Launch HN: Slashy (YC S25) – AI that connects to apps and does tasks

70 points by hgaddipa001 ↗ HN
Hi HN! – We’re Pranjali, Dhruv and Harsha, building Slashy (https://www.slashy.ai). We’re building a general agent that connects to apps and can read data across them and perform actions via custom tools, semantic search, and personalized memory. Here’s a demo: https://www.youtube.com/watch?v=OeApHMHhccA.

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 :)

23 comments

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> we build own MCP

> 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?

Slashy is great and the founders are so talented. I've been following Pranjali on Twitter for a while -- they've got great weekly videos where they keep releasing new features.

The team ships fast and I'm excited to see where they go

> scraping LinkedIn profiles

is this legal? last time I checked linkedin.com/robots.txt do not allow scraping, unless explicit approval from linkedin

LinkedIn has api. So why to scrap?
How does the scraper work? e.g. LinkedIn aggressively blocks scraping and you'd need to be logged in to see most things you'd care about. How do you handle that?
(comment deleted)
I really hate to be the curmudgeon here but won't foundation models end up having their own AI workflows like the GPT store but with MCP?

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.

This is quite useful where has this been all my life

Email drafting is decent since it reads my drive, previous emails, and everything else so it has a good bit of context

How much time do you spend in gmail now? have you continued to track that?
Looks nice, but little hesitant to give access to emails. What model is being used on backend ?
> We use a single agent architecture (as we found this reduces hallucinations)

Do you have a benchmark for this? in my experience, hallucinations have nothing to do with what framework you use.

nice launch!

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.

Honestly, what have HN become? These AI projects are looking more and more like shitcoins and their creators are shitcoin shillers.
Anything that gives any sort of system access to sensitive data and lets agents carry out actions on basically unchecked input sounds like a complete security and privacy nightmare by design.
Congratulations on the launch. I think it's a smart move to not use MCP here. Because your LLM really needs to understand how the different integrations work together.

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?

It seems like this collection of tools gives you a ton of lethal-trifecta risk for prompt injection attacks. How have you mitigated this—are you doing something like CaMeL?
what's interesting about this one is that their claims about what makes slashy different are almost entirely wrong... almost all the big models let you connect and do all of the things mentioned. Not understanding MCP at all is hilarious. If an agent has toosl to access multiple data sources it will make calls across them to resolve things, not sure whatever claiming but there's no way you are actually indexing at scale and probably doing just the exact same thing.
Regarding: “Find the emails of everyone who reacted to my latest LinkedIn post and send personalized outreach”

A forewarning and maybe advice to apply some heuristics — on another HN thread I received the brilliant advice to prefix my first name on LinkedIn with an emoji.

Now, whenever I get a message on LinkedIn that starts with “Hello [emoji] [first name]” I know it was automated spam, and I reflexively block the sender.