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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. 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. 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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. 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Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering 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. 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 LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users
100Hires MCP Server — Connect Claude, ChatGPT and Cursor to Your ATS
kravetsss · 2026-06-16 · via Hacker News - Newest: "LLM"

Developer Portal — MCP

Connect 100Hires to Claude, ChatGPT, Cursor, and any AI assistant

100Hires is an AI-native applicant tracking system (ATS). The 100Hires MCP server gives Claude, ChatGPT, Cursor, and any MCP-compatible AI assistant secure access to 130 tools for candidates, jobs, applications, and interviews — driven entirely by natural-language prompts.

One MCP endpoint, 130 tools — manage candidates, jobs, applications, interviews and messages with natural language.

Endpoint https://mcp.100hires.com/mcp

  • MIT License
  • Glama.ai verified
  • Official MCP Registry

Recruiting MCP, ATS MCP, hiring AI integration — bring your applicant tracking system into Claude, ChatGPT and Cursor with secure OAuth 2.1. Building a direct integration instead? See the 100Hires REST API documentation. New to 100Hires? Start a 14-day free trial.

What is MCP?

Model Context Protocol is an open standard from Anthropic that lets AI assistants connect to external systems through a uniform interface — think of it as USB-C for AI tools.

Hosts

AI assistants and IDEs that talk to MCP servers — Claude (web, Desktop, Code), ChatGPT, Cursor, VS Code, Codex, Windsurf, Zed.

Clients

The transport layer inside the host that speaks MCP over Streamable HTTP or stdio.

Servers

100Hires MCP server — exposes 130 tools backed by the public REST API, secured by OAuth 2.1.

100Hires runs a remote MCP server, not a local one. There’s nothing to install, deploy or update on your machine — every client, including Claude Desktop and Cursor, points at https://mcp.100hires.com/mcp and authorizes via OAuth. New tools and bug fixes land server-side and reach all your assistants the next time they call tools/list.

What you can ask

Ten real prompts that work today, in plain English.

  • “Show me candidates who applied this week for the Senior PM role”
  • “Move all rejected candidates from this job to the talent pool”
  • “Schedule a 30-minute interview with Sarah next Tuesday for the Backend Engineer role”
  • “Draft a follow-up email to candidates who haven't replied in 5 days and send it”
  • “Open a new Senior Product Designer job in New York and publish it to LinkedIn”
  • “Find every LinkedIn candidate with Python on their resume and tag them 'pythonista'”
  • “What's on my interview calendar tomorrow?”
  • “How many candidates do we have at each stage in the Sales Rep pipeline?”
  • “Reject everyone still in 'Phone Screen' for the Marketing Manager job with reason 'No response'”
  • “Pull the resume and last 3 notes for the candidate I interviewed yesterday”

Quickstart

From zero to your first MCP prompt in under five minutes.

  1. Prerequisites

    A 100Hires account on any plan, including the 14-day free trial. The MCP endpoint is available to every workspace.

  2. Pick your AI client

    One-click install for Cursor and VS Code; copy a snippet for everything else. All clients connect to https://mcp.100hires.com/mcp over Streamable HTTP; stdio-only clients use the mcp-remote shim.

    Open 100Hires in ChatGPT

    Or in ChatGPT go to Settings → Apps and find 100Hires in the directory.

    Click Connect, sign in, and click Allow.

    In claude.ai, open the sidebar and click Customize.

    Claude sidebar with the Customize menu item
    Step 1 — open the sidebar → Customize.

    Go to Connectors, click +, and choose Add custom connector.

    Claude Customize → Connectors with the + menu showing Add custom connector
    Step 2 — Connectors → +Add custom connector.

    In the Add custom connector dialog, fill in:

    • Name: 100Hires
    • Remote MCP server URL: https://mcp.100hires.com/mcp
    • Click Add.
    Claude Add custom connector dialog with Name and Remote MCP server URL fields
    Step 3 — fill in the form and click Add.

    Claude opens the 100Hires consent screen. Sign in, click Allow, and you’re connected.

    Install in Cursor

    Click Install in Cursor to add 100Hires MCP automatically. Or paste the snippet below into ~/.cursor/mcp.json.

    Install in VS Code

    Opens VS Code and registers the server. Or paste the snippet into .vscode/mcp.json in your workspace.

    Add the snippet to your Claude Desktop config and restart the app:

    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json

    Run this in your terminal:

    OpenAI Codex CLI — add the server, then run codex mcp login 100hires to authorize.

    From OpenAI Responses API

    Connect 100Hires MCP headless from your own scripts, backends, and AI agents — no desktop client required. Pass the server as a tool with type: "mcp" and the OpenAI platform calls the endpoint directly. The example exposes only the read-only hires_list_jobs tool and skips approval for that tool — add more tools to allowed_tools only after deciding which actions require approval.

