A few months ago I got obsessed with OpenClaw, an open-source AI chatbot framework. I loved the idea of having my own personal AI assistant on Telegram — one that actually remembers who I am across conversations.

The problem: setting it up is a pain. You need a VPS, Docker, Node.js 22+, a config file, an AI API key, volume mounts, restart policies… you get it. I set it up for myself, then for a friend, and by the third person asking me “can you set this up for me too?” I realized there might be a product here.

So I built LobsterLair.

It’s a managed hosting platform for OpenClaw. You sign up, connect a Telegram bot (takes 30 seconds with BotFather), pick a personality for your bot, and you’re live. The whole thing takes under 2 minutes. No servers, no API keys, no Docker knowledge needed.

How it works under the hood

Each customer gets their own isolated Docker container running OpenClaw. The containers sit on an internal Docker network with no port mapping — they only make outbound connections to the Telegram API. Everything is managed through a Next.js dashboard that talks to Docker via dockerode.

Stack: – Next.js 16 (App Router) + TypeScript – PostgreSQL + Drizzle ORM – dockerode for container orchestration – NextAuth v5 for auth (email + Google OAuth) – Stripe for payments – Nginx + Let’s Encrypt for SSL – SendGrid for transactional emails

The AI model (MiniMax M2.1 with 200k context window) is included — I pay for a central API key so users don’t have to deal with that. Each bot has persistent memory, so it actually learns about you over time and gets better the more you use it.

The business model

Simple: $19/month per bot, with a 48-hour free trial (no credit card required). No free tier. I wanted to keep it sustainable from day one.

Where I’m at after one week

• 39 total signups
• 8 active instances running right now (6 trials, 2 paying customers)
• About 72% of signups never start a trial, which tells me there’s friction in the funnel I need to figure out
• The 2 paying conversions happened organically — no marketing yet

It’s tiny numbers, but seeing real people actually use the thing is incredibly motivating. One user has been chatting with their bot for 3 days straight.

What I learned building this

1. Container orchestration is harder than it looks. Getting permissions right between the host app (running as one Linux user) and the containers (running as another) took days of debugging. I ended up needing a specific sudoers rule just for chown.

2. Trial-first is the way. Originally I had payment upfront. Nobody converted. The moment I added a 48h no-card trial, signups went from zero to actual users within hours.

3. Include the hard part. The biggest barrier for users wasn’t the hosting — it was getting an AI API key. By bundling the AI model centrally, the entire setup became friction-free.

4. Internationalization early. I added i18n (English, German, Spanish) from the start using next-intl. Surprisingly, a good chunk of signups came from non-English speakers.

What’s next

• Figuring out why 72% of signups drop off before starting the trial
• Adding Discord and Slack as channels (OpenClaw supports them, I just haven’t wired up the onboarding UI yet)
• Possibly a “bring your own API key” option for power users who want to use different models

I’d love to hear your thoughts. Is $19/month the right price point for something like this? Any ideas on reducing that signup-to-trial drop-off?

Site is at lobsterlair.xyz if you want to check it out.

I’m honestly exhausted with the filter on Character.AI. There are moments when it suddenly feels less restrictive and I think they finally relaxed it or fixed the over-flagging… and then it snaps right back and it becomes hard to do anything again. Super frustrating.

So with that in mind, how good is Janitor AI really? I’ve heard bits and pieces, but I’d like real opinions from people who’ve used it.

How does it handle memory, staying in character, creativity, storytelling, and roleplay overall? Does the bot actually feel like the person it’s supposed to be?

I been roleplaying to set up ideas but lately the one that i used more, Ai janitor, is been working very bad, so say, what would be the best places now to have an roleplay with bots?.

Okay I’m genuinely curious and not even trolling.

It feels like AI girlfriend sites went from random niche corner of the internet to fully mainstream in record time. Every other week there’s a new one claiming better memory, more realism, fewer restrictions, deeper personality, more immersive conversations, etc. At this point I can’t tell what’s actually good and what’s just marketing with a slick landing page.

