#knowledge

#Woodworking #Knowledge #Tools

Your agents can now answer with the business definitions your analysts already trust, no metrics to rebuild. Now GA in Knowledge Catalog: semantic model ingestion from Looker, BigQuery, dbt, Snowflake, Databricks, and SAP, all in one governed context graph:

Claude Code’s architecture, explained visually: (bookmark this) Claude Code is a lot more than a CLI that invokes the Claude models. The actual system has six layers, and the model is just one node inside the loop. The diagram below explains every component: 1) Input layer handles session management, permission gating, and YAML-based trust tiers before anything reaches the model. 2) Knowledge layer holds the skill registry, context compressor, task graph, and cross-session memory store. This is where harness intelligence lives outside the weights. The context compressor is a 5-layer cascade that kicks in when the context window hits roughly 95% capacity. It doesn’t summarize your conversation the way ChatGPT does. Instead, it runs structured extraction on file paths, code snippets, and error histories while pruning redundant tool outputs. The goal is to keep the context usable, not just smaller. 3) Execution layer runs tool dispatch through a typed registry with one handler per tool, like bash, read, write, grep, glob, and revert. The streaming runtime handles parallel execution, and the prompt cache reuses stable prefixes at roughly 10% of the original cost. 4) Integration layer connects the MCP runtime to external servers (filesystem, git, custom). Tools register inward, and memory writes outward to a markdown file (agent_memory.md) that persists across sessions. 5) Multi-agent layer is the most underappreciated piece, and it works very differently from what most people assume. Claude Code supports two levels of parallelism: - Subagents are lightweight workers that run inside your session. They get their own context window, do a focused task (search the codebase, explore a file tree), and return results to the parent. They can’t talk to each other, and they can’t spawn their own subagents. It’s a strict parent-child hierarchy. - Agent teams go further. One session acts as a team lead, and it spawns independent teammates, each running as a full Claude Code instance with its own context window. The team lead breaks a task into subtasks, assigns them, and monitors progress. The coordination happens through two mechanisms → a shared task list (JSON files on disk) and a mailbox system for peer-to-peer messaging. Each teammate gets git worktree isolation. It’s a separate working directory with its own branch, sharing the same repository history. This means agents can write to overlapping parts of the codebase without file conflicts. When they finish, worktrees with no changes are cleaned up automatically. Worktrees with changes persist for human review before merging. 6) Observability layer wraps everything. An event bus with lifecycle hooks logs all tool calls and messages, creating a complete audit trail of the agent’s actions and decisions. Background executors run daemon threads non-blocking, so observability never stalls the main loop. Finally, the master agent loop sits at the center of all six layers. It assembles context, calls the model, receives a tool request, executes it, feeds the result back in, and repeats. Every iteration is one turn. Within a turn, the model might request a tool call. That request flows through the permission system, gets executed, and the output feeds back into the loop as the next input. The loop itself is single-threaded on purpose. All the intelligence lives in the layers around it, not in the loop logic. Anthropic calls it a dumb loop because the model reasons, and the harness mediates. This is the architecture behind Claude Code. If you want to dive deeper into Claude Sugagents and agent teams specifically, I recently wrote an article about them. It explains the difference between Claude sub-agents (isolated, fire-and-forget workers) and agent teams (persistent, peer-communicating instances with a shared task list), and when to use each. Read it below.