    Requires an OAuth access token — your app obtains it via the OAuth 2.1 flow (see Authentication). OpenAI does not store the token: send it with every request and refresh it yourself when it expires (access tokens live 1 hour).

    from openai import OpenAI
    
    client = OpenAI()
    
    response = client.responses.create(
        model="gpt-5",
        tools=[{
            "type": "mcp",
            "server_label": "100hires",
            "server_url": "https://mcp.100hires.com/mcp",
            "authorization": "YOUR_OAUTH_ACCESS_TOKEN",
            "allowed_tools": ["hires_list_jobs"],
            "require_approval": "never"
        }],
        input="List all open jobs in 100Hires"
    )
    
    print(response.output_text)

    From Anthropic Messages API

    Connect 100Hires MCP headless from the Anthropic SDK — the MCP connector lets the Messages API call the server for you via mcp_servers, no local MCP client needed. The connector runs tools without approval prompts, so the example enables only the read-only hires_list_jobs tool — extend the allowlist in configs deliberately.

    Requires an OAuth access token — your app obtains it via the OAuth 2.1 flow (see Authentication). Anthropic does not store the token: send it with every request and refresh it yourself when it expires (access tokens live 1 hour).

    import anthropic
    
    client = anthropic.Anthropic()
    
    response = client.beta.messages.create(
        model="claude-opus-4-8",
        max_tokens=16000,
        messages=[{"role": "user", "content": "List all open jobs in 100Hires"}],
        mcp_servers=[{
            "type": "url",
            "url": "https://mcp.100hires.com/mcp",
            "name": "100hires",
            "authorization_token": "YOUR_OAUTH_ACCESS_TOKEN"
        }],
        tools=[{
            "type": "mcp_toolset",
            "mcp_server_name": "100hires",
            "default_config": {"enabled": False},
            "configs": {"hires_list_jobs": {"enabled": True}}
        }],
        betas=["mcp-client-2025-11-20"]
    )
    
    for block in response.content:
        if block.type == "text":
            print(block.text)

    For stdio-only clients (Zed, Windsurf, n8n, older builds) use the mcp-remote shim. It’s a third-party open source bridge: github.com/geelen/mcp-remote.

    Full list of MCP-compatible clients: modelcontextprotocol.io/clients.

  3. Authorize

    Your client opens the 100Hires consent screen. Sign in, review the requested scope, click Allow. Tokens are stored on the client; 100Hires never sees its credentials.

  4. Verify

    Ask your assistant: “List all open jobs in 100Hires.” If you get a list back — you’re wired up. If not, see FAQ.

  5. Next steps

    • Browse all 130 tools
    • Understand OAuth and scopes
    • Review the safety model

Tools

Keep human confirmation on for destructive tools. Feedback: support@100hires.com.

Authentication

OAuth 2.1 with PKCE and Dynamic Client Registration. Hosts onboard themselves — no API keys to copy and paste, no client secrets to leak.

Flow

AI client → GET  /.well-known/oauth-authorization-server   (RFC 8414)
          → POST /oauth/register                            (DCR, RFC 7591)
          → GET  /oauth/authorize  + PKCE                   (consent in 100Hires)
          → POST /oauth/token                               (code → access_token)
          → POST /mcp  Authorization: Bearer <token>

Scopes

A single scope, mcp:full, mirrors the permissions of the authorizing user.

Token lifetime & refresh

  • Access tokens expire after 1 hour.
  • Refresh tokens are issued automatically; clients refresh transparently.
  • Tokens are bound to the authorizing user and revoked when they leave the workspace.

Manage sessions

Settings → Integrations → Connected AI clients lists every active session: client name (from DCR), last used, IP, scope, and a one-click Revoke.

Safety

  • Access is controlled by your API key permissions.
  • Read operations are always safe.
  • Destructive actions (delete, reject) require explicit IDs.
  • Rate limiting with automatic retry is built-in.
  • API key is never sent to third-party hosts.

Access model

Every token — OAuth or API key — is bound to a single 100Hires user. The MCP server only ever sees data that user could see in the UI; cross-company access is impossible. Sessions are listed under Settings → Integrations → Connected AI clients.

Destructive tools

These tools modify or remove data and always require explicit IDs — assistants cannot batch-delete by query:

  • hires_delete_*
  • hires_reject_application
  • hires_disqualify_candidate
  • hires_batch_reject_applications

Best practices

  • Don’t hand AI assistants a blanket “delete by filter” permission — keep human confirmation on for destructive tools.
  • Use a separate token per client so you can revoke one without disrupting the others.
  • Revoke unused sessions on a regular cadence.
  • For CI, prefer a restricted API key over a personal token.

FAQ

I’m getting 401 Unauthorized or Internal server error

Your access token has likely expired or was revoked. Disconnect and reconnect the MCP server in your client.

Claude clients store tokens locally. To force a fresh OAuth flow:

rm -rf ~/.mcp-auth/100hires
I’m getting an error from the server

Email support@100hires.com with the failing tool name, the time of the request and the error message your client showed.

WSL / Windows quirks with npx mcp-remote

Run mcp-remote from the same shell as your client (e.g. WSL → WSL, not WSL → Windows host). Clear the on-disk token cache (~/.mcp-auth/100hires) when switching environments.

Does the server support Streamable HTTP?

Yes. Both application/json and text/event-stream responses are supported. If you’re reverse-proxying, set proxy_buffering off for the SSE path.