I’ve tried a couple just to see what the hype was about, and the biggest difference I’ve noticed isn’t even the theme. It’s memory and consistency. Some of them feel like they forget everything after five messages. Others actually maintain tone and context and don’t randomly switch personalities mid-conversation.

But here’s the thing. Are they actually that different from each other? Or are most of these platforms basically running similar models with slightly different tuning and branding?

Also, what are people even ranking when they say “best”?

Memory?

Personality depth?

Less censorship?

UI?

Voice features?

Because I swear some of them look impressive at first and then fall apart after a longer conversation.

I’m not looking for promo replies or obvious marketing. I just want real user opinions.

So what’s actually worth trying right now in 2026?

What’s overrated?

What surprised you?

And is there even a clear winner, or is it all preference and hype cycles?

Let’s hear the unfiltered takes.

How Retail Leaders Are Mistaking Interfaces for Autonomy

by Rafael Esberard

Lately I have been vetting and analysing a wave of promised “agentic” solutions in retail. At NRF here in New York City, the pattern was impossible to ignore. Almost every booth carried the word AI. A large majority proudly displayed agent or agentic. The signal was clear. The market has decided that “agent” is the next badge of innovation.

I did not walk the floor as a spectator. I was there with a responsibility. My job is to evaluate these solutions rigorously before recommending anything to my clients. I sat through demos. I asked uncomfortable questions. I pushed past polished scripts. When you represent companies that will invest serious capital, you learn to separate theater from capability.

Here is the uncomfortable truth… Most of what is being presented as an AI agent today is not an agent. It is a chatbot, enhanced with an LLM, sometimes connected to a tool, but still fundamentally reactive. The word agent is being stretched beyond its meaning because it sells. And when a word sells, it spreads quickly.

If we do not define this properly now, executives will make expensive decisions based on a label rather than a capability. So let’s step back and review some definitons…

The Core Distinction (chatbot vs agent)

The Chatbot is a reactive system. It waits for you to ask. You type a request, it responds. You give another instruction, it executes a bounded action. The user drives the sequence. Even when powered by a large language model, it remains fundamentally conversational. It answers, suggests, and occasionally triggers a predefined action. It does not own the outcome.

The Agent is different in principle. An agent is a system that owns an end to end outcome, not a single command. It can plan multi step work, execute across systems with proper permissions, run asynchronously, and handle exceptions without requiring the user to guide every move. The user defines the objective. The agent advances the task.

Here are my initial line in the sand tests:

• If the user must drive every step, it is a chatbot.
• If the system cannot run without the chat window open, it is not an agent.
• If it cannot handle exceptions and recover intelligently, it is not an agent.

This distinction matters because language can create the illusion of capability. A fluent interface feels intelligent. But fluency is not autonomy. A conversational wrapper does not transform a reactive tool into an outcome driven system. Executives must discipline themselves to ask one simple question: who is really doing the work, the user or the system?

The Hype Myths (dismantling)

Let us dismantle the most common myths, because these are the exact claims being used to sell “agentic” solutions right now.

Myth 1: “If it uses an LLM, it is an agent.” An LLM is not an agent. An LLM is a language and reasoning engine.

• It can write, summarize, explain, and recommend
• It can sound confident
• It can even propose a plan

But if it cannot execute that plan end to end, it is still a chatbot. A smarter chatbot, but a chatbot.

Myth 2: “If it calls an API once, it is an agent.” Calling an API is not agency.

• A single API call is an action
• Agents are systems of actions
• Agency is not “can it do something,” it is “can it complete the outcome”

Tool calling is a feature. Agents require orchestration.

Myth 3: “If it can add to cart, it is an agent.” This one is the easiest to expose.

Retail has had:

• intent recognition
• conditional bots
• scripted automation
• add to cart triggers

for well over 15 years.

So when someone shows “add to cart” as agentic, you are not seeing a breakthrough. You are seeing a familiar capability with a new label.