A GRAD STUDENT WITH 32 FOLLOWERS JUST HIT 56,400 STARS. His name is Yuxiang Lin. GitHub handle: Lum1104. He's at Georgia Tech. He researches multimodal emotion recognition. He plays badminton in his spare time. His personal site is a .dev subdomain. His pinned bio still says he's "currently working on LLM agents." A few months ago he had 655 stars on a side project called Understand-Anything — a plugin that turns any codebase into an interactive knowledge graph. Then everything broke open. The project got picked up by Egonex (Infinite Universe, Inc.). It went from a personal repo to an org-backed release. It got translated into Chinese, Traditional Chinese, Japanese, Korean, Russian, and Spanish. It hit Trendshift. 56,400 stars later, it's becoming the default code-understanding plugin for nearly every AI coding tool that exists — Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode, OpenClaw, Antigravity, Pi Agent, Vibe CLI, Hermes, Cline, KIMI CLI, and Trae. Here's what the plugin does: → Tree-sitter parses your codebase for the structure → LLM agents add the meaning → Builds a knowledge graph of every file, function, and class → Opens an interactive dashboard you can pan, zoom, and search → Generates guided tours so you learn a 200K-line codebase in the right order → Tells you what your changes break before you commit → Auto-updates on every commit via a post-commit hook 568 commits. 7 releases. MIT license. v2.7.3 shipped May 19, 2026. One honest note: the LLM passes cost real tokens on large repos. The structural graph is free and reproducible. The semantic layer is where your model bill shows up. The README still credits him by name. The license still has his name on it. © Yuxiang Lin and Infinite Universe, Inc. A researcher who studies how machines understand human emotion built the tool that finally teaches machines to understand code. This is what open source was supposed to feel like. 100% Open Source. Repo in the first comment.

The Power of Knowledge Knowledge opens doors that strength alone cannot. Never stop learning, never stop growing, and never underestimate the power of an enlightened mind. #Knowledge #Enlightenment #Wisdom

अपनी बहन को बचा लो 😱 शिक्षा, जागरूकता ही उन्हें बचा सकती है। शेयर करें, जागरूक बनें और दूसरों को भी जागरूक करें। #knowledge #education #awareness #RespectWomen #StopAbuse

The purpose of knowledge is not to provide answers, but to teach us how to question everything.... #philosophy #knowledge #education

RAG isn’t just vector search + LLM. 5 RAG architectures to know: → Hybrid — better retrieval → Graph — connected knowledge → Agentic — plan & verify → Adaptive — search when needed → Multimodal — text, images & charts Choose the right architecture for the problem. #AI #ML

