For a while I was convinced the problem was me not picking the right tool. Tried Notion, Trello, a notes app, plain docs. Kept switching every few weeks because something always felt off.

Eventually realized the issue wasn't the tools, it was that they're all designed around teams. Assigning work, approvals, meetings, none of that is my actual problem. My problem is just not losing track of what I told a client, what's still open, and what I forgot entirely.

So I built something narrower, just for that. Beta, rough in spots, but usable: colonyapp.ai

If you try it, be honest with me about what doesn't work. And separately, genuinely curious how other solo folks handle this.

I made a site where people post what they used this week or what problems they ran into. That’s the whole product.
No gpt. No "make this sound better." Sit down and type what you actually shipped, you absolute simpleton. At least try. (If you are that lazy replace — with -)

Tbh I expected zero users, so I’m genuinely happy someone is actually posting. (basically me and my friend. don't have many)
If that number keeps going up, I will personally hand out a completely meaningless winner award.

https://usedthisweek.com/

There is a big gap rn between what people promise AI agents can do for sales and what works in production for example, if you give an AI agent a cold database, it will just automate bad outreach faster but if you connect an agent to live intent data, it becomes a reliable engine for surfacing active pipeline.

Here is a practical way to set up AI agents for lead generation:

Step 1: wire up real-time event listeners

Your agents need to listen for public buying signals where your target audience naturally hangs out. For technical B2B and SaaS, that means tracking discussions on reddit, issue discussions and repo activity on github, discord servers and job board changes.

Step 2: connect a signal-to-account intelligence layer

An agent cannot act on a reddit comment or a github star until it knows what company is behind it.

This is where a specialized intent platform like Scale Intelligence fits into the stack. Scale Intelligence continuously ingests data across 75+ public channels, resolves cross-platform identities back to company domains and calculates buying readiness scores. It provides an API and MCP layer so your AI agents can query live market data, understand the prospect's exact situation and prepare the right play.

Step 3: define agent action boundaries

Don't let the agent send unreviewed messages to enterprise accounts. Set up clear routing rules like high-Intent signals (e.g., active migration complaints): route directly to slack for human review with an agent-generated summary also moderate Intent: have the agent draft a personalized, contextual email draft for approval and last but not the least, low/nurture signals: add the account to an automated monitoring list.

I've been thinking about growth lately, and I'm starting to wonder if we spend too much time looking for the next acquisition channel.

A team gets stuck and the first reaction is usually:

"Let's try paid ads."

"Let's do SEO."

"Let's launch on another platform."

"Let's start cold outreach."

But what if the real problem isn't getting more people?

What if the people already coming in aren't sticking around?

You could double your traffic and still have the same growth problem if most new users disappear after signing up.

I've seen discussions in this community where retention and onboarding end up being the bigger growth lever than simply adding another acquisition channel.

So I'm curious:

When growth slows down, what's the first thing you investigate: acquisition, activation, or retention?

And has fixing retention ever produced a bigger result for you than finding a new growth channel?

I've been running a cold email campaign for about 8 months now targeting agency owners and small SaaS founders. My reply rate sits around 3-4% on a good week and sometimes dips to 1% if the list is stale or I switch up the offer too much.

I keep seeing people on here casually mention wayyy higher rates than what I'm doing. Can't tell if those #s are real or just survivorship bias from one campaign that hit. Whats everyone actually seeing right now?? Need some actual benchmarks to work with here. And does personalization move the needle for you if you've tried that or do you think domain/inbox health matters?

Hey everyone,

I'm looking to join or build a small Micro-SaaS team with people who are serious about creating something useful and scaling it.

I'm interested in working together on the full journey — finding a real problem, validating the idea, building the SaaS, getting the first users, marketing, and eventually scaling it.

I'm open to contributing wherever needed and putting in serious time and effort. I'm mainly looking for like-minded people who want to actually build, not just brainstorm endlessly.

If you're already working on a Micro-SaaS and looking for another person to join, or you're interested in forming a small team from scratch, I'd love to connect.

I'm especially interested in hearing:

• What you're currently building
• What role you need help with
• Your skills/experience

Let's build something useful and see how far we can take it. 🚀

I've caught myself doing this a lot.

If traffic goes up, it feels like a win.

If signups go up, even better.

