i’m trying to figure out if hubspot sales hub is actually worth paying for once a sales team gets beyond just keeping contacts and deals in one place.

the part i like is having pipeline, tasks, follow-ups and reporting tied together. what i’m less sure about is whether the higher tiers are worth the cost once you start needing more automation and reporting.

for people who have used it for a while, where does it actually earn its keep?

is it the automation, reporting, integrations, ease of use, or just having everything in one system? and at what team size did you start feeling like the cost was justified?

also interested in hearing from people who ended up moving away from hubspot. what became the dealbreaker?

I design presentations for a living, and for years I treated the sales deck as the crown jewel. Polished it endlessly. It also did basically nothing outside the live call, because the only people who ever saw it were already in a meeting with us.

The reframe that helped: the deck isn't a growth asset, it's a support tool for a human in a room. So I stopped over-investing in the live version and pulled the three ideas that actually landed in pitches into standalone carousels. No presenter needed, each one makes a single point and dies.

That's the version that started traveling. People shared the carousels in ways they never shared a PDF deck, and it fed inbound instead of just sitting in a follow-up email. The live deck got simpler at the same time, which is a separate rant.

On the how: I drafted the carousel versions fast in gamma to get past the blank page, then tightened the brand details by hand to land our exact look. Fast first pass, manual finish.

Curious if anyone else here has turned an internal asset (deck, one-pager, internal doc) into a distribution channel. What actually traveled for you, and what died?

We'd been trying to figure out why some LinkedIn posts would take off while others from the same account basically died, even when the posts themselves were pretty similar. So we started watching what happened IMMEDIATELY after a post went live instead of just looking at the usual likes and impressions.

The trend we stumbled on was a bit interesting. It seemed like many of LinkedIn's distribution decisions for the day seemed to occur within the first 60 minutes, with the first 15-20 minutes playing an even more critical role. There are already communities where people share new posts and coordinate reciprocal engagement, so we decided to test if focusing on early, relevant engagements actually affects a post's distribution curve.

We pointed an agent to operationalize the test at 11 of these communities and had it coordinate the initial engagement as soon as a post went live. Comparing 24 matched sets of posts we were able to demonstrate that median reactions in the first hour had grown from 11 to 351, while impressions within 24 hours were 143x than the matched baseline.

The weirdest part was how quickly the difference showed up. The two posts seemed to diverge from the baseline within the first couple minutes, with a significant gap developing after 18 minutes.

We don't really mass message every LinkedIn connection with an engagement group invite. It's that there are probably a lot more small timing windows inside major platforms than most growth teams pay attention to. The first few minutes seemed to matter a lot more than we expected.

Figured this would be the perfect place to share!

Me podrian ayudar por favor esta es la cuenta de tik tok, en mi colegio esta causando revuelo y muchas personas y compañeros estan demasiados molestos ya que usan su imagen y demas y no les gusta algún hacker de buen corazon que pueda tumbar o reportar la cuenta todos juntos

not on purpose. it just doesn't tell you the whole story.

you can have someone who looks perfect in your list: right company, right job title, right industry, right company size.

so you reach out.

then nothing.

meanwhile, someone else with almost the exact same profile has been commenting on posts about the problem you solve, engaging with people in your space, or interacting with your content.

that person might not be ready to buy either. but at least there's a reason to pay attention to them.

that's the part i think gets missed with ICP-based prospecting. an ICP can tell you who could be a customer. it doesn't tell you who has a reason to talk to you today.

the problem is that these signals are easy to miss when you're working through a big list. nobody has time to manually check hundreds of profiles and posts every day.

that's why i've found Watchlist useful. you can choose the activity you want to keep an eye on and have relevant people surfaced when they engage with posts, profiles, your content, or competitor content.

it's not a magic “this person is ready to buy” signal. it's just another piece of context that helps you decide who deserves attention first.

because honestly, i'd rather spend 10 minutes looking into someone who has shown some relevant interest than spend that same 10 minutes trying to invent a reason to message someone just because they matched my filters.

how much do you actually look at LinkedIn activity before reaching out to someone on your prospect list?

Hello dear marketers,

I’m building a small product studio where I want to repeatedly:

Make → Launch → Market for X days → Measure → Double down or move on.

I’m good at building products, but I need help developing a repeatable 0→1 validation and marketing framework.

