A friend of mine who's been in B2B sales for over 15 years shared something recently that changed how I think about prospecting.

No reply. No meeting booked. No intro, no referral. Just a single like from a prospect on one of his LinkedIn posts.

That was it. And it turned into a $120,000 deal.

What struck me wasn't the outcome, it was the process. He's stopped chasing people with cold DMs and generic lists. Now he pays attention to what he calls dynamic social signals: small behaviors that hint at interest or intent, even when they're subtle.

Things like someone viewing his profile. Liking a competitor's post. Following someone in his space. Commenting on a post about the problem his product solves. Attending an event. Jumping into a niche discussion.

To most people none of that looks like a lead. To him it's the start of a conversation.

When someone engages with something even loosely related to what he does, he uses it as context for a thoughtful message. Not a pitch, not a sequence. A relevant insight, a short piece of content, or a question tied to what they showed interest in.

One example: a VP viewed his profile after reading a post about ROI in telecom. Instead of pitching, he sent a quick case study on how his product improved ROI for a similar company. The VP replied the same day.

Another: a director liked an influencer's post about a problem his product solves. He followed up with a simple message, no pitch. That became a call the same week.

The result: way more replies, more accepted requests, and most of the people who reply are actually qualified.

High intent plus good timing is what makes the difference. You're paying attention to what people already show you instead of guessing.

And it works.

Hiring VA
2-3$ /h depending on experience
Social Media Data Entry

I've been using Folk as my personal agent for about a month, I text it using IMessage.

Right now I'm mostly using it for low level tasks like a morning brief with my calendar and weather, reminding me to reply to people I forget, and I feel like I'm leaving some of its capacity on the table.

I'm not really a tech guy so I'm pretty new to this. I wanted to know what are some other use cases that I can implement with my AI agent to maximise its potential.

Thanks!

I've been using Folk as my personal agent for about a month, i text it using IMessage.

Right now I'm mostly using it for low level tasks like a morning brief with my calendar and weather, reminding me to reply to people I forget, and I feel like I'm leaving some of its capacity on the table.

I'm not really a tech guy so I'm pretty new to this. I wanted to know what are some other use cases that I can implement with my AI agent to maximise its potential.

Thanks!

A few companies in and I've seen all the playbooks and frameworks. It still comes down to sell harder and hit the number.

Has anyone actually worked under a GTM that was real strategy and not just pressure?

This is r/AskGTM , a place for people working on go-to-market. You can ask questions, share what is working, post problems, compare notes, talk about careers, or just write the thing you cannot say on LinkedIn.

What go-to-market means

Go-to-market is the work of getting a product into the hands of the right customers and turning that into revenue. It includes who you sell to, how you reach them, what you say, how you sell, how you retain them, and how the whole system gets better over time.

It is not just outbound. It is not just sales. It is the full path from market to customer to revenue.

Founder go-to-market

For founders, go-to-market usually starts with doing the work yourself. Finding the first customers, choosing an ICP, picking a sales motion, writing the first emails, doing calls, handling demos, closing deals, and learning what people actually care about.

This is the place for questions about first customers, positioning, pricing, distribution, founder-led sales, when to hire, and how to know if a channel is working.

Sales

Sales is a big part of go-to-market. SDRs, AEs, AMs, founders, and sales leaders can talk about prospecting, cold email, cold calls, LinkedIn, sequences, discovery, qualification, MEDDIC, BANT, demos, objections, negotiation, closing, pipeline, quota, and comp.

The small details matter. A bad list kills a good email. A weak discovery call kills the demo. We will circle back usually means something else is broken.

Marketing and growth

Marketing and growth are also go-to-market. Inbound, content, SEO, demand gen, community, brand, product-led growth, ABM, paid, events, and lifecycle all belong here.

This is for the people trying to create demand, explain the product clearly, bring the right people in, and make sales easier before a call ever happens.

Data, signals, and outbound infrastructure

A lot of go-to-market comes down to knowing who to contact and when. That means segmentation, account selection, list building, scraping, verification, enrichment, buying signals, intent, funding, hiring, expansion, tech changes, job posts, and other triggers.

It also means deliverability. Domains, inboxes, warmup, sending volume, bounces, spam, reply rates, and why your emails are not landing.

RevOps and CRM

RevOps is the part behind the scenes that decides whether the team can see what is happening. CRM hygiene, routing, territories, reporting, attribution, forecasting, pipeline stages, handoffs, data quality, and dashboards all matter.

Bad ops makes good teams look confused. Good ops makes problems visible.

GTM engineering

GTM engineering is the newer technical side of go-to-market. It sits between revenue, data, tools, and code.

People here are building enrichment systems, replacing expensive tools, wiring APIs, scraping data, monitoring signals, cleaning lists, building internal tools, using AI coding agents, and testing whether AI SDRs are useful or just noisy. The role is new enough that some job posts ask for ten years of experience in something that barely existed two years ago.

