My therapist suggested I turn my journaling habit into something I could share with others. Someone mentioned using an ai app builder but I have no coding background at all, like I studied social work. Is this actually doable for someone like me or am I in over my head?
Tried powerquery to pull data from scanned PDFs but it doesn't really work well on low quality scans with tables in it. I know nothing will be perfectly accurate, but what’s the best data extraction tool you’ve used so far? Not sure if there's another way to do it via excel but i'm kinda desperate rn
my SDR team just got teh renewal notice and holy shit. we're at like 150/user/month for Professional and they want to bump us to over 200. thats nearly 50% more for the same damn features weve been using.
i get inflation and all that but this feels excessive. were burning through 10k exports a month across 8 reps and data quality has been declining. getting way more bounces lately, especally on mobile numbers. our ops guy pulled the numbers and its not great.
started shopping around - looked at Lusha briefly but thier apollo pricing is honestly not much better for what you get. also been testing Prospeo after someone on here mentioned them. getting way more accurate results on emails vs the 70-80% we see with Apollo. would save us a decent chunk monthly too. but im wondering if anyone else made a similar switch and regretted it? or found somthing better?
the apollo.io cost projections for next year based on our growth are insane for a 15 person sales team. like what are other growing teams doing about sales intelligence costs right now? feels like every b2b data provider is just jacking prices
i kept blaming Dubai water for my hair fall and then realised i’ve never even checked the boring stuff.
women here keep mentioning vitamin D, ferritin, B12, thyroid etc. which is annoying because i wanted the answer to be “buy filter, problem solved” lol.
my current plan is not glamorous:
bloodwork
sleep better
eat actual protein
stop tight buns
Be Bodywise hair growth serum at night
conditioner only on lengths
Nizoral only if dandruff comes back
no new random oil every week
has anyone here found low ferritin/vitamin D was actually making hair fall worse?
We have a divergence problem and I suspect we're not alone.
Our LLM judge passes a response. A human reviewer looks at the same response and flags it as unsafe (subtle policy violation, or a tone that's technically compliant but would upset a real customer, or an answer that's correct but inappropriate for the context).
The judge isn't wrong on the literal rubric. It's that the rubric doesn't capture what the human knows. And we can't put a human on every eval, that's the whole point of the LLM judge.
So how are people actually calibrating LLM judges against human judgment? Not "use an LLM judge" (we do), but specifically closing the gap when judge and human disagree on safety.
most cold email advice floating around assumes your target is a VP of Engineering at some mid-market SaaS company. and honestly thats fine if thats your market. but about 9 of our 14 clients right now are selling into other B2B SaaS companies, and even within that vertical the advice breaks down fast when youre a bootstrapped startup with zero brand recognition trying to book meetings with people who get 47 cold emails a day from companies that look exactly like you.
we run a 2 person cold email operation out of denver. me and my VA who is based in the philippines and is honestly better at list building than i am at this point. $28k/mo in revenue across 14 clients, bootstrapped, no investors, learned everything from screwing things up and reading way too many threads on here at 2am.
the first problem that almost killed a few campaigns early on was targeting. sounds obvious right. but when your client sells, say, a pipeline analytics tool to series A and series B startups... the decision maker could be the founder, the head of growth, the VP of sales, or sometimes a director of demand gen who got hired 3 weeks ago. at bigger companies you know its the CRO or the VP of Sales and you move on. with startups the org charts are a mess, titles change every quarter, and half the people on LinkedIn still have their old title from 2 jobs ago. we burned through like 6 weeks of a client engagement early on just emailing the wrong people because we assumed "VP Sales" was always the right call. turns out at PLG companies under 80 employees the person who actually cares about outbound pipeline is often the founder or a head of growth who doesnt even have "sales" in their title. my VA and i started manually checking company size and recent hires on LinkedIn before building lists which slowed everything down but reply rates went from like 1.1% to around 3.8% once we got the targeting right.
