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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. I thought I had a bug 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 - 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 - Donnyb369/mcp-spine: Context Minifier & State Guard — Local-first MCP middleware proxy
2026-04-11 · via Hacker News - Newest: "LLM"

mcp-spine MCP server

The middleware layer MCP is missing. Security, routing, token control, and compliance — between your LLM and your tools.

MCP Spine is a local-first proxy that sits between Claude Desktop (or any MCP client) and your MCP servers. One config, one entry point, full control over what goes in, what comes out, and what gets logged.

57 tools across 5 servers. One proxy. Zero tokens wasted.

The Problem

You've connected Claude to GitHub, Slack, your database, your filesystem. Now you have 40+ tools loaded, thousands of tokens burned on schemas every turn, no audit trail, no rate limits, and no way to stop the LLM from reading your boss's DMs. MCP gives agents power. Spine gives you control.

What It Does

Layer What it solves
Security Proxy Rate limiting, secret scrubbing, path jails, HMAC audit trail
Semantic Router Only relevant tools reach the LLM — local embeddings, no API calls
Schema Minifier 61% token savings by stripping unnecessary schema fields
State Guard SHA-256 file pins prevent the LLM from editing stale versions
Token Budget Daily limits with warn/block enforcement and persistent tracking
Plugin System Custom middleware hooks — filter, transform, block per tool
HITL Confirmation Destructive tools pause for human approval before executing
Multi-User Audit Session-tagged audit trail for shared deployments

Demo

MCP Spine Doctor

Runs on Windows, macOS, and Linux. CI tested across all three.

Install

pip install mcp-spine

# With semantic routing (optional)
pip install mcp-spine[ml]

Quick Start

# Interactive setup wizard — detects your servers, asks about features
mcp-spine init

# Or quick default config
mcp-spine init --quick

# Check everything works
mcp-spine doctor --config spine.toml

# Start the proxy
mcp-spine serve --config spine.toml

Claude Desktop Integration

Replace all your individual MCP server entries with a single Spine entry:

{
  "mcpServers": {
    "spine": {
      "command": "python",
      "args": ["-m", "spine.cli", "serve", "--config", "/path/to/spine.toml"],
      "cwd": "/path/to/mcp-spine"
    }
  }
}

Features

Security Proxy (Stage 1)

  • JSON-RPC message validation and sanitization
  • Secret scrubbing (AWS keys, GitHub tokens, bearer tokens, private keys, connection strings)
  • Per-tool and global rate limiting with sliding windows
  • Path traversal prevention with symlink-aware jail
  • Command injection guards for server spawning
  • HMAC-fingerprinted SQLite audit trail
  • Circuit breakers on failing servers
  • Declarative security policies from config

Semantic Router (Stage 2)

  • Local vector embeddings using all-MiniLM-L6-v2 (no API calls, no data leaves your machine)
  • ChromaDB-backed tool indexing
  • Query-time routing: only the most relevant tools are sent to the LLM
  • spine_set_context meta-tool for explicit context switching
  • Keyword overlap + recency boost reranking
  • Background model loading — tools work immediately, routing activates when ready

Schema Minification (Stage 3)

  • 4 aggression levels (0=off, 1=light, 2=standard, 3=aggressive)
  • Level 2 achieves 61% token savings on tool schemas
  • Strips $schema, titles, additionalProperties, parameter descriptions, defaults
  • Preserves all required fields and type information

State Guard (Stage 4)

  • Watches project files via watchfiles
  • Maintains SHA-256 manifest with monotonic versioning
  • Injects compact state pins into tool responses
  • Prevents LLMs from editing stale file versions

Human-in-the-Loop

  • require_confirmation policy flag for destructive tools
  • Spine intercepts the call, shows the arguments, and waits for user approval
  • spine_confirm / spine_deny meta-tools for the LLM to relay the decision
  • Per-tool granularity via glob patterns

Tool Output Memory

  • Ring buffer caching last 50 tool results
  • Deduplication by tool name + argument hash
  • TTL expiration (1 hour default)
  • spine_recall meta-tool to query cached results
  • Prevents context loss when semantic router swaps tools between turns

Token Budget

  • Daily token consumption tracking across all tool calls
  • Configurable daily limit with warn/block actions
  • Persistent SQLite storage (survives restarts within the same day)
  • Automatic midnight rollover
  • spine_budget meta-tool to check usage mid-conversation
  • Token estimation via character-count heuristic (~4 chars/token)

Plugin System

  • Drop-in Python plugins that hook into the tool call pipeline
  • Four hook points: on_tool_call, on_tool_response, on_tool_list, on_startup/on_shutdown
  • Plugins can transform arguments, filter responses, block calls, or hide tools
  • Plugin chaining — multiple plugins run in sequence
  • Allow/deny lists for plugin access control
  • Auto-discovery from a configurable plugins directory
  • Example included: Slack channel compliance filter

