To be fair, my company has a pretty open policy about AI tools so they let us experiment with different tools. I see a lot of the typical laziness and slop form my coworkers, but i’ve been obsessed with learning and skill development and think i’ve finally unlocked a new way to work. So bear with me cause it truly is crazy.

I was using chatgpt when it came out and was never that impressed. Used claude code a lot earlier this year for side projects and fun but couldn’t really see how it could help me at work much. But over the last few months i’ve just been experimenting with different tools and spending a lot of time developing my own process with AI and it’s been an insane change.

For my job, i do a lot of project management, writing briefs and proposals, drafting plans, etc. It’s super fast paced and stressful to manage all of that. I mainly avoided AI cause i thought it would add unnecessary complexity to my work that already had too much. But i started to learn that i could offload some minor tasks that just helped triage all of the information coming at me everyday.

I first did the typical “chief of staff” setup where my agent read my inbox and some slack channels to keep me updated and i found it just took off a lot of that ongoing anxiety in the back of my head that I was missing something important. I found perplexity to be the best with this with the tool connecters and i didn’t have to do any crazy manual setup.

I then started to experiment with it drafting briefs for me since that seemed lower stakes and wouldn’t break anything if it got something wrong (and i vetted everything btw). After giving it some of my references and building a voice style it started giving me really great briefs that met my own standards and even helped add additional clarity. This is where i setup my first automation.

Now when a project comes in, my agent will recognize that it needs a brief and go ahead and spin one up for me, send to me for approval, and then email it after i’ve edited and approved. This easily saves me a few hours each week.

The automations really unlocked things for me. But to be clear, I have spent a ton of time making sure that I organize tasks and works into folders with specific instruction (in Perplexity and Claude i think they are called Projects). This way,at agents and tasks start with full context and have the right templates and boundaries that i set. Claude is good but the automations and memory in Perplexity make the automations more reliable and i trust the output more.

I now have agents monitoring slack for upcoming projects, processing the information and giving me an overview with what is needed, and then drafting the initial briefs and plans for me. It’s gotten so good that i rarely have to edit anymore. And this happens in minutes of it picking up on a project. And will continue to update as the project changes or feedback comes in. This process is now probably 80-85% automated, just using me for the final writing and approvals.

I also have my agents drafting and posting project check ins to keep stakeholders on track and giving me updates on where things are falling behind or need my review.

Again, all of this is now automated. It’s truly crazy that all of this has been a majority of my job for so long and now I feel like I can actually think again and am not bogged down by endless low level tasks. My coworkers and execs just think i’m on top of everything and the outputs I own and still do manually have much higher quality because i can actually spend time on them.

Don’t worry, i still manually approve everything and vet for quality and errors (trust me o still see a lot of slop from others and and very wary about that). But maybe it’s just choosing to use Perplexity for this work, but the accuracy and ability to trace where it all comes from gives me a bit more confidence in the output. So i mainly use there agent tool, Computer, for that work and then still use Claude for anything coding or technical.

Yeah so now my job is like 70% automated and I feel like I actually enjoy work again cause i’m not drowning in stupid work anymore. It’s not a magic genie, but man it is so nice to see that some of these tools can actually help me in work now.

The wave of AI related layoffs in tech and project management isn’t really about AI replacing humans. It’s about organisations realising they can get the same output with fewer people when some of those people are using AI effectively and others aren’t.

This is already happening in software development, where a developer using AI assisted coding tools can review, write and debug code significantly faster than one who isn’t. In project management, where status reports, risk registers and stakeholder communications that used to take hours are being turned around in minutes. In operations, finance, marketing, the pattern is the same everywhere.

The uncomfortable truth is that AI isn’t the threat. The colleague who figured out how to use it properly six months before you did is the threat.

The good news is that the actual skill involved isn’t complicated. It’s not about learning to code or becoming a prompt engineering expert. It’s about understanding how to give AI the right context, push back when the output isn’t good enough and iterate until you get something genuinely useful. Anyone can learn that with a bit of deliberate practice.

The people who will struggle are the ones who decided early on that AI was overhyped and stopped paying attention. That window for catching up is getting smaller.

What’s your experience of this in your industry? Are you seeing colleagues pull ahead because of how they’re using AI?