the bigger issue though was enrichment and data quality when youre going after startups. these arent fortune 500 companies with 14 entries in every database. half the contacts at a series A company simply dont exist in ZoomInfo. i tried pulling lists from there for a client selling competitive intelligence software to vertical SaaS companies and got maybe 40% coverage on the contacts we actually wanted. we switched to running Prospeo for enrichment first, then filling gaps with Clay workflows for the edge cases, and that got us to a much better place. Prospeo found valid emails for about 80% of our target list which was a huge improvement. the remaining 20% we either got through Clay scraping LinkedIn profiles or just skipped. theres a point where chasing the last 15% of a list costs more in time than just building a fresh segment.
what really caught me off guard was the messaging piece. and i dont mean subject lines or whatever, i mean the actual value prop framing. when youre emailing a CRO at an enterprise software company, you can talk about pipeline coverage gaps and CAC payback periods and they get it immediately. but when youre emailing a founder at a 30 person startup who just raised their series A, they dont care about your ROI calculator. they care about one thing: are they going to hit the growth numbers they promised their investors. took me probably 4 months of mediocre results to figure out that the pain points are technically the same (churn, pipeline, competitive displacement) but the emotional framing is completely different. a startup founder losing 3 deals to a competitor in one quarter feels that in their chest. a VP of Sales at a 500 person company sees it on a dashboard. the emails that work for startups are shorter, more direct, and reference something specific about their situation. we started pulling recent funding announcements, new hires, and product launches as signal data and weaving that into the first line. reply rates on signal-based sequences are running around 4.2% to 5.7% depending on the client.
infrastructure wise we keep it pretty simple. Maildoso for inboxes, usually 3-4 per client depending on volume. Woodpecker for sending because the warmup is built in and my VA can manage campaigns without me babysitting. MillionVerifier for verification before anything goes out. Prospeo handles the enrichment step, Hunter if we need to cross-reference something that looks off. we track everything in... google sheets. i know. i know. we have a HubSpot account for one client who insisted but honestly for a 2 person team sheets just works and i can build custom tracking that does exactly what i need without paying $800/mo for a CRM we'd use 20% of.
oh wait i should mention the warmup thing. when youre sending to startup people, deliverability matters even more because a lot of them use Google Workspace and googles spam filters are aggressive right now. we warm every inbox for minimum 21 days, usually closer to 28 before sending any live campaigns. i tried cutting it to 14 days about 8 months ago because a client was impatient and we got 2 inboxes flagged within the first week of sending. not worth it.
the last thing that tripped us up was volume expectations. clients who are bootstrapped startups themselves want to send 5000 emails a week because they think more volume equals more meetings. and for some markets maybe. but when your total addressable list of series A SaaS companies with a head of growth or VP sales is like 1,200 people... you burn through that list in a month at high volume and then what. we cap most startup-focused clients at 40-50 emails per inbox per day, run 3 inboxes, and focus on making every email actually relevant. one client selling a churn prediction tool to vertical SaaS companies books 8-12 meetings a month off maybe 2,800 emails. thats a 0.3% to 0.4% meeting rate which sounds low until you realize each meeting is worth $3-4k in potential ARR to them.
anyway none of this is groundbreaking but i feel like the startup-to-startup cold email motion is different enough from generic B2B outbound that its worth talking about. the data is harder to get, the targeting is messier, the messaging needs to hit different, and the volumes are smaller by necessity. Prospeo plus a good VA who knows how to research is honestly 80% of the battle on the data side. the other 20% is just not being lazy with your copy.
ok this got longer than i planned and i have a sheet to update for a client before tomorrow morning so im gonna stop here
I have been thinking about this a lot recently because Instagram feels way more driven by engagement than it used to be.
Whenever a post already has decent likes, people seem much more likely to stop scrolling, interact, and even trust the content more. But when a post has barely any engagement, most users ignore it instantly even if the content itself is actually solid.
That honestly made me curious why buying Instagram likes has become so common lately.
I’m not talking about trying to fake influencer status or anything like that. More about giving posts enough early activity so they don’t look completely dead right after posting.
It honestly feels like first impressions matter a huge amount on Instagram now. Posts with engagement already on them just seem to perform differently compared to posts sitting at low numbers.