Config Hot-Reload

  • Edit spine.toml while Spine is running — changes apply in seconds
  • Hot-reloadable: minifier level, rate limits, security policies, token budget, state guard patterns
  • Non-reloadable (requires restart): server list, commands, audit DB path
  • All reloads logged to the audit trail

Multi-User Audit

  • Unique session ID generated per client connection
  • Client name and version extracted from MCP handshake
  • All audit entries tagged with session ID
  • mcp-spine audit --sessions lists all client sessions
  • mcp-spine audit --session <id> filters entries by session
  • Enables compliance and usage tracking for shared deployments

Transport Support

  • stdio — local subprocess servers (filesystem, GitHub, SQLite, etc.)
  • SSE — legacy remote servers over HTTP/Server-Sent Events
  • Streamable HTTP — MCP 2025-03-26 spec, single-endpoint bidirectional transport with session management
  • All transports share the same security, routing, and audit pipeline

Diagnostics

# Check your setup
mcp-spine doctor --config spine.toml

# Live monitoring dashboard
mcp-spine dashboard

# Usage analytics (includes token budget)
mcp-spine analytics --hours 24

# Query audit log
mcp-spine audit --last 50
mcp-spine audit --security-only
mcp-spine audit --tool write_file
mcp-spine audit --sessions
mcp-spine audit --session <session-id>

Example Config

[spine]
log_level = "info"
audit_db = "spine_audit.db"

# Add as many servers as you need — they start concurrently
[[servers]]
name = "filesystem"
command = "npx"
args = ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/project"]
timeout_seconds = 120

[[servers]]
name = "github"
command = "npx"
args = ["-y", "@modelcontextprotocol/server-github"]
env = { GITHUB_TOKEN = "ghp_..." }
timeout_seconds = 180

[[servers]]
name = "sqlite"
command = "uvx"
args = ["mcp-server-sqlite", "--db-path", "/path/to/database.db"]
timeout_seconds = 60

[[servers]]
name = "memory"
command = "npx"
args = ["-y", "@modelcontextprotocol/server-memory"]
timeout_seconds = 60

[[servers]]
name = "brave-search"
command = "node"
args = ["/path/to/node_modules/@modelcontextprotocol/server-brave-search/dist/index.js"]
env = { BRAVE_API_KEY = "your_key" }
timeout_seconds = 60

# Remote server via legacy SSE
# [[servers]]
# name = "remote-sse"
# transport = "sse"
# url = "https://your-server.com/sse"
# headers = { Authorization = "Bearer token" }
# timeout_seconds = 30

# Remote server via Streamable HTTP (MCP 2025-03-26)
# [[servers]]
# name = "remote-api"
# transport = "streamable-http"
# url = "https://your-server.com/mcp"
# headers = { Authorization = "Bearer token" }
# timeout_seconds = 30

# Semantic routing
[routing]
max_tools = 15
rerank = true

# Schema minification — 61% token savings at level 2
[minifier]
level = 2

# Token budget — track and limit daily token spend
[token_budget]
daily_limit = 500000    # tokens per day (0 = unlimited)
warn_at = 0.8           # warn at 80% usage
action = "warn"         # "warn" = log warning, "block" = reject tool calls

# State guard — prevent context rot
[state_guard]
enabled = true
watch_paths = ["/path/to/project"]

# Plugins — custom middleware hooks
[plugins]
enabled = true
directory = "plugins"
# allow_list = ["slack-filter"]  # optional whitelist
# deny_list = ["debug-plugin"]  # optional blacklist

# Human-in-the-loop for destructive tools
[[security.tools]]
pattern = "write_file"
action = "allow"
require_confirmation = true

[[security.tools]]
pattern = "write_query"
action = "allow"
require_confirmation = true

# Security
[security]
scrub_secrets_in_logs = true
audit_all_tool_calls = true
global_rate_limit = 120
per_tool_rate_limit = 60

[security.path]
allowed_roots = ["/path/to/project"]
denied_patterns = ["**/.env", "**/*.key", "**/*.pem"]

Security Model

Defense-in-depth — every layer assumes the others might fail.

