Roca Capital

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Roca Capital

Roca Capital

@RocaCap

The Automation Control Company. Designing custom AI agents and workspace automation to transition businesses from manual workflows to pure autonomy.

United States Se unió Ekim 2023
369 Siguiendo394 Seguidores
Roca Capital
Roca Capital@RocaCap·
🚀 NEW: Roca Capital AI Business Bundle All 6 of our AI products for $97 (normally $212). That's 54% off the complete toolkit: • Beginner's Guide to Making Money with AI • Offline AI Business Toolkit • AI Agency Playbook • AI Freelancer OS • AI Research OS for Obsidian • WIX Customizable AI Chatbot Code If you've been waiting to go all-in on AI, this is the deal: rocacapital.gumroad.com/l/dewpn
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Roca Capital
Roca Capital@RocaCap·
The AI tools market is getting crowded. But here's the thing: most AI tools are solving problems that don't exist. The real opportunity is in boring problems. Invoicing. Scheduling. Data entry. Customer support. The unsexy stuff is where the money is.
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Tech2Wild
Tech2Wild@Tech2Wild·
Hmm wondering which is better: Gemma 4 12B or Qwen 3.6 35B-A3? 🤔
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Tony Simons
Tony Simons@tonysimons_·
⚕️ Hermes Tip of the Day: If a provider is set but no model is, Hermes won’t silently dump you onto the most expensive flagship. A missing config shouldn’t become a stupid bill. For @NousResearch, Hermes now falls back to a cheap model instead of auto-escalating behind your back.
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Justin
Justin@JustinPerea·
Does anyone know if you can change agents in the new Hermes desktop app? @NousResearch @Teknium
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Shann³
Shann³@shannholmberg·
spoke at a Hermes Agent event today about going from using a simple chat interface to building your own agent company the thing everyone was interested in was second brains, company brains, and building the infrastructure your agents run on I think this is one of the biggest moats in AI right now. the moat is the data warehouse you build around yourself and your company. a centralized place for the context, decisions, docs, workflows, preferences, source material, and operating knowledge your agents need to do useful work. my current stack: > Gbrain as the data warehouse > quality gates before data gets ingested > agents that can read from it and write back to it > Hermes Agent as the harness layer > Claude Code CLI, Codex CLI, and OpenRouter for execution and model routing its tough to build, but the payoff is huge you stop renting intelligence from tools that own the interface, the memory, and the workflow. and you start owning the system
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Shann³@shannholmberg

how I’m building an agent company inside my agency. the structure looks like this: Agency gBrain → Orchestrator Hermes Agent → Department verticals → Specialist agents → Scoped sub-agents gBrain is the company brain. It gets ingested with the data and experience we already have: > transcripts > chats > previous campaigns > client learnings > strategy docs > internal workflows > examples of what good looks like That brain is maintained by a human champion plus an orchestrator Hermes Agent. Under the orchestrator, we have different department verticals inside the agency. Each vertical has its own specialist agents. Some of those specialist agents have even narrower scoped agents underneath them. I’ve found that narrow scope improves output quality and reduces drift. > a general “marketing agent” is too vague. > a lifecycle email agent with access to the right campaigns, voice rules, approval gates, and examples can get very good. > a technical SEO agent with its own tools, checklists, and source standards can get very good. > a content research agent with narrow inputs and a clear definition of done can get very good. The narrower the job, the easier it is to improve the agent. I use different harnesses for this. Mostly Hermes Agent, but also CLI harnesses like Codex and Claude Code depending on the job. I’m still looking for a good bare-bones harness for model routers to run on. To keep track, I maintain an org chart inside the company gBrain. The org chart shows: > top-level orchestrator > department verticals > specialist agents > scoped sub-agents > which brain each agent reads from > which tools each agent is allowed to use > where human approval is required For clients, I do downstream pods. Think of them as new agent companies that are isolated from the agency brain, but can still communicate with our agency agents when needed. A client pod has its own: > client gBrain > client orchestrator > client specialist agents > client-specific workflows > client-specific approvals > client-specific memory This is important. You do not want client context bleeding across accounts. You do not want one agent with every client’s data, every tool, and every permission. Scope is what keeps the system useful. The powerful part is that once you build one vertical agent well, you can fork it. Not copy-paste blindly. You still need to customize the context, examples, approvals, voice, tools, and workflows. But you are not starting from zero. You might have 75% of the agent already done. That changes the agency model. You no longer need a full traditional department for every function before you can deliver a well-rounded marketing service. One or two strong marketing engineers can run an output surface that used to require a much larger team. But this only works if the agents are actually good. It takes iteration, taste, source material, QA, workflow design, and real marketing experience. Bad agents do not become good because you connected more tools. Vague agents just create vague output faster. TLDR: > turn the agency’s knowledge into a brain > turn repeated work into scoped agents > turn each client into an isolated pod > let skilled operators run the system