🧬🩺 — WHAT IF THE NEXT GENERATION OF MEDICINE COULD RECOGNIZE DISEASE BEFORE THE SYMPTOMS BECOME IMPOSSIBLE TO IGNORE? THE MOST EXPENSIVE MOMENT IN HEALTHCARE IS OFTEN THE MOMENT WE DISCOVER SOMETHING TOO LATE. A patient experiences unexplained fatigue. Another develops subtle neurological symptoms. A routine blood test reveals an abnormality. A radiology scan contains a finding that deserves closer attention. A family history suggests elevated risk. A physician must connect information scattered across multiple systems, appointments, and medical records. And somewhere inside that complexity lies one of healthcare's greatest challenges: TURNING MEDICAL INFORMATION INTO TIMELY, RELIABLE CLINICAL INSIGHT. Modern medicine has extraordinary capabilities. Advanced imaging. Molecular diagnostics. Genomics. Biomarkers. Digital pathology. Wearable monitoring. And increasingly sophisticated artificial intelligence. Yet healthcare information remains fragmented. Clinical teams face enormous workloads. Patients often struggle to understand their results. And many diseases are still discovered after they have progressed. WHAT IF THE NEXT BREAKTHROUGH IN HEALTHCARE ISN'T JUST A NEW TEST? WHAT IF IT'S THE INTELLIGENCE THAT CONNECTS THE TESTS, THE EVIDENCE, AND THE CLINICAL DECISIONS? Introducing a potential next-generation HealthTech venture: DIAGNOSED™ INTELLIGENCE THAT HELPS MEDICINE SEE FURTHER. Built around AN AI-NATIVE CLINICAL INTELLIGENCE PLATFORM DESIGNED TO SUPPORT EARLIER RISK RECOGNITION, DIAGNOSTIC WORKFLOWS, MEDICAL DATA INTERPRETATION, AND EVIDENCE-BASED CLINICAL DECISION SUPPORT. In our proposed brand positioning: .SI = SUPER INTELLIGENCE. Together: — SUPER INTELLIGENCE FOR THE FUTURE OF DIAGNOSTICS. Not autonomous medical diagnosis. Not a replacement for physicians. Not a system that promises to detect every disease. A RESPONSIBLE, CLINICIAN-SUPERVISED INTELLIGENCE LAYER THAT HELPS HEALTHCARE PROFESSIONALS INTERPRET COMPLEX INFORMATION AND MAKE MORE INFORMED DECISIONS. THE FUTURE OF DIAGNOSIS MAY NOT DEPEND ON ONE PERFECT ALGORITHM. IT MAY DEPEND ON CONNECTING THE RIGHT INFORMATION AT THE RIGHT TIME. ━━━━━━━━━━━━━━━━━━━━ 🧠 1. DIAGNOSEDBRAIN™ — CONNECT THE CLINICAL DOTS A diagnosis rarely depends on a single piece of information. Symptoms. Medical history. Laboratory results. Imaging findings. Medication records. Family history. And changes over time can all matter. But relevant information may be distributed across different clinical systems. DIAGNOSEDBRAIN™ — MULTIMODAL CLINICAL INFORMATION INTELLIGENCE. Potential capabilities: 🧠 Clinical information synthesis 📋 Medical history summarization 🧪 Laboratory result trend analysis 🩻 Imaging report organization 💊 Medication context review 🔍 Evidence-linked clinical considerations 📊 Longitudinal health information visualization 🛡️ Clinician review and approval workflows Imagine a physician evaluating a patient with persistent, unexplained symptoms. DiagnosedBrain™ could organize available clinical records, highlight relevant changes, and present evidence-linked considerations for professional review. NOT A MACHINE DECLARING WHAT DISEASE SOMEONE HAS. A SYSTEM HELPING CLINICIANS SEE IMPORTANT INFORMATION MORE CLEARLY. ━━━━━━━━━━━━━━━━━━━━ 🧬 2. BIOMARKERINTEL™ — FIND THE SIGNAL IN COMPLEX LABORATORY DATA Laboratory testing produces valuable clinical information. But individual values don't always tell the full story. Reference ranges. Historical changes. Biological variation. Patient context. And relationships between measurements can influence interpretation. BIOMARKERINTEL™ — INTELLIGENCE FOR LABORATORY DATA ANALYSIS. Potential capabilities: 🧪 Longitudinal biomarker tracking 📈 Laboratory trend visualization 🔍 Abnormal result prioritization 🧬 Molecular diagnostic data organization 📊 Reference interval contextualization 🧠 Clinician-defined pattern analysis 📋 Follow-up workflow support 🔐 Secure laboratory data integrations Imagine a healthcare team reviewing several years of laboratory results. BiomarkerIntel™ could help identify changes that warrant professional attention and organize the underlying evidence. The system would need to account for laboratory methods, clinical context, and uncertainty. A SINGLE NUMBER CAN BE IMPORTANT. BUT THE STORY BEHIND THE NUMBER MAY BE EVEN MORE IMPORTANT. ━━━━━━━━━━━━━━━━━━━━ 🩻 3. IMAGEINTEL™ — HELP CLINICIANS NAVIGATE MEDICAL IMAGING INFORMATION Modern medical imaging is extraordinarily sophisticated. MRI. CT. Ultrasound. X-ray. Digital pathology. And specialized imaging modalities generate substantial amounts of clinical information. IMAGEINTEL™ — AI-ASSISTED MEDICAL IMAGING WORKFLOW INTELLIGENCE. Potential capabilities: 🩻 Imaging report summarization 🔍 Finding comparison across examinations 📊 Longitudinal imaging history 🧠 Validated image-analysis model integrations 📋 Follow-up recommendation tracking ⚠️ Clinician-defined critical finding workflows 🔐 Secure