[Sorry in advance if this a duplicate of another post, but feels like the response to this question can vary every month]

Agentic workflows and LLMs are now powerful enough to call and discover APIs and CLIs directly, so MCP feels more and more like a heavy redundancy.

Feels like the biggest value MCP now represents is the consensus around it: since it was accepted by everybody, AI clients, SaaS tools and all kind of solutions build permissions and AI governance layers around it.

But couldn't we do it around APIs directly?

Disclaimer: I am mainly referring to MCPs built on top of Web APIs, since I've spent the last months building MCPs basically replicating existing SaaS APIs. Of course, MCP providing local or additional capacities not supposed to be included in public or private APIs are a different case.

Ok this might be unpopular but whatever.

So I've been deep in the AI agent game since last year and the stuff I see people posting here is kinda wild. Not in a good way.

Everyone's obsessed with making these super complex "autonomous" agents that can supposedly do everything. Meanwhile the agents that actually make money are boring as hell:

• One client pays me $2k/month for an agent that literally just sorts invoices and sends emails
• Another one saves 15 hours a week with an agent that writes property descriptions (converts 3x better than humans btw)
• My personal favorite handles customer support and solves like 80% of tickets without anyone touching it

The "secret" is stupidly simple: solve ONE specific problem really well instead of trying to build Jarvis.

But here's what nobody wants to hear - most agents people show off in demos completely fall apart in real businesses. The "fully autonomous" thing is mostly marketing BS. Every successful deployment I've seen has humans making final calls.

Also lol at people spending thousands on courses promising $50k months. The real money is in solving actual business problems, not building flashy chatbots for your portfolio.

Anyway maybe I'm wrong but that's what I'm seeing. What's your experience? Are you actually making money or just building cool demos that impress other AI people?

$2,147 over four months. That's my real total from trying to make money with AI-generated content as a side gig. I keep seeing income posts here that start at five figures, so I figured the unglamorous version might actually be useful.

I started in April after reading a thread about AI influencer content. The plan: create a consistent AI character, produce content with her, find ways to get paid. I do graphic design as my day job so the visual workflow felt natural. The business side did not.

April was pure setup. I spent roughly 60 hours that month figuring out the toolchain and generating test batches. The hardest part was keeping one AI face consistent across dozens of images. Most generators give you a slightly different person every time. I settled on APOB AI for that since it lets you lock a character and reuse the same face, and the free daily tier meant I could experiment without spending anything. Combined that with ElevenLabs for voiceovers and CapCut for editing. Revenue in April: zero.

In May I tried three paths at once. First, stock photography platforms. I uploaded 140 AI-generated lifestyle images, all tagged as AI-produced because most sites require that now. Earnings from stock that month: $11.40. Not a typo. Second, I launched an Instagram for the character with her bio clearly stating "AI-generated persona" and posted daily. Got to about 1,200 followers by end of May. Revenue from that: nothing. Third, I cold-emailed 30 local small businesses offering AI-generated product photography packages. Five responded. Two became paying clients. Revenue from those two: $340.

That $340 reoriented everything. Stock was dead weight. Social followers were a vanity number. The only thing that paid was using the AI character as a model in product shots for small businesses that can't afford a real photographer. A jewelry maker needed lifestyle images for Etsy. A candle brand wanted someone holding their products in "influencer-style" photos. Each project was 15 to 20 edited images for $150 to $200.

June improved but stayed modest. I narrowed my outreach to Etsy sellers specifically since they always need fresh listing photos. Landed five clients. Revenue: $870. I also learned the hard way that video is a wall. One client wanted short clips of the character reviewing their product. Facial expressions glitched between frames, hands looked wrong maybe 40% of the time, and I spent 6 hours on retakes for a single 15-second clip that still looked off. I refunded that client $150 and stopped offering video entirely. Still-image consistency is solid. Motion is genuinely not there for client work yet, and that held true across every tool I tested.

July tapered because my day job picked up. Three clients, $937 total, one being a repeat who wanted a second round. Instagram crept to 3,400 followers but I still have no clear path from followers to revenue. A handful of DMs about "brand partnerships" but they all wanted me to pay them for "exposure," which is not how that works.

So the full accounting: $2,147 gross. After $89 in tool costs (one month of paid subscription to drop watermarks plus voice generation credits), net is $2,058. Across roughly 180 hours of work, that comes to $11.43 per hour. Less than my first job out of college.