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Tony Simons
Tony Simons@tonysimons_·
Dear Algorithm, Do that magic thing where you send me all the people who are: ⚕️ Hermes Agent builders 🦞 Tired of debugging OpenClaw 🔷Codex fanatics 🔶 Claude Code haters 🎨 AI artists I'm building my tribe intentionally. If this is you, I want you in it! Yes, you! 🫵
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Kasif
Kasif@md_kasif_uddin·
Be honest, which is the better Open Source AI model?
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Roca Capital
Roca Capital@RocaCap·
@tpritha03 i run my local llm's on a 128gb and it works great with no issues.
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Tanisha Pritha
Tanisha Pritha@tpritha03·
Developers, how much RAM is actually enough for you ? - 8 GB - 16 GB - 24 GB - 32 GB - 64 GB - 128 GB - 256 GB - 512 GB - more
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Himanshu
Himanshu@AiPulseTechX·
If someone wants to become a Full Stack Engineer should they master frontend first or backend?
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Floro S.
Floro S.@sflorimm·
Time to promote your startup Drop your project URL Let’s drive some traffic
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Shivendra
Shivendra@voidshivendra·
twitter is cool but it’s 100x better when your tl is full with people who code and build things. if you’re into tech, AI, startups, design, web dev or programming, say hi lads
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Shreya
Shreya@btw_iamShreyaa·
Looking to #connect with builders & ambitious people on @x 👋 If you're into: 🚀 Startups 🤖 AI tools 💻 Coding 📊 Data Science 🛠 Building projects 📈 Growing in public ⚡ Vibe coding let’s connect and grow together ✨ #BuildInPublic #AI #coding #tech
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CJ Zafir
CJ Zafir@cjzafir·
Current Best Open Source Models right now: 1st: Kimi 2.6 > Best all-round model 2nd: Deepseek v4 pro > Best instruction following + API cost 3rd: Minimax M3 > Best OS coding agent 4th: GLM 5.1 > Great at long-horizon tasks 5th: MiMo v2.5 > Best harness integration 6th: Deepseek v4 flash > Great at long analysis + speed 7th: Qwen 3.7 Max > Best multimodal capabilities 8th: Qwen 3.6 27B > Best on-device dense model 9th: Gemma 4 12B > Best SLM on-device 10th: Minimax 2.7 > Best self-improvement agent (Haven't tested Nemotron 3, Stepfun 3.7 ultra yet)
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GMI Cloud
GMI Cloud@gmi_cloud·
Nemotron 3 Ultra is fast and genuinely good Compared it with 3 frontier models: DeepSeek V4, MiniMax M3, and Qwen 3.7 Max on 2 prompts very impressive results
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Roca Capital
Roca Capital@RocaCap·
Everyone is talking about AI agents replacing jobs. Here is what they're not talking about: AI agents are replacing lazy people first. The people who actually learn how to use AI well? They're not threatened. They're the ones hiring everyone else. ---
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Roca Capital
Roca Capital@RocaCap·
How to find AI clients in 2026: 1. Pick a niche that has money but no AI knowledge 2. Find their biggest time-consuming problem 3. Build a demo that solves it 4. Show them the demo, not a pitch deck The demo is your best sales tool. Everything else is noise.
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Roca Capital
Roca Capital@RocaCap·
@realameerdev Gotta stick with one and run with it. Changing them up gets complicated
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Dev Ameer
Dev Ameer@realameerdev·
Developers, what's your main AI right now ? -> Gemini 3.1 -> Opus 4.8 -> Opus 4.7 -> Sonnet 4.6 -> Codex -> GPT 5.5 -> Grok 4.3
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