Just came across claimmoney.com and would like to get some feedback from current users. What's the ROI like ? How much have you guys claimed so far ?
Really looking for a good class action platform, but there are so many scams in this space so I'm really trying to be careful.
I rented an apartment in Minnesota for a couple months before buying my house and just learned that I qualify for the RealPage rental settlement. Just received $300 and it feels amazing. Rent there was awful, like 1,500$ or smth.
The settlement is for renters who lived at qualifying properties between October 18, 2018 and November 21, 2025, over allegations that landlords used RealPage's software to inflate rents (as a group).
If you rented an apartment during that period, I would heavily recommend checking it out.
The deadline is January 29th, 2027.
Three stages, and there was no first round.
Recorded video: Seven behavioral questions.
From what I remember: Why Wells Fargo. Tell me about a time you missed a deadline. What would you do if a client asked for something you knew was wrong. Tell me about a time you changed someone's mind. You get three attempts per question.
Then a preference survey by email asking me to rank coverage groups and locations. I put NYC first, then Charlotte.
Superday: Three interviews, 30 minutes each, over Zoom. Everyone sat in a waiting room first and got dropped into breakout rooms.
MD, technical: walk me through a DCF. How do you get to free cash flow. What is the Gordon Growth formula and what happens to your value if you move the terminal growth rate up 50 basis points. Explain working capital and what a build in working capital does to cash.
Associate, technical and macro: Where is the 10-year right now. What has happened to tariffs this year and what does it do to a client's cost structure. Difference between senior and subordinated debt, and who gets paid first in a restructuring. What is gold doing (weird question lmao).
Global head, behavioral: no technicals at all. Resume, why banking, what I do outside of school, and one long stretch about a team project that went badly.
Where I am now: Six days post-Superday. HR called and said the decision would come "early next week" so expect about a week and a half of waiting time. I know two people heard the same day they interviewed and the rest of us are sitting here.
Questions ?
i replaced most of my GTM stack with Claude Code but let me be precise about "most" because the honest version is more useful than the hype version lol
tbh for a while my stack looked like everyone's: a sequencer, an enrichment tool, some no-code automation platform gluing it together, and me babysitting all of it. i've torn out the middle of that. what's left is Claude Code and a folder of instructions doing the orchestration and reasoning i used to pay a no-code platform to do badly.
the correction up front, because technical people will call it out otherwise: Claude Code didn't replace my data or my sending. enrichment data still comes from an API. email still goes out through a real sending service. those endpoints don't disappear, a folder of markdown can't enrich a lead or send a cold email by itself. what died is the glue: the no-code automation platform in the middle, and the hours i spent shuttling data between tools by hand. Claude Code drives those endpoints directly through MCP, API calls, and scripts, instead of a platform brokering every handoff and losing a field at each hop. for a solo operator or small team, that middle layer was most of the cost and most of the fragility.
the core shift was to stop thinking in "tools" and start thinking in skills. a skill in Claude Code is literally a folder with a SKILL.md file, plus whatever scripts or reference docs it needs. Claude reads the folder name and description every session and pulls in the full instructions only when the task matches. so i've got one for enrichment logic, one for how we qualify a lead, one for the copy rules and voice, one for how a campaign gets structured. each is a repeatable process written down once, loaded only when it's relevant.
the piece that makes it cohere is CLAUDE.md, the persistent memory file. our ICP, positioning, the "we don't do X" calls, the real definition of "qualified". all of it lives there and loads into every session, so every skill runs against the same source of truth. one honest caveat i learned the hard way: CLAUDE.md is strong guidance, not a hard guarantee. the model reads it and mostly follows it, but it can still drift. so it fixes the "my rules live in five places and quietly contradict each other" problem, which is real and worth fixing. but it is not a lock. the actual locks come later. still, consolidating every rule into one inherited file fixed more than any tool ever did.
for heavy work i use subagents. Claude Code can spin off a separate session with its own clean context window and its own tool access. so if i'm researching 40 accounts, that runs off to the side and comes back as a clean result instead of clogging the main session. the deep, messy work stays isolated.