imaging system connections 👨‍⚕️ Radiologist oversight and review Imagine a radiology department managing thousands of examinations. ImageIntel™ could help organize previous findings, compare reports, and support follow-up tracking. Where image interpretation algorithms are used clinically, they would require appropriate validation and regulatory authorization. THE GOAL ISN'T TO REMOVE RADIOLOGISTS FROM THE PROCESS. IT'S TO GIVE THEM BETTER TOOLS FOR MANAGING COMPLEXITY. ━━━━━━━━━━━━━━━━━━━━ 🧬 4. GENOMICPATH™ — CONNECT MOLECULAR INFORMATION WITH CLINICAL CONTEXT Genomic medicine is transforming our understanding of certain diseases. Inherited conditions. Cancer biology. Rare disorders. Pharmacogenomics. And molecular diagnostics increasingly contribute to clinical decision-making. But genomic information can be difficult to interpret. GENOMICPATH™ — CLINICAL GENOMICS KNOWLEDGE AND INTERPRETATION SUPPORT. Potential capabilities: 🧬 Genetic variant information organization 📚 Curated evidence references 🔍 Variant classification workflow support 🧠 Genotype-phenotype research assistance 📋 Molecular report summarization 💊 Pharmacogenomic knowledge integrations 👨‍⚕️ Specialist review workflows 🔐 Sensitive genomic data governance Imagine a specialized clinical genetics team evaluating a rare disease case. GenomicPath™ could help organize relevant findings and connect them with curated scientific evidence. GENETIC INFORMATION REQUIRES EXPERT INTERPRETATION. The platform would support that process, not replace it. ━━━━━━━━━━━━━━━━━━━━ ❤️ 5. EARLYSIGNAL™ — RECOGNIZE WHEN A PATTERN DESERVES ATTENTION Many clinical conditions develop gradually. Some changes are subtle. Others appear across multiple measurements or encounters. The challenge is distinguishing meaningful signals from ordinary variation. EARLYSIGNAL™ — CLINICIAN-SUPERVISED RISK RECOGNITION AND MONITORING. Potential capabilities: 📈 Longitudinal trend monitoring 🧪 Laboratory change detection ❤️ Validated physiological monitoring integrations 📊 Risk model evaluation 🔍 Clinician-configured alerts 🧠 Evidence-linked risk explanations 📋 Follow-up coordination 🛡️ False-alert and performance monitoring Imagine a healthcare organization monitoring patients who have already been identified as requiring regular follow-up. EarlySignal™ could help surface relevant changes according to validated clinical protocols. EARLIER ATTENTION CAN BE VALUABLE. But earlier detection is not automatically better unless it improves patient outcomes and appropriately balances false positives, unnecessary testing, and other harms. THE REAL OBJECTIVE ISN'T MORE ALERTS. IT'S BETTER CLINICAL DECISIONS. ━━━━━━━━━━━━━━━━━━━━ 🤖 6. DIAGNOSEDAGENTS™ — SPECIALIZED AI ASSISTANTS FOR CLINICAL WORKFLOWS Healthcare involves complex coordination. Records. Results. Research. Appointments. Referrals. Follow-ups. And communication between clinical teams. DIAGNOSEDAGENTS™ — A MULTI-AGENT CLINICAL WORKFLOW PLATFORM. Potential agents: 🧠 ClinicalAgent™ — Organizes clinical information for professional review. 🧪 LabAgent™ — Summarizes laboratory trends and relevant changes. 🩻 ImagingAgent™ — Connects imaging reports and historical findings. 🧬 GenomeAgent™ — Supports genomic evidence navigation. 📚 EvidenceAgent™ — Retrieves relevant clinical literature and guidelines. 📋 FollowUpAgent™ — Tracks clinician-approved follow-up tasks. 🔍 QualityAgent™ — Checks documentation completeness and consistency. 🛡️ SafetyAgent™ — Monitors defined validation and governance requirements. Imagine a specialist preparing for a complex patient consultation. ClinicalAgent™ summarizes the history. LabAgent™ organizes laboratory trends. ImagingAgent™ retrieves relevant prior reports. EvidenceAgent™ provides source-linked references. FollowUpAgent™ identifies outstanding clinician-approved tasks. ONE CLINICAL WORKFLOW. MULTIPLE SPECIALIZED ASSISTANTS. ONE RESPONSIBLE HUMAN DECISION-MAKING PROCESS. ━━━━━━━━━━━━━━━━━━━━ 📚 7. EVIDENCECORE™ — CONNECT CLINICAL INTELLIGENCE TO MEDICAL EVIDENCE Healthcare knowledge changes. New studies are published. Clinical guidelines are updated. Diagnostic criteria evolve. And new technologies require evaluation. EVIDENCECORE™ — A CURATED MEDICAL KNOWLEDGE AND RESEARCH INTELLIGENCE LAYER. Potential capabilities: 📚 Medical literature retrieval 🔍 Clinical guideline navigation 📊 Evidence quality indicators 🧠 Source-linked research summaries 📋 Guideline version tracking 🧪 Clinical trial information organization ⚠️ Conflicting evidence identification 👨‍⚕️ Expert-reviewed knowledge workflows Imagine a physician researching a complex clinical question. EvidenceCore™ could organize relevant publications and present concise, traceable summaries. IN MEDICINE, CONFIDENCE WITHOUT EVIDENCE IS A LIABILITY. A trustworthy system must distinguish established knowledge from emerging research and uncertainty. ━━━━━━━━━━━━━━━━━━━━ 🔐 8. CLINICALTRUST™ — MAKE SAFETY