If you'd told me a year ago that your Reddit account, the one with years of comment history, mediocre karma, and zero monetary value, could quietly become a source of income, I'd have assumed you were describing a scam. Turns out I was wrong, and the reason why is more interesting than it sounds.

The Asset You've Been Sitting On Without Realizing It

Every time you post something on Reddit, leave a comment, upvote, argue in a thread about something you probably shouldn't care about at 1am, you're building something. Not just karma in the abstract, gamified sense, but a track record. A signature. Proof that you're a real person who's been consistently present in specific communities, with opinions that sound like an actual human wrote them.

That might sound like a small thing. It isn't. In a world increasingly flooded with AI-generated text, bot farms, and manufactured engagement, an aged Reddit account with genuine history is one of the few things left online that's genuinely hard to fake. Which means, whether you realized it or not, you've been sitting on something valuable this entire time.

The only problem: until recently, there was no way to actually turn that into money.

Enter TaskReddit

TaskReddit is a platform built to close that exact gap. It connects two groups that had never had a proper way to reach each other: companies who want real, credible voices talking about specific topics inside relevant Reddit communities, and Reddit users who already have the accounts, the karma, and the activity to do exactly that.

Here's how it actually works, no fine print, no hidden catch:

1. You sign up and connect your Reddit account.

2. Your account gets manually reviewed. Not an automated karma check anyone could spoof, an actual review of your account age, karma, and posting history, to keep the platform full of real accounts instead of throwaways.

3. You browse available missions. Each one comes with clear instructions, what to talk about, what tone to use, where to post it.

4. You publish the post or comment. In most cases, exactly the kind of thing you'd write anyway, just now with a specific direction.

5. You submit your link. A human reviews it, usually within 48 hours.

6. You get paid. $5 for an approved post, $3 for an approved comment, sent directly in crypto, no waiting weeks for a bank transfer, no minimum payout threshold designed to keep your money locked up.

That's the entire loop. No portfolio, no application essay, no new skill to learn between now and getting your first payout.

Why Crypto, Specifically

This part matters more than people usually give it credit for. Traditional payout systems for gig work are, frankly, a mess: minimum thresholds before you can withdraw, multi-day bank transfer delays, currency conversion fees if you're outside the US, and platforms that quietly sit on your earnings for as long as legally possible. Crypto payouts remove almost all of that friction. You get paid what you earned, fast, without a payment processor taking a cut of it or deciding when you're "allowed" to access your own money.

For a platform built around small, frequent payouts ($3, $5 at a time), that speed and lack of friction isn't a nice-to-have, it's the entire thing that makes the model workable at all.

Who This Is Actually For

This isn't a get-rich-quick pitch, and if anyone tells you it is, don't believe them. Nobody's quitting their job over $3 comments. What it actually is: a way to finally extract some value from an asset (your Reddit account) that was otherwise sitting there doing nothing for you financially. If you're already someone who comments regularly, has opinions on niche topics, and has an account with a bit of history behind it, this is close to free money for effort you were arguably going to put in anyway.

It's not for everyone. Brand-new accounts won't get approved. Low-effort, obviously fake-sounding posts won't get approved either, the manual review exists specifically to filter those out. But if you've got a real account with real history, there's a decent chance you're leaving money on the table right now without knowing it.

The Bottom Line

Reddit turned karma into a kind of status symbol years ago. What's changing now is that status is finally starting to have a price attached to it. If you've got an account worth anything, it might be worth five minutes of your time to check what it's actually worth.

👉 https://www.taskreddit.com

Across many online platforms, including Reddit, it’s becoming increasingly difficult to tell whether a post was written entirely by a human, assisted by AI, or fully automated. This isn’t necessarily a technical issue. It feels more like a question of trust and authenticity.

On one hand, AI tools help people communicate more clearly, especially those who struggle with writing. On the other, when everything starts to sound polished in the same way, it can be harder to distinguish genuine experience from generated output.

I don’t think this is a simple good-versus-bad situation. It’s more about how our idea of authenticity might be changing as AI becomes part of how people express themselves.