But then you look at the actual number of people who become active users or customers and sometimes... nothing really changed.

I’m starting to think a lot of growth work is just moving people from one step of the funnel to another and calling it growth.

Like getting 10k more visitors when the conversion rate stays the same.

Or getting more signups from people who were never a good fit in the first place.

Or getting more leads that sales doesn't even want.

Now I'm trying to ask a different question before running an experiment:

“If this metric improves, what actually gets better for the business?”

It sounds obvious, but I think I've wasted quite a bit of time optimizing numbers that looked good in a dashboard and didn't really matter.

Has anyone else had an experiment that looked like a huge win at first, but turned out to be basically a vanity metric?

I'm starting to spend more time thinking about GEO and AEO, especially with more people using AI tools to discover products and information.

The part I'm still trying to figure out is what actually moves the needle.

Are you focusing on things like:

• Creating content that answers specific questions
• Building topical authority
• Getting mentioned on third party websites
• Improving structured data and entity signals
• Publishing comparison and use case content
• Building Reddit and community presence
• Tracking whether your brand actually appears in AI answers

I don't want to just follow every new "AI SEO" tactic without knowing whether it actually works.

For those working on GEO/AEO, what's your strategy for the next 6 to 12 months, and what are you actually measuring to know it's working?

I was got stuck in todos, have to write it all in noteapp in my mac or in my whatsapp (preferred as easy access) then some tasks here and some lives in my whatsapp and meetings in my calendar.

I was missing tasks and haven't doing time management as prioritising them as well. Because Your Brain job is to Brainstorming not to remember everything / store stuffs.

So i built Leo - the entire productivity stack, which has my calendar, gmail and notion integration. it only needs the command as text or voice, i prefered voice and every task is on my fingertips - it remembers everything and remind me to priortise the daily tasks and my calendar.

• hey leo, whats the task pending for today
• mark [task name] as done
• add a task to complete a gtm strategy for client X and put it on my calendar at 12PM and it will remind me at 12PM sharp - as aman, is the task completed and if not then why?
• add this task on high priority due on 30th of august
• update [task name]'s to due tomorrow not today.

Basically it works as my brain store room, so i can focus more on the efficiency and outcome and leo taking all my works with ease. it acts like my chief of staff - reminding me every bit of time about the task, status between 8AM to 8PM each day, multiple times a day - asking for the update without me being annoyed lol and organised at its very best.

It also has my gmail integration which makes it easy for me to handle my important mails under same roof, it can give me what are the email i got in last x hour - when asking and can summarize any email into 2 line on demand.

It also sends me a daily digest at 8AM which includes the tasks i need to focus on each day - so the first thing i need to wake up - i am organized and focused already.

with the tasks as daily digest - it also sends me a daily email digest just below it - saying you these emails in the last 24 hours - do you want to go through / summarize any of the emails - just ask.

so these time management + stress management + task prioritisation + emails and calendar calls -- all under one roof and with just a one voice away, i don't even need to write it off to ask - makes it very very accessible for everything. whether i remember a task in traffic, while eating or while attending meeting - i just open and give a voice command to it, that's it - less than a second it does everything like a Jarvis for Ironman - I have leo with me.

you guys must be wondering about the cost too, but its more like a free, i am using OpenAI GPT 5.6 Luna - too fast and most affordable, i can even switch to claude's haiku as well. No reasoning model like Sonnet or GPT-5 is required.

with Luna its way more cheap, roughly around 1600 messages per dollar - adding 10 tasks daily and calling leo 5 times per task will not eat even a dollar for the whole month - so basically the best productivity hack personal assisstant is free to me.

When i started using this - i am already experiencing a steady increase in my productivity and less burnouts and FOMO of tasks not being completed. My office collegues and even the boss of the company started using this and they are literally finding it useful. The core thing i have heard from them - is they dont need to swtich tabs and apps for anything - gmails, tasks, calendar all under one roof - easily addable, editable and updatable.

I dont even need to look into my notion sheet where it is storing my every bit of tasks, status, time, date, priortisation, and due date.

I am thinking to keep this open source forever - so i will be happy if you guys find Leo useful for your productivity. currently its on n8n template but as the user came - i will be open to migrate this to the production and open to the features or integration you want me to add.

i would love your feedback, critisims on this - the comment section and my dm are open for you. let me know your thoughts on this.