PART OF THE ARCHITECTURE Healthcare AI cannot be treated like an ordinary consumer application. Clinical errors can have serious consequences. Medical information is sensitive. Algorithmic bias can affect patient care. And performance can vary across populations and clinical settings. CLINICALTRUST™ — HEALTHCARE AI GOVERNANCE, VALIDATION, AND OVERSIGHT. Potential capabilities: 🛡️ Clinical performance validation 🔍 Model bias and subgroup evaluation 📊 Ongoing performance monitoring 🔐 Patient data access controls 📋 Audit trails ⚙️ Model change management 👨‍⚕️ Human oversight requirements 🌍 Jurisdiction-specific regulatory compliance support Any clinical decision-support functionality would require careful assessment of applicable medical device regulations, privacy obligations, and intended use. THE MOST VALUABLE HEALTHCARE AI COMPANY WON'T SIMPLY BE THE ONE WITH THE MOST ADVANCED MODELS. IT WILL BE ONE THAT CAN DEMONSTRATE CLINICAL VALUE, RELIABILITY, AND TRUST. ━━━━━━━━━━━━━━━━━━━━ 🧬 THE DIAGNOSED CLINICAL INTELLIGENCE GRAPH™ Imagine connecting: Symptoms. Medical history. Laboratory results. Imaging reports. Genomic findings. Medication records. Clinical guidelines. Scientific publications. Follow-up requirements. And validated clinical decision-support tools. A healthcare professional evaluates a complex case. DiagnosedBrain™ organizes available information. BiomarkerIntel™ highlights laboratory trends. ImageIntel™ connects imaging findings. GenomicPath™ supports molecular evidence review. EvidenceCore™ retrieves relevant research. EarlySignal™ evaluates approved monitoring criteria. ClinicalTrust™ applies governance and validation controls. FROM FRAGMENTED INFORMATION TO STRUCTURED CLINICAL CONTEXT. FROM STRUCTURED CONTEXT TO EVIDENCE-INFORMED REVIEW. FROM REVIEW TO BETTER-COORDINATED CARE. The potential competitive advantage? SECURE HEALTHCARE INTEGRATIONS + VALIDATED CLINICAL WORKFLOWS + MULTIMODAL DATA ORGANIZATION + EVIDENCE TRACEABILITY + STRONG GOVERNANCE. ━━━━━━━━━━━━━━━━━━━━ 🔄 THE DIAGNOSED FLYWHEEL More healthcare organizations adopt validated workflows ↓ More clinical teams use structured information tools ↓ More workflow outcomes can be evaluated under appropriate data governance ↓ More performance and usability improvements are identified ↓ More specialized clinical modules are validated ↓ More healthcare organizations can assess the platform's value ↓ A STRONGER CLINICAL INTELLIGENCE ECOSYSTEM. The long-term moat wouldn't simply be access to a general-purpose AI model. IT COULD BE THE COMBINATION OF CLINICAL VALIDATION, SECURE INTEGRATIONS, EVIDENCE-BASED WORKFLOWS, AND INSTITUTIONAL TRUST. ━━━━━━━━━━━━━━━━━━━━ 💰 THE BUSINESS MODEL could support several enterprise healthcare revenue streams. HEALTHCARE ENTERPRISE SAAS Subscriptions for approved clinical workflow and information management tools. CLINICAL DECISION-SUPPORT LICENSING Licensing of appropriately validated and authorized software. LABORATORY DATA INTELLIGENCE Specialized analytics and reporting tools for diagnostic organizations. MEDICAL IMAGING WORKFLOW SOFTWARE Integrations supporting radiology reporting and follow-up coordination. GENOMICS KNOWLEDGE PLATFORMS Research and clinical interpretation workflow software. HEALTHCARE AI GOVERNANCE Validation, monitoring, and audit infrastructure. RESEARCH ORGANIZATION TOOLS Secure research intelligence and data organization workflows. ENTERPRISE INTEGRATIONS Implementation services for hospitals, clinics, and healthcare technology companies. The strongest initial commercial opportunity? CLINICIAN-SUPERVISED MEDICAL RECORD SUMMARIZATION AND DIAGNOSTIC WORKFLOW INTELLIGENCE. Why? Healthcare professionals spend significant time navigating fragmented information. Information organization is a clear operational problem. The product can begin with carefully defined, lower-risk workflows. Value can be evaluated through time savings, documentation quality, and clinician satisfaction. And the platform can expand into more advanced clinical applications only after appropriate validation. START WITH CLINICAL INFORMATION. PROVE WORKFLOW VALUE. ESTABLISH TRUST. EXPAND INTO VALIDATED DIAGNOSTIC INTELLIGENCE. ━━━━━━━━━━━━━━━━━━━━ 🚀 THE GO-TO-MARKET STRATEGY PHASE ONE — SOLVE ONE CLINICAL WORKFLOW PROBLEM Partner with a defined healthcare specialty or provider organization. Focus on information synthesis and documentation support. PHASE TWO — LAUNCH DIAGNOSEDBRAIN™ Build secure clinical data integration and summarization capabilities. PHASE THREE — INTRODUCE BIOMARKERINTEL™ Add longitudinal laboratory data organization and visualization. PHASE FOUR — DEVELOP EVIDENCECORE™ Connect clinical workflows with curated research and guideline references. PHASE FIVE — EXPAND INTO SPECIALIZED MODULES Evaluate imaging, genomics, and risk recognition applications through rigorous clinical validation and regulatory pathways. ONE SPECIALTY. ONE MEASURABLE