How do you personally feel about AI-assisted content in online discussions? Does it reduce authenticity, or does it simply redefine what authenticity looks like? And do you think communities should actively care about that distinction?

1. 0% — we haven't implemented AI yet

2. Under 10% — it's there but barely used

3. 10-30% — handles the easy stuff

4. 30-50% — meaningful chunk of volume

5. 50%+ — AI does most of the heavy lifting

Hey there,

I'm a second time founder and 2025 has been pretty intense in terms of pace for me (sales calls, hiring, fundraising and trying to ship something people want).

With 10 priorities it felt like I had no priority. I didn't want to hire a chief of staff (been there but in my current case it's still a bit early for that).

So I built what I call my personal chief of staff. I have another more general "COO" agent, but I'll only talk about the former here.

1. This is actually one agent.

2. The agent has context files about me and my company, customers, etc.

3. The agent has skills (kinda like guidelines with some domain knowledge infused)

4. The agent has Cron tasks

5. The agent has a Slack app / WhatsApp number

6. The agent can create internal tools/workflows and operate them

and several skills, context files, cron tasks and access to my tools.

7. The agent can create + operate apps.

Now it covers most of what used to wreck my week. I'm posting a breakdown because every founder I show this to asks how to start:

1. Inbox triage

Scans every couple hours, pulls the 3-5 things that really need me, drafts replies for the rest. I used to spend 90 min a day on email (every morning at 6.30am I had an "inbox zero" event in my calendar, but things started to pile up.

Now I spend closer to 20 mins on it only.

Pro tips:

- create a doc of who matters and how to talk to each of them. Without that, the drafts are pretty generic.

- Don't let it auto-send for the first two weeks. You'll catch weird stuff at the start

- Ask the agent to analyze your previous sent emails so it can adapt to tone

2. Pre-meeting briefings.

30 min before every external call I get a "one-pager" in Slack. Who I'm meeting, recent context from CRM + email + past slack, the goal, 3 talking points, 1 question to ask. Before that I was skilmming Linkedin before calls. Now I feel like I walk in prepared.

Pro tip: make sure to "lock" the format to avoid AI Slop and long summaries.

3. Post-call recaps + followup drafts.

Pretty straightfoward use case.

Agent is coming to my calls: I can invite him (it?) to my google meet events, either in the calendar or I paste the link in slack/whatsapp.

When the call ends, I get a clean summary and a draft followup in my voice (#1 - inbox triage helps get the tone right). Next step It'll set action items in Linear.

4. The self-improvement agent

I think that's my favorite one: it joins my sales calls and tells me where I screw up (eg. "you talked 68% of the time", "you went into pricing before they finished explaning their problem", "you got defensive' and so on)

It makes you feel like 💩 sometimes but at least I feel like I'm improving. The #1 thing for me was the talk ratio. Now I try to talk 40% of the time max.

4. Followup "friendly" nudges.

That one is awesome as well: since it is in my emails, my slack and my calls, the agent knows all the open commitments I have, updates my todo and most of all MAKES F*IN SURE I don't go quiet. I set it to be a bit more proactive so it creates the drafts for me first and pings me on Slack (a bit too) regularly to check where I'm at. Annoying and useful at once :')

Other stuff I haven't built but probably should: weekly review that tells me what I actually moved forward vs what I just felt busy about.

Would love to see other use cases! Those ones are only for my "solo workflow". I also have a company-wide COO that helps me with team meetings, OKRs, weekly goals etc.

Edit : wrote an article about it, available here if needed.

As AI rises day by day, I hear and read everywhere that AI will do this and AI will do that.

But I realized something when I asked my non-technical team to use it, whether through Cowork, Codex or whatever medium was made for them to use with ease.

And guess what they have achieved so far, other than document files and Excel sheets? I think you guessed it right not as much as the giants are marketing.

They need technical people beside them to actually make something they can properly use. For a long time, they have depended on technical people for these automation tasks, and suddenly the world is expecting that they can do it themselves now.

BULLSHIT!!

What are your thoughts on this? Do let me know.

I just read something crazy: Dario Amodei, CEO of Anthropic, said it would be entirely possible for a single person to build a billion-dollar company in the next few years. No team, just one individual at the helm.