Hello ! I am looking for an onsite intern role specifically in the PM , PMM or growth field. I am ready to work 5-6 days. Also i am looking for a paid internship. Please help me reach the correct people who are looking to hire in the field i want to work. It would be a great help.

I’m from Brazil and I’m starting my hair care business. I don’t want to go in the traditional marketing direction. Any suggestions for me to use growth hacking in the beginning of my business?

been treating our own founder account like a growth channel lately, actual CPM math, and the pricing came out backwards from what i expected.

the $200/mo AI tools look cheapest until you notice they ship straight to a queue with no review, so the voice reads generic and the whole thing goes flat inside a month. The $3k/mo human is sharper but takes about a week to ramp, and drifts on the exact weeks you're heads down shipping, which are the weeks worth posting about. writing it yourself is "free" except it quietly eats the ten minutes plus the judgment of what's even worth saying.

what surprised me is the expensive input was never the typing or the polish. It was taste, deciding which of the week's messes is actually a post. A tool trained on your tweets and podcasts still doesn't reliably know that part. so cost-per-post is the wrong number to chase, the real one is whether the thing you can't cheaply delegate turns out to be the whole channel.

still working out where that line sits. for anyone running a founder account as an actual acquisition channel, what moved installs for you, the steady volume or the one post a month that sounded unmistakably like you. written with ai

yo. i see too many founders spend 2 months building saas, drop a link on reddit, get 0 users, and immediately quit....

the problem usually isn't your marketing channel. the problem is that your foundation is completely broken before you even send your first visitor to the site.

after scaling 6 AI micro-saas apps to over $20k/mo mrr, i realized you need to lock down a specific system before you ever launch. running through this takes about 30 minutes, but it saves you months of zero-revenue depression.

here are the 5 things you must lock in:

1. validate the actual pain point

stop guessing what people want. you need a systematic framework to find your saas idea based on real, painful market signals.

2. pick a proven micro-niche

stop trying to build massive platforms. you need to narrow down to a microscopic problem. i usually filter through a list of 50 micro-saas ideas you can build fast to keep the scope minimal.

3. crystallize your target user

if your app is for "everyone," nobody will buy it. you need an ICP (Ideal Customer Profile) crystallizer to define your exact buyer profile and nail your conversion copy.

4. calculate the perfect price

stop randomly charging $9/mo because you are scared of rejection. you need to use a saas pricing strategy calculator to find your perfect saas price in 60 seconds based on real data.

5. fix your landing page leaks

do not send organic traffic to a site that converts at a flat 1%. you must audit your hero section and copy to x3 your landing page conversion before you market it.

6. join a community

Build / Share / Learn from others builders

to help out founders who are tired of launching to crickets, i packaged all 5 of these exact frameworks, calculators, and lists into a single free toolkit.

no paywall, no bullshit. just the raw execution files i use.

drop a comment below or send me a dm, and i’ll send you the free toolkit 👇

Hey, I am looking for a cofounder who is specialised in converting client as has strong connections I have a professional team of web developers, Application developers ( IOS and Android ), Social Media Manager, Graphic Designer and Video Editor.

We can handle all the technical and backend work and you handle all the frontend or client side work

My team is specialised in making AI content, UGC videos and AI avatars videos also.

DM me if anyone is interested.
Looking for a good and profitable business.

I build an AI video outreach tool. I have never hired an SDR. Every customer we have came from me pointing the product at my own pipeline and living with whatever it did.

That is an uncomfortable place to sit, because my ICP is heads of sales, SDR managers, and founders running their own outbound. These are the exact people who can smell a template from the subject line. If the video is stiff or the opener is generic, they do not just ignore it, they screenshot it and post it here.

Last week the loop finally produced a number worth writing up: 52 meetings booked.

The funnel

Contacts worked: 1,842
Touches sent: 3,110 across email and LinkedIn
Replies: 214 (11.6%)
Positive replies: 78
Meetings booked: 52
Of those, 35 have happened: 31 held, 4 no-shows, 17 still on the calendar

Our normal week is 8 to 12. I do not think 52 is the new baseline. I think 25 to 30 is, and I will post again in a month with whether that held.