WORKFLOW IMPROVEMENT. ONE TRUSTED CLINICAL PARTNERSHIP. THEN SCALE RESPONSIBLY. ━━━━━━━━━━━━━━━━━━━━ 💎 WHY Because the name connects one of healthcare's most important concepts with an ambitious technological direction. DIAGNOSED A universally recognizable medical term. Direct. Memorable. Relevant to patients, physicians, researchers, and diagnostic technology companies. It immediately evokes: 🩺 Medical diagnosis 🧬 Disease identification 🧪 Laboratory testing 🩻 Medical imaging 🧠 Clinical intelligence 🔬 Scientific investigation ❤️ Patient care Then comes the extension. .SI Officially Slovenia's country-code top-level domain. But in this proposed brand concept: SI = SUPER INTELLIGENCE. Together: A concise identity for a technology company working at the intersection of medicine and advanced AI. The domain is especially compelling because the word Diagnosed already carries strong relevance to healthcare. Potential brand applications: 🧠 AI-assisted clinical decision support 🧪 Diagnostic laboratory intelligence 🩻 Medical imaging software 🧬 Genomic interpretation tools ❤️ Preventive healthcare technology 📊 Medical data analytics 🔬 Clinical research platforms 🤖 Healthcare AI agents 🛡️ Medical AI validation software 🏥 Hospital workflow intelligence SOME DOMAIN NAMES REQUIRE A LONG EXPLANATION. COMMUNICATES ITS MEDICAL CATEGORY IMMEDIATELY. Its distinctive extension adds a forward-looking technology identity. Prospective buyers should conduct appropriate trademark, regulatory, and domain eligibility checks. ━━━━━━━━━━━━━━━━━━━━ 🎯 WHO SHOULD ACQUIRE 🏥 Healthcare technology startups 🧠 Medical AI developers 🧬 Precision medicine companies 🧪 Diagnostic laboratory technology providers 🩻 Medical imaging software companies ❤️ Digital health platforms 🔬 Clinical research technology businesses 📊 Healthcare data analytics companies 🤖 AI clinical workflow developers 🛡️ Medical AI governance companies 🚀 HealthTech venture studios 💰 Healthcare technology investors For a medical AI startup, could become the identity of a next-generation clinical intelligence platform. For a diagnostic technology company, it could anchor a specialized AI product. For a healthcare software provider, it could support a dedicated diagnostic workflow division. For a venture studio, it offers a recognizable medical brand with multiple potential enterprise applications. THE DOMAIN IS AVAILABLE. THE HEALTHCARE TECHNOLOGY COMPANY BEHIND IT IS YOURS TO BUILD. ━━━━━━━━━━━━━━━━━━━━ 🚀 THE ACQUISITION THESIS Healthcare is entering an era of extraordinary information abundance. More tests. More imaging. More molecular data. More medical literature. More digital records. And more opportunities to apply artificial intelligence. But information abundance does not automatically produce better healthcare. THE CHALLENGE IS TURNING COMPLEX INFORMATION INTO RELIABLE CLINICAL UNDERSTANDING. Imagine physicians receiving structured summaries of relevant medical history. Laboratory specialists identifying meaningful longitudinal patterns. Radiologists having easier access to previous findings. Clinical geneticists navigating complex molecular evidence. Healthcare teams coordinating follow-up requirements. And hospitals evaluating AI systems through rigorous governance. THE NEXT GENERATION OF DIAGNOSTIC TECHNOLOGY SHOULD NOT BE BUILT AROUND REPLACING MEDICAL JUDGMENT. IT SHOULD BE BUILT AROUND MAKING MEDICAL JUDGMENT BETTER INFORMED. That's the opportunity behind An AI-assisted clinical intelligence platform. A medical data interpretation business. A diagnostic workflow software company. A precision medicine technology venture. A healthcare AI governance platform. Or a global HealthTech brand built around one principle: BETTER INFORMATION. BETTER CLINICAL DECISIONS. BETTER OPPORTUNITIES FOR TIMELY CARE. 🧠 CONNECT THE INTELLIGENCE. 🧬 UNDERSTAND THE EVIDENCE. 🩺 SUPPORT THE CLINICIAN. 🛡️ EARN THE TRUST. ❤️ ADVANCE THE FUTURE OF DIAGNOSTICS. Available now at the marketplace. We're also open to strategic partnerships with experienced healthcare entrepreneurs, medical AI researchers, diagnostic technology founders, clinical informatics specialists, HealthTech venture studios, serial technology founders, and investors interested in building a responsible, scalable healthcare intelligence company around this domain. THE FUTURE OF MEDICINE ISN'T ABOUT MACHINES KNOWING EVERYTHING. IT'S ABOUT GIVING HEALTHCARE PROFESSIONALS BETTER TOOLS TO UNDERSTAND WHAT MATTERS. — INTELLIGENCE THAT HELPS MEDICINE SEE FURTHER. 🧬🩺🚀 #DiagnosedSI #DotcomsIO #SuperIntelligence #HealthcareAI #MedicalAI #DigitalHealth #HealthTech #ClinicalAI #MedicalDiagnostics #PrecisionMedicine #ArtificialIntelligence #AIAgents #ClinicalDecisionSupport #MedicalImaging #Biomarkers #Genomics #PreventiveHealthcare #HealthcareInnovation #ClinicalInformatics #MedicalTechnology #FutureOfMedicine #VentureCapital #StartupIdeas #PremiumDomains #DomainsForSale