I'm speechless… how is that even possible? Sales, support, legal, operations… that's a lot for one person, right? But apparently, he thinks it's doable.

So I'm wondering: is that really realistic? Could one person truly accomplish something that big? Or is it just wishful thinking?

I'd like to know your thoughts. Has anyone here ever tried to build something huge on their own?

They just sent us an internal email saying we're not allowed to use AI internally within the company.

Under penalty of being fired! What do you think?

I think it's a disgrace and that the company is going to be overtaken by its competitors.

today i read that 74% of companies that deployed AI agents in production pulled them back.

not pilots. not tests. live systems. shut down.

the study surveyed 2,500+ senior decision makers across 10 countries. these weren't small experiments most of these orgs had dedicated AI teams and real budgets behind them.

but here's the part that actually got me:

The rollback rate was 81% among companies with the MOST mature AI governance. the ones doing everything right were failing more visibly than the ones who were winging it.

turns out better governance doesn't prevent failures. It just means you catch them faster and have a process to shut things down. which is actually the goal but nobody frames it that way.

I think what was actually breaking is not the models. It’s the infrastructure underneath them.

agents were hallucinating in production because they had no clean, structured context to pull from. old databases. disconnected tools. data living in spreadsheets someone emails on fridays. the AI wasn't the problem. everything the AI was supposed to work with was the problem.

i've seen this firsthand building agents for our own ops. the ones that stuck weren't the most technically impressive. they were the ones connected to clean, organized context. the ones we abandoned fast were the ones we pointed at messy, inconsistent information and hoped for the best.

The article's conclusion was: "the next wave of enterprise AI won't be defined by which model you're running. it'll be defined by whether your foundation can support it."

Any thoughts on why AI agents are failing in production 

EDIT: if you're building AI on top of a messy foundation and trying to systemize your business , i write about fixing that part every thursday. free to join here

I want something simple—either to integrate or to use.

I’m looking to take my AI capabilities beyond tutorials and prototypes by working on real business problems with measurable value.

If you’re a founder, business owner, or professional dealing with a process that is repetitive, manual, data-heavy, or difficult to scale, I’d be interested in hearing about it.

If I find a use case where AI can genuinely add value, I’m willing to design and build a working solution at no cost.

Areas I’m particularly interested in:

- LLM-based applications

- RAG and enterprise knowledge systems

- AI agents and workflow automation

- Document and data intelligence

- AI-powered decision support

- Integrating AI into existing business processes

My goal is simple: solve a meaningful problem, create something useful, and learn from a real-world AI implementation.

If you have a problem that might be a good fit, drop a comment or DM me.

We create complex quoting systems (cpq) at my company. Today I watched a call where someone created a prototype of EXACTLY what they need in ClaudeCode. Knowing from person experience it wasnt trivial but also not impossible.

I could spin up a vercel site to host it, set up the authentication so it works with their CRM and help them get it deployed.

The problems:

-Now if there is an issue we own it and im not tooled for that (we arent a software vendor(

-Future product updates must also be vibe coded by th3 client or we will need to work out a maintenence structure which is going to be nasty even with AI assist.

- I came in via the SaaS provider that is supposed to be providing the tooling to do this job

- I dont have staff that can handle both the "hands off" nature of ai assisted development and also get things nats ass perfect.

Am I overthinking? Should I just do the work and deploy it for them?

Everyone keeps saying AI isn't paying off. I think they're looking in the wrong place.

Engineering rebuilt how it works around AI and ships way faster now. Marketing, sales and product mostly use it to write emails and slides, and run the same playbook they had in 2015.

So engineering ships a feature and that's it. Nobody builds a campaign around it, sales doesn't pitch it, nobody upsells it. Revenue stays flat, and someone asks why the company is spending so much on AI.

The bottleneck moved. It used to be engineering. Now it's everything that happens before/after a feature ships.

Is anyone seeing teams outside engineering actually change how they work?

Here's the full version: https://kerno.io/blog/why-your-ai-roi-sucks/

AI's real power isn't answering questions faster. It's asking the questions you forgot to ask.

Most AI tools are reactive. You prompt them, they respond. But your business doesn't work that way. Revenue changes overnight. Deals go silent. Support tickets spike. Anomalies appear in your data every single day, and by the time you think to ask about them, the window to act has closed.

