Launch HN: Slashy (YC S25) – AI that connects to apps and does tasks
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 :)
Need to read up on how CaMel does it. Do you have any good links?
Regardless, here’s the CaMeL paper. Defeating Prompt Injections by Design (2025): https://arxiv.org/abs/2503.18813
Here’s a paper offering a survey of different mitigation techniques, including CaMeL. Design Patterns for Securing LLM Agents against Prompt Injections (2025): https://arxiv.org/abs/2506.08837
And here’s a high-level overview of the state of prompt injection from 'simonw (who coined the term), which includes links to summaries of both papers above: https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/
Question: you say you do semantics search. If I understand correctly that means you must somehow index all data (Gmail, GDrive, ...) otherwise the AI would have to "download/scan" thousands of files each time you ask a question. So how do you do the indexing?
For some background: I'm working on something similar. My clients are architects. They have about 300k files for just one building. With an added 50k issues and a couple of thousand emails. And don't forget all subcontractors.
Would Slashy be able to handle that?
We use indexing similar to glean (but a bit less elegant without the ACLs)
Can talk more about your use case if you'd like to.
Send me a text at 262-271-5339
Do you worry that AI browser agents (comet etc) will eat this market of light integrations? Since the user is already logged in to various services like linkedin/email etc it's easy for tasks to be scripted together - or fully prompted.
also what did you use to make the video? looks better than most looms.
Thanks for the compliment.
Not worried about browser agents, as we actually have pretty deep integrations (we include semantic search as well as user action graphs).
Naturally apis will always be better than browsers as apis are computer languages and browsers are human language.
The sale of Browser Company today too I think shows there's not that much of a ceiling for agentic browsers.
Have a waterfall approach in case one source doesn't have the requested information!
> we use existing models via their API
> we use existing tools/services/platforms
> ChatGPT/OpenWebUI-like web interface
> mostly uses text, no image, no desktop control (?)
hardly can see what this app brings. also, it is paid and requests are routed to someone else? shouldn't this be free, local, and with bring-your-own–key already with things like ollama/llama.cpp?
We just make our own tools in-house :)
Hmm the local open source model is something we've thought of, but currently haven't found open source models to be usable
For example we can read and attach pdfs to gmail which not a lot of people can, since we have our own internal storage api.
Security I understand, but if you consent to giving it access would it not be fine for privacy.
https://x.com/raidingAI/status/1955890345927172359
The team ships fast and I'm excited to see where they go
Do you have a benchmark for this? in my experience, hallucinations have nothing to do with what framework you use.
We find that the less the agent knows, the more it hallucinates
I could really envision saving an 'AI Workflow' template with integrated MCP clients that will balloon once adoption is reached. Right now adoption is low so its not a priority for them, once it is, they will tack it on.
I really wish this the best of luck its a great concept, but surely you must be thinking ahead to plan for this situation.
We do think there's a good chance they'll make their own version.
But we view it as a Dropbox situation where, the foundation models much like Apple and Google know that this will be the future, but are a bit slow to act on it.
That's our goal long term to get better templates
is this legal? last time I checked linkedin.com/robots.txt do not allow scraping, unless explicit approval from linkedin
See: https://www.webspidermount.com/is-web-scraping-legal-yes/
There's a relevant footnote in the cited HiQ Labs v. LinkedIn case:
"LinkedIn’s cease-and-desist letter also asserted a state common law claim of trespass to chattels. Although we do not decide the question, it may be that web scraping exceeding the scope of the website owner’s consent gives rise to a common law tort claim for trespass to chattels, at least when it causes demonstrable harm."
They also said: "Internet companies and the public do have a substantial interest in thwarting denial-of-service attacks and blocking abusive users, identity thieves, and other ill-intentioned actors."
It's a good idea to take legal conclusions from media sites with a grain of salt. Same goes for any legal discussion on social media, including HN. If you want a thorough analysis of legal risk--either for your business or for personal matters--hire a good lawyer.
Yes, all LLM caveats apply. Due your diligence. But they are quite good at this now.
Can tell you :)
I'd say maybe to get comfortable try out the non email features first, but we don't have access to any of your data.
We don't, and agent pulls in data only when executing queries
Yeah we store our user credentials on our side and manage them. Along with refreshing tokens and so forth
Email drafting is decent since it reads my drive, previous emails, and everything else so it has a good bit of context
Let me know how it goes, and feel free to text/call me at 262-271-5339 with any feedback
Gosh, I hope it also does things too!
What makes you say that
Maybe HN/ycombinator is just not interesting anymore. I saw some of you commenting that this might be similar to the famous Dropbox situation. That could not be more delusional and representative of what HN became, a meme of itself.
Rinse and repeat.
You're right though ... these YC batches are not what they used to be. AI is hot right now, so it seems YC is throwing money at anything that seems like it can at least actually do something (not that it is necessarily good). If that product doesn't get hot, who cares? Plenty more money to go around on the next batch, because one of them probably will!
I'd like to think the fact we do what we promise is exciting, but without trying the product hard to convey that well :)