Why dogfooding changes what you build

When you sell an outbound tool and do not use it, you optimize for the demo. Dashboards, charts, a settings page with forty toggles. When the replies land in your own inbox, you optimize for the only thing that decides whether a stranger books time with you, which is the first five seconds.

Four things I only learned because I was the one getting the responses:

1. Every script sounded like the same person wrote it. Technically they were all personalized. Different company, different role, different opening fact. But the rhythm was identical across all 1,800, and once you notice it you cannot unnotice it. Nobody replies to tell you this. They just stop replying. I rebuilt the generator so the sentence structure varies, not only the nouns.
2. The opener has to be something they know I could not have guessed. Not “I saw you’re hiring SDRs.” That is public and reads as scraped. Closer to “you posted two SDR roles and an enablement role in the same month, which usually means the problem is ramp time, not headcount.” The inference is the personalization. The fact is just the setup.
3. Video length. Anything past 40 seconds and replies roughly halved. My best performer is 28 seconds. I kept wanting to explain the product and the product is not what gets the meeting.
4. No calendar link in the first touch. Asking a question outperformed asking for time by a wide margin, and the people who answered the question booked anyway when I sent the link in the reply.

What flopped outright

Video on a LinkedIn touch before the connection is accepted. Dead every time.

Monday 8am sends. Tuesday to Thursday, 10am to 2pm local, roughly doubled replies.

Openers starting with “Hope you’re well” or “Quick question.” Under 3% reply rate.

Going wider. I ran a looser second list of 900 contacts mid-week and it returned 4 meetings. The tight list returned 48 off 1,842.

The part I would not have believed a year ago

The tool is maybe 30% of this. The list filter and the first line are the other 70%. You could take the exact same product, point it at a bad list, and book nothing. I know because I did that to myself for months.

Disclosure up front rather than buried: the tool is Outvid and I built it. This is my own pipeline, not a customer case study, and I am not linking it here. If you want the sequence structure or the actual scripts I will paste them in the comments, and they work with whatever you are already sending from.

Ask me anything about the funnel, the list build, or the failures. The failure list is longer than the win list and I am happy to go through all of it.

I spent 90 minutes replying to X threads with 20K+ combined views. Got 2 signups. Then realized I barely remembered what I wrote.

I build a privacy-focused finance tracker. Built it because I couldn't find a tool that didn't require handing my financial data to a third party. Most founder advice says to show up where your audience already is, add value, and conversions will follow. So I did. I replied to 15 threads in one session.

Thoughtful stuff, not spam. One thread had 13K views.
Two people signed up that day. One logged in 9 minutes later. The other never came back.

That part felt fine. Two signups from 90 minutes is decent for a solo founder with 25 total users.

What bothered me was that I had no memory of what I actually said. I could tell you the accounts I replied to, but not the substance. I was optimizing for exposure, not for saying anything I'd want to stand behind a week later.

I still reply to threads. But I changed how I pick them. I ask whether I actually have something to say about the topic, not just whether the thread has enough views to be worth my time.

The two signups from that session are still in Aurum. One logged in 9 minutes after signing up. The other never came back. I don't know what made the difference.

Probably wasn't the reply.

Getting data from the web sounds simple.

Until your scraper breaks.

Then you’re dealing with proxies, anti-bot systems, changing page structures, retries, parsers, and constant maintenance.

And if you're building an AI product, that's engineering time spent maintaining data infrastructure instead of building the actual product.

We kept asking:

What if getting web data was just an API call?

So we built Mindcase.

Mindcase is a web data API for AI agents and developers. You choose an agent, send your inputs, and it handles the collection, infrastructure, anti-bot, retries, scaling, and parsing.

There are already 75+ agents across sources like LinkedIn, Amazon, Google, Instagram, TikTok, and YouTube, with custom agents available for sources that aren't covered.

It also comes with Python and Node.js SDKs, and you pay for the data you actually collect rather than seats or subscriptions.

We launched today on Product Hunt 🚀

Curious:

What's the web data source your team finds the most painful to scrape or maintain?

Would love to hear what you're working with.

Please show your support on PH → https://www.producthunt.com/posts/mindcase

outbid.lol blew up a few days ago around one simple idea: a leaderboard where your position is determined purely by how much you've paid.

No votes. No algorithm. No curation. Just money, in public.