Post-sale

Go-to-market does not stop when a deal closes. Onboarding, customer success, support, adoption, retention, expansion, renewals, referrals, and net revenue retention are part of the same system.

A bad-fit customer is often created before the contract is signed. Expansion often starts with selling the right thing the first time.

Partnerships and channel

Partnerships, agencies, resellers, affiliates, marketplaces, integrations, and channel deals are part of go-to-market too.

This is where people can talk about partner-sourced pipeline, rev share, co-selling, channel conflict, attribution, enablement, and whether the partnership is real or just two logos on a slide.

AI in go-to-market

AI now touches almost every part of the work. It can research accounts, write drafts, summarize calls, update notes, monitor signals, build tools, enrich data, and act like a 24/7 coworker.

It can also make teams faster at doing dumb things. Five hundred lazy sequences are still worse than fifty thoughtful ones. Deleting the CRM and telling an agent what happened each day might be the future, or it might be chaos. Worth discussing.

Careers, comp, and hiring

Go-to-market is also a career path. Breaking in, BDR to SDR to AE, moving into RevOps, becoming a GTM engineer, switching into marketing, joining an agency, getting laid off, surviving a bad market, interviewing, negotiating comp, and sharing real numbers all belong here.

Post the wins too. First deal, biggest deal, first commission check, new job, better title, clean dashboard, fixed deliverability, first customer, whatever.

Post here

Ask what you are stuck on. Share what worked. Share what failed. Post the messy version.

Use the closest flair so the right people find it: Founder GTM, Sales, Marketing/Growth, RevOps, GTM Engineering, Post-Sale, Partnerships, AI, Careers, Comp, Hiring, or Beginner.

I'm not saying this to make anyone feel guilty because I also use ChatGPT but people are just ignoring the environmental impact of AI and it's really bad.

Yes one query on its own is tiny, but each of those is talking a fraction of a watt-hour and a few milliliters of water to cool the servers, on its own its not a problem but do you know how many people are sending these queries each second everyday, its billions a day and the number climbs every month. Inference, meaning the actual everyday use of AI, now is consuming more energy than training the models ever did. So every time the user base grows, the negative environmental footprint grows with it, and right now it's growing faster than the changes to counter its impact. Data centers drove half of all new US power demand last year. AI data centers went through hundreds of billions of gallons of water in 2025 alone, and the projections are worse. The UN put out a report saying AI data centers could be pulling close to 945 TWh of electricity by 2030, with a water footprint on the scale of an entire region of the planet.

And the infuriating part about this is AI companies are making a choice to not build their models in an eco friendly way. They could make it more efficient, and make it use cleaner power, they could offset their ecological footprint. But they don’t since it costs more and they can’t scale as fast if they do.

A few alternatives i've found:

- mistral AI
- Cohere
- EcoGPT
- Liquid AI

Curious what do you guys think of all of this? (Currently writing an essay on this)

Do yourself a favor and do not use any other tool.

I am not asking you to use InterviewCoder, but it is better not to use anything else.

InterviewCoder works in a very different way. There are a lot of scammers out there trying to build duplicates of InterviewCoder and spreading FUD about it because they might want to profit from this business.

I know a lot of you here do not come from prestigious universities, but it is commonly understood at many universities that using other tools can get you instantly blacklisted.

Even if you do purchase InterviewCoder, make sure to practice with it so your eyes get prepared.

The latency is the best on the market, so you should be able to have a perfect and genuine interaction with your interviewer.

Everything else is fake.

The only other tool I would advise is FinalRound, if you have normal interviews. But for CodeSignal, use InterviewCoder.

When someone posts about an interview here, we ask for proof of the interview and proof that they used InterviewCoder.

We take this subreddit very seriously.

I freelance solo and about half my clients are overseas, so I invoice in USD, GBP and EUR depending on who it is.

The money comes in fine, but at tax time it is a headache working out what I actually earned in dollars and tracking the conversion on each payment.

Right now it is a spreadsheet and a folder of PDFs.

Looking for accounting software that can handle multiple currencies without me doing the math by hand, and that won't be overkill for a one person operation.

What do other contractors with foreign clients use?

I’ve been testing a bunch of AI assistants that work through messaging instead of making you open another app.

I don’t think there is a perfect product yet, but the category is getting interesting. Texting an AI feels more natural for quick tasks, reminders, follow-ups, research, and things you want to offload without switching context.

Here are the ones I’d look at, with pros and cons.

1. Folk

Website: getfolk.app Best for: memory-first daily assistance

This is the one I’d probably start with if you want a general AI assistant that remembers you over time.

Pros:

• Memory seems to be the main focus, not just an add-on
• Works across iMessage, Telegram, Discord, and WhatsApp
• Feels more like a personal assistant than a normal chatbot
• Good fit for daily context, reminders, follow-ups, and ongoing conversations

Cons:

• Still early, so reliability and features may change fast
• If you need mature enterprise workflows, it may not be the safest pick yet
• Hard to judge long-term quality until more users have tested it for a while

My take: Folk gets the edge for everyday use because memory matters a lot in this category. If an assistant does not remember your context, it quickly becomes just another chatbot in a different interface.