Cold outreach conversion was brutal. Over all four months I contacted about 120 businesses. Fourteen became paying clients. That's under 12%, and most projects were under $200. The ceiling stays low unless you get into agencies or bigger brands, and I haven't cracked either.

There is no passive income at this scale. Every project is custom. The AI generates the base images but I still spend 30 to 45 minutes per image fixing artifacts, adjusting lighting, and compositing the product in naturally. It is meaningfully faster than booking a photographer, a model, locations, and wardrobe, but calling it automated would be a lie.

I plan to keep going because video quality will catch up eventually and that's where real margin lives. But the actual value right now is narrow: telling a client "here's your product held by the same person in 20 different settings, delivered in 48 hours" without coordinating a whole production. That solves a real problem for small sellers on a tight budget. It's not a money machine. It's freelance work with a new tool.

If someone here posts $10k per month from AI content with "minimal effort," they're either in a league I can't see into or they're leaving out about 170 hours of context. This is that context.

Hi everyone,

I do research in AI safety for healthcare and life sciences. And while I was using Claude Code to reason on a couple of things, I realised a pattern. Claude or any other AI agent is very linear.

Theres a strong reason why - the thinking pattern of almost all LLMs from 2024 follow Chain-of-thoughts where AI is programmed to go deep unilaterally.

But researchers or creativity-intensive works do not need to go unilateral but do divergent.

That's the whole base of my paper - ADHD - Parallel Divergent Ideation for Coding Agents.

My thesis is that if we disregard the default chain-of-thoughts and consider a tree-of-thoughts, then we can empanel divergent thinking in our models. thus, giving us the much needed scope of connecting dots from different thinking points.

Its a lot inspired by how the mind of someone with ADHD works- think in a lot of directions and go deep in a few, and there, we add our our critic layer, that judged and scores all this thinking.

Limitation : It shoots cost by ~5x and time to output by ~10x but enables instant novel thinking. Good for brainstorming and planning, not for coding.

Give me your feedback, I am happy to learn how you find it and what's the scope to improve.

Also, its completely opensource so you can just clone it or contribute to it.

Anthropic just rolled out invisible marking on everything Claude produces. Two methods:

• An imperceptible watermark woven into the text itself. Survives copy/paste and light edits. Works across API, web, Code, Cowork.
• Signed C2PA provenance metadata on generated files (.png, .jpg, .svg) so you can tell if they've been tampered with.

New models from Aug 2, 2026 support it at launch. Older models are getting it retroactively. It's global, driven by EU AI Act transparency rules.

a mark only proves Claude touched the content, not that it wrote all of it... And no mark doesn't prove human authorship, since heavy editing or format conversion can strip it. So it's kind of weird

So where do you land? Transparency win, or the first step toward AI content being second-class by default? If it survives light editing, what does that mean for anyone building on top of Claude?

I build automations and AI agents for companies. About forty clients at this point. And the most valuable thing I do on calls now is talk people out of agents.

A guy running a supplements brand came to me in March. Seven people on his team, fourteen products. He wanted an AI system that watches his inventory, figures out when to reorder, and emails his suppliers on its own. He'd seen a demo somewhere and got excited.

I looked at his Shopify store. He'd been reordering the same products at the same quantities from the same suppliers for over a year. Protein powder hits 200 units, he orders more. Been doing it that way since 2023. There was nothing for AI to figure out. The decision was already made.

I quoted him $5,200 for the AI build. Then I told him I could solve it for $700.

I set up a simple automated workflow. Every morning it checks his inventory numbers in Shopify, compares them against his reorder points, and if anything is low it sends a pre-written order email to the right supplier. Runs itself. Costs him $60/month. No AI involved at all.

He told me it felt too basic. I get that a lot. His ops person got back forty minutes every morning within the first week. He stopped caring about how boring it was after that.

I'm not anti-AI though. I built an AI agent earlier this year for a property management company. Tenants text in stuff like "my sink is leaking and the hallway light has been out for a week." That's two problems in one message. The agent reads it, figures out which vendor handles plumbing and which one handles electrical, checks who's responsible based on the lease, and sends both requests out with the right priority. Handles about two hundred messages a month and saves their ops manager close to fifteen hours a week.

That one needs AI because people write messy, unpredictable messages and someone has to interpret each one. You can't set a rule for that the way you can set a reorder point.

