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

V
Vulnerabilities – Threatpost
T
The Blog of Author Tim Ferriss
S
SegmentFault 最新的问题
D
DataBreaches.Net
博客园_首页
罗磊的独立博客
B
Blog
T
Threat Research - Cisco Blogs
C
Cisco Blogs
GbyAI
GbyAI
Engineering at Meta
Engineering at Meta
WordPress大学
WordPress大学
G
GRAHAM CLULEY
H
Help Net Security
酷 壳 – CoolShell
酷 壳 – CoolShell
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
爱范儿
爱范儿
SecWiki News
SecWiki News
T
Threatpost
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
Schneier on Security
Schneier on Security
T
The Exploit Database - CXSecurity.com
Google Online Security Blog
Google Online Security Blog
T
Tor Project blog
小众软件
小众软件
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
Y
Y Combinator Blog
H
Hacker News: Front Page
V
V2EX
Security Latest
Security Latest
Cloudbric
Cloudbric
Simon Willison's Weblog
Simon Willison's Weblog
Attack and Defense Labs
Attack and Defense Labs
D
Darknet – Hacking Tools, Hacker News & Cyber Security
P
Proofpoint News Feed
博客园 - 三生石上(FineUI控件)
NISL@THU
NISL@THU
S
Secure Thoughts
Blog — PlanetScale
Blog — PlanetScale
博客园 - 司徒正美
V2EX - 技术
V2EX - 技术
Vercel News
Vercel News
P
Palo Alto Networks Blog
IT之家
IT之家
MyScale Blog
MyScale Blog
有赞技术团队
有赞技术团队
Application and Cybersecurity Blog
Application and Cybersecurity Blog
D
Docker
Google DeepMind News
Google DeepMind News
Webroot Blog
Webroot Blog

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 - blak0p/git-courer: MCP server for Git with local Ollama — zero tokens for git operations
blakok14 · 2026-06-01 · via Hacker News - Newest: "LLM"
Gemini_Generated_Image_g9lcw7g9lc

Release Build MIT License

Issues & Bugs: @blak0p/git-courer/issues · Discussions: @blak0p/git-courer/discussions


Quick Links

Doc Description
Architecture Codebase structure, patterns, and how to add features
Troubleshooting Fix: Ollama not running, MCP not detected, permission errors
MCP Clients All 14 supported clients, config formats, manual setup
Config Options All ~/.config/git-courer/config.yaml and .git-courer/config.json settings
Commands Complete reference for all 22 MCP tools
Models Guide Tested models, token usage, and which one to pick
Contributing Setup, running tests, and how to collaborate

Install

go install github.com/blak0p/git-courer@latest
curl -fsSL https://raw.githubusercontent.com/blak0p/git-courer/main/scripts/install.sh | sh

That's it. It installs the binary and auto-configures every AI tool it detects on your machine.

Requirements: Git · llm local

Homebrew (macOS / Linux):

brew install blak0p/tap/git-courer

Windows

# Windows (PowerShell)
irm https://github.com/blak0p/git-courer/releases/latest/download/git-courer_windows_amd64.tar.gz | tar -xz -o git-courer.exe
.\git-courer.exe setup

Manual install:

# macOS / Linux
curl -fsSL https://github.com/blak0p/git-courer/releases/latest/download/git-courer_$(uname -s | tr '[:upper:]' '[:lower:]')_$(uname -m).tar.gz | tar -xz -C /usr/local/bin git-courer
chmod +x /usr/local/bin/git-courer
git-courer setup

The only MCP git server that understands your code before it commits.

Most tools wrap git diff | llm "write a commit". git-courer does something different: it parses your AST, builds a dependency graph, classifies the change type in Go without touching the LLM, and only calls the model to write the human-readable message.

One staged area = one commit. Always.


Why it's different

1. Commit type determined in Go, not by the LLM

git-courer uses go-enry and go-tree-sitter to parse your AST and apply deterministic rules — new public function, modified signature, deleted symbol, breaking change. The LLM writes the message, not the decision.

2. Dependency graph

Before committing, git-courer builds a graph of what your staged files affect across the codebase. Real scope, not just which files were touched.

3. Local + cloud working together

Go and the local LLM handle everything they can. Your cloud LLM receives structured commit context — WHY + WHAT — instead of raw code.

4. Commits as LLM context

A git-courer commit is a compressed, structured summary any LLM can consume directly. Better input, fewer tokens, fewer hallucinations.

5. Automatic backup on every mutation

Every write operation creates a backup before executing. One command undoes anything.

6. The complete git pack — one server, nothing else needed

Why install one tool for commits, another for releases, another for PR checks? git-courer covers the full cycle:

Read: status, diff, history, blame, pr-review
Write: commit, amend, revert, stage, reset, stash
Branch: branch, merge, rebase, cherry-pick, tag
Utility: sync, backup, undo, remotes, config

All returning structured JSON. No pager hangs, no text parsing, no extra tools.


Workflows

Commit

Preview the change → review proposed commits → apply. git-courer splits your staged files into atomic commits by dependency graph automatically.

Before any PR or merge

Call pr-review — a pre-PR gate that runs in one shot:

  1. Tests — runs test_command from .git-courer/config.json (e.g. go test ./...)
  2. Conflicts — detects merge conflicts with the target branch and returns AST-annotated hunks ([NEW_FUNC], [MOD_SIG ⚠BREAKING])
  3. Diff stats — files changed, additions, deletions
  4. Divergence — ahead/behind count, mergeable status

If it's not green, you don't merge. Set test_command via git-config SET_TEST_COMMAND or edit .git-courer/config.json directly.