How I personally trade Directional bias & Structure Liquidity Poi. +20RR trade on USOIL #knowledge #consistency

Let's check in with this firearms expert (must be with a neck tattoo like that...) and see what his advice for winning a gunfight "in an urban setting" is 🤨 Use optics and sights accordingly... #WTF #sights #knowledge #nope #education #urban #learning

Holy sh*t I spent weeks turning Claude Code into a design studio. no Figma license. no agency. no plugins I had to pay for. here's the stack I used and it's embarrassingly simple: -> graphify: teach Claude your brand dump your guidelines, logos, PDFs and old projects into one folder. graphify turns it into a knowledge graph, so every design Claude makes follows your rules instead of guessing. 134k stars, Y Combinator. uv tool install graphifyy -> graphify install -> /graphify . -> Figma-Context-MCP: Figma straight to code it hands your actual Figma file to the agent. the README says it builds designs in one shot, and does it better than pasting screenshots. -> Penpot: the open-source Figma free, open source, and its MCP server lets AI read and edit your designs, then hand them to code and back. -> React Bits: the "expensive" look, for free the biggest open-source library of animated React components. those smooth hover effects and text animations you see on high-end sites? copy, paste, done. -> HyperFrames: designs that move write HTML, get a finished video. made by HeyGen for AI agents. your landing page can now have its own launch video by tonight. claude plugin marketplace add heygen-com/hyperframes -> claude plugin install hyperframes@hyperframes -> MCP for Blender: 3D without learning Blender connects Claude to Blender. describe a scene, the agent builds and edits it in real 3D. the order that works: brand into graphify -> design in Figma or Penpot -> Claude builds it with React Bits -> HyperFrames makes the promo -> Blender adds the 3D hero shot the uncomfortable part: tools used to be the moat. now everyone has them for free. the only thing that still separates good design from AI slop is taste. and taste is the one thing you can't install. bookmark this. your next client will ask why you're suddenly so fast.