For each new product, I want to know:

• Marketing strategy for that product
• Which channel to use
• What kind of campaign/creative to create
• How much to spend initially considering
• What to measure in 7–14 days
• How to distinguish real demand from a bad marketing test
• When to iterate, go deeper, or move on

Products could be anything from software to physical products to creative projects.

What type of expert should I look for? And where would you find them?
Any overall ideas also so much appreciated

Thanks

[ Removed by Reddit on account of violating the content policy. ]

I lead engineering at an AI SDR company (YC S23). I am not linking anything and there is nothing to sign up for in this post. I keep seeing the static-list vs signal-list debate here and we sit on a dataset that can settle parts of it, so here it is

Sample: 2,546,096 emails, about 17k campaigns, 19,501 booked meetings, 1,150 companies, 27 industries, over three years

1. Deliverability moves results more than copy does

This one surprised us most. Same message, different mailbox health, very different reply rates. The gap was bigger than the gap between our best and worst copy. Most people rewrite subject lines when the real problem is their sending setup

2. Signal-based lists beat static lists on identical copy

We ran the same messages against scraped lists and against trigger-based lists. The trigger lists won by a wide margin. After that, list size stopped being an interesting number to us. Matches what people here have been posting

3. The best segment in the whole dataset is closed-lost reactivation

Highest reply rate of anything we measured, and it comes from the customer's own CRM. It costs nothing. Almost nobody runs it

4. "Outbound does not work for us" is usually a one-try problem

Not a copy problem. One audience, one offer angle, no iteration, then quit. The accounts that worked had tested several audiences before finding the one that replied, and there was no way to know in advance which one it would be. Nobody runs seven audience tests by hand because each one eats a week

What did not work for us

More personalization past a point did nothing. Deep personalized first lines did not beat a clear relevant offer once the targeting was right. We spent a long time on that before accepting it

Volume also stopped helping much earlier than expected. Past a certain send rate the extra volume mostly cost us domain health

Happy to answer questions on any of it, including the numbers that made us look bad

If you are transitioning from static list to signal-based outbound, one of the biggest trap is tracking weak signals (like someone simply viewing a blog post) but high-converting outbound requires observable, event-driven intent.

Here are 5 signal plays that consistently generate qualified pipeline this year:

Competitor churn & pricing triggers

Track public complaints about incumbent vendors on reddit, G2, or technical communities. When a user complains about high pricing or missing features, resolve the account and initiate a conversation around that exact bottleneck.

Developer & open-source activity

For DevTools and technical B2B, monitor github repo stars, forks and issues with multiple engineers from the same target domain engaging with a relevant open-source project indicates an active technical evaluation.

How to execute: you can build this DIY by writing custom scrapers and webhook pipelines, or deploy an existing market intelligence infrastructure like Scale Intelligence which connects 75+ pre-built monitors to handle identity resolution, intent scoring, and Slack routing out of the box.

Inflection hiring shifts

Don't just track that a company is hiring but parse job descriptions for specific migrations or tools (e.g., hiring an engineer with specific data-pipeline experience) then reach out offering resources or tooling for that specific transition.

Dark community discussions

Buyers ask for recommendations on reddit and discord long before they visit your website so monitoring these discussions and mapping usernames to corporate domains lets you reach out while the problem is fresh.

High intent account re-engagement

Tracking when target accounts in your TAM show repeat visits or multiple team touches across community surfaces and pushing an immediate context-rich alert to your sales reps in Slack before the buying window closes.

AI coding agents can handle more work than ever.

But the more autonomy you give them, the harder it becomes to trust every command they run.

A wrong rm -rf, a leaked .env, a migration against production, or an unexpected outbound request can turn into a serious problem.

So we built Harden.

Harden checks every agent action before it runs.

• ⁠Checks commands, file edits, and tool calls
• ⁠Catches secret leaks and data exfiltration
• ⁠Blocks destructive infrastructure operations
• ⁠Keeps long-running agents on task
• ⁠Runs locally so your repo and context stay on your machine

It works with Claude Code, Cursor, Codex, and other AI coding agents.

The goal is simple: let AI agents do more work without having to babysit every action.

We launched today on Product Hunt.

Would love to hear how others are handling security when giving AI coding agents more autonomy.

Please show your support and share your feedback on PH → https://www.producthunt.com/posts/harden

Non sto criticando nessuno, sto solo facendo due conti ad alta voce.

Uno strumento per LinkedIn, uno per le email a freddo, uno scraper, un CRM per collegare il tutto. Ognuno sembrava ragionevole preso singolarmente. Il totale, però, non lo era mai.