Myth 4: “Chat interface equals agentic workflow.” A chat window is not a workflow engine.

• Chat is an interface
• Workflows require state, permissions, monitoring, exception handling, and recovery
• Chat makes weak systems look powerful, because language is persuasive

And that is where executives get trapped.

A real example I just saw this week I watched a demo from a well known retail search vendor now branding an “agentic experience.” The demo was a chat window. The user typed: “Please add this product to the cart for me.” AGENTIC!!?? Ps: And the add to cart button was literally one inch away. It was a high-level session, with extreme hi-level retail executives and consultants present. That is not an agent. That is theater. And theater is expensive when you mistake it for capability.

The Maturity Ladder

To bring discipline to this conversation, I use a simple maturity ladder. Not to criticize vendors, but to clarify where a solution truly sits.

1. Rules based bot: Predefined flows, scripted responses, conditional logic. Intent recognition, basic understanding of user intent, mapped to predefined actions.
2. LLM Chatbot: Natural language reasoning, dynamic responses, better context handling, still reactive. Can do tool calling assistant, can trigger APIs or systems when prompted, executes single bounded actions. (90% the “agentic” promisses I have seem solutions in the market today have not crossed it further)
3. Supervised Agent: Can plan multi step workflows, operate across systems with permissions, handle exceptions, and run asynchronously, with oversight.
4. Autonomous Agent: Owns the outcome end to end, manages execution, monitors performance, and escalates only when necessary.

The critical shift happens between tool calling assistant and supervised agent. At level 2, the user still drives the process. The system reacts and executes isolated commands. At level 3, the system begins to plan. It sequences actions. It checks results. It recovers from errors. It runs without constant prompting. It operates within defined permissions and governance structures.

Conclusion – Let’s Bring Home

This is not a semantic debate. It is a capital allocation issue. When executives confuse chatbots with agents, two predictable things happen:

• First, companies overpay for rebranded interfaces. The price reflects the promise of autonomy, but the capability remains reactive. You end up funding a better conversation layer, not a system that reduces labor or owns outcomes.
• Second, strategy gets distorted. Teams are told that “agents are coming,” expectations rise, roadmaps shift, and real infrastructure work, integration, permissions, monitoring, orchestration, gets postponed. Capital is deployed toward visible demos instead of durable capability.

Language is persuasive. A fluent interface creates the perception of intelligence. But perception does not execute workflows. And perception does not generate ROI. So here is the discipline I recommend to my clients before approving any “agentic” investment.

Ask for evidence of these five capabilities:

1. End to end outcome ownership, not isolated task execution
2. Asynchronous execution without constant user prompting
3. Exception handling and recovery logic
4. Persistent memory and personalization across time
5. Evaluation and monitoring with measurable reliability

If a vendor cannot clearly demonstrate these in production, not in theory, you are not buying an agent. You are buying a chatbot.

The market will continue to use the word agent because it signals progress. But as leaders, we are responsible for precision. Most of what is called agent today is not. If you must type every step, it is not an agent. If it cannot run without the chat open, it is not an agent.

Stop buying interfaces. Start buying outcomes. And internally, stop using the word agent until the capability earns it.

Thank you!

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Rafael Esberard is a Digital Innovation Architect and Strategic Consultant with over 20 years of experience in the eCommerce and Software Development industry. As the founder of KORE Business, he helps companies design, govern, and evolve their digital ecosystems through a pragmatic, business-driven approach to composable, MACH architecture, Agile and AI integration. Rafael is a MACH Ambassador and works alongside retailers and industry leaders to guide the selection, validation, and orchestration of best-fit solutions across complex multi-vendor landscapes, ensuring scalability, agility, and long-term ecosystem health. His expertise spans omnichannel strategies, AI-driven ecosystem optimization, and accelerating time-to-value and time-to-market across digital transformation projects. By bridging technology evolution with real-world business needs, Rafael enables clients to transform ambition into sustainable competitive advantage.