what actually replaced the no code automation platform is headless mode. Claude Code runs as a one shot command from the CLI, no interactive session, so i can wire it into a cron job or a pipeline and it just executes. and it reuses the same hooks and permission rules as when i run it by hand. that's where the automation platform line item died. i don't need a platform to run a scheduled workflow when Claude Code can run headless on a timer and actually reason about what it's doing instead of following rigid if-this-then-that rules. it still calls out to my enrichment API, CRM, and sender to do anything real, headless is the trigger and the reasoning (not the hands)
to keep it from doing anything dumb, i lean on hooks and tool restrictions. hooks are deterministic. they fire at set points and enforce a check whether the model feels like it or not, which is exactly the thing CLAUDE.md can't guarantee. the allowed-tools setting hard-limits what a given run is even permitted to touch. for anything destructive or high-impact i require explicit approval instead of letting it run. the model reasons; the guardrails are rigid on purpose. this is the layer most people skip and then wonder why their agent nuked something.
so the actual end to end, a new inbound lead. it comes in, Claude Code loads the enrichment skill and pulls what it needs from the enrichment API, loads the qualification skill and scores the lead against the ICP in CLAUDE.md. here's the honest bit, that score is a judgment, not gospel. the model can still misjudge fit, which is exactly why a human reviews the edge cases and a hook enforces the hard rules (suppression, send limits, "never touch X segment"). if it's a fit, it loads the copy skill and drafts the first touch in our voice with a real reason for reaching out. sending itself still goes through a proper sending service. deliverability, warmup, suppression, unsubscribes are their own problem, and drafting good copy doesn't solve any of them. i review anything a prospect actually reads. the reasoning is Claude's, the rules are mine, the endpoints are still endpoints. there's just no platform in the middle taking a cut and dropping fields between systems.
concrete before and after. old way: a lead enrichment and scoring flow lived across an enrichment tool, a no-code automation platform, and the CRM three integrations, and data quietly degrading at each handoff. now: a couple of skill folders and a CLAUDE.md, all reading one source of truth, with Claude Code calling the enrichment API and writing to the CRM directly. the data sources didn't go away. the glue between them did. when i want to change how we qualify, i edit one file instead of reconfiguring three tools.
i'm not going to pretend it's hands off. anyone who says their system runs with zero humans is lying or shipping garbage. i review anything a prospect reads. i go in when something breaks and i need to see what actually happened. the model is genuinely good at the mechanical middle. research, drafting, routing. but the judgment layer (is the message right, is the account a real fit) is mine and should stay that way. and the non deterministic part is real: an LLM on a timer over a live CRM will double-touch or mis-route if you don't wrap it in hard dedup and hard limits. that's what the hooks and the review are actually for.
the hardest thing here wasn't technical. spinning up a skill folder takes ten minutes. it's that i'd built an identity around managing a stack. the subscriptions and the integrations felt like sophistication. but that was complexity i was mistaking for capability. the leverage was writing down what we do clearly enough that the model could execute it, and getting out of the way on the parts that don't need me.
what i'm still working out is how to structure CLAUDE.md as it grows. the direction i've landed on: keep the root file thin, push the procedural stuff into skills that load on demand, and move the "only applies sometimes" rules into path-scoped rule files so they don't load every session. but i haven't nailed the clean pattern yet.. if someone else run ops mostly on Claude Code instead of a tool stack tell me plz how you figured it out
Let's be honest. People and companies are never going to stop using AI just because it's bad for the environment, so why whine about it?
What can we actually do to reduce AI’s environmental impact, and what are some more eco-responsible ways to use AI?
last month i ran a test. pulled 30 target accounts, ran them through all four of our intent providers at the same time. two flagged account A as high intent. one called it medium. the fourth didn't have it at all. same account, same week, four different reads.
the sales team didn't know what to do so they ignored the signals entirely, which is probably the worst possible outcome after spending this much money.
i've started thinking the problem isn't which provider is "right." it's that we're treating intent scores as a destination when they're supposed to be a starting point. but i don't have a clean operational answer for what to do when the signals contradict each other.
how are you handling conflicting intent data across multiple providers? do you pick one as primary and ignore the rest, or is there some aggregation logic that actually works in practice?
I've come across so many class action websites that turned out to be scams that now, I’m super suspicious of anything.
I recently found Claimoney and it looked legitimate so I want to ask before I pay for a subscription: How is the experience and how much money does it make you gain in say a month?
Tell me in the comments (no sponsored accounts pls).
if you paid for Tinder Plus or Tinder Gold in California any time after March 2, 2015 while you were over 29 (or over 28 after March 2016), you're probably owed money from the age-pricing settlement.
deadline to pick your payout method is August 18, 2026.
there's no claim form to fill out, payment goes out automatically since you'd have already gotten notice, you just choose PayPal, Venmo, Zelle, ACH.
it's pro rata off a $60.5M pool split by how much you actually spent, so heavier subscribers get more.