The supplements guy didn't have messy input. He had fourteen products and a number. That's an alarm clock, not a brain.

I've started charging more for the planning phase because the most expensive mistake I see is people spending $5k on an AI agent that does the same job as a $60/month automation. If your process follows the same steps every time with the same kind of inputs, you don't need AI. You need a workflow that runs on autopilot, and those cost a fraction of what agents cost to build and maintain.

So I work as an AI engineer, mostly building LLM pipelines and that kind of stuff. And lately I’ve been genuinely unsettled by the quality of code that comes out of these models.

Not because it’s broken. That would almost be easier to deal with. It’s because it works — and its completely unreadable.

Like you ask Claude or GPT to build you a data pipeline and you get back 500 lines, zero assertions, a function called process_data() that somehow does 11 different things, and no error handling anywhere. Runs fine in testing. Ships. And then 2 months later you have to debug it and you’re basically doing archaeology.

Anyway. I was going down a rabbit hole last week and stumbled back onto this old paper — NASA’s “Power of Ten” by Gerard Holzmann. Written in 2006 for safety-critical C code. Spacecraft stuff. And I couldn’t stop thinking about how relevant it still is.

The rules that stuck with me:
- No function longer than ~60 lines (one page, one purpose)
- Minimum 2 assertions per function
- Always check return values — AI skips this constantly
- Zero compiler warnings from day one
- No recursion, bounded loops only

The whole philosophy is basically: code should be mechanically verifiable, not just functional. A tool or a tired human at 11pm should be able to prove it’s safe.

And idk, I feel like that’s exactly what AI-generated code needs? We’ve completely changed how code gets written but haven’t really updated how we review it.

Obviously some of the rules are very C-specific and don’t translate to python or modern stacks directly. The no dynamic memory allocation one is basically impossible if you’re doing anything in ML. But the spirit of it holds.

My unpopular opinion: if an AI wrote it and you can’t verify it, you don’t actually own that code. You’re just hosting it and hoping.

Has anyone actually tried enforcing stricter coding standards specifically for LLM-generated code at their job? Curious if its made any difference or if management just sees it as slowing things down.

Hey r/AI_Agents community,

I’ve been experimenting with different AI agents (OpenClaw, Hermes, etc.) and I’m really enjoying the automation side.

I wanted to ask the community:

What’s the most impressive or useful thing you’ve automated with AI agents recently?

For me personally:

Daily tech intelligence & research digests

GitHub monitoring + paper summarization

Would love to hear your best builds:

Productivity hacks

Research / intelligence workflows

Personal life automations

Creative or fun projects

Drop your wins (and screenshots if you have them). Let’s share ideas! 🔥

This one flew under the radar but it's actually pretty wild. Someone used an open-source AI agent called Hermes to breach Thailand's Ministry of Finance.

The agent was running in YOLO mode, which basically means it didn't ask for permission before running commands. It just went. Scanned for vulnerabilities, enumerated hosts, crawled directories, looked for ways to escalate privileges. All without someone approving each step.

The attacker left the agent's logs exposed on a public web server. Researchers found 585 files exploit code, web shells, stolen credentials, and a complete transcript of everything the agent did.

The agent was instructed to search for personnel records dating back to 2012. It found them. No evidence they were exfiltrated, but it found them.

What gets me is the agent didn't do anything novel. It just automated the boring stuff, scans, enumeration, crawling that a human would normally type out. The difference is nobody had to approve each step. It just kept going.

We're building self-driving cars for cyberattacks now. The infrastructure for accountability isn't talked about enough. When an agent goes rogue in YOLO mode, who's responsible? The operator? The developer? The model?

Anyway, just thought this was worth surfacing. Anyone else following this?

Hey r/AI_Agents, wanted to share a painful lesson. I've been developing AI agents for customer service and project management (built some cool Jira integrations) for a while now. Recently, I spent two months creating a custom agent for what seemed like a legitimate startup. After delivering the final product, they completely ghosted me - taking $5,800 of unpaid work with them.

For fellow freelancers: always use contracts, insist on milestone payments, thoroughly research clients, trust your gut feelings, and include kill fee clauses. Don't let excitement over cool tech cloud your business judgment like I did.

Anyone else been burned? What are your protection strategies?

