Nikolay Samokhvalov

Run Powerful LLMs Locally — No Cloud. No API Costs. Most people think you need OpenAI or Gemini APIs to build AI apps. But the truth is, you can run state-of-the-art models completely offline using Ollama — no internet, no API bills. This free guide explains how to use Ollama with LangChain, LlamaIndex, and Haystack — while keeping your data fully private and secure. Here’s what you’ll learn 🔵 1️⃣ What is Ollama? A lightweight local framework for running models like Llama, Mistral, and CodeLlama directly on your machine. No cloud dependencies No API fees Total privacy 🔵 2️⃣ Why Use It? Run private, offline LLMs Build RAG pipelines and AI agents locally Perfect for experimentation, prototyping, and compliance-focused projects 🔵 3️⃣ Integrations LangChain: Build chatbots, memory-based agents, and reasoning systems LlamaIndex: Create retrieval-augmented generation (RAG) apps Haystack: Deploy enterprise-grade NLP pipelines Direct API: Use HTTP requests for custom workflows 🔵 4️⃣ Privacy & Cost Benefits Data never leaves your machine No usage limits or hidden API costs Fast, low-latency inference Ideal for secure and local AI systems 🔵 5️⃣ Popular Local Models Llama 3 Mistral CodeLlama Gemma Why this matters: Running models locally gives you control, speed, and privacy — without paying for every single API call. CC: Naresh Edagotti

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🔑 Why ECAI leaves ION, IPFS & JSON-LD behind Most “decentralized” tools solve fragments of the trust problem: ION → Anchors identifiers, but doesn’t verify semantics. IPFS → Stores files, but can’t prove meaning. JSON-LD → Links data, but is mutable text without cryptographic finality. ⚡ ECAI (Elliptic Curve AI) fuses these into one: Every fact (Subject–Predicate–Object–Context) maps to a unique elliptic curve point. Meaning is cryptographically bound, not just described. Querying is decentralized by default on æternity. Results can be realtime via Erlang channels or notarized on-chain when needed. 👉 The result: immutable, verifiable knowledge — identity, semantics, and persistence converged into a single truth layer. ECAI isn’t “another protocol.” It’s the foundation of decentralized intelligence. #ECAI #DamageBDD #blockchain #aeternity #AI #verification

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Live talk about deploying LLMs in productions. Insightful notes about importance of evaluation data by Lena Shakurova She discussed the use of G-eval and RAG-eval as key techniques for evaluating LLMs in production environments.

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AI can build workflows now. That doesn’t mean your GTM system will work. Hot take after playing with Claude Code for a while: A lot of GTM teams are about to confuse workflows with systems. Claude Code is honestly impressive. You describe something in English and it builds it. I’ve been using it myself, I even vibe-coded the design of our upcoming website with it and the result was better than I expected. For experiments, prototypes, and internal automations, it’s incredible. You can ship things in hours that used to take weeks. But building a quick workflow and building a revenue system are two very different problems. A workflow is just logic. A RevOps system has to deal with data ownership, reliability, permissions, and all the messy handoffs between marketing, sales, and ops. And most importantly, it has to keep working six months later when nobody remembers how it was originally built. That’s the part most teams underestimate. Version one ships fast. Version two starts breaking. Version three suddenly needs someone maintaining it full time. Most GTM teams aren’t engineering teams. They don’t have someone debugging enrichment pipelines at 9:30am while SDRs are waiting for leads. That’s where tools like Clay play a different role. Claude Code gives you raw flexibility. Clay gives you flexibility inside a system that’s already built to handle the boring but critical stuff: data orchestration, enrichment, integrations, shared workflows across teams. So the real question usually isn’t custom vs SaaS. It’s this: Does your team actually have the capacity to own the system long term? Because flexibility is great… until it becomes operational risk. 🤔 Curious what others are seeing right now. are AI tools pushing teams toward more custom GTM infrastructure, or back toward platforms that manage the complexity for you? –– Hey I’m Marcos Stu Founder automate rev.ops. Clay Solutions Partners, Advanced Artisans