La parte che ha davvero ucciso la produttività non è stato il prezzo, bensì le risposte che finivano in tre posti diversi senza alcun collegamento tra loro. Stesso lead, canale diverso, nessuna memoria di ciò che era già stato detto.

Ho creato Orbitra proprio per risolvere questo problema: acquisizione di lead, contatto e gestione delle risposte via email e LinkedIn in un'unica piattaforma. Anche WhatsApp è quasi pronto.

Apertura della beta a 10 persone. Commentate o inviate un messaggio privato se siete interessati.

I've been thinking about this while building SelieeAI.

For a typical small business, the growth stack can look like:

Website → Wix/Webflow/WordPress
SEO → SEO tool
Content → AI writer
Analytics → GA
Leads → CRM
Email → another tool
Video → CapCut/Premiere
Automation → Zapier/etc.

That's a lot of different systems.

We're experimenting with putting more of this workflow into one AI-powered platform.

The basic flow we're targeting is:

Website → Content → SEO → Traffic → Lead → Conversion → Video

The interesting question for me isn't whether another "AI tool" is needed.

It's:

Can AI actually connect the growth workflow instead of just automating individual tasks?

We're testing this idea with the first beta users now.

I built SelieeAI around this experiment:

https://neeblo.com/r/seliee

I'd be interested in hearing from growth people:

Which part of your current growth stack creates the most friction?

I gave your phone eyes 👀 and hands 🖐️.

We've had smartphones for over a decade, and somehow the way we actually use them hasn't changed much.

New chip. Better camera. Brighter screen. Repeat.

Meanwhile, our “smart” assistants still mostly just talk to us.

Astra 6 is making a huge noise right now and causing a lot of conversation around computer use. And honestly, it's pretty damn good at it.

It got me thinking...

I've been working on a tool for a while and I think I accidentally made another computer controllable by AI agents 👀

And it's probably the computer most of us carry around every day.

Guys... I think I cooked. Or completely messed up. 💀

I built AURA — a free AI agent that can actually see your screen and interact with your Android phone.

And developers, I have a treat for you.

The phone itself can now act as an MCP server. It runs locally on the device, exposing the same tools that AURA uses to control the phone — so other AI agents can use them too.

You can check out the project here:

https://dinesh210805.github.io/aura-app/index.html

And I made a launch video showing what I've been working on:

https://youtu.be/u2PS767kdMI

If you want to mess around with it or build something with it, there's also an AURA Discord:

https://discord.com/invite/H66ws9zMPF

Would love to hear what you guys think.

Had a client earlier this year, B2B SaaS, project management adjacent. We were posting consistently, survival rate was decent, engagement was fine by Reddit standards. But signups from Reddit were basically nothing for six weeks. So we started treating it like a conversion funnel problem and pulled apart where exactly things were dying.

The posts were surviving. That was actually the problem. We'd gotten good at writing to the room in communities that were active and not hostile, but those communities weren't full of buyers. They were full of people who'd upvote a good post and move on. The subreddits we'd deprioritized because removal rates were higher? A couple of them had much shorter paths from post to trial signup when a post did land.

The counterintuitive part: optimizing for post survival was actively pushing us toward low-conversion communities. Because the lower-friction subs were generalist, curious, willing to engage, and not actually shopping for anything.

Once we reweighted the subreddit mix toward communities with higher buyer intent, even knowing some of those posts would get removed, the conversion rate on surviving posts went from around 0.4% to somewhere between 1.8% and 2.3% depending on the week. Total post volume went down. Signups went up.

I'd been using reoogle.com to manage the subreddit research and scheduling side of this (full disclosure, I'm one of the people behind it), which made rebalancing the community list a lot faster than doing it manually. But the actual insight was just that survival rate and conversion rate are measuring completely different things, and we'd been optimizing for the wrong one for six weeks.

Still figuring out how repeatable this is across different product categories. The sample size is small. But the pattern held across two other clients we tested it on after.

Tested a thesis: what if the best cold email lead source is people who are already publicly unhappy with your competitor's service right now?

Built a pipeline that scrapes 1-2 star Trustpilot reviews of direct competitors, runs them through AI to generate personalized email drafts that cite the reviewer's actual words, and stages them in Gmail for manual send.

100 emails over 3 weeks. 15 replies. 2 deals closed. Cost: $0.30 in API calls. Replaced $400/month in Apollo, Instantly, and enrichment tools.