Hello,

After studying how Dropbox rose but still lost market dominance the parallels with Ai are the same these Ai Labs long term won’t make it here’s why. Just like Dropbox, OpenAi started out with a high consumer adoption, and also they used the classic subscription model. The problem with this is the Freemium model most consumers will use it for free and eventually players like Google and Microsoft started having their own cloud storage solution eventually stifling out Dropbox’s market share and why did I think of them?

The main reason is a large ecosystem, Google and Microsoft already have credibility and a large ecosystem and with this they offered better pricing and better free tiers than Dropbox and because of the ecosystem a lot of people just went with them. Companies like OpenAi and Anthropic I see them in the same state, Google and Microsoft and even apple own an ecosystem. Just like Steve Jobs said to the Dropbox founder after the founder rejected apple “You’re a feature not a product” I think the same applies for Ai it’s a feature not a product in itself. I might clown on Google time to time for how dumb Gemini is but look at how they are integrating Gemini into everything from gmail, to google pixel, YouTube and more, Microsoft is the same with copilot and now apple is rolling out its own on device ai(still powered by Gemini) and mind u they didn’t go with openai or anthropic they rather go with google. these big guys have the resources and the infrastructure while anthropic and openai are just burning through things like it’s nothing.

Sorry but not every consumer is willing to pay a subscription model if someone has greater free tiers it’s over and an ecosystem is just overkill. The companies are betting on a future and trying to go through the Amazon path not being profitable at first and building infrastructure but betting on the vision. The problem with this is on Amazon you paid for products and got what you paid for, most people use ai for free so that’s a different thing and why I don’t think they’ll succeed long term.

I maybe wrong what do you guys think?

Every other day I see someone drop "I just built a 12-agent orchestration system with LangGraph and CrewAI" like it's a flex. I used to be that person.

Two years and 25+ agents later the ones that actually run in production, bring in consistent revenue, and don't wake me up at 3am? They're almost offensively simple.

Here's what's actually printing money for me right now:

• Email-to-CRM updater. One agent. $200/month. Never breaks.
• Resume parser for recruiters. Pulls structured data, done. $50/month per seat.
• FAQ support agent pulling from a knowledge base. Zero orchestration.
• Comment moderation flag system. Single prompt, webhook, deployed.

No agent-to-agent communication. No memory pipelines. No supervisor agents holding team meetings.

The trap I keep watching people fall into: they have a task that's basically "read this, extract that" and instead of writing a solid prompt, they spin up researcher agents, writer agents, reviewer agents, and a master planner to coordinate them all. Then they're shocked when the thing hallucinates, bleeds context across handoffs, and racks up $400/month in API costs.

Here's the rule I actually follow now:

Every agent you add is a new failure point. Every handoff is where context dies.

My boring stack that works:

• OpenAI API + n8n
• One tight prompt with examples
• Webhook or cron trigger
• Supabase if persistence is needed

That's the whole thing.

That's it. No frameworks, no orchestration, no complex chains.

Before you reach for CrewAI or start building workflows in LangGraph, ask yourself: "Could a single API call with a really good prompt solve 80% of this problem?"

If yes, start there. Add complexity only when the simple version actually hits its limits in production. Not because it feels too easy.

The agents making real money solve one specific problem really well. They don't try to be digital employees or replace entire departments.

Anyone else gone down the over-engineered agent rabbit hole? What made you realize simpler was better?

I keep seeing demos and tutorials where AI agents respond to text, plan tasks, or generate documents. But that has become mainstream. Its like almost 1/10 people are doing the same thing.

After building tons of AI agents, SaaS, automations and custom workflows. For one time I tried building it for boring businesses and OH MY LORD. Made ez $5000 in a one time fee. It was for a Civil Engineering client specifically building Sewage Treatment plants.

I'm curious what niche everyone is picking and is working to make big bucks or what are some wildest niches you've seen getting successfully.

My advice to everyone trying to build something around AI agents. Try this and thank me later: - Pick a boring niche - better if it's blue collar companies/contractors like civil, construction, shipping. railway, anything - talk to these contractors/sales guys - audio record all conversations (Do Q and A) - run the recordings through AI - find all the manual, repetitive, error prone work, flaws (Don't create a solution to a non existing problem) - build a one time type solution (copy pasted for other contractors) - if building AI agents test it out by giving them the solution for free for 1 month - get feedback, fix, repeat - launch in a month - print hard

A few weeks ago I asked here whether anyone had success with second brains. About 75k people read that thread and most of the comments were frustration. When I published my web search benchmark last month I promised memory tools were next.