Curious if anyone here has actually tested buying Instagram likes and whether it helped with reach, engagement, or overall post performance long term.
early on, doing outreach manually feels useful because you learn what people care about.
but after a point it becomes hard to track follow ups, replies, bounced emails, linkedin touches, crm notes, etc.
for people selling b2b, when did you move from manual outreach to a proper system?
after first 5 customers? 10? or from day one?
AEO tooling for the last two years has basically been one thing: measurement. Citation tracking, visibility scores, prompt volume dashboards. You look at the data, you figure out what to do, you go do it yourself. The tool stops at the insight.
That's changing.
The loop is starting to close. Citation share drops on a key prompt. System identifies what entered the supply chain. Writes the brief. Drafts the content. Pushes it to the CMS. Pings the team in Slack. No manual steps in between.
We just renamed Workflows to Agents at Profound. Sounds like a small thing, it's not. The CMS integrations and Slack node that shipped with it are what actually close the loop rather than just producing smarter alerts.
You can see more about it here: https://www.tryprofound.com/blog/workflows-are-now-agents-january-release-roundup
If you're still doing anything manually that happens on a fixed schedule, that's a sign you have room to automate.
Is anyone actually letting their AEO stack take automated action yet or still keeping a human in the loop for everything?
I knew German had gender.
I accepted that.
I made peace with der/die/das.
Then German said: cute, now meet den/dem/des.
My current problem is not understanding the idea of cases. I can read an explanation and go:
“okay, nominative = subject, accusative = direct object, dative = indirect object-ish, genitive = possession/formal fancy pain.”
But in real sentences, especially while speaking, I don’t have time to run a legal investigation before choosing an article.
Current plan:
Question for people who survived this:
Did articles/cases become automatic from exposure, or did you actively drill them?
Because right now I can explain cases better than I can use them.
running multi leg stuff, iron condors and hedged strangles. my broker keeps charging me near full margin on positions that are clearly hedged because it evaluates leg by leg instead of as a basket.
ties up way more capital than the actual risk. is there a broker that evaluates the basket properly or do i just live with this.
When people say “supports diarization,” I don’t know what that means anymore.
Diarization in a 2-minute clean demo?
Or diarization after:
Because the painful diarization bug is not “it labeled Speaker 1 and Speaker 2.”
It’s:
Speaker 2 slowly becomes Speaker 4.
The action item is assigned to the wrong person.
A short correction is attributed to the wrong speaker.
A late joiner breaks the rest of the transcript.
I’m thinking of doing a pyannote-assisted annotation workflow where each STT API gets scored on:
Smallest AI Pulse includes diarization, so for me the question is not “does it have the feature?”
The question is:
can the speaker labels survive a messy real call long enough to trust the summary?
Anyone running diarization in production? What broke first?
I’m trying to figure out the credibility bar for the new polished tDCS headsets. DIY NeuroMyst/Caputron-style rigs are cheap and transparent, but montage/current/electrode placement are on you. Flow is the opposite bucket: condition-framed, more clinical guardrails, and depression-specific evidence. The new wellness/performance headsets seem to win on adherence, but that is not the same as efficacy.
My current filter would be: disclose montage and current, show sham-controlled or at least preregistered data, publish adverse events/dropout rates, and track 3-6 week adherence rather than only “felt better after session 1.” Wearable-correlated outcomes would be interesting too: Oura/Apple Watch sleep, HRV, resting HR, plus simple self-ratings.
Practical test I’d use before trusting my own impressions: 14 days baseline, then 21 days device use at the same time daily, no new caffeine/supplements, and pre-pick two outcomes like “first 90-min focus block completed” and “time to wind down after work.” Stop counting vibes after the fact. I’m asking partly because I’ve been looking at Mave Health which packages 20-min forehead tDCS as a focus/stress routine rather than treatment. I’m also the kind of person who will overthink a $20 desk lamp but somehow impulse-buy coffee gadgets, so I need a better filter. Weird analogy, but this whale detection network is the kind of real-world signal validation I wish consumer neurotech had more of.