Rahul Rejeev

@ycombinator @tryresult super hype!

Result (@tryresult) is an operating system for starting a business. It helps users go from an idea to building a product, incorporating their companies, managing finances, filing taxes, and marketing— all on a single platform. Result is where the internet makes money. Congrats on the launch, @kushwah_aaryan & @saviomartin7! ycombinator.com/launches/QkS-r…

More AI-generated code doesn't make your team faster. It might actually slow you down.

for years, product and sales could outrun engineering teams with promises. now as engineering throughput improves, the constraint shifts elsewhere: sales capacity, buyer trust, implementation, support, customer behavior, internal coordination.

talking to businesses, it feels like the marginal dollar of AI spend has stopped compounding like everyone assumed it would

@patrickc @watolabs

I want some kind of LLM workflow tool. • Ability to manage a set of input files (Markdown or similar), plus other general-purpose context. • With real-time collaboration. (And maybe some concept of snapshots or VCS integration.) • And the ability to create/manage a inference workflows and a stored set of prompts. • Access to general-purpose coding agents (and not just chat models). • Some concept of compiled outputs/inference results (which ideally can be shared externally). Many projects have this feeling: "there is all this stuff, which I want to process/compute over in this iterated way, with some build artifacts being important/worth saving." GNU Autotools x Notion or something. Is anyone building this?

@Yeshua_tree @watolabs yup, we try to make everything as auditable and traceable as possible

@watolabs The trust map matters as much as the data map. Agents need to know not just where knowledge lives, but which human judgments made a source worth trusting.

Most companies already have memory. It’s just scattered across Salesforce, Slack, Linear, GitHub, dashboards, docs, spreadsheets, and the people who know where to look. That’s why “company memory” for AI agents should be structured to help agents navigate this convoluted mess. Try to create a map: Write where knowledge lives, what sources to trust, who has access, and which workflows have been reviewed. Wrote more here: blog.watolabs.com/company-memory…

@NinjaNeko42 @ycombinator @watolabs @arihanxv yo chill, it was a long day haha 😅

@ycombinator @watolabs @arihanxv @rahulrejeev atleast tell nigaas to smile i know this shit is hard but talking about ur product with this energy is kinda sad also i would like to raise 200 milli for 1% equity it has AI in it

Wato (@watolabs) is building the collaboration layer for teams working with AI agents: shared knowledge, cloud agents, automations, and permissioned tools across the AI subscriptions companies already use. Congrats on the launch, @arihanxv & @rahulrejeev! ycombinator.com/launches/Qet-w…

super excited to finally launch!

@ycombinator @DraftedAI don't need to scroll zillow for house inspo no more

It’s never been easier to design your dream house. Draw a shape. Define your rooms. Set your constraints. @DraftedAI generates complete floor plans, elevations, and 3D home designs in seconds. Over the last month, 120,000 people generated 325,000+ home designs with Drafted.ai.

it’s been great living in sf, learning by doing need to leave the apartment more often

enterprises love predictable pricing but nowadays one team can accidentally turns Claude into a line item bigger than headcount, I feel for the CFOs

Burning Bridges @Drake

Enterprise AI shouldn't mean pricier seats and bigger admin dashboards. Current GPT and Claude enterprise plans still leave teams stitching work together across scattered tools. A real enterprise plan makes AI work collaborative by default. That’s what we’re building.

@arihanxv aren't you the ceo?

In 2026, "engineers" are just product managers

@MaanavA51108 i think its a unique space, agents feel super silo'rd rn

what yall think about multi agent coordination and communication? the concept of moltbook was kinda cool i feel like ever since the acquisition, it kinda went under the radar and is hella underreacted to

AI token futures would be interesting is anyone working on this?

@MaanavA51108 agreed, imo the thing i'd change the most about yc is that i've never had the chance to fully engage in neighborhood life in sf, a shame considering we live in such a great city and it's the cornerstone of a good society. then again i do love working so theres that lol

the biggest company in 10 years will be one that fosters human connection one of the craziest things to think about after sam’s talk at yc today i think the concept that AI will eventually run parts of the world autonomously is insane but it really shows how important it is to stay connected as humans

JUST IN: Sam Altman offers $2 million in OpenAI tokens to each startup in YC's current batch in exchange for equity.

AI adoption inside companies is increasing, but super uneven. Some people are becoming super users. Others are still hesitant. This creates friction, as teams deal with different speeds, workflows, and expectations. Companies are going to have to deal with this soon.

@nghoihin @watolabs this is super cool. love the wiki-first angle. extracting durable facts from chat, then letting MCP query the generated memory/wiki feels like the right shape. curious how it handles as the knowledge base scales

@watolabs Shipped Beever Atlas — open-source LLM Wiki for teams. Native MCP. github.com/Beever-AI/beev…

We just built the most scalable company brain. Institutions are naturally noisy. Wato is built to solve for that. Most “AI memory” systems are solving the wrong problem. They treat memory as retrieval: put documents in a vector database, search across them, and hope the right context comes back. But company knowledge does not stay clean on its own. It gets stale, duplicated, contradictory, and hard to trust. Real memory needs structure: source of truth version history rollback permissions exact search human-readable records This is where a lot of graph RAG / embedding-first systems break down. They can retrieve plausible context, but they don’t answer the more important questions: Who said this? Is it still true? Which team owns it? What changed? At Wato, we think memory should work more like a versioned filesystem. Keep the source as readable Markdown, organized by teams and folders. Then layer search on top. You still get semantic search, keyword search, and exact search at scale, but the source stays inspectable and editable. The agent needs to both “remember" and the institution needs to know what it knows, keep it current, and make it safely usable.