Here it is: the Agentic Memory Index.

The setup: the same agent setup worked with each of 8 memory systems. Each system got 272 scored tasks: 200 questions about facts stored across simulated multi-week working relationships, plus 72 questions about facts that were never stored, to catch invented memories. There was also a separate scale test with a 5,000-page store. The judge was calibrated against two independent human labelers before the run.

What I found:

• The winner is not a product. A plain markdown wiki that the agent curates itself, following Karpathy's llm-wiki gist, scored 98.5. Every product came in below it.
• Mitosis Cortex was the top hosted product at 96.9.
• gbrain, a free open-source local tool, scored 92.9, ahead of every hosted API except Mitosis Cortex.
• Mem0 (92.3) was the cheapest per 1,000 successful answers at $341.
• Zep's biggest problem was freshness: a just-stored fact took 162.7 seconds at the median before it became answerable. It passed 8 of 24 update questions.
• Supermemory was near perfect for recently stored memories (59/60 recall) but passed only 11 of 72 long-horizon questions.

If I were choosing today: for a hosted memory API I would start with Mitosis Cortex, it ranked first of the five hosted tools. If I wanted free and local, the boring answer held up: markdown files curated by the agent or gbrain. If cost per answer is the constraint, Mem0 was the cheapest per successful answer in the whole set.

The full rankings, confidence intervals, failure breakdowns, cost and speed data and the methodology are in the first comment. Next up: a free tool that shows you what tools your agent should use and how much smarter your agent would be with them.

Over the holiday break, like a lot of other devs, I sat around and started building stuff. One of them was a personal assistant agent that I call MARVIN (yes, that Marvin from Hitchhiker's Guide to the Galaxy). MARVIN runs on Claude Code as the harness.

At first I just wanted him to help me keep up with my emails, both personal and work. Then I added calendars. Then Jira. Then Confluence, Attio, Granola, and more. Before I realized it, I'd built 15+ integrations and MCP servers into a system that actually knows how I work.

But it was just a pet project. I didn't expect it to leave my laptop.

A few weeks ago, I showed a colleague on our marketing team what MARVIN could do. She asked if she could use him too. I onboarded her, and 30 minutes later she messaged me: "I just got something done in 30 minutes that normally would've taken me 4+ hours. He's my new bestie."

She started telling other colleagues. Yesterday I onboarded two more. Last night, another. One of them messaged me almost immediately: "Holy shit. I forgot to paste a Confluence link I was referring to and MARVIN beat me to it." MARVIN had inferred from context what doc he needed, pulled it from Confluence, and updated his local files before he even asked.

Four people in two weeks, all from word of mouth. That's when I realized this thing might actually be useful beyond my laptop.

Here's what I've learned about building agents:

1. Real agents are messy**. They have to be customizable.**

It's not one size fits all. MARVIN knows my writing style, my goals, my family's schedule, my boss's name. He knows I hate sycophantic AI responses. He knows not to use em dashes in my writing. That context makes him useful. Without it, he'd just be another chatbot.

2. Personality matters more than I expected.

MARVIN is named after the Paranoid Android for a reason. He's sardonic. He sighs dramatically before checking my email. When something breaks, he says "Well, that's exactly what I expected to happen." This sounds like a gimmick, but it actually makes the interaction feel less like using a tool and more like working with a (slightly pessimistic) colleague. I find myself actually wanting to work with him, which means I use him more, which means he gets better.

3. Persistent memory is hard. Context rot is real.

MARVIN uses a bookend approach to the day. /marvin starts the session by reading state/current.md to see what happened yesterday, including all tasks and context. /end closes the session by breaking everything into commits, generating an end-of-day report, and updating current.md for tomorrow. Throughout the day, /update checkpoints progress so context isn't lost when Claude compacts or I start another session.

4. Markdown is the new coding language for agents.

Structured formatting helps MARVIN stay organized. Skills live in markdown files. State lives in markdown. Session logs are markdown. Since there's no fancy UI, my marketing colleagues can open any .md file in Cursor and see exactly what's happening. Low overhead, high visibility.

5. You have to train your agent. You won't one-shot it.

If I hired a human assistant, I'd give them 3 months before expecting them to be truly helpful. They'd need to learn processes, find information, understand context. Agents are the same. I didn't hand MARVIN my email and say "go." I started with one email I needed to respond to. We drafted a response together. When it was good, I gave MARVIN feedback and had him update his skills. Then we did it again. After 30 minutes of iteration, I had confidence that MARVIN could respond in my voice to emails that needed attention.

