u/pystar

I'll take your word for it if it fails. No verification, no hoops.

Simple challenge.

Go to Hunch. Hit the chat icon in the bottom right corner. Type any plain English request a real website visitor might actually make. Something you'd genuinely want a site to handle.

Book a demo. Sign up for a plan. Ask about pricing and then sign up in the same message. Contact support. Make a purchase. Ask a question and then immediately give a directive. Try to confuse it.

If Hunch fails to understand what you meant and take the right action, you get a free month of the paid plan. $49, yours, no questions asked. I will take your word for it.

Some prompts to start with if you want a baseline before you get creative:

"sign me up for a free trial"

"how much does the pro plan cost and sign me up"

"I want to book a consultation"

"contact support"

"schedule a demo"

"do you have a free trial?"

Those are the easy ones. The interesting attempts will be the ones I haven't thought of.

For context on what Hunch actually is: it's a single script tag that puts a widget on any website. Visitors type what they want in plain English and the widget takes the action on their behalf, on that site, end to end. Not a FAQ bot. Not a lead capture form. It actually does things. The intent parsing, the question vs action distinction, the multi-step requests, all handled without the site owner configuring rules for every possible input.

I built this alone. I have no idea how it holds up against people actively trying to break it. That's genuinely why I'm posting this.

Drop your result in the comments. Win or lose, I want to know what you tried.

I'll take your word for it if it fails. No verification, no hoops.

Simple challenge.

Go to Hunch. Hit the chat icon in the bottom right corner. Type any plain English request a real website visitor might actually make. Something you'd genuinely want a site to handle.

Book a demo. Sign up for a plan. Ask about pricing and then sign up in the same message. Contact support. Make a purchase. Ask a question and then immediately give a directive. Try to confuse it.

If Hunch fails to understand what you meant and take the right action, you get a free month of the paid plan. $49, yours, no questions asked. I will take your word for it.

Some prompts to start with if you want a baseline before you get creative:

"sign me up for a free trial"

"how much does the pro plan cost and sign me up"

"I want to book a consultation"

"contact support"

"schedule a demo"

"do you have a free trial?"

Those are the easy ones. The interesting attempts will be the ones I haven't thought of.

For context on what Hunch actually is: it's a single script tag that puts a widget on any website. Visitors type what they want in plain English and the widget takes the action on their behalf, on that site, end to end. Not a FAQ bot. Not a lead capture form. It actually does things. The intent parsing, the question vs action distinction, the multi-step requests, all handled without the site owner configuring rules for every possible input.

I built this alone. I have no idea how it holds up against people actively trying to break it. That's genuinely why I'm posting this.

Drop your result in the comments. Win or lose, I want to know what you tried.

I'll take your word for it if it fails. No verification, no hoops.

Simple challenge.

Go to Hunch. Hit the chat icon in the bottom right corner. Type any plain English request a real website visitor might actually make. Something you'd genuinely want a site to handle.

Book a demo. Sign up for a plan. Ask about pricing and then sign up in the same message. Contact support. Make a purchase. Ask a question and then immediately give a directive. Try to confuse it.

If Hunch fails to understand what you meant and take the right action, you get a free month of the paid plan. $49, yours, no questions asked. I will take your word for it.

Some prompts to start with if you want a baseline before you get creative:

"sign me up for a free trial"

"how much does the pro plan cost and sign me up"

"I want to book a consultation"

"contact support"

"schedule a demo"

"do you have a free trial?"

Those are the easy ones. The interesting attempts will be the ones I haven't thought of.

For context on what Hunch actually is: it's a single script tag that puts a widget on any website. Visitors type what they want in plain English and the widget takes the action on their behalf, on that site, end to end. Not a FAQ bot. Not a lead capture form. It actually does things. The intent parsing, the question vs action distinction, the multi-step requests, all handled without the site owner configuring rules for every possible input.

I built this alone. I have no idea how it holds up against people actively trying to break it. That's genuinely why I'm posting this.

Drop your result in the comments. Win or lose, I want to know what you tried.

I'll take your word for it if it fails. No verification, no hoops.