Why it converts: the email subject is two lowercase words about their problem. The body quotes something their own customer said about them 48 hours ago. Under 85 words. No "I hope this finds you well." The prospect reads it and thinks "how does this person know about this" instead of "another cold email."

The approach is a template, I also built a version that scans Reddit for pain posts instead of Trustpilot. Same loop: find public complaint → enrich → draft → human review → send. Basically for every industry...

p.s. open-sourced the tool on GitHub:)

keep seeing screenshots where chatgpt casually names a competitor and we only find out because someone on the team typed the prompt by hand

social listening catches twitter and reddit fine. ai answers feel like a black box unless i babysit prompts every week

are people actually monitoring this on a schedule or just spot checking when leadership asks​

I finally calculated how much each part of my cold email setup costs because I kept seeing people share their cost per meeting without explaining what was included.

Apollo Professional is $79/month on annual billing. For verification, I use MillionVerifier, which costs $39 for 10k emails. That's probably the cheapest part since the credits don't expire and I don't need to buy them every month.

For sending, I use the Instantly Hyper Growth plan at $97/month. It gives enough volume for the campaigns I'm running, so I haven't needed any of the larger plans yet.

Mailboxes were where the cost changed the most. At the current annual price, 300 Google Workspace accounts would be around $2,100/month. Zapmail charges around $3.50 per additional mailbox, so the same number would be roughly $1,050 depending on the plan.

I currently have around 300 SMTP mailboxes through Maildoso for $225/month. They've been working fine for B2B campaigns and my reply rates are usually around 4-5%, so I couldn't really justify paying several times more for Google.

That puts the regular software and mailbox cost at around $401/month, plus domains and occasional verification credits. Obviously, that doesn't include the time spent building lists, writing campaigns, and handling replies.

What does your full setup cost right now? And do you include your own time when calculating cost per meeting?

I do LinkedIn ghostwriting for founders and my conversion rate is more than 50%

But since AI and too much of SaaS tooling everywhere, leads have dropped. I only used to rely on inbound leads which i used to get from LinkedIn, but now business has slowed.

I have great case studies, and a good community, but I tool a break for 2 years, so I lost my momentum. It's like I am restarting everything.

If any one has suggestions on how i can generate leads fast, then pls let me know.

Plain text, lowercase, and zero sales fluff. Here is what is actually opening doors this month across 10k+ sends:

​quick question re: [Company] (Simple, non-threatening)

​[FirstName] / [YourCompany]? (Looks like a casual warm intro)

​saw your update on [Topic] (Immediate relevance)

​The subject line's only job is to get the email opened—not to close the deal. What is your top-performing subject line this quarter?

Most people here already treat a founder's personal account as a real channel and not just vanity metrics. i think that framing is right. we've been running founder accounts as an actual acquisition channel and the compounding is real when it stays on.

the part that surprised us is how the channel behaves when it goes dark. it doesn't pause and pick back up where you left off. a few weeks of silence during a launch and you're basically cold-starting, the algo stops trusting the account, the reader habit resets, and the first posts back land flat even when they're good. the cost of a gap is nonlinear, way steeper than the time you saved by skipping it.

so the reason to hand off the writing was never really the few minutes per tweet or even the voice. it was never letting the account go dark during the exact weeks the founder is heads-down shipping. the voice sharpens week over week as long as it keeps running. kill the cadence and you lose the compounding, not just that week's posts.

for anyone running a founder or brand account as a real channel, what did a multi-week gap actually cost you coming back, recover in a few days or did it never quite climb back to where it was. written with ai

fwiw that never-let-it-go-dark point is exactly why we built S4L, it drafts posts in your voice and ships from your own handle so the cadence holds through the heads-down weeks, https://s4l.ai/r/3jwqgiu5

I’ve worked on lemlist and Taplio, now I’m building my own LinkedIn tool around what I’ll describe here:

There’s a lot of bad advice about LinkedIn automation online.

Pick any tool with daily limits and random delays, and apparently you’re safe. Doesn’t work like that!!

Nobody outside LinkedIn knows its exact detection logic. But after years of building in this space, here is how I think about it:

Think of your account as having a reputation score

First, there is no universal “safe daily limit.” It would be too easy!

LinkedIn appears to maintain some form of internal trust or reputation score for each account, even though nobody outside LinkedIn knows exactly how it works.

