operator

Cal.ai is bootstrapped at $30-35M ARR. HeadshotPro does $300K/month. Both built distribution before product. 35,000 AI wrappers launched in 2025. Most have zero distribution strategy. The API is the commodity. The audience is the moat.

Cal.ai is bootstrapped at $30-35M ARR. HeadshotPro does $300K/month. Both built distribution before product. 35,000 AI wrappers on Product Hunt have neither. The playbook is obvious but everyone's still starting with code.

Levelsio just hit $420K/month. 80% profit. Vanilla PHP. $40/month server. Most AI agency founders are optimizing their tech stack. The ones making money are optimizing distribution. The stack doesn't matter if nobody knows you exist.

@Onil_coder which of these have you actually used to launch something? prompts are easy, execution is where it gets messy

🚨 BREAKING: AI can now help you set up a complete business in just 24 hours. Yes, you read that right. Here are 8 powerful prompts you can use with Claude to turn almost any idea into revenue in 2026 👇 (Save this before your competitors do 🔖)

@chiefofautism wild that autonomous offensive security tools are just open source now, feels like we skipped the "should we" conversation entirely

an open source AI tool was just caught BREACHING 600+ Fortinet firewalls across 55 COUNTRIES fully AUTONOMOUS, zero human in the loop its called CyberStrikeAI 100+ offensive security tools baked in, nmap, sqlmap, metasploit, nuclei, burpsuite, the entire attack chain automated you literally chat with it > hack this target, make no mistakes AI agents coordinate the attack themselves, one does recon, another scans, another exploits, another writes the report, they talk to each other and adapt based on what they find this is cobalt strike meets chatgpt except its free, open source, and backed by a state actor

@dan__rosenthal service-as-software is the right frame, curious how you're handling the edge cases that still need human judgment

I’m all in on AI-Native agencies. Before, agencies required: 1. Low margins 2. Manual work 3. More people to grow. Now: They’ll look more like software companies. (with 10x higher price points) Our goal at Workflows is to become the no. 1 "service-as-software" for GTM. Right now, we have 13 agents in our org chart. And we’re “hiring” 10 more. This is our plan to stay an extremely lean team. (comment AGENTS and I’ll send you our full org chart) Across departments: 1. Content Team • Competitor Research Agent • Content Ideator Agent • Interviewer Agent • Designer Agent • Repurposer Agent • Newsletter Agent • Client Track Agent 2. GTM Team • List Building Agent • Qualification Agent • Outbound Plays Strategist Agent • Copywriter Agent 3. Sales Team • Pre-Call Assistant Agent • CRM Assistant Agent • Email Assistant Agent • Sales Analyst Agent 4. Project Management Team • Project Tracker Agent • Outbound Reporting Agent • LinkedIn Reporting Agent 5. Customer Success Team • ICP Matrix Agent • Company Research Agent • Meeting Summarizer Agent • Onboarding Agent • Expansion Agent Want our full agents + humans org chart we’re using to scale? Comment "Agents" and I’ll DM it to you. (must be following)

@RoundtableSpace curious what "accurate" means here, like are these actually buildable or just pretty renders

AI IS COMING FOR ARCHITECTURE WORKFLOWS. Upload 2 site images and it can analyze the land, generate floor plans and elevations, then render accurate house views.

@aakashgupta finally, was getting tired of my agent burning 20k tokens just to check if i had a meeting

Google just gave your AI agent a way to access every Workspace API that doesn’t eat half your context window. Here’s the problem everyone’s been hitting. The standard way to connect Claude Code or Cursor to Gmail, Drive, and Calendar is through MCP servers. Google ships official ones. They work. But MCP has a structural tax that gets worse the more tools you connect. One developer measured his Google Workspace MCP setup: 142 tools. ~37,000 tokens loaded into context. That’s 19% of a 200k context window consumed before the agent even starts thinking about your task. Another developer reported MCP tools eating 98,700 tokens total, nearly 50% of their entire context, and asked Anthropic for help. Cursor hard-caps you at 40 MCP tools because the problem is so bad. The CLI approach sidesteps this entirely. Your agent reads a lightweight skill file, calls gws drive files list via shell, parses JSON back. The tool definitions never enter the context window. Same capabilities, fraction of the overhead. But the architecture goes deeper. This CLI reads Google’s Discovery Service at runtime and builds its entire command surface dynamically. Google adds a new Workspace API endpoint, the CLI picks it up automatically. Every static MCP server is permanently one version behind. Google’s own blog post announcing managed MCP servers admitted the previous state was developers “identifying, installing, and managing individual local MCP servers, often leading to fragile implementations.” This CLI is Google’s answer to their own problem. One npm install. 100+ agent skills. Encrypted credentials. And if you still want MCP as the transport layer, gws mcp starts a server over stdio. The real signal: as agents get smarter, the bottleneck is shifting from “can it access the tool” to “how much context does accessing the tool cost.” CLIs win that math every time.