Simple challenge.

Go to Hunch. Hit the chat icon in the bottom right corner. Type any plain English request a real website visitor might actually make. Something you'd genuinely want a site to handle.

Book a demo. Sign up for a plan. Ask about pricing and then sign up in the same message. Contact support. Make a purchase. Ask a question and then immediately give a directive. Try to confuse it.

If Hunch fails to understand what you meant and take the right action, you get a free month of the paid plan. $49, yours, no questions asked. I will take your word for it.

Some prompts to start with if you want a baseline before you get creative:

"sign me up for a free trial"

"how much does the pro plan cost and sign me up"

"I want to book a consultation"

"contact support"

"schedule a demo"

"do you have a free trial?"

Those are the easy ones. The interesting attempts will be the ones I haven't thought of.

For context on what Hunch actually is: it's a single script tag that puts a widget on any website. Visitors type what they want in plain English and the widget takes the action on their behalf, on that site, end to end. Not a FAQ bot. Not a lead capture form. It actually does things. The intent parsing, the question vs action distinction, the multi-step requests, all handled without the site owner configuring rules for every possible input.

I built this alone. I have no idea how it holds up against people actively trying to break it. That's genuinely why I'm posting this.

Drop your result in the comments. Win or lose, I want to know what you tried.

I'll take your word for it if it fails. No verification, no hoops.

Simple challenge.

Go to Hunch. Hit the chat icon in the bottom right corner. Type any plain English request a real website visitor might actually make. Something you'd genuinely want a site to handle.

Book a demo. Sign up for a plan. Ask about pricing and then sign up in the same message. Contact support. Make a purchase. Ask a question and then immediately give a directive. Try to confuse it.

If Hunch fails to understand what you meant and take the right action, you get a free month of the paid plan. $49, yours, no questions asked. I will take your word for it.

Some prompts to start with if you want a baseline before you get creative:

"sign me up for a free trial"

"how much does the pro plan cost and sign me up"

"I want to book a consultation"

"contact support"

"schedule a demo"

"do you have a free trial?"

Those are the easy ones. The interesting attempts will be the ones I haven't thought of.

For context on what Hunch actually is: it's a single script tag that puts a widget on any website. Visitors type what they want in plain English and the widget takes the action on their behalf, on that site, end to end. Not a FAQ bot. Not a lead capture form. It actually does things. The intent parsing, the question vs action distinction, the multi-step requests, all handled without the site owner configuring rules for every possible input.

I built this alone. I have no idea how it holds up against people actively trying to break it. That's genuinely why I'm posting this.

Drop your result in the comments. Win or lose, I want to know what you tried.

I've been building Hunch for months. It's a script tag that makes your website usable by AI agents.

When ChatGPT or Claude tries to use a site, it usually fails. Forms aren't labeled. Buttons don't say what they do. There's no structured layer for an agent to act on. So the agent gives up or hallucinates.

Hunch fixes that by putting a lightweight action layer on your site. Visitors can type what they want to do ("book a meeting," "get a quote," "find pricing") and the agent handles it end to end, using your actual website content and workflows. Not a generic chatbot. Not a popup. Something that actually understands what your site does and can do it.

The product is ready. What I don't have is enough real people running it on real websites.

So here's the deal: reply here or DM me, we get on a call, and I personally install Hunch on your site from start to finish. Next.js, Webflow, WordPress, Framer, raw HTML, doesn't matter. I handle everything. You just show up.

What I'm asking in return: use it, tell me honestly what's broken or confusing, and if it does something genuinely useful for you, consider the paid plan when you're ready. No pressure on that last part. The call is free regardless.

I'll be straight with you. I'm a solo founder. No paying customers. No team. I built this by myself and I'm at the stage where I need people actually using it in production more than I need anything else. I would rather spend this week on ten setup calls with real people than write one more post that gets three upvotes and zero signups.

If you have a website and any interest in making it work for AI agents, reply here or DM me. I will respond today.

hunchbank.com

Of course, I'm comparing apples to oranges.