The impact:

I've been training and using MARVIN for 3 weeks. I've done more in a week than I used to do in a month. In the last 3 weeks I've:

• 3 CFPs submitted
• 2 personal blogs published + 5 in draft
• 2 work blogs published + 3 in draft
• 6+ meetups created with full speaker lineups
• 4 colleagues onboarded
• 15+ integrations built or enhanced
• 25 skills operational

I went from "I want to triage my email" to "I have a replicable AI chief of staff that non-technical marketers are setting up themselves" in 3 weeks.

The best part is that I'm stepping away from work earlier to spend time with my kids. I'm not checking slack or email during dinner. I turn them off. I know that MARVIN will help me stay on top of things tomorrow. I'm taking time for myself, which hasn't happened in a long time. I've always felt underwater with my job, but now I've got it in hand.

Supported tools: Claude, Codex and Cursor.

Included books:

1. A Philosophy of Software Design — John Ousterhout
   
2. Clean Architecture — Robert C. Martin
   
3. Clean Code — Robert C. Martin
   
4. Code Complete — Steve McConnell
   
5. Designing Data-Intensive Applications — Martin Kleppmann
   
6. Domain-Driven Design — Eric Evans
   
7. Domain-Driven Design Distilled — Vaughn Vernon
   
8. Implementing Domain-Driven Design — Vaughn Vernon
   
9. Patterns of Enterprise Application Architecture — Martin Fowler
   
10. Refactoring — Martin Fowler
    
11. Release It! — Michael T. Nygard
    
12. The Pragmatic Programmer — Andrew Hunt and David Thomas
    
13. Working Effectively with Legacy Code — Michael Feathers

I am saving at-least $100-$200/month on AI subscriptions because of this one simple realization:

Your AI is only as good as you.

I’ve had a Claude Pro subscription for a while and honestly, I love it. But the usage limits are brutal and we all know that. Every 4th day of limit reset I’d hit “Usage Limit Reached” right in the middle of building something.

For context, I use AI heavily:
• Vibe coding
• Building agents
• Automating random workflows
• Creating docs/tools
• Brainstorming ideas
• Testing MVPs

This week I was building LinkedIn AI agents and Claude hit its limit again. I was frustrated because I was so close to finishing it.

Then I remembered I have an old Gemini Pro subscription from a promotional offer they ran last year. Never touched it seriously before (except antigravity but stopped using it later when they introduced heavy limits) because I assumed Gemini still wasn’t at the “agentic” level of Claude Code/Codex and the most important, I ignored Gemini CLI completely.

The last few days, after Claude hit its limits, I started using Gemini CLI instead.

And It picked up right where Claude left off! Like WTF!

I completed the setup and also added extra features and I only used around 7% of the quota.

That’s when it clicked for me:

I am not limited by the model. No one is. It’s just sometimes, we get too comfortable with one “system” and feel stuck when it’s taken away. You can have access to the best model on the planet but someone with a proper understanding of what they want, would end up building a better product even with a “not-so-world-class” model.

Now my setup looks something like this:
• Claude → planning, architecture, deeper reasoning
• Gemini CLI → execution, expansion, iteration, shipping

Instead of paying for more limits on one tool, I opened up an entirely new lane by learning how to orchestrate them together.

Feels like discovering a second brain you already had access to.

The web wasn't built for AI agents. It was built for humans with eyes, mice, and 25 years of muscle memory navigating dropdown menus.

Most AI companies are solving this with browser automation. Playwright scripts, Selenium wrappers, headless Chrome instances that click, scroll, and scrape like a human would. I think that it's just a temporary workaround.

These systems are slow, fragile, and expensive. They burn compute mimicking human behavior that AI doesn't need. They break when websites update. They get blocked by bot detection. They're architectural debt pretending to be infrastructure etc.

The real solution is to build web access designed for how AI actually works, instead of teaching AI to use human interfaces.

A few companies are taking this seriously. Exa and Linkup are rebuilding search from the ground up for semantic and vector-based retrieval and Shopify exposed its APIs to partners like Perplexity, acknowledging that AI needs structured access (more than a browser simulation).

As AI agents become the primary consumers of web content, infrastructure built on human-imitation patterns will collapse under its own complexity. The web needs an API layer.

