... problem: The "garbage" bottleneck We all know that 90% of enterprise data is unstructured (PDFs, docs, emails). Traditional RAG pipelines "collapse" this structure into flat vectors, losing the narrative context. Current solutions are either too slow or prohibitively expensive. 2/ The solution: NeuroBlock's smart hybrid architecture We didn't want to use brute force. The NeuroBlock Research Team designed a framework that combines the best of two worlds: Speed: Classical NLP ( spaCy ) for initial NER (10-50x faster ... ... fragment loses the thread, the system detects the topic shift via embedding similarity to decide exactly where to cut. 4/ The "Secret sauce": Context preservation This is where NeuroBlock transforms the data. We don't just store vectors

... Discord ? It hits Enter. In Notes ? It just pastes. I can reply to messages without touching my keyboard once. 2. App-Specific Profiles It context-switches for me. Outlook: Automatically polished to "Professional". Telegram: Switches to "Casual & Lowercase". VS Code: Formats code blocks automatically. 3. Ghost Morph (Agent Skills) It s not just for chatting. Hold a modifier key to trigger custom agents: "Turn this rant into a structured Jira Ticket ." "Turn this thought into a Twitter Thread ... ... based on my intent and style. And yes, it s Privacy-Focused. Since I'm an indie dev and can't afford cloud storage for everyone, I built it on a Local-Storage architecture. Your writing history and style vectors

... users unless you build a ton of infrastructure first. This means every AI dev eventually will end up building this infra to provide the best user experience needs for their agent and app. What rolling your own really means: Vector DBs + embeddings + tuning Extracting memories from conversations (and resolving conflicts) Designing user profile schemas and keeping them in sync Managing long chat history + summarization pipelines Juggling different formats across OpenAI, Claude, etc. Hosting, scaling, backups, monitoring Timeline: 2 4 weeks ... ... plus ongoing maintenance) Cost: engineering time + infra + vector

... years) Skills: Data Analysis, Budget Management Salary: 75K 95K Step 3: VP Marketing (4-6 years) Skills: Strategic Planning, Board Experience Salary: 95K 140K Don't stay >3 years in Senior role (kills momentum) Key Features Vector Map Visual metro-style career path Job Offer Reality Check Scores toxicity (0-100), flags red flags Skills Dashboard Prioritized learning roadmap Transition Intelligence Find "bridge roles" for career pivots B2B Dashboard For schools tracking placement rates Why It's Different ... ... board. Not a personality test. Not a coaching marketplace. - Built on 1M+ real LinkedIn profiles (data, not opinions) - Instant (2 min) vs

... buried, and I was missing important signals worth thousands of dollars. The Solution TgRAG uses semantic AI search to solve this: 1. Select channels - Choose any public or private Telegram channels 2. AI indexes - We process every message with vector embeddings 3. Search naturally - Ask questions in plain language, get instant answers with sources Technical Details - Semantic search using vector

... free, fully featured, privacy-focused URL shortener. What s Live Now One-click shortening (lightning fast) Time-bound links (auto-expire at a chosen date/time) Custom endpoints (clean vanity slugs like /summer-sale) Tags (organize links and filter analytics) QR codes (vector-based, print-ready) Unlimited analytics dashboard (clicks over time, top links, tag breakdowns, simple graphs) Privacy First No tracking (no cookies, no hidden scripts) We only collect your email address You can permanently delete everything (profile + all links) anytime ...

... obsess over orchestration but production agents collapse when their data plane can t keep up. That s the problem we set out to solve with GraphBit. Why a new data layer matters RAG pipelines break without fresh, reliable data. Vector DBs alone can t handle hybrid recall or scale gracefully. Enterprise teams need auditability + efficiency, not black-box memory. What GraphBit s Data Layer does differently Unified Retrieval One interface across pgvector, Qdrant, Weaviate, Elastic. No lock-in. Hybrid Search ... ... rehman): The Rust-powered execution core caught my eye. Any benchmarks you can share yet? Comment from Imrul Kayes(@imrulkaayes): Data really is the silent failure point for most agents. Makes me wonder, how are teams today balancing freshness vs

... While You re Still Planning Your Competitors Are Already Launching The Problem Many founders and investors have great ideas and funding but products remain stuck in planning. Meanwhile, competitors

... Creativity is currency Community is the new capital Young people are more powerful than ever But in Africa, many Gen Z creators still feel stuck. Too many ideas. Too little guidance. 3/ That s why I m building VectoRise A creative ecosystem that helps Gen Z: Discover their identity Unlock their potential Build purpose-driven lives Launch digital ideas All in one platform. 4/ It s a mix of: Personal branding Tools for digital creativity Community-driven discovery Values that ... ... ground, not distract Think: LinkedIn meets Notion meets Inner Compass made for Africa. 5/ Why share this here? Because Product Hunt helped many bold ideas take off. And I want to build VectoRise

... Makers & Builders using LLMs! Anyone else building cool stuff like PDF Q&A or custom bots with RAG, but finding the context retrieval step... frustrating? Most AI app data stacks these days use vector search (Pinecone, Weaviate, etc.) to grab text chunks for the LLM. But sometimes it feels like it finds stuff that's keyword-similar while totally missing the actual point the user asked for. Leads to those slightly weak or "confidently wrong" LLM answers ... ... what's your single biggest headache right now in getting truly relevant context for your RAG pipeline? Is it nailing the chunking strategy? The embedding choice? Hitting the limits of pure vector

... real, practical AI skills without coding experience. That s exactly why we created this course. We ve designed it to be hands-on, teaching real-world AI automation using no-code and low-code tools like N8N, CrewAI, and VectorShift so you can build smart workflows, optimize business processes, and save hours of manual effort. We ve worked with companies and professionals integrating AI into their workflows, and one thing is clear: AI is no longer optional. Whether ... ... code workflows, and how to leverage AI in your business. If you're ready to move from questions to hands-on skills, our AI Automation Course will guide you through real-world use cases using N8N, CrewAI, and VectorShift

... search tool - Gives the API access to leverage web search and cite sources Computer use tool - Gives the API access to use a remote machine, this is huge File search tool - Upload a batch of files to a vector store and then query them via an endpoint Web search tool I read this as we can now give API calls the ability to search the web and the return object can contain citations. This is interesting to me because ... ... this programmatically and unlocks a ton of new use cases for automation. Computer use tool pricing: $3 per 1M input tokens and $12/1M output tokens. File search tool The new file search tool allows you to create vector

... built, for anyone who wants to build AI apps fast. It has pretty much every piece that every successful AI app uses right now. RAG read from local documents (pdf,text) Tools fetching data from 3rd parties DB Vectors vectors