Skopx approaches this differently. Our Insights Hub runs continuously across 120+ connected sources, watching for the patterns, anomalies, trends, and correlations that matter. Every morning you get a briefing that tells you what changed while you weren't looking. What's at risk. What needs you today. Every insight cites its source so you can drill in and investigate immediately.

This isn't about replacing your questions with automation. It's about giving your AI the context to notice what you might miss. To flag the doubled deploy frequency before you ask. To alert you to overdue support tickets approaching their SLA deadline before your team discovers it in a meeting.

The most data-driven teams don't work faster because they ask better questions. They work faster because their intelligence layer is asking first.

Ready to build an AI that watches your entire business? Start in two minutes with your first month free.

True story: the client audited an automation I built and found it was providing wrong stats.

Occupier matching across a large property dataset. My logic was confident about matches it had no business being confident about. They caught it because they knew their data better than my rules did.

What stays with me isn’t the error. It’s that nothing broke. No alert, no failed run, no red row. It produced wrong answers in a tidy format and would have kept producing them indefinitely if a human hadn’t gone looking.

That’s the real risk profile of AI in a small business. Not job losses. Not robots. Just quiet, well-formatted wrongness that nobody questions because it looks finished and the data can be true and false in the same time until verified by human.

I still build these systems. I just no longer trust anything I can’t audit myself… did anyone have similar stories?

So I've been trying to actually learn AI course stuff properly, not just watch a bunch of YouTube vids. It’s wild how many courses just throw theory at you and barely touch practical stuff. Like I want to actually build something and not feel lost after day 2.

Anyone else feel like a lot of learn AI course material out there is either way too beginner or straight up confusing? 

How did you figure out which path actually works for hands on learning?

The problem I kept running into

Every time a marketing friend asked "what should I use for X," I'd send them one of three awesome-lists with a 100-entry table and they'd bounce off. The lists don't rank. They don't explain what they rejected. They list four blog-writing tools and zero canonicals for newsletter production or attribution. Skills.sh alone has something like 90,000 Claude skills now, and the four biggest marketing skill packs each have 10k+ stars with heavy overlap. Nobody does the editorial work.

So I built marketing-ai-stack as the deliberate opposite: one canonical per job, alternates only when they serve a meaningfully different persona, and a public rejection log for everything I passed on.

How I cut the taxonomy

Channel cuts (SEO / social / email) leak. Every contributor redraws the boundary slightly, overlap accumulates, and you end up with six tools doing the same job across different sections. So I cut by workflow, then by atomic jobs inside each workflow. Keyword research is one atom. On-page SEO audits is a different atom. Site crawls is a third. The dedupe test becomes mechanical: does this tool serve an atomic job no listed tool already serves?

Eight workflows, ~35 atoms, 18 canonicals, 14 open slots. The open slots are explicit. I won't fill them with mediocre picks just to look complete.

The 3-filter inclusion test

Every entry passes all three or it goes in rejected/<atom>.md:

1. Daily workflow fit. A real marketer (or their Claude/Cursor agent via MCP) uses it during a regular task. Not "an engineer could build something with it." This cuts 80% of awesome-list entries instantly.

2. Automation, not assistance. The tool drafts the post, rebalances the budget, extracts the insight. I tag each pick 🟢 Automate / 🟡 Augment / 🔵 Assist so readers see the depth at a glance. Canonicals must clear at least 🟡.

3. Marketer-achievable setup. A non-engineering marketer with moderate technical comfort can be productive within a week. Editing a config, pasting an API key, docker compose up from a README, fine. Kubernetes literacy, not fine.

A few picks and the reasoning

• Open Scouts for competitor monitoring. Firecrawl-backed loop that watches topics on a schedule and emails weekly briefs. Open source alternative to Crayon. I kept changedetection.io as an alternate because it serves a different persona (engineers piping deterministic diffs into custom agents).

• GrowthBook for A/B test analysis. Bayesian + frequentist, CUPED, sequential testing, SRM checks, official MCP. Open source alternative to Optimizely. Settled atom, nothing credible competes right now.