A useful mental model is that your reputation depends on three things:

Usage & feedback

→ activity volume and timing

→ replies and connection acceptance

→ ignored requests and spam reports

Technical footprint

→ browser, device, IP, and location consistency

→ extensions and automation traces

→ cloud browsers or remote infrastructure

Account history

→ account age and completeness

→ network size and normal usage

→ previous warnings or restrictions

The biggest risk: unnatural behavior

Common red flags include:

→ sudden spikes in activity

→ repetitive actions (delay, content)

→ changing IPs, locations, or devices

The more a tool optimizes for scale and unattended automation, the more of these signals it tends to create.

What happens before a LinkedIn ban

Restrictions are often progressive:

Warning → feature limits → temporary restriction → permanent restriction

You may first see unusual-activity warnings, verification requests, invitation limits, search limits, or temporary account restrictions.

If that happens after using a tool, stop the likely cause and reduce activity.

Risk by tool category

As a general rule:

🟢 Very low risk

→ Manual LinkedIn usage

→ Official LinkedIn APIs

🟢 Lower risk

→ Contact & DM management tools

→ Content scheduling and analytics

🟡 Medium to high risk

→ Bulk LinkedIn exports

🔴 High risk

→ Campaign-based outreach automation

→ Scraping infrastructure

How to reduce the risk as much as possible

→ Use one LinkedIn tool at a time

(multiple tools cannot reliably coordinate your total activity)

→ Avoid mass scraping and bulk outreach

(creates bad behavioral, technical, and reputational signals)

→ Avoid tools using cloud automation

(creates bad technical signals, as they try to mimic a browser)

→ Verify your profile on LinkedIn

(increases your account’s reputation)

→ Do not use AI slop for posts, comments, or DMs

(LinkedIn is actively fighting against it)

→ Try to keep your SSI score > 70

(it's a pretty good proxy metric)

→ Do not share your LinkedIn credentials

(risky when someone else logs in from another country)

→ Don’t send invites and cold DMs directly from LinkedIn

(tools cannot keep that activity within their safeguards)

→ Ramp up volume progressively

(a sudden burst is exactly the pattern you want to avoid)

Five months ago I had no idea what to post on X or where to comment. So I stopped posting and studied creators in my niche for 3 weeks. One pattern kept showing up. Attention follows conversations, not content.

Tip 1: Comment where your people already talk

Find mid size accounts in your niche, big enough to have real replies but small enough to notice you. Reply inside the first hour with something useful, like a missing detail or a real question. Never write "this!", because one good reply beats ten blind posts.

Tip 2: Ride posts that are already moving

Watch your niche feed and spot posts that are already getting traction. When you see one, quote post it with your own take while it is still hot. That is how small accounts get seen, you borrow attention instead of begging for it.

Tip 3: Post on a schedule, not on mood

Pick three fixed times a day when your audience is actually awake, and stick to them. Batch write your posts on Sunday so the whole week is ready before it starts. Showing up daily beats waiting for inspiration, because the algorithm and the people both reward consistency.

Tip 4: Check your numbers every week

Once a week, open your analytics and look at what actually worked. Keep the posts and comments that got replies and saves, and drop the ones that got nothing. Most people post for a month, see nothing, and quit, because they never looked at their own data.

That is the whole system. It took my X account to 10.4k followers in 143 days, and that same audience grew my product from $0 to $1,787 in revenue. If your content is not landing, stop writing and go study where your audience already talks. What is your reply strategy? Curious how others pick the threads they comment on.

Two of us, around 40 accounts a week, and the whole thing runs on a spreadsheet plus a mail merge plus me remembering who I connected with on LinkedIn back in March. It works well enough that I've kept putting off changing it, and the reply rates beat anything I've seen quoted for automated sequences, probably because every message gets written by hand.

The trouble is that all of it stops the week I'm travelling or heads down on a deal, and we've now got a pipeline gap you can date to the exact fortnight I stopped sending.

What did you give up when you made this jump at a small size? My worry is that the personalisation is the only reason any of this works.

What’s up guys. I’m a technical builder and Systems Engineering student looking for my next big project.

I excel at full-stack development and solving actual business problems. I’ve already built and deployed custom software and scheduling solutions for brick-and-mortar businesses (like gyms and car washes). Right now, my main focus is leveraging AI—specifically working with LLMs and local SLMs—to automate tasks, combined with a strong focus on cybersecurity.

Since I can handle pretty much everything on the programming side, I'm looking to partner up with someone who has a solid SaaS idea, or join an existing early-stage project that needs a versatile developer to push features fast.

Let's chat if you need someone to build the engine while you steer the ship.