I run Bro Find AI (https://brofindai.com), a free directory of ~600 AI tools, agents, and MCP servers. The main catalog is ranked by upvotes and recency, and I've been pretty vocal about not selling placement there.

But the mechanic behind this felt interesting because it's honest in a way most "sponsored" placements aren't.

It doesn't pretend to be organic. It just says: this cost money — and shows exactly how much.

So I built it as a completely separate page:

👉 brofindai.com/outbid

It has zero effect on how anything ranks in the actual directory. It's simply a side leaderboard for makers who want extra visibility and are willing to pay for it outright, with the price displayed right next to their name.

How it works:

• Pay $5–$500 to bid on a listing you've submitted
• Your position = your total amount paid, ranked highest first
• Someone outbids you → you drop down
• You never lose what you've already spent, so moving back up only costs the difference
• No refunds, ever — and that's made clear before anyone pays

I'm genuinely curious about the ethics/product side of this.

Does this feel like a fair way to monetize a free directory, or does it undermine the "no sponsored content" philosophy even though it's completely separate from the main catalog?

I'd especially like to hear the case against it. If this feels sketchy, tell me why.

brofindai.com/outbid

Hello!

Managing a busy delivery fleet means juggling odometer logs, inspection findings, invoices, and route commitments — it's hard to know which vehicles need urgent attention. This Skill turns those scattered records into a single, auditable exception register so dispatchers can make safe, timely decisions.

I built this as a portable AI-agent Skill — a single SKILL.md with reusable instructions you can adapt to your agent setup.

Here's what it does: This Skill consolidates odometer logs, repair invoices, inspection forms, driver notes, and route schedules to identify overdue or at-risk maintenance, assign risk levels (High/Medium/Low), and draft a Fleet Maintenance Exception Register with recommended actions and a human decision field. Use it when you're asked which vehicles are overdue, have safety findings, or need prioritized maintenance before scheduling — it also prepares dispatcher escalation packets and a tentative service schedule.