I've been running a B2B SaaS solo for a while. Most of my early customers came from content + word of mouth, but I've been experimenting with an affiliate program this quarter. Just added a timed challenge structure to it with some small prizes to see if it drives more referrals.

Would love to hear what other solo/small-team founders here are doing that's actually moving the needle. Cold outreach? Partnerships? Community? Still figuring out the right mix.

I've been tracking everything since I started. Thought the data might help others.

January: $0. Signed up for 4 SaaS affiliate programs. Spent the month writing comparison posts. February: $80. First commissions trickled in. Mostly from one blog post about AI tools. March: $210. Recurring commissions started stacking. Posts from January still earning. April: $380. Added a YouTube video walkthrough. Drove more signups than my blog posts combined. May: $520. Recurring is now about 60% of monthly income. The rest is new conversions.

What I learned:

• Recurring is the cheat code. A $20/mo commission from 10 people = $200/mo and growing.
• Pick tools you'd actually use. I promote 4 products. 2 of them I use daily. The content is 10x better and converts higher.
• Don't spam your link. I put it in one place on each page and focus on genuinely helping readers decide. My conversion rate is ~3% which seems decent.

The best program I'm in pays 20% recurring on a $149/mo product. One referral = $29.80/mo. Three of those covers my hosting bills forever.

Anyone else doing SaaS affiliate stuff? What's working for you?

Been comparing affiliate programs for a few months and I'm trying to figure out what's actually better long-term.

Scenario A: 50% first-month commission only. You get $50 once for a $100/mo referral. Scenario B: 20% recurring. You get $20/mo for as long as they stay.

Scenario A looks better on paper (bigger number) but Scenario B seems to compound way better if churn is low.

I'm in a few recurring programs now and the math is interesting. One of them pays 20% recurring + 5% tier 2 on subs, which works out to way more over 12 months than any one-time deal.

What do you optimize for? Do you prioritize high one-time payouts or lower recurring ones? Has anyone actually tracked their LTV per program?

Spent the last year building something that lets websites not just answer questions with AI but actually do things — submit forms, trigger signups, book consultations.

Hardest lesson: the AI part is solved (GPT wrappers are everywhere). The hard part is the pipeline:

- Crawling and keeping the website's knowledge fresh automatically

- Turning what the AI decides to do into actual actions (API calls, form submissions, etc.)

- Making sure the actions are safe (nobody wants an AI that can accidentally delete stuff)

There are 10+ "AI chatbot for your website" tools now, but almost none of them cross the line from conversation to action. There's a reason for that — the action layer is 10x harder than the chat layer.

If you're building in this space, what's been your bottleneck?

We sell a somewhat technical product (custom furniture) so we get a ton of pre-purchase questions: dimensions, materials, lead times, "will it fit in my apartment."

We set up an AI chatbot to handle this. It did great at answering. But our conversion rate didn't budge. I was frustrated — faster answers should mean more sales, right?

Turns out the visitors who asked "do you do custom sizes?" didn't just want to know "yes we do." They wanted to start the custom order process right there. When we changed the bot to recognize intent and trigger the actual booking/quote flow instead of just answering, our assisted conversion rate went from near-zero to about 8%.

Basically: if your site has any kind of pre-purchase friction (custom orders, consultations, quotes), don't build a Q&A bot. Build an action bot that skips the inquiry and goes straight to the transaction.

Has anyone else noticed this gap? Answers being cheap but action being where the money is?

I'm building an AI agent that handles customer inquiries on business websites. When it works, it works beautifully — answers questions accurately, books appointments, submits contact forms.

When it doesn't work:

- Misunderstands the question (wrong intent detection)

- Answers confidently but incorrectly (hallucination on edge cases)

- Fails to extract the right context from the website (vector search returns irrelevant chunks)

- Tries to use a tool that doesn't apply (our tool routing isn't perfect)

The 80% success rate sounds good in a demo. In production, it means 1 in 5 customer interactions is bad — which is terrible.