Release (CLI)

Run git-courer release. The CLI reads commits from .git-courer/commits.json (captured during each git-commit APPLY on this branch) and groups them by the areas defined in .git-courer/config.json. If the branch store is empty, it falls back to git log since the last tag. Go calculates the version bump; the local LLM writes the human-readable changelog per area.

Undo

Every destructive operation has an automatic backup. One command restores the previous state.


How it works

You: "commit my changes"
        ↓
AI delegates to git-courer (via MCP)
        ↓
Go reads AST + dependency graph → classifies type deterministically
        ↓
Local LLM writes the human-readable message from the annotated diff
        ↓
Security scan (5 layers) → auto-backup → commit
        ↓
"✓ feat(auth): add OAuth2 token refresh"

For the full list of tools: docs/commands.md
For workflow details: docs/workflows.md


Supported Tools

Tool Auto-configured
Claude Code
Cursor
Windsurf
OpenCode
Cline
Roo Code
VS Code
Claude Desktop ✓ macOS/Win only
Continue
Zed
Codex
Gemini CLI
pi
Antigravity

Run git-courer mcp setup to configure all detected tools at once, or git-courer mcp setup <client> for a specific one.

Interactive TUI

Run git-courer with no arguments to launch the interactive installer:

Interactive TUI

The TUI walks you through 4 steps:

  1. MCP Configuration — select which AI tools to configure (auto-detects installed clients)
  2. General Settings — configure your LLM backend, model, and project context
  3. Review — preview your config before saving
  4. Finish — config saved to ~/.config/git-courer/config.yaml

You can also update the binary or uninstall directly from the menu. Navigation: j/k or arrow keys, esc to go back.

Commands

git-courer runs as an interactive TUI when launched without arguments. It also provides MCP server and management commands:

Command Description
git-courer Launch interactive TUI (requires terminal)
git-courer mcp Run MCP server
git-courer mcp setup Configure all detected AI tools
git-courer mcp setup <client> Configure a specific tool (e.g. cursor)
git-courer release Automated semver releases and changelogs (CLI only)
git-courer remove Remove git-courer from the current project
git-courer uninstall Uninstall the binary globally
git-courer update Update to the latest version
git-courer version Show current version

MCP Tools

For the 22 MCP tools and their arguments, see docs/commands.md.

Configuration

git-courer uses two config levels:

Global (~/.config/git-courer/config.yaml) — personal settings: LLM backend, model.

Per-project (.git-courer/config.json) — committable, shared with team. Stores description, areas, test_command, excluded. Better results = edit this file per project.

All options: docs/config.md

Background Jobs

When you call git-commit PREVIEW, the server may return immediately (FAST) or start a background job (SLOW). In the SLOW case:

  1. PREVIEW returns {status:"processing", job_id}
  2. The agent must call git-commit STATUS with that job_id to poll
  3. When STATUS returns {status:"done"}, the plan is ready
  4. Then call git-commit APPLY with the same job_id

The agent continues working — it does NOT block waiting for the job.

Every successful commit via git-commit APPLY is captured to .git-courer/commits.json (branch-scoped under .git-courer/branches/<branch>/commits.json). When you later run git-courer release, the CLI reads those captured commits — grouped by the areas defined in .git-courer/config.json — to generate the changelog. No re-parsing git log.

Why this matters: Git history is frequently rewritten — PR squashes, rebases, force-pushes — destroying the real commit narrative. The CommitStore preserves every commit message as it was written, independently of what happens on the remote. Your release changelog survives git log being flattened into a single squashed commit. This is local documentation that outlives git history rewriting.

Troubleshooting

Having issues? Check docs/troubleshooting.md for:

  • Ollama not running / model not configured
  • MCP not detected by your AI tool
  • Permission errors during install
  • Secrets detected in commits (false positives)

MCP config file locations: docs/mcp-clients.md


FAQ

Who decides the commit type? Go, via AST analysis. The LLM only writes the human-readable message.

Do I need Ollama? You need some LLM backend. Ollama is the recommended default, but git-courer works with any OpenAI-compatible server: LM Studio, vLLM, LocalAI, or a custom endpoint. Without a configured backend, all AI operations (commits, releases, branch names, security auditor) fail. Basic git reads (status, diff, log) still work.

Is my code sent anywhere? No. Everything runs on your machine — git-courer, Ollama, your data.

Who decides the version number in a release? Go, not Ollama. Version is calculated from commit types (feat: → minor, feat!: → major). Ollama only writes the human changelog.

My tool isn't listed. Open an issue: @blak0p/git-courer/issues. If it supports MCP, adding it is usually a few lines.

How do I mark a breaking change? Use ! after the commit type (feat!:) or include BREAKING CHANGE: in the body. git-courer picks this up automatically for version bumping and changelog generation.


Contributing

Want to collaborate? Here's everything you need:

Architecture & Codebase

Read docs/architecture.md for:

  • Directory structure and tech stack
  • Hexagonal architecture patterns
  • Key packages and their responsibilities
  • How to add a new feature
  • Testing approach

Reporting Bugs

Found a bug? Open an issue: @blak0p/git-courer/issues

Include:

  • Your OS and git-courer version (git-courer version)
  • AI tool you're using (Claude Code, Cursor, etc.)
  • Steps to reproduce
  • Relevant logs or error messages

How to Collaborate

  1. Read the docs: Start with docs/architecture.md and CONTRIBUTING.md
  2. Pick an issue: Check @blak0p/git-courer/issues for good first issue labels
  3. Discuss: Use @blak0p/git-courer/discussions for questions or feature ideas
  4. Submit PR: Follow conventional commits (feat:, fix:, chore:)

Adding a New MCP Client

If your AI tool supports MCP but isn't listed, adding it is usually 5 lines of code in internal/installer/mcp_config.go. See docs/mcp-clients.md for the format.