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Show HN

GitHub - astefanutti/shaderbang: Shebang for Shaders Show HN: AI agents for UK GDAD PCF roles and their skills The Two Pillars: Mixer Mode and Meta-Software in the Reorganization of Software Work After AI GitHub - JaiCode08/teleport-env What 1,000+ Harness Experiments Taught Me About Self-Improving Agents Show HN: Liiists, a Markdown-first, iOS and CLI list app SwiperTab – Get this Extension for 🦊 Firefox (en-US) GitHub - kouhxp/fftext: Summarize, explain, fact-check, or translate any text, URL, or file. No GPU. No cloud. One command GitHub - sweetpad-dev/sweetpad: Develop Swift/iOS projects using VSCode GitHub - dogmaticdev/IRON: IRON a.k.a. Intermediate Representation Object Notation is a Interpreter/Database that is used to create Programming Languages. GitHub - sjhalani7/vaen: Package your AI coding harness into a portable .agent file, and share it across repos, teams, & the community without ever having to copy-paste instructions, skills, MCP config, or secrets. Show HN: Gandalf the Grader Show HN: Citadeld – replay any CI failure locally from a single file GitHub - tdortman/cuSBF: High-Performance GPU Super Bloom Filter coral-ai/claude-code-token-xray at main · Coral-Bricks-AI/coral-ai GitHub - ulyssestenn/funes: Funes is a Git-based framework for LLM-managed knowledge work: an AI Librarian ingests raw sources, builds an interlinked Markdown knowledge base, and uses it to produce cited reports, analyses, and other outputs. GitHub - ThatXliner/gah: Git Add Hunk, built for agents to use GitHub - harmont-dev/harmont-cli: Command-line client for the Harmont CI platform GitHub - brooksmcmillin/mcp-authflow: OAuth 2.0 Authorization Server framework for MCP servers GitHub - javaid-codes/audit-supply-chain-agents GitHub - amorey/gochan: A small library of common channel architectures for Go, inspired by Rust GitHub - arifozgun/OpenGem: Free, Open-Source AI API Gateway with Gemini, OpenAI & Anthropic Compatibility in 1 file GitHub - Pranesh950/BioPetals: 🌸 Run BIOxAI models at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading GitHub - cnguyen14/bounty-doctor: Diagnose a GitHub bounty issue before you waste hours: detects honeypot scam repos, AI-bot attempt swarms, and stale contests. Show HN: CoreMCP – MCP Server for On-Prem DBs Show HN: KittyHTML – Render HTML/CSS as an inline image in your terminal GitHub - bingud/filemat: Web-based file manager Show HN: TruthLens – Free multi-signal deepfake image detector GitHub - apexlocal-jz/claude-usage-tray: Windows system-tray app showing your Claude Code rate-limit usage at a glance. Zero deps, ~300 lines of PowerShell. Cross-IDE (works regardless of VS Code, Cursor, plain terminal). Release v0.1.2.1 · kouhxp/yapsnap
Draper — AI research tool for founders
tomchill · 2026-06-19 · via Show HN

You build the product.We’ll find who wants itand how to reach them.

Make the decisions a founder can’t afford to get wrong — grounded in research, not a hunch.

I built a habit-tracker for runners — where do these people actually hang out online?

RETURN

TikTokRedditInstagramXLinkedInNewsForums

TikTokRedditInstagramXLinkedInNewsForums

From idea to traction.

From the first rough idea to your first hundred customers, the workflow doesn’t change — you ask, and Draper answers.

  1. 01

    Ask in plain language.

    Type the question you’d put to a sharp advisor. Idea validation, market research, marketing ideas, channel decisions.

  2. 02

    Draper reads the social internet.

    TikTok, Reddit, Instagram, X, news and the long-tail forums — read in parallel, the small threads included.

  3. 03

    Get a sourced answer back.

    A structured read on your market, your audience, or your moment — every claim linked to where it came from.

Don’t just take our word for it. Bring your own question.

Start free

“I just did eight hours of strategy in thirty minutes.”
— OK200
“Draper makes the general LLMs look like Boomers.”
— Tombras
“This is going to make me look like a rockstar.”
— Tracksuit
What is Draper?

Draper is a research tool. It works exceptionally well for founders and small businesses, but the use cases run wide — marketers and agencies running competitor and creator research, and enterprise and mid-market teams pressure-testing positioning, use it the same way. Ask a question about your audience, market, or competition in plain language, and Draper reads the social internet — Reddit, TikTok, Instagram, X, LinkedIn, news, and the long-tail forums — and hands back a grounded answer with its sources attached.

How does Draper actually find answers? Does it hallucinate?

Draper reads the small threads, the obscure forums, the sub-100-comment Reddit posts — the corners of the social web where the conversation actually happens, and where most tools don’t look. Every answer comes back with its sources attached, so you can see exactly where each claim came from. Same class of model as Claude, plugged into a much wider read of the internet. The extra data is what stops it from guessing.

What’s free, and what costs?

7 days free to start. $20/mo gives you a healthy amount of usage, plus a monthly allocation of full reports — the deeper, multi-source pulls like the one above. Cancel anytime — risk free.

What kinds of questions work best?

Anything where the answer lives in conversation, content, or community — not in your dashboard. The more specific the question, the sharper the answer.

Is my question private?

Your questions are private. We don’t sell them, and they don’t leave Draper.

Eight hours of strategy in thirty minutes.