Threat Mitigation
Prompt injection via tool args Input validation, tool name allowlists
Path traversal Symlink-aware jail to allowed_roots
Secret leakage Automatic scrubbing of AWS keys, tokens, private keys
Runaway agent loops Per-tool + global rate limiting
Command injection Command allowlist, shell metacharacter blocking
Denial of service Message size limits, circuit breakers
Sensitive file access Deny-list patterns for .env, .key, .pem, .ssh/
Tool abuse Policy-based blocking, audit logging, HITL confirmation
Log tampering HMAC fingerprints on every audit entry
Destructive operations require_confirmation pauses for user approval
Runaway token spend Daily budget limits with warn/block enforcement
Unvetted plugins Allow/deny lists, directory isolation, audit logging
Sensitive data exposure Plugin-based response filtering (e.g., Slack channel compliance)

Architecture

Client ◄──stdio──► MCP Spine ◄──stdio────────► Filesystem Server
                       │      ◄──stdio────────► GitHub Server
                       │      ◄──stdio────────► SQLite Server
                       │      ◄──stdio────────► Memory Server
                       │      ◄──stdio────────► Brave Search
                       │      ◄──SSE──────────► Legacy Remote
                       │      ◄──Streamable HTTP──► Modern Remote
                   ┌───┴───┐
                   │SecPol │  ← Rate limits, path jail, secret scrub
                   │Router │  ← Semantic routing (local embeddings)
                   │Minify │  ← Schema compression (61% savings)
                   │Guard  │  ← File state pinning (SHA-256)
                   │HITL   │  ← Human-in-the-loop confirmation
                   │Memory │  ← Tool output cache
                   │Budget │  ← Daily token tracking + limits
                   │Plugin │  ← Custom middleware hooks
                   │Audit  │  ← Session-tagged multi-user trail
                   └───────┘

Startup Sequence

  1. Instant handshake (~2ms) — Responds to initialize immediately
  2. Concurrent server startup — All servers connect in parallel via asyncio.gather
  3. Progressive readiness — Tools available as soon as any server connects
  4. Late server notificationtools/listChanged sent when slow servers finish
  5. Background ML loading — Semantic router activates silently when model loads

Windows Support

Battle-tested on Windows with specific hardening for:

  • MSIX sandbox paths for Claude Desktop config and logs
  • npx.cmd resolution via shutil.which()
  • Paths with spaces (C:\Users\John Doe\) and parentheses (C:\Program Files (x86)\)
  • PureWindowsPath for cross-platform basename extraction
  • Environment variable merging (config env extends, not replaces, system env)
  • UTF-8 encoding without BOM
  • Unbuffered stdout (-u flag) to prevent pipe hangs

Project Structure

mcp-spine/
├── pyproject.toml
├── spine/
│   ├── cli.py              # Click CLI (init, serve, verify, audit, dashboard, analytics, doctor)
│   ├── config.py           # TOML config loader with validation
│   ├── proxy.py            # Core proxy event loop
│   ├── protocol.py         # JSON-RPC message handling
│   ├── transport.py        # Server pool, circuit breakers, concurrent startup
│   ├── audit.py            # Structured logging + SQLite audit trail + sessions
│   ├── router.py           # Semantic routing (ChromaDB + sentence-transformers)
│   ├── minifier.py         # Schema pruning (4 aggression levels)
│   ├── state_guard.py      # File watcher + SHA-256 manifest + pin injection
│   ├── memory.py           # Tool output cache (ring buffer + dedup + TTL)
│   ├── budget.py           # Token budget tracker (daily limits + persistence)
│   ├── plugins.py          # Plugin system (hooks, discovery, chaining)
│   ├── dashboard.py        # Live TUI dashboard (Rich)
│   ├── sse_client.py       # SSE transport client (legacy)
│   ├── streamable_http.py  # Streamable HTTP transport (MCP 2025-03-26)
│   └── security/
│       ├── secrets.py      # Credential detection & scrubbing
│       ├── paths.py        # Path traversal jail
│       ├── validation.py   # JSON-RPC message validation
│       ├── commands.py     # Server spawn guards
│       ├── rate_limit.py   # Sliding window throttling
│       ├── integrity.py    # SHA-256 + HMAC fingerprints
│       ├── env.py          # Fail-closed env var resolution
│       └── policy.py       # Declarative security policies
├── tests/
│   ├── test_security.py    # Security tests
│   ├── test_config.py      # Config validation tests
│   ├── test_minifier.py    # Schema minification tests
│   ├── test_state_guard.py # State guard tests
│   ├── test_proxy_features.py  # HITL, dashboard, analytics tests
│   ├── test_memory.py      # Tool output memory tests
│   ├── test_budget.py      # Token budget tracker tests
│   └── test_plugins.py     # Plugin system tests
├── examples/
│   └── slack_filter.py     # Example: Slack compliance filter plugin
├── configs/
│   └── example.spine.toml  # Complete reference config
└── .github/
    └── workflows/
        └── ci.yml          # GitHub Actions: test + lint + publish

Tests

pytest tests/ -v

190+ tests covering security, config validation, schema minification, state guard, HITL policies, dashboard queries, analytics, tool memory, token budget tracking, plugin system, and Windows path edge cases.

CI runs on every push: Windows + Linux, Python 3.11/3.12/3.13.

License

MIT