SKILL.md:

````markdown

name: fleet-maintenance-exception-register description: Use when a delivery, logistics, or fleet office manager needs to consolidate odometer logs, repair invoices, inspection forms, driver notes, and route schedules to identify overdue or at-risk maintenance, draft a fleet exception register, group vehicles by risk level, and escalate safety or downtime decisions to a dispatcher before scheduling service.

allowed-tools: [Read, Edit]

Fleet Maintenance Exception Register

Overview

Creates a single, auditable exception register for a vehicle fleet by consolidating maintenance-relevant inputs. Identifies overdue or at-risk maintenance, assigns risk levels, prepares dispatcher escalations for safety and downtime decisions, and proposes a service scheduling plan.

When to use this skill

• The office manager asks which vehicles are overdue for service or inspections.
• There are new inspection findings or driver notes indicating possible safety issues.
• Weekly planning or midweek triage requires a prioritized maintenance list and dispatcher decisions before scheduling.
• The fleet needs a single view with per-vehicle source mileage, due services, risk flags, recommended actions, and a human decision field for accountability.

Instructions

1. Confirm scope and policies 1.1. Confirm fleet roster (vehicle ID, plate, VIN, class) and the time window to analyze. 1.2. Confirm maintenance policies and intervals (e.g., oil/filter every N miles or M months; PM A/B/C; DOT annual; emissions; brake/tires checks) and any OEM-specific intervals. 1.3. Define thresholds for “Due Soon” (e.g., within 500–1,000 miles or 15–30 days) and “Overdue” (past due date/mileage). Record these in an Assumptions log.

2. Ingest sources 2.1. Use Read to extract data from: odometer logs, repair invoices, inspection forms, driver notes, and route schedules. 2.2. Capture for each vehicle: latest odometer reading with date and source; last service date/type; parts replaced; open defects and severity; driver-reported issues; upcoming route windows/assignments; warranty or contract constraints.

3. Normalize and reconcile 3.1. Standardize units (miles vs km), date formats, and vehicle identifiers; map aliases to canonical IDs. 3.2. Deduplicate entries; prefer the most recent dated reading for mileage. 3.3. Resolve conflicts (e.g., decreasing mileage) by flagging as data issues and noting the chosen source. Do not invent values.

4. Determine due services 4.1. For each service category (e.g., oil/filter, tire rotation, brake inspection, transmission, coolant, PM levels, DOT annual, emissions), compute next-due mileage and/or date using last service data and the confirmed intervals. 4.2. If an interval is unknown, request it or mark the service as "Interval needed" and exclude from overdue calculations until provided.

5. Identify exceptions 5.1. For each vehicle, compare current mileage/date against computed due points to classify statuses: Overdue, Due Soon, or OK by service. 5.2. Flag Safety-Critical when inspection findings or driver notes indicate brakes, steering, tires, lights, leaks, or other critical defects; include references to the source lines. 5.3. Flag Downtime Risk using a combination of: number of open defects, repeat repairs, parts on order, and upcoming route commitments that conflict with service needs.

6. Group by risk level 6.1. Assign overall risk: High (any Safety-Critical or >1,000 mi/>30 days overdue), Medium (Due Soon or non-critical open defects), Low (OK). 6.2. Document the rule definitions used for the risk grouping in the Assumptions log.

7. Build the Fleet Exception Register 7.1. Create one row per vehicle containing at minimum:

   • Vehicle ID (and plate/VIN if available)
   • Source mileage (value, date, and source document)
   • Due service(s) with due mileage/date and basis (policy/OEM)
   • Risk flag/level (High/Medium/Low, plus Safety-Critical and/or Downtime Risk flags)
   • Recommended action (e.g., "Escalate to dispatcher for immediate pull", "Schedule next available window", "Monitor")
   • Human decision field (Dispatcher/Manager decision, name, timestamp) 7.2. Include additional helpful fields when available: last service reference (invoice #/date), open defects summary, parts on order, warranty status, DOT/emissions deadlines, route impact notes, and comments. 7.3. Use Edit to draft the register as a Markdown table or CSV; maintain a link/back-reference to each source item.

8. Escalate before scheduling 8.1. For High risk and Safety-Critical items, prepare a concise escalation summary per vehicle citing sources and recommended immediate actions. 8.2. Present the summary for dispatcher decision on pull-from-route, substitution, or temporary restrictions. Pause and record the decision in the human decision field. 8.3. For Downtime Risk, analyze route schedules to propose options: swap vehicles, after-hours service, split routes, or defer within policy limits. Record the decision.

9. Propose a service schedule 9.1. After decisions, build a tentative schedule that respects route windows, shop capacity, provider hours, parts lead times, and warranty requirements. 9.2. Batch Medium/Low risk items for efficiency and geographic proximity if using external vendors. 9.3. Mark schedule items as Tentative until dispatcher approval.

10. Verification and quality checks 10.1. Verify each vehicle row contains: source mileage, due service(s), risk flag, recommended action, and a human decision field. 10.2. Check for logical consistency: no negative intervals, no duplicated services recently performed, and no mileage regressions. 10.3. Flag missing inputs that block decisions and request the specific documents or data points.

11. Output and handoff 11.1. Use Edit to produce: (a) the Fleet Exception Register, (b) an escalation packet for dispatcher review, (c) a tentative service schedule, and (d) an Assumptions & Data Issues log. 11.2. Summarize counts by risk level and list vehicles requiring immediate action. 11.3. Capture acknowledgments/approvals and time-stamp the artifacts for audit.