I keep seeing GTM systems that scrape someone’s LinkedIn, summarize their homepage, feed everything into an LLM, and generate something like: “Loved your recent post about scaling!” then the next sentence is the exact same pitch sent to 4,000 other people. teams call that one-to-one personalization. But It isn’t. it’s a mail merge wearing an ai costume. real personalization isn’t proving that you found a random fact about someone; it’s explaining why that fact changes the reason they should care about your offer right now. even LinkedIn’s own sales guidance makes this distinction: adding someone’s name, school, or a familiar detail isn’t the same as creating actual contextual relevance.
the confusing part is that personalization genuinely matters. lets get some facts here (that gtm people dont care about) : McKinsey found that 71% of consumers expect personalized interactions and 76% get frustrated when they don’t receive them. But people hear those numbers and conclude that every email needs a fake compliment and three scraped facts. Hmmm but that’s not what buyers are asking for. They want relevance, not proof that your robot found their podcast. and trust matters even more once AI is involved: Salesforce’s 2024 research found that 60% of consumers believed advances in AI made trust more important.
AI itself isn’t the problem, btw. (some facts noone cares about again)Salesforce reported that 83% of sales teams using AI saw revenue growth, compared with 66% of teams not using it. the core problem is where teams apply it. AI is extremely good at producing thousands of different-looking sentences, but different wording doesn’t mean different thinking. you can generate 10,000 technically unique emails that all contain the same generic idea. “Saw you’re hiring.” “Congrats on the funding.” “Loved the recent launch.” Cool. Why does any of that make your product relevant huh? If the system can’t answer that question, it isn’t doing personalization...it’s simply decorating a template.
the actual workflow should be:
signal; hypothesis; relevance; message
Signal: the company hired a new sales leader.
Hypothesis: they may be rebuilding process, territories, tooling, or reporting.
Relevance: your product solves one of those problems.
Message: explain the connection in a few sentences.
Fake personalization says, “Congrats on becoming VP of Sales at Acme! We help sales teams grow pipeline.”
Real personalization says, “You joined Acme (i made it up whatever company) while they’re hiring six AEs but still don’t have a RevOps hire. Usually that means routing, territories, and CRM cleanup land on the new sales leader. We help teams fix that before the new reps ramp.”
The first one contains personal data. The second contains a point of view.
And this matters beyond reply rates. At enough volume, bad AI personalization becomes a deliverability problem. When the model references the wrong person, invents a compliment, uses an old job title, or sends an irrelevant message, people don’t think, “Their enrichment workflow needs better QA.” They hit spam. Google’s guidance says senders should aim to keep reported spam rates below 0.1% and avoid ever reaching 0.3%. So scaling weak personalization isn’t harmless and it can damage the infrastructure carrying the rest of your outbound.
my rule is pretty simple: AI should help decide who deserves a message, why now, and what the signal probably means. Writing the first line should be the final step, not the entire strategy.
most teams automate the easiest 10%. writing the sentence. and call it personalization.
And the valuable 90% is figuring out whether there was anything worth saying in the first place :)
my son's class teacher called in a parents' meeting today & asked everyone not to give their child a smartphone. I witnessed a 14-year-old cry on the floor when her phone got confiscated, it was def not a normal tantrum and an elementary teacher educated us on the difference between kids with phones and kids without is night and day within a few weeks of the school year starting. A Girl Scout leader i know dissolved her whole troop after a camping trip because the girls wouldn't put their phones down for anything, not even the activities they used to beg to do outside.
so we've gone a different route. Our son has an Apple Watch for calls and texts only, and we control the contact list. No smartphone yet, and if we ever do give him one, it's zero YouTube, no browser, no WhatsApp until he's older, and it comes back to us every night like it's ours, because it is.
anyone else holding off on smartphones, what's actually working for you?
The Silk and Great Value plant based drink recall from summer 2024 (the Listeria one) has a settlement now, $7.5 million fund.
If you bought or drank the recalled products in Canada between August 2023 and July 2024 you can file. How sick you got and how much you can claim: symptoms up to 48 hours: $400 48 hours to a week: $1,500 longer than a week, no hospital: $7,000 hospitalized: $10,000 to $30,000 severe or lasting complications: up to $150,000 death of a family member: up to $300,000
Basic claims need a declaration plus a receipt or packaging. The higher tiers need medical records. The deadline to file is October 16, 2026. This outbreak was serious, 3 deaths were linked to it, so if you or someone in your family got sick around then it's worth filing.