2. Poke

Website: poke.com Best for: integrations and proactive nudges

Pros:

• Strong integrations with Gmail, Calendar, Notion, and other tools
• Can text you first, which is important for an actual assistant
• More polished and visible than most products in the category
• Good for reminders, scheduling, and connected workflows

Cons:

• Memory does not feel as central as Folk
• More focused on integrations and proactive messages than becoming your long-term personal context layer
• Pricing and access can be a bit harder to understand

My take: Probably the best option if you care more about integrations than memory.

3. Tomo

Website: tomo.ai Best for: accountability and habits

Pros:

• Clear use case around habits, routines, and check-ins
• Good if you want something to keep you accountable
• Feels more focused than a generic assistant

Cons:

• More narrow than the others
• iMessage-only
• Not really built for general assistant tasks

My take: Great if you want a coach for habits. Less useful if you want one assistant for everything.

4. Orchid

Website: orchid.ai Best for: work email and meetings

Pros:

• Useful for drafting replies and booking meetings
• Approval-before-sending flow makes sense for work
• More executive-assistant-like than chatbot-like

Cons:

• Mostly focused on work coordination
• Less useful if email and meetings are not your main pain point
• Not as broad as a daily personal assistant

My take: Interesting for people who spend a lot of time coordinating through email and calendar.

5. Ollie

Website: ollie.ai Best for: family and household logistics

Pros:

• Clear focus on parents and households
• Useful for appointments, school runs, calendars, and family planning
• Messaging makes sense for messy coordination

Cons:

• Less relevant if you are not managing a household or family schedule
• More family-focused than work-focused

My take: Probably very useful for the right user, but not built for everyone.

6. Zo

Website: zo.computer Best for: technical users

Pros:

• Very powerful
• You are basically texting a cloud computer with its own Linux environment
• Can handle more technical and hands-on tasks than a normal assistant

Cons:

• Overkill for most people
• Less obvious value if you are not technical
• Probably has a steeper learning curve

My take: Most interesting for builders, developers, and power users.

7. Lindy

Website: lindy.ai Best for: inbox-heavy work

Pros:

• Strong for email triage, follow-ups, and work automation
• More mature as a work assistant
• Good if your work life runs through your inbox

Cons:

• Requires more setup
• Feels priced more like a business tool
• Not the first pick if you want a lightweight personal assistant

My take: Good for work automation, especially if email is your main bottleneck.

Extra use case: Kalshi / prediction markets

One use case I think is underrated is using these assistants for prediction markets like Kalshi.

I would not want an AI placing trades for me, but I do think texting an assistant for market research, reminders, event tracking, and alerts is a very natural workflow.

For example:

• “Remind me before this market closes”
• “Track new information about this event”
• “Summarize the latest context before I make a decision”
• “Watch for relevant news and tell me what changed”

That kind of workflow feels much better over messaging than inside a normal app.

Overall

My current pick would be Folk for everyday use.

The reason is simple: for a messaging-based AI assistant, memory is probably the most important feature. Integrations are useful, but if the assistant does not build context over time, it does not feel that different from opening ChatGPT or Claude.

Folk still looks early, so I would not treat it as a guaranteed winner. But if I had to pick one to try first, I’d start there.

I charge people $400 to help them cheat interviews with Cluely and honestly I think this is going to become a normal thing.

Before everyone starts crying about ethics, the whole interview process is already cooked. Companies use AI to screen resumes, recruiters barely read anything, hiring managers pull questions from banks, interviewers follow rubrics they didn’t write, and candidates are expected to do 6 rounds, system design, leetcode, behavioral stories, fake culture fit and sometimes a take home for free. But somehow the candidate is supposed to be the only one not using tools? lol.

I started helping people prep for this because most people are actually terrible at cheating. They think you just open Cluely and it magically gets you a job. That’s how you get caught. You pause too long, read stuff that doesn’t sound like you, answer the wrong question perfectly, or stare at the screen like you’re being held hostage.

The real skill is knowing what to prepare before the call. What stories to load, what company context matters, what system design notes to keep visible, what behavioral frameworks to use, what coding patterns to refresh, what not to put in the prompt window, how to recover when you blank, how to make the answer still sound like you and not like some AI corpse reading STAR method slop.

That’s what I charge for. $400/session. Mostly senior-ish backend, infra, data and PM candidates. Fintech, AI companies, late stage startups, sometimes public companies. These are not idiots trying to fake being engineers from zero. Most of them know their job. They’ve shipped real systems. They just don’t want one random staff engineer with a bad mood at minute 47 of round 5 to kill a $250k offer.

Some people will call it cheating. Fine. It is. I’m not going to pretend it’s some noble education business. But I also think the fake moral panic is hilarious. Hiring has been an arms race for years and candidates were the last ones showing up unarmed.

So yeah. I’m a Cluely cheating coach now!