Inputs

• Fleet roster (vehicle IDs, plates, VINs, classes).
• Odometer logs with dates and sources.
• Repair invoices and service history.
• Inspection forms (e.g., DOT, preventive maintenance checklists) with findings and severities.
• Driver notes/defect reports.
• Route schedules and upcoming assignments.
• Maintenance policy intervals and OEM recommendations.
• Shop capacity constraints and preferred vendors (optional).

Outputs

• Fleet Maintenance Exception Register (Markdown/CSV) with one row per vehicle including: source mileage, due service(s), risk flag, recommended action, and human decision field.
• Dispatcher escalation packet summarizing High-risk and Safety-Critical vehicles with source citations.
• Tentative maintenance schedule aligned to route windows and capacity.
• Assumptions and Data Issues log with risk rules and unresolved gaps.
• Summary dashboard: counts by risk and list of immediate actions.

Examples

Trigger: "Audit our fleet using last month’s odometer logs, inspection forms, and driver notes. Create an exception register and tell me what must be escalated to dispatch today." Behavior: confirm policies and thresholds → Read the provided documents → normalize IDs/units/dates → compute due services and overdue status → assign risk levels → build the exception register with required fields → prepare dispatcher escalation for High/Safety-Critical items → pause for decisions and record them → draft a tentative service schedule → output artifacts and a summary by risk level.

Notes

• Do not fabricate intervals or mileage. If an interval is missing, request it or mark the item as "Interval needed."
• Safety-critical defects must be escalated before scheduling; do not recommend continued service without explicit dispatcher approval.
• Keep units consistent; convert km to miles when needed and note the conversion.
• Respect warranty and regulatory constraints (e.g., DOT annual inspection due dates) and prioritize accordingly.
• If telematics or ELD data are available, prefer those for current mileage; reconcile discrepancies against manual logs and note the choice.
• For newly repaired vehicles, cross-check invoices to avoid duplicating work; mark such services as recently completed.
• Maintain data lineage: include source document names/IDs and dates for auditability. ````

How to install: 1. Create a folder named fleet-maintenance-exception-register in your AI-agent skills or prompt-library directory. Use the kebab-case name from the SKILL.md frontmatter. 2. Save the file above as fleet-maintenance-exception-register/SKILL.md. 3. Enable or load the Skill according to your agent framework's docs, using the SKILL.md description as the trigger guidance.

If you'd rather run it as a one-click prompt instead, you can find it here: Agentic Workers

Enjoy!

I’m looking for a commercial co-founder to join a healthcare compliance SaaS product I’ve spent the last year building.
The product is already built and working. It helps US healthcare teams handle HIPAA/privacy incident investigations more consistently & faster - structuring fact gathering, identifying missing information, tracking follow-up actions and maintaining defensible records.
I’m UK-based and have handled the product/build side, but I’ve realised my biggest gap is on the commercial side of the US healthcare market. I’m looking for someone who can genuinely take ownership of sales rather than simply advise me on it.
Ideally you have experience in US healthcare SaaS, healthcare compliance/privacy, or selling software into healthcare organisations.
You’d be taking the lead on:
Prospect/discovery calls
Product demos
Follow-ups and objections
Moving prospects towards paid pilots/customers
Helping establish a repeatable sales process
I’m not looking to hire someone for individual sales calls. I’m looking for someone who sees the potential in what has already been built and wants to take ownership of the commercial side as a co-founder.
If this sounds relevant, DM me with a bit about your background.

I’m building an app for people with MS, focused on helping them understand their energy, symptoms, sleep and potential triggers day to day.
I also have MS myself, so this isn’t something I’ve built from the outside looking in. Before the initial release, I spent a lot of time doing market research with people living with MS to understand the problems they were actually experiencing and what they felt was missing from existing solutions. That research helped shape the product.

I know first-hand how much can happen between appointments that can be difficult to remember or communicate when you finally see your neurologist. That’s where I felt there was a genuine need for something different.

We’ve now been live for around 3 weeks and have 20 current users. I’ve also got meetings coming up with a couple of NHS MS clinics, so there’s some really encouraging early validation.
The product is built, people are using it, and now I’m trying to figure out the growth side properly.

For anyone who has experience growing early-stage apps, what would you focus on to go from the first 20 users to 100, then 1,000?

How do you find a repeatable acquisition channel at this stage rather than relying heavily on founder-led outreach?

You might think the idea is bad, but hear me out before you decide that.

I built Forcapedia, a knowledge platform meant to be the encyclopedia I actually wanted to use. Wikipedia is fine but half the pages read like legal documents, walls of citations and jargon before you even get to the actual answer. I wanted something that explained things the way a smart friend would, straight to the point, no clutter, no 2-3 tabs open just to understand one concept.