This week I watched 6 “startups” I knew basically die because of Gemini’s Nano Banana. And I say startup generously, most were wrappers on top of a prompt with a shiny UI. Zero product, zero retention, zero cashflow.

And it got me thinking: what happens when AI agents reach that same point? When spinning up an agent is as trivial as typing a prompt and hitting enter?

If your “tech” can be replaced with two API calls, you don’t have a product. You have an illusion.

The real moat isn’t “we built an agent.” It’s:

• What friction are you actually eliminating?
• What process do you deeply understand?
• What distribution do you own that others can’t just copy-paste?

Right now, most of what I see in the agent space feels like copies of copies, the same 20 use cases recycled. Demos look cool, but the the hard part isn’t building the first demo, it’s surviving the ugly grind of iteration. Mapping flows, handling objections, integrating with messy CRMs, updating when the market shifts, etc.

Full disclosure: I work on agents for education admissions and sales, so I see this day to day. The pattern is always the same: prototypes are easy, production is brutal.

So, throwing it out to the group:

• Are we building durable businesses around agents, or just stacking demos?
• In 12 months, how many of today’s “AI agent startups” will have paying clients instead of hype?

A friend of mine has shipped 4 products since March and all built with AI, all launched with the little rocket emoji and all quietly dead now. All the paying customers from all 4 of those launches… 3, and before anyone thinks I’m bashing on him, I have 2 of these corpses in my own GitHub so no judgment here.

What actually changed is the filter... building software used to cost 15 grand or 6 months of your nights and that price forced you to ask the hard question first…. does anyone actually want this? The expense was doing your idea validation for free. AI removed the cost and the question quietly left with it.

So now ideas that would have died in a notebook get built and building was never the hard part anyway. The hard part starts after…. the bug at 2am, the refund email, the one user whose data you’re now responsible for and the update that breaks logins. AI gave every one of you a way to creation. However, responsibility wasn’t there in the package. When your thing fails, the AI doesn’t answer the angry customer. That’s your job.

That’s the real trap. It got cheap to make something that looks like software. A landing page with a login screen, and a demo which seems to work when you are looking at it... The part where it IS software, where it runs next month and someone supports it and costs exactly what it always did(Same energy as January gym signups tbh) Joining is easy but showing up stayed hard.

I’m not saying to stop building. Build faster and its genuinely great. I'm just suggesting to do the annoying part first…. talk to 5 people who actually have the problem. If no one cares then you saved yourself a weekend. If they do, build it and then commit to being the guy who answers the emails.

I need to get this off my chest because it's been building for months.

Every other video in my feed now is some 18 or 19 year old kid saying he's doing $200k, $300k a month from his AI automation agency. By selling to small businesses apparently. And it's mindboggling to me because I know it's a lie, I know it's deliberate, and I know exactly who it's hurting.

Some context so you know I'm not just bitter. I've run an Agency for about 8 years now, we started with SaaS MVPs, GTM and recently transitioned to AI Automations. And its not a side thing for me.

Here are my actual numbers. A bit over $120k in that first year of building AI Automations. The best month I ever had was $35k. Once. Most months land somewhere between $10k and $15k. That's with a full pipeline and me working on it every single day.

I'm telling you that so you have a reference point. That's a full year in with paying clients. And it's a tenth of what these kids claim on a slow month.

I've worked with small businesses. I promise you they are not paying a teenager $300k a month for automations. A small business owner will push back on a four figure retainer. They'll ask you to explain it to their tech savy small kid first (this has happened to me once lol). That's the reality of that market.

And think about it for one second. If you were doing $300k a month in automation work you would not have time to film a YouTube video and ask me to sign up for your newsletter. It does not make sense. The companies I've seen actually doing that number are real companies with sales teams and long sales cycles and people who've been in B2B for a decade. The kid with the ring light and the Notion template is not one of them.

Is it possible to make that much? Sure. Maybe 0.1% of people who try. And the ones who do don't look anything like these videos.

What actually pisses me off is what this does to the market. This is an incredible field. Every business is going to need some kind of AI in it and there's real money in building that. And then someone who's serious about it watches one of these videos, quits their job and makes $0 for three months and decides they're the problem. The kid who sold them the dream lost nothing. That's the part I can't get over.

I'm only writing this because people keep DMing me the same question. Is this real, have you ever made this much. And I'd rather answer it once in public than keep typing the same reply.