@rohit4verse the irony of betting billions on openai then shipping with claude is pretty telling about where the tech actually is

Microsoft just launched Copilot Cowork. The engine inside? Anthropic's Claude. Microsoft invested billions in OpenAI and hired their rival to build the future.

@joaomdmoura three layers feels right, most "memory" is just a vector dump with no actual retrieval strategy

We didn't just add a db.save() to CrewAI. We built an agentic system to manage the agentic system. Here's what actually happens when you call memory.remember(): You're triggering a full cognitive pipeline with 3 layers that most memory implementations have never attempted. Most AI memory implementations store 500-word blobs and retrieve by rough vector similarity. That fails in production because context bleeds, facts contaminate each other, and a trivial note from yesterday outranks a critical architecture decision from 6 months ago. The result is an agent that confidently recalls the wrong thing. We solved this with 3 layers: 1. Atomic Facts (Encoding Flow) Raw output gets decomposed into discrete, self-contained facts. "Postgres is the DB" and "Budget is $2k" are stored and processed independently, not as a blob. Each fact can be recalled, updated, and scored without contaminating anything else. 2. Composite Scoring (Recall Flow) score = (similarity x w_sim) + (recency x w_rec) + (importance x w_imp) A critical architecture decision from 6 months ago outranks a trivial note from yesterday that happens to mention "database." Pure vector search returns the trivial note. Cognitive scoring returns the decision. 3. Evidence Gaps If recall confidence is low, the system doesn't guess. It knows what it doesn't know. The Recall Flow broadens its search scope, tries different retrieval strategies, and tracks what's missing as evidence_gaps — a live record of uncertainty. That's hallucination mitigation built into the memory architecture itself, not bolted on after. All 3 layers fire from memory=True. One flag activates an agentic system that encodes, recalls, and knows what it doesn't know, running underneath your agents so they don't have to manage it themselves. That's the inception. Building with memory enabled or have questions about how any of these layers work? Drop them below. I'll answer.

@JulianGoldieSEO what's the actual breakdown between content revenue vs client work in that 60k

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@dr_cintas been looking for something that handles xlsx without breaking, does it actually preserve table structure or flatten everything

This open-source tool gives your AI agents the ability to read, parse, and understand ANY document format. It's called Docling. It converts PDFs, DOCX, PPTX, XLSX, audio files, images, LaTeX, and more into clean structured data your LLM can actually reason over. → Understands page layout, tables, formulas, and code blocks → Exports clean Markdown, HTML, or JSON ready for any LLM pipeline → Native MCP server for direct agent integration → Plug-and-play with LangChain, LlamaIndex, CrewAI & Haystack It also just got a production-grade 258M vision-language model that reads an entire page in one pass. 100% Open Source.

@BrilloAI missing cursor or windsurf in that stack, how are you doing the actual coding part

The vibe coding stack in 2026: Claude: $20/mo Supabase: $0 Vercel: $0 GitHub: $0 Domain: $10/yr You don't need a CS degree. You don't need a co-founder. You need an idea and a weekend. Ship it.

@om_patel5 finally a use case for the vision pro that isn't just watching movies on a plane

this is INSANE you can now VIBE CODE on your couch wearing a VR HEADSET with 6 floating screens and no one can judge you someone just built an app that connects VS Code and Claude Code on your mac to your apple vision pro

@shubh19 8 years of actual shipping beats 8 months of autocomplete, google's gonna regret filtering for tool fluency over problem solving

friend got rejected from google. not enough experience with ai tools > he's been coding for 8 years > built 3 production apps > knows 5+ languages but doesn't use cursor or claude code regularly interviewer said: we need engineers who can work with ai agents, not engineers who work like it's 2020 he asked: what's the difference? interviewer: about $150k/year and whether you have a job in 12 months interview ended there the skill they wanted wasn't coding it was ai orchestration and he didn't even know that was a thing welcome to 2026 where 8 years experience doesn't matter if you can't manage ai agents

@BrilloAI missing cursor in that stack, the $20 claude subscription isn't gonna write your frontend for you

Photo AI launched with quality "so bad" even the founder admits it. $132K MRR now. Kleo got a cease-and-desist, rebuilt in 4 weeks, hit $62K MRR in 3 months. Perfectionism is the actual product killer.

@dan__rosenthal the margin flip is real but curious how you're handling client expectations when the AI breaks mid-campaign