smoke test.

License

BSD 3-Clause. See LICENSE.