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𝐁2𝐁 𝐀𝐄𝐎: How JSON-LD schema code bypassed a 12,50€ CPC auction and ranked a Deep Tech product at position 1 Schema.org is the most undervalued asset in showing up in Search Engines and LLMs. On January 26th, we implemented multilayered JSON Code into a clients 𝐦𝐨𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐩𝐚𝐠𝐞. Using schema, this product is now clearly communicated to crawlers. Every spec is machine readable, every claim is verified by either an external link or an internally linked testimonial. While this all may sound complex, the concept is simple: 𝐈𝐭’𝐬 𝐚 𝐔𝐗 𝐮𝐩𝐠𝐫𝐚𝐝𝐞 𝐟𝐨𝐫 𝐜𝐫𝐚𝐰𝐥𝐞𝐫𝐬 & 𝐚𝐠𝐞𝐧𝐭𝐬. The result: Within 6 weeks, this product page climbed from position 13 to the 𝐭𝐨𝐩 𝐨𝐟 𝐭𝐡𝐞 𝐬𝐞𝐚𝐫𝐜𝐡 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 (position 1). Generating 5 clicks everyday on a query that Google charges 12,50€ cpc. That is roughly 1.800€ in monthly ad-spend equivalent generated by a single block of Schema Code. And yet, not many people talk about schema and even less actually know how to apply it. I’m currently building an interactive tool to test exactly how LLMs read and extract your product pages with the amazing Federico De Ponte. DM me for early access.

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We've just released v0.7.0-beta of the AI Workflow System. When we started with v0.1, the goal was simple: provide a set of structured playbooks to guide AI agents. But as we used it, we realized that guidance wasn't enough—we needed enforcement. The Journey So Far: ✅ v0.4 - v0.5: We introduced mandatory workflow enforcement. No more skipping steps or hallucinating requirements. The agent must follow the TDD cycle. ✅ v0.6: We added PRD Planning. You can now plan multi-feature projects upfront, and the system handles the dependencies. ✅ v0.7 (Today): The system is now Language-Agnostic. Whether you use TypeScript, Python, Java, Go, Rust, or C#, the playbooks adapt automatically. What's Next (Road to v1.0): We are now focused on production hardening: ◻️ Integration testing with real-world projects. ◻️ CI/CD pipeline examples. ◻️ Beta testing with diverse teams. We are building the standard for reliable, agentic coding. https://lnkd.in/d7ZBmj57 #AI #SoftwareEngineering #OpenSource #AgenticWorkflow #DevTools PS: AI generated message

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San Francisco-based Onos Health has raised $6.3 million in an oversubscribed venture round to transform how health plans manage behavioral health. Onos Health is a healthcare AI company tackling the estimated $60 billion lost each year to care misalignment and administrative waste. Its platform uses proprietary AI models to analyze payer data in real time, helping health plans identify optimal care pathways, eliminate outdated processes like prior authorizations, and direct more dollars toward high-quality care. The round was co-led by Haystack and Pathlight Ventures, with participation from pre-seed investors Bertelsmann Healthcare Investments and Nebular. The funding will be used to support growing demand from the nation's largest health plans and to expand its clinical and data science teams. Congratulations to founder & CEO Akshay Agrawal and the Onos Health team. Business Wire: https://lnkd.in/dD95z7ZZ Deals like this are reshaping the AI landscape. For weekly analysis on the funding, valuations, and strategic moves that matter, subscribe to the Charted Growth newsletter: chartedgrowth.beehiiv.com #AI #HealthTech #BehavioralHealth #InsurTech #DigitalHealth #Funding #VentureCapital

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Claude Sonnet 4 now supports 1M-token context, joining Gemini as the only other frontier model at that scale. Pricing: $6/M input, $22.5/M output (over 200K). Now you can feed it whole codebases or novels in one go. Big for long-form dev workflows. 👀

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