When I was building Webhound, I'd spend hours a day manually clicking through supabase rows to see if it was behaving as expected.
Since joining LiteLLM Al Gateway, I've spoken to other developers building agents on top of our gateway and surprisingly, they're all doing the same thing.
LiteLLM Lens solves that problem. Hyped to be working on it.
Agents are lazy at research. They stop at the first confident answer, even when more research could change it.
Webhound is your agent’s research engine. You use budget to decide how much research a question deserves.
We gave it $5 to investigate a border wall contract. It found an unreported $18M change.
Agents are lazy at research. They stop at the first confident answer, even when more research could change it.
Webhound is your agent’s research engine. You use budget to decide how much research a question deserves.
We gave it $5 to investigate a border wall contract. It found an unreported $18M change.
Day 4 in SF and the conversations keep getting better!
AM: got breakfast with @seanthorneai, ex-CEO of @PeopleDataLabs, now building @agentscoretrust. Talked hiring people who are "n" of 1, watching competition without obsessing over it, and how to think about angel investors
PM: hung out with the boys at @WebhoundAI - @TheoDevSchmidt and @Mokhalil01 (no pic of Mo, but go check his Substack, he's been posting great stuff). Keep your eyes out for what they're cooking up soon and if you're ever in SF and need a rotisserie chicken or a long-running research agent hit them up
Have found the same to be true in doing web research on a space or a problem.
Deep research tools are able to find stuff you know you’re missing, but I have been disappointed by their ability to surface what I’ve missed entirely.
I started forcing a kind of bfs lit review stage in my deep research and it has been notably more useful.
two things I would def take from this if you’re building a tool that will be used by agents:
Descriptive error messages reduce agent misuse of your tool drastically
Adding an onboarding endpoint to your MCP solves so many user setup problems
We asked 50 top investors what they expect founders to know about their space, then distilled the answers into the Founder Stack.
10 reports backed by ~15 continuous hours of research that go deeper on your market, customers, competitors, wedge, upside, and the case against you than anything else out there.
If you don't learn something new, you get your money back.
As a “business guy” I’m slightly embarrassed to share what I’ve been vibe coding. I’ve built a pipeline for doing deep research on San Francisco public policy issues using Manus, https://t.co/5JuOEAIx7r, and Dropbox. When I share I hope you all will have a ton of useful advice (instead of flaming me).
My new deep research setup: Manus to create research prompts / exec summaries, Webhound to run$100 deep research reports (Manus uses the Webhound API), Dropbox to store all the research reports (Manus saves/ organizes reports using the Dropbox API)
My new deep research setup: Manus to create research prompts / exec summaries, Webhound to run$100 deep research reports (Manus uses the Webhound API), Dropbox to store all the research reports (Manus saves/ organizes reports using the Dropbox API)
I always wanted an AI deep research product with the following characteristics:
1. I set the price and it runs for as long as I'm willing to pay
2. Ability to produce reports or structured data
3. Great data validation
I think I might have found what I'm looking for:
https://t.co/tNs7t1FLhb