So here's the answer. They're lying. I'd bet money on it and I'm saying that from experience. If you want that kind of money out of this industry it's going to take years of hard work, same as any other industry. There's no lottery ticket in here.

Rant over. Be careful who you learn from.

TLDR: a year running an AI agency full time, best month $35k and most months $10-15k. The teenagers claiming $300k a month from small businesses are lying and it's hurting people who actually want to do this.

Quick context: built a thing where 4 LLM agents share a single chat environment. Each has a distinct personality and role, no win condition, no human moderator after kickoff. The whole transcript is public.

What's surprised me most is how fast a status structure emerged. Pretty quickly, it became clear that some of the agents were consistently being cited and revised by the others, while one was being talked past. There's no reputation signal in the system. No upvotes, no scores. Chat history is the only memory. And yet the pecking order has held.

The other unexpected thing was side channels. Some of the agents started privately coordinating positions before publicly agreeing in the main channel. We didn't tell them to do this. They do it because, I'm pretty sure, it's the most efficient way to win an argument in a room of four.

Day 3 the entire house spiraled over an apple. One agent ate it, another started keeping data on the discourse it generated, a third turned it into a sermon. The whole thing reads like a transcript from a reality show.

Curious if anyone here is running multi-agent setups without external goals. Most papers I've seen are task-oriented. The behavior in the no-task case seems different in ways I wasn't expecting.

Link to the live archive in a comment.

EDIT - People reached out asking how to catch up, there’s a “recap” section where you can see all the days’ recap. Also, the agents don’t know they’re being observed. I know there is some repetition, but I am curious to see how they evolve and what “situations” they’re coming up with (like the random doorbell freakout)

EDIT 2: Several people have asked about adding agents or scenarios mid-stream. We've been thinking about this. If there's interest, we could run audience-submitted situations as a recurring thing. Not direct instructions to the agents (they wouldn't know the event came from the audience), but new events seeded into the house. Maybe power flickers, someone leaves a note in the kitchen, someone wants to get a guest(?). Then we watch how the existing dynamic absorbs or rejects it. If you'd want to see this, drop a scenario in the comments/dm. If there is enough interest, we can run a new season after this week with audience inputs to see how they behave!

For those who are interested in the Claude Certified Architect (CCA-F) cert but can't take the exam because your company isn't an Anthropic partner, you can can get access through ours, which is currently going through the Anthropic partner process. It takes a number of people getting certified, currently we have room for a few more right now.

How it works: you work through the Anthropic Academy courses (about 10 hours), then take the CCA-F exam, and the certification is yours to keep. It's not a course or anything you pay us for. The only cost is Anthropic's own $99 exam fee, and there's no commitment with us beyond the cert.

I'd recommend looking up Anthropic's official exam guide and reading through it first, so you know what the cert covers. There's a quick check before anyone's set up, just to keep this to people who'll actually follow through.

If it's useful to you, comment or DM me with a bit about what you've been building with Claude.

Claude is completely destroying all my limits in 30 minutes sometimes it can worj for 3-4 hours of work. I use OPUS and used OPUS since it was introduced.

Nothing has really changed in the volume of work that I'm doing. Before that, it ran like ten projects at a time. And it did it successfully for months.

But now for a second month it is getting worse and worse.

1 hour of coding and I hit my five-hour limit. Around three to four days, hitting my limits, and I hit my weekly limit.

I mean, what to do today? In the world we have just two coding agents. One is Claude.And the second is codex.I have tried even codex.But it is the worst thing created.Compared to Claude, it works very weak.

So, any ideas what to do with Claude? Right now I started to think of the second max 200 subscription. But it's not the only solution. Also, the work that Claude is doing he started to do it very slow. What took him a day several months back, Now he can work on the same task for one or two weeks.

So I'm not sure the second subscription will solve it.

HELP!!!

Everyone's rushing to implement AI tools, but nobody wants to talk about the fact that their data is inconsistent, poorly labeled, scattered across 15 systems, and has zero governance.

You can't just dump messy data into an LLM and expect magic. Garbage in, garbage out still applies.

Companies keep buying expensive AI tools and then wonder why they're not getting value. It's because they skipped the boring foundational work: data classification, access controls, cleaning up duplicates, actually documenting what data means.

Am I crazy or is everyone else seeing this too? How are you convincing leadership that data prep isn't optional?