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Introducing: OpenRouter for everything. One prepaid balance for every API your agents and products need. It’s the billing and metering layer every AI team needed from day one. An OpenRouter for everything. Every model call, search, scrape, or data request - one key, one balance, charged as it happens... at the price you set. The markup is yours. Failed calls cost nothing. Full ledger down to the individual user. 286+ providers. 2,404+ endpoints. And now it fuels our consumer product, Locus Founder. No more juggling provider accounts, month-end invoices, or building your own usage billing system. Teams building agentic products can finally treat their API spend like any other line item: priced, capped, and billed through to their users. Comment “LOCUS” for priority early access. Book a call atshow more

cole

1,927 subscribers

76,129 views • 1 month ago •via X (Twitter)

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45 Comments

Samuel Spitz1 month ago

@techbyved This is cool, how did you make the video? Remotion? Looks good. You should try Replit Animation sometime.

cole1 month ago

@techbyved Actually video was made with Fable 5, took some iteration of course. I'll give it a try! Shot you a dm as well :-)

cole1 month ago

I’ve been wanting to use this track for a launch for awhile, glad I finally could :-) Also docs here:

Aman1 month ago

this is gonna make agent billing so much simpler

cole1 month ago

Indeed!

ved luhana1 month ago

Can’t wait to see adoption go up and up

cole1 month ago

Indeed, we’ll keep pushing

Alexandre Dennis priv/acc1 month ago

Agents got a budget. Now give them a mandate.

s1 month ago

You'll only ever need one api endpoint again

cole1 month ago

Model routing❌ API routing✅

Kai Lennox1 month ago

This is actually a really clean idea. One balance for the whole AI stack would save so much billing pain.

Shiyi Peng1 month ago

congrats!! this is so cool🚀

Raj Nagulapalle1 month ago

billing consolidation across 2,400+ endpoints is genuinely hard to pull off. failed calls costing nothing is the detail that'll matter most to agent builders running at scale.

Shreyans Bhansali1 month ago

i've built enough agent infra to hate provider billing. one key and one ledger sounds boring in exactly the right way

cole1 month ago

🫡

Nick Khami1 month ago

this product makes a ton of sense for all the vibecoding solutions out there

Sreeram Garlapati1 month ago

The interesting part isn’t consolidating API keys, it’s turning fragmented AI infrastructure into a predictable cost layer. Once teams can meter, cap, and pass through every model and data call, AI usage starts looking like a normal infrastructure expense instead of a billing nightmare.

Sebastian Buzdugan1 month ago

one leaked key can drain every service, are spend caps enforced per agent

cole1 month ago

Yup

P1 month ago

great idea

cole1 month ago

🫡

Ken Wattana1 month ago

Great stuff!

Roger R.1 month ago

Agents are gig workers

alissa (nyc!)1 month ago

This is sick!!

P1 month ago

locus team is cooking

cole1 month ago

Thank you sir!

Gsci1 month ago

LOCUS

prapa1 month ago

LOCUS

armandoki1 month ago

@ycombinator This is smart

aaron1 month ago

LOCUS. 🫡

Phillip Yan1 month ago

Congrats on the launch!

Kovan1 month ago

super cool

Sina Matian1 month ago

There should be a feature to sell unused AI credits. For example, If i have $200 in deepseek credits, since deepseek is on locus I can sell those credits to Locus, then get $200 Locus credits or $180 cash etc.

Tykoo1 month ago

LOCUS

John Wei1 month ago

the agent handles the mess, you just pay one bill

cole1 month ago

Exactly 🫡

Sahib Nanda1 month ago

@ycombinator Portfolio: GitHub: SDE @CRED. Built an AI portfolio backend with Orchestrator/Worker logic and a custom MCP tool-calling layer. I know the infra cost of running agents. DMing.

Ian Janicki1 month ago

Congrats! @zincdotcom would love to be a provider!

Fahim Ahmed1 month ago

This is awesome, congrats Cole!

cole1 month ago

Thank you thank you sir

Chloe1 month ago

finally, a way to stop chasing 20 different developer API keys.

Keita1 month ago

COOL! LFG Cole & Locus team🔥

Dariya1 month ago

Epic

cole1 month ago

E🅿️ic

Malibu1 month ago

wow

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Israfil

14,705 views • 17 days ago

we’re launching Codag (Y Combinator S26) - the compression and control layer for agent tools. we started with logs. then customers pulled us into searches, tests, builds, and api responses. the pattern was always the same: agents burning context on repetitive output, with teams unable to see where the tokens went. Codag now cuts input-token usage ~3x at answer parity and gives teams visibility across their agent stack. agents are becoming the primary users of software - companies are adding agents, models, providers, and MCP servers faster than they can measure or control them. most cannot answer which tools their agents invoke, what those calls cost, or where the waste is. we’re building the layer between them and everything they read. 90+ engineering orgs already use Codag today to reduce that spend and see agent tool usage across provider. find out more and book a demo at

we’re launching Codag (Y Combinator S26) - the compression and control layer for agent tools. we started with logs. then customers pulled us into searches, tests, builds, and api responses. the pattern was always the same: agents burning context on repetitive output, with teams unable to see where the tokens went. Codag now cuts input-token usage ~3x at answer parity and gives teams visibility across their agent stack. agents are becoming the primary users of software - companies are adding agents, models, providers, and MCP servers faster than they can measure or control them. most cannot answer which tools their agents invoke, what those calls cost, or where the waste is. we’re building the layer between them and everything they read. 90+ engineering orgs already use Codag today to reduce that spend and see agent tool usage across provider. find out more and book a demo at

Michael Zhou

163,267 views • 1 month ago

🚨 What happens when you give your employees an employee? Say hello to @GetDiana: the new, enterprise-grade OpenClaw. She just launched to give every single person on your team their own dedicated AI counterpart. She lives right inside your Slack DMs and handles the heavy lifting across Finance, GTM, Product, and Ops. In a single morning, she can: → Audit 5 vendor invoices for discrepancies. → Pull your HubSpot pipeline and format a daily summary. → Analyze 42 user interviews to highlight product priorities. She connects to 3,000+ tools via API. A tool doesn’t have an API? No problem! Diana securely logs in through a browser agent and gets it done anyway. Plus, every action is monitored by the Governor security layer, meaning she can be shut down mid-task before any damage is DONE... pretty handy! I got a sneak peek at the demo from the @GetDiana team, and I think it’s such a huge productivity boost for lean teams 💥

🚨 What happens when you give your employees an employee? Say hello to @GetDiana: the new, enterprise-grade OpenClaw. She just launched to give every single person on your team their own dedicated AI counterpart. She lives right inside your Slack DMs and handles the heavy lifting across Finance, GTM, Product, and Ops. In a single morning, she can: → Audit 5 vendor invoices for discrepancies. → Pull your HubSpot pipeline and format a daily summary. → Analyze 42 user interviews to highlight product priorities. She connects to 3,000+ tools via API. A tool doesn’t have an API? No problem! Diana securely logs in through a browser agent and gets it done anyway. Plus, every action is monitored by the Governor security layer, meaning she can be shut down mid-task before any damage is DONE... pretty handy! I got a sneak peek at the demo from the @GetDiana team, and I think it’s such a huge productivity boost for lean teams 💥

Charly Wargnier

15,009 views • 6 months ago

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

Vikas gupta

20,121 views • 1 month ago