> scraping LinkedIn profiles
is this legal? last time I checked linkedin.com/robots.txt do not allow scraping, unless explicit approval from linkedin
replies(3):
While working on a previous startup, we realized we were spending more time doing busywork in apps than actually building product. We lost hundreds of hours scraping LinkedIn profiles, updating spreadsheets, updating investor reports, and communicating across multiple Slack channels. Our breaking point happened after I checked my screen time and realized I spent 4 hours a day in Gmail. We decided that we could create more value solving this than by working on the original startup (a code generation agent similar to Lovable).
Slashy is an AI agent that uses direct tool calls to services such as Gmail, Calendar, Notion, Sheets and more. We built all of our tools in-house since we found that most MCPs are low quality and add an unnecessary layer of abstraction. Through these tools, the agent is able to semantically search across your apps, get relevant information, and perform actions (e.g. send emails, create calendar events, etc). This solves the problem of context-switching and copy-pasting information from an app back and forth into ChatGPT.
Slashy integrates to 15 different services so far (G-Suite, Slack, Notion, Dropbox, Airtable, Outlook, Phone, Linear, Hubspot, and more). We use a single agent architecture (as we found this reduces hallucinations), and use our own custom tools—doing so allows the model to have higher quality as we can design them to work in a general agent structure, for example we use markdown for Slack/Notion instead of their native text structure.
So what makes Slashy different from the 100 other general agents?
- It Actually Takes Action: Unlike ChatGPT or Claude that just give you information, Slashy researches companies, creates Google Docs with findings, adds contacts to your CRM, schedules follow-ups, and sends personalized emails – all in one workflow.
- Cross-Tool Context: Most automation tools work in silos (one of the biggest problems with MCP). Slashy understands your data across platforms. It can read your previous Slack conversations about a prospect, check your calendar for availability, research their company online, and draft a personalized email. What powers this is our own semantic search functionality.
- User Action Graphs: Our agent over time has memory not just of past conversations, but also forms user actions graphs to know what actions are expected based on previous user conversations.
- No Technical Setup Required: While Zapier requires building complex flows and fails silently, Slashy works through natural language. Just describe what you want automated.
- Custom UI: For our tool calls we design custom UI for each of them to make the UX more natural.
Here are some examples of workflows people use us for:
▪ "Every day look at my calendar and send me a notion doc with in-depth backgrounds on everyone I’m meeting"
▪ "Find the emails of everyone who reacted to my latest LinkedIn post and send personalized outreach"
▪ "Can you make me an investor pitch deck with market research, competitive analysis, and financial projections"
▪ "Doing a full Nvidia Discounted Cash Flow (DCF) analysis"
Slashy.ai is live with a free tier (100 daily credits) along with 500 credits for any new account. You can immediately try out workflows like the ones above and we have a special code for HN (HACKERNEWS at checkout).
Hope you all enjoy Slashy as much as we do :)
is this legal? last time I checked linkedin.com/robots.txt do not allow scraping, unless explicit approval from linkedin
Can tell you :)
We like to believe we're pretty trustworthy, and do our best to make everything secure.
i just won't use it, and nobody should, unless they can understand exactly how it works and reason for themselves about the risks they are taking. you clearly work hard and care deeply about what you are building, and it will be very useful. but it has the potential to cause widespread harm, no matter how trustworthy you are, how much you care about it, or what your intentions are.
with respect to user security and privacy, doing your best is not much better than yolo security. the minimum standard should be to research the threat landscape, study the state of the art in methods to mitigate those threats, implement them, and test them thoroughly, yourselves and through vendors. iterate through that process continuously, alongside your development. it will never end. or, you can open source it and the internet does this for you for free. build something people love, grow traction, convert that to money. THEN figure out how to make money from them.. not the other way around. or, more likely, some combination of all of the above.
someone else linked you to simon wilson's lethal trifecta page, i would absolutely start there, and read everything linked as well. pangea and spectreops both do good work in the llm pentesting space, i'm sure there are more.