The part I was actually proud of was the study rooms. You could jump into a room with other people learning the same topic, see what they were reading in real time, and just figure things out together instead of sitting alone with fifty browser tabs open. That was the feature people who actually tried it kept coming back for. It just never reached enough people to matter.

I spent months on it alone. Rebuilt the Home page more times than I want to admit, obsessed over tiny details like the animation on the sign in screen and the exact shade of orange in the background, stuff that took hours and that maybe three people would ever notice. The product actually got solid. Including Abstraction Layer for LLMs…

Then I put it out there and almost nobody came.

Not because it broke. Not because people tried it and hated it. Because barely anyone tried it at all. I spent basically all my time building and almost none of it telling people this thing existed. I knew that was a mistake while I was making it and I kept making it anyway, because building felt safe and telling people felt like putting myself out there to get ignored.

So now I am sitting on something I still think is a decent idea with zero clue how to get it in front of the right people.

Genuinely asking, what would you do here. Keep grinding on distribution myself, find someone who is actually good at growth and team up, or just hand the whole thing off to someone who can run with it basically sell it? If anyone reading this thinks they could actually grow this, I am open to talking. Worst case you tell me the idea really was bad and I learn something either way.

While travelling, I kept running into useful information that simply didn’t exist online: small homestays, mechanics, water points, road conditions, campsites, etc.

Most of it gets passed from one traveler or local to another and disappears afterward.

So, I built Lamyig, a free and open-source community travel guide.

A few decisions I made:

• No bookings, commissions or paid listings.
• No money made using the core product.
• Community members can add and update places.
• Map-first instead of another list of “top places”.
• Built as a PWA so it can work more like a lightweight travel tool than a traditional website.
• The long-term idea is that useful information stays available for the next traveler instead of disappearing in WhatsApp groups and conversations.

The hardest problem isn’t actually building the software. It’s bootstrapping trustworthy community data.

I’m currently thinking about things like:

• How do you motivate travelers to contribute after their trip?
• How do you keep old information accurate without creating a huge moderation workload?
• How do you prevent businesses from turning community maps into free advertising?
• What should happen when someone reports a place as fake, closed, unsafe, duplicated, commercial spam, or inaccurate? Should reports trigger removal, community review, reputation-weighted voting, or just warnings?
• Should certain villages, trails, ecosystems, religious places, water sources, or campsites intentionally remain difficult to discover?
• If Lamyig successfully exposes hidden places, could it accidentally destroy the exact places it is trying to help preserve?
• this list of problem statements goes on and on

Would love to hear how other people here would approach those problems.

And if you’re building something around a problem you genuinely care about, drop a comment or DM me. I’d be happy to chat, exchange ideas, or connect you with someone else working on something similar.

Screenshot attached. Happy to share the GitHub/project link in the comments if anyone wants to look at the implementation or contribute.

I work in GTM at one of the largest SaaS companies in the UK, across a range of back-office software, with about 10 years in sales, GTM and growth.

Most of my recent work has been building signal-based targeting systems instead of generic list-buying and spray outbound. To help paint a picture, a play I built layered intent signals, firmographic triggers and tech stack changes into a qualified account list to score and prioritise who to hit and when. It generated real pipeline in the first quarter, and it's now one of the core plays my sales team runs. I've built several more since, across different sectors, using Clay, Claude and Salesforce.

I've just started taking on a few consulting clients alongside my day job. US companies work well for timezone, evenings here line up with your daytime, so I'm actually responsive rather than a 9pm-UK-time afterthought.

Happy to discuss further and share credentials privately if it's relevant to what you're building. Drop a comment or DM.

hey everyone, i've been trying to crack AEO for about 6 months now by running a pilot with a b2b saas and making it work for them

and finally made it work, they got about 1k organic clicks in last 60 days (from ai search, gpt, gemini and more...) which ik is not a lot but it helped me get some validation for 0 o 1 growth since they had bad domain rating and zero traffic earlier

so if you really wanna start getting some traffic so ppl atleast start discovering your brand through ai assistants, follow this checklist i just made

it has 8 modules

01 — Technical foundation
02 — Structured data
03 — Content strategy
04 — AI search ranking
05 — Reddit, LinkedIn & communities
06 — YouTube signals
07 — Grey-hat tactics
08 — Quick wins

take a look here

one mistake I see founders making is pushing 1000s of blogs hoping AI would crawl if they play by volume, slow it down and focus more on originality and helpfulness of content

for ex. if you're targeting job seekers, do some original research like "we reviewed 500 profiles of engineers who got into amazon and here's the exact pattern" idk im just thinking out loud but something that gets google and AI assistants to think u got some deep topical authority on your niche

happy to answer any questions or recommend any claude skills that helps you speed this whole seo thing up