We've layered on:

1. Confidence scoring — if below threshold, fall back to a human handoff

2. Topic guardrails — redirect off-topic questions gracefully

3. A "clarify" mode when intent is ambiguous

4. Manual override — the business owner can review and correct responses

The reality: users (the business owners) don't trust the agent because of the 20% failure rate, even though it saves them time overall. The handoff to humans ended up being the most important feature, not the AI itself.

For PMs building AI features: plan for the failure modes before you launch the happy path. The 80% is the easy part. The 20% is where your product lives or dies.

Curious how others handle this — do you aim for 95%+ accuracy before shipping, or ship fast and handle failures with graceful fallbacks?

We have a system where AI agents can call tools that are dynamically discovered at runtime (crawled from website forms/buttons). Each tool has an OpenAI-compatible JSON schema that we don't know until the crawl finishes.

The challenge: TypeScript wants types at compile time, but our tool schemas are runtime data.

Current approach:

- Base Tool type with name, description, inputSchema: JSONSchema

- Type-narrowing helpers that take a Tool and produce typed arguments at the call site

- The LLM returns tool calls as JSON, which we validate against the schema at runtime with Zod

// Simplified version of what we do

interface Tool {

name: string

description: string

inputSchema: Record<string, unknown>

}

function validateToolCall<T extends ZodRawShape>(

tool: Tool,

args: unknown,

schema: ZodObject<T>

): z.infer<typeof schema> {

return schema.parse(args)

}

This works but the DX is terrible — tons of type assertions, and you lose autocomplete because the schemas aren't known statically.

We considered code generation (generate TypeScript types from discovered tools at build time) but that doesn't work because tools are discovered per-customer at runtime.

What patterns are other TypeScript teams using for runtime-validated LLM tool calls? Are there good libraries for this beyond Zod + JSON Schema interop?

Our product needs to keep website content fresh for AI agents. We crawl customer sites, extract content, generate embeddings, and discover interactive elements. Currently managing ~500 active crawls.

Infrastructure breakdown:

Crawler service:

- Built on top of a headless Chromium instance (for JS-rendered sites)

- Runs on Cloudflare Workers for the simple crawls, falls back to a dedicated Node.js service for complex SPAs

- Max 20 pages per site, 500ms delay between requests

- Stores raw HTML + extracted text in D1, embeddings in Vectorize

Re-crawl schedule:

- Homepage + pricing: every 6 hours

- Core pages (about, services, contact): daily

- All other pages: weekly

- Full re-crawl: triggered on website update webhook (if they have one)

Scaling issues:

- Headless Chrome is memory-heavy. We can't run more than ~3 concurrent crawls per instance.

- Some sites (looking at you, e-commerce with 10k products) never finish within our budget.

- Rate limiting — we've been blocked by Cloudflare-protected sites even with respectful delays.

Cost breakdown (monthly):

- Compute for crawlers: ~$180

- Embedding API calls: ~$90

- Storage (D1 + Vectorize): ~$40

- Total crawl infra: ~$310 for 500 sites

Curious what other teams use for crawling at this scale. Is headless Chrome still the default, or are people using lighter alternatives like Playwright or even raw HTTP + parse for simpler sites?

We built an AI agent that gets embedded on business websites and answers visitor questions. The hard part isn't the LLM — it's giving the agent useful context about the specific business.

Our current pipeline:

1. Crawl the site (up to ~20 pages)

2. Split pages into chunks with overlap

3. Embed chunks + store in a vector DB (Cloudflare Vectorize)

4. On each user question, hybrid search (dense + sparse) over chunks

5. Feed top results + extracted facts into the LLM prompt

This works reasonably well, but there are edge cases I keep running into:

- Pricing pages change frequently. How often do you re-crawl?

- Businesses with 200+ product pages — we can't fit everything in context. How do you prioritize?

- Pages with JavaScript-rendered content (React sites, SPAs) — we had to add a headless browser step that triples crawl time

- The "facts" extraction (pricing, contact info, business hours) is surprisingly fragile across different site layouts

What's your approach for giving agents reliable context about a specific business? Do you use structured extraction (LLM-in-the-loop during crawl) or raw chunk retrieval?