u/Environmental-Bus178

Okay so I've been learning Serbian for a few months and I went through a bunch of apps trying to find something that actually helps you have real conversations, not just flashcards and grammar drills.

Duolingo, Babbel, Busuu, Pimsleur, a few others. Tbh most of them felt the same. Good for vocab, useless the moment you actually want to open your mouth and speak.

Then last month someone in this community mentioned Issen and I figured I'd try it. That was crazy because I had been looking for something like this for months and it was right here the whole time.

Hands down the best conversational language learning app I've used in 2026. You just speak Serbian, it responds, it corrects you, keeps the conversation going in real time. No tutor to book, no scheduling, no feeling embarrassed about your grammar in front of a real person. Just actual speaking practice every single day whenever you want.

I can vouch for it anytime. If you're stuck in that "I can study Serbian but I can't actually speak it" phase this is genuinely the thing that breaks you out of it.

Horrendous cable management, yes, will focus on it some other time.

Just want some thoughts and maybe ideas for the next upgrade.

Managing outbound for a B2B SaaS company. We sent 26,412 cold emails in Q1 2026 across 6 campaigns. Instead of keeping this data internal, sharing it because most “benchmark” articles don’t share raw numbers from real campaigns.

The numbers that actually tell you something:

Reply rate: 4.1% average across all campaigns. Our best campaign hit 7.8%. Our worst: 1.9%. Industry average per Instantly’s 2026 report (billions of emails analyzed): 3.43%. Per Hunter.io’’s 2025 analysis of 31M emails: 4.5%. We’re slightly above average. The 7.8% campaign was our most targeted segment (under 200 recipients). The 1.9% was our broadest.

Bounce rate: 1.8% average. This was our #1 focus. We use SalesTarget.ai for lead sourcing. They pull from a bunch of different data sources so the verification is better than what we were getting from Apollo. When we were on Apollo we bounced at 8–11%. After switching, 1.8%. That single change improved everything downstream. Anything above 2% starts compounding domain damage fast.

Positive reply rate: 1.7% (about 41% of replies were positive/interested). This matters more than total reply rate. A 5% reply rate where 80% are “not interested” is worse than a 3% rate where 60% are interested.

Meeting booked rate: 0.9% of total sends converted to meetings. That’s roughly 1 meeting per 111 emails. At 200 sends a day, that’s about 9 meetings/week. For context, industry conversion rates for cold outreach are 0.2–2%.

Stuff we stopped caring about:

Open rate: We saw 38–52% across campaigns. But Apple Mail Privacy Protection inflates open tracking data by roughly 18 percentage points. We stopped optimizing for opens entirely. Reply rate is the only reliable engagement metric in 2026.

Send volume: We could send more. We deliberately stay at 200 a day because smaller, targeted batches outperform blasts. Hunter.io found that sequences targeting 21–50 recipients achieved 6.2% reply rates vs. 2.4% for 500+ recipients.

Honestly the biggest lever in our outbound performance was data quality, not copy. When we switched from Apollo to SalesTarget.ai, bounce rates dropped, deliverability improved, reply rates went up. Better data means emails actually reaching inboxes which means more replies. Everything else is optimization.

One thing we still haven’t solved: SalesTarget.ai’s data is weaker on very senior titles at enterprise companies. We still supplement with manual LinkedIn research for C‑suite contacts at companies over 1,000 employees. No platform nails that segment yet.

One thing I underestimated when scaling content was how slow search engines can be at discovering new pages.

This came up while running SEO for two small SaaS side projects and a directory‑style site. We did a content push of about 80–100 pages across blog posts, landing pages, and some programmatic pages. A few weeks later I checked Search Console expecting at least most of them to be indexed.

Roughly half were still sitting in either “discovered currently not indexed” or “crawled currently not indexed.” Some had not even been crawled yet. That was a surprise because the assumption most marketers make is publish + sitemap = Google will figure it out quickly.

After dealing with this a few times I realized indexing is more of an operational workflow than a one‑time setup. When you publish in batches you actually have to push discovery a bit or pages can sit idle for weeks.

The first thing that helped was internal links immediately after publishing. When we add new pages now, we go back and link them from 3–5 existing indexed pages in the same topic cluster. That alone noticeably sped up initial crawling compared to just leaving them in the sitemap.

Second was keeping the XML sitemap fresh and resubmitting it after large batches. When we dropped around 90 new URLs into a directory section and updated the sitemap the same day, Googlebot picked up a lot of them within 48 hours instead of the usual week‑plus delay.

For the most important pages we still use manual requests in Google Search Console. It is slow and you obviously cannot do hundreds that way, but doing 10–15 of the highest value URLs right after publishing seems to help the rest of the cluster get discovered faster.

When the batches got bigger we also experimented with IndexNow pings, some small API scripts, and a couple indexing tools. One of the tools we tested was IndexerHub, mainly to push sitemap URLs through Google’s indexing API and IndexNow automatically. The main difference operationally was not having to manually submit 100+ URLs every time we shipped a content batch.

Curious how other people here handle indexing when publishing large batches of pages. Are you mostly relying on Search Console and sitemaps, or running some kind of automated submission workflow?

For the last few months I've been doing the usual indie hacker distribution loop. SEO pages, posting on Twitter/X, launched on Product Hunt once, submitted to a bunch of startup directories, a couple old blog posts that still get traffic from Google.

Traffic looked fine. A few thousand visits a month depending on the week. Signups coming in. Stripe occasionally pinging with a payment.

But I had this weird moment last week. I was standing in line waiting for coffee, checked Stripe on my phone, and saw a new payment come in. Good feeling obviously. Then I realized I had absolutely no idea where that person came from.

Not SEO? Not Twitter? Maybe a directory? Maybe an old blog post? I honestly couldn't tell. Which is kind of insane when you think about it. I was spending time on marketing channels without knowing which ones actually produced revenue.

I was using Google Analytics like most people. GA is powerful and obviously the default. But once I tried to connect marketing to signup to Stripe payment in a way that made sense, it got messy pretty fast. Events, conversions, Stripe data, attribution windows. Doable, but it felt like a part time analytics job.

So I ran a small experiment. My goal was simple: see the path from first visit to Stripe payment. I installed Faurya and connected it to Stripe so I could see which traffic source actually led to paying users.

The surprising part was what showed up. One tiny startup directory I submitted to months ago sent maybe 60 visitors in the last 30 days. Basically noise compared to SEO or Twitter. But it produced 3 paying customers. Meanwhile one of my blog posts pulled around 1,200 visitors and converted exactly zero. Without tying traffic to revenue I would have doubled down on the blog content and ignored the directory completely. I'm still playing with the setup. One downside so far is Faurya isn't as deep as something like GA or PostHog if you want really heavy product analytics. It's more focused on traffic to revenue attribution. But for a solo founder that might actually be the point. Do you actually trust your attribution setup between marketing and Stripe revenue, or are most of us just guessing based on traffic numbers?