Most GTM advice is cope. Here’s what actually worked.
I know this sounds like another founder bro post, but GTM got way easier once I stopped doing the stuff everyone on LinkedIn keeps yelling about.
No giant funnel. No 14-channel strategy. No “thought leadership engine.” No hiring SDRs before anyone knew what to say.
Just figuring out who actually had a painful problem right now and putting the right message in front of them.
Here’s the breakdown.
Nobody cares that your SaaS has AI workflows, dashboards, integrations, automations, or whatever else is on the landing page.
They care that something is broken.
Pipeline is weak. Churn is creeping up. Sales reps have empty calendars. Onboarding takes forever. Data is a mess. Leadership wants answers yesterday.
Once the message became about the pain instead of the product, people actually replied.
Wild concept, I know.
Most GTM teams build lists like this:
VP Sales B2B SaaS 51 to 200 employees United States
Congrats, you built the same list as 900 other teams.
The better question is:
Why would they care this month?
New VP hired. Team expanding. Funding announced. Hiring SDRs. Launching a new market. Replacing old tools. Posting about growth targets.
That’s where the money is.
Fit is nice. Timing is better.
More leads sounds great until your team is chasing people who were never going to buy.
A small list of companies with real urgency beats a giant graveyard of stale contacts.
Bad data is not harmless either.
Wrong numbers. Old emails. People who left the company. Fake intent signals. Outdated titles.
That stuff makes your team look sloppy.
10 million dead contacts is not an asset. It is a liability with a dashboard.
Most buyers do not want a demo from a stranger.
They do not want a 30-minute call.
They do not want to “explore synergies.”
They do not want to see your platform tour.
They want to know if you understand their problem.
So the better move is a smaller ask.
Want the breakdown? Should I send the idea? Worth showing you what I mean? Want me to map the angle?
Tiny yes first.
Then the meeting.
AI made lazy GTM louder.
That’s it.
If your outbound sounds like it came from a prompt template, buyers can smell it in two seconds.
AI is useful for research, sorting accounts, spotting triggers, summarizing calls, and testing angles.
But if the final message still feels like it could go to 50,000 random companies, it is cooked.
This one hurts because every founder wants to do everything.
Cold email. LinkedIn. SEO. Paid ads. Webinars. Partners. Events. Communities. Founder content.
Then three months later nothing works and everyone blames the market.
Most teams do not have a channel problem.
They have a focus problem.
Pick one main channel. Pick one support channel. Pick one conversion motion.
Get that working before cosplaying as HubSpot.
A buyer liking your product does not mean the deal is real.
Can they explain it to finance? Can they justify the budget? Can they get the team to switch? Can they prove the ROI? Can they make it feel urgent internally?
Most deals die inside the company, not on the sales call.
Good GTM helps the buyer sell the thing when you are not in the room.
Sent emails is not progress.
Website traffic is not progress.
Demo requests are not always progress.
Content impressions are definitely not progress.
The real question is:
Are the right accounts moving closer to a decision?
If not, congrats, you are just very busy.
The boring formula still wins.
Right account. Right pain. Right timing. Clear message. Small next step. Strong proof. Clean follow-up.
That’s it.
Most GTM does not fail because the team is not smart enough.
It fails because the message is vague, the list is lazy, the data is stale, and the ask is too much too soon.
If your pitch could work for any company, it probably works for none.
If it feels painfully specific to a company in a moment where they actually care, now you have a shot.
What GTM “best practice” do you think is mostly bullshit now?
Came across this one and it's about as easy as these get. Google agreed to an 8.25 million dollar settlement over Play Store apps collecting data from kids under 13.
If your child used or downloaded any app from Google Play while they were under 13, anytime from April 2015 to now, they qualify. That's a massive net, basically any kid who ever played a game on an Android phone or tablet. Most of them are still minors so you file as the parent or guardian.
Payout is estimated around 40 to 200 dollars per kid depending on how many people file.
You don't upload any receipts or documents, but you do enter some basic info: your mailing address, the kid's birth month and year, and a box confirming they used a Play Store app before turning 13. It's signed under penalty of perjury so only file if it's true.
Deadline is September 14, 2026. Official site is COPPAPrivacyClassAction.com (administrator is Kroll), just make sure you're on that one and not a copycat.