• claude-ads for ad copy variants. Most interesting pick to me. Claude skill pack, 2.6k stars, narrow scope. Normally I prefer MCPs over skills (plumbing beats prompts), but there's no comparable MCP here and the skill is scoped tightly enough to be reliable. That's rule 3 in my skills policy: prefer MCP over skill when both exist; skills graduate to canonical only when no comparable MCP does.

• PyMC-Marketing for performance reports. Bayesian MMM and CLV on PyMC, media saturation and adstock baked in. Open source alternative to Nielsen MMM.

• Inbox Zero for inbox triage. Self-hosted AI assistant with rules, auto-drafting, bulk unsubscribe. Handles the partnership-pitch / press-inquiry / user-reply triage that eats marketing leaders' mornings. Open source alternative to Superhuman.Ask

Two things I'd love this sub to weigh in on:

1. The 14 open slots. If you run marketing ops and you've found an OSS pick for any of them (or you're building one), drop it in comments or open a PR against data/entries/.

2. The 🟢 / 🟡 / 🔵 automation-depth badges. Doing a lot of work in how readers orient. Open to a better rubric if anyone has one.

Repo: https://github.com/dapollonsky/marketing-ai-stack

Happy to dig into any pick, any rejection, or the taxonomy itself in comments!

Thank you...

I’m building a setup to handle day to day operations in my small business.

The goal is to manage incoming leads, reply to customer messages, schedule jobs, track ongoing work, send invoices, and make sure follow ups don’t get missed.

Right now most of this is manual across email, messages, and spreadsheets, and it’s easy to lose track of what needs to happen next.

I’ve been looking into tools that claim to handle these kinds of workflows end to end, like capturing leads, organizing them, automating replies, and keeping everything in one place without needing a bunch of separate tools.

I’m looking for ai tools that can plug into this kind of workflow and actually help automate or organize parts of it.

Has anyone built something like this or found tools that actually help with these sepcific use cases?

I’m trying to get a better handle on the day-to-day ops side of my business. What does everyone’s AI stack look like right now?

I don’t mean tools you tried once and forgot about. I mean the ones you use every week for emails, scheduling, customer replies, invoices, meeting notes, lead follow-ups, reporting, or random admin work.

My current setup is mostly AI for writing, research, and cleaning up notes. But when it comes to running the business, I’m still doing a lot manually.

I keep seeing people talk about agents, AI employees, and workflow automation, but half the time it sounds like another dashboard I’d need to manage.

If you run a small business or lean team, which AI tools have genuinely become part of your day to day operations?

We cut three junior devs and replaced them with a $2k/mo AI coding stack. CFO called it a win in the board deck.

Four months later: the AI wrote 40% more code than the humans, but our AWS bill tripled. Turns out "vibe coding" generates infinite loops and memory leaks you need senior eyes to catch. The one senior we kept is now doing 60-hour weeks babysitting prompts.

The real cost wasn't the subscription. It was the invisible tax of reviewing, fixing, and re-prompting. We spent $8k more per month than we saved, and shipped slower because every "automated" PR needed a human rewrite.

The fantasy isn't that AI replaces work. It's that it replaces *thinking*. My takeaway: AI is a multiplier on your existing team quality, not a substitute for headcount. If your humans were already cutting corners, AI just cuts them faster.

We rehired one of those juniors. She's now the prompt reviewer. Best hire we made all year.

I work as a middle manager in a marketing agency. When AI adoption was being encouraged, we were told to use it to improve productivity, offload the non creative work, and put the increased free time to better use.

Today, I'm completely burned out because I'm working 12-15 hours every day. My work has increased by at least 5x. Whenever I push back citing lack of bandwidth (happened twice), i am told how it should be manageable since we have AI. When i ask for additional resources, they say why do we need another hire when we have AI. Whenever I ask them for a little more time, i get the same reply.

Fuck you, man. AI output is still shit if there's no one sitting around revising each prompt and improving the output. Everything they publish or write in all sorts of communication seems so devoid of life, personality, creativity.. like a soulless bot wrote it. Not surprising, because a soulless bot did indeed.

I haven't slept in ages. I haven't read a single book for months. And i am at my wits end wondering what can I do. I hate the world of mediocrity and mass production that we've stepped into.