How Many and How Much? Contrastive Learning with Negative Samples for Knowledge Graph Embedding Sooho Moon, Yunyong Ko [𝚌𝚜.𝙻𝙶]

Tried every note plugin before one Karpathy gist automated the whole vault Karpathy published llm-wiki.md. Seven repos already run it. About 255k stars between them. Obsidian is the IDE. The LLM is the programmer. The wiki is the codebase. Three operations run everything. 1. Ingest reads a raw source, writes a wiki summary, updates every related page in the vault 2. Query answers from the wiki and files good answers back as new pages 3. Lint runs periodic checks for contradictions, stale claims, orphan pages, missing cross-references 4. graphify turns codebases, docs and PDFs into a queryable knowledge graph, 137.9k stars 5. claude-obsidian files sources into linked cited notes and answers from the vault, 15.9k stars 6. mcp-obsidian connects one MCP server with 7 tools to read, search and write the whole vault, 4.5k stars Seven repos. ~255k stars. Three gist operations. The gist names a method, not a model. The model is a slot you fill with whatever runs your vault. Drop a source in. The wiki rewrites itself. Lint catches what drifted. Save this ↓

नौ मन सूत उलझिया, ऋषि रहें झकमार । सतगुरु ऐसा सुलझा दें, उलझे ना दूजी बार ।। Satlok Ashram Nepal #kabir #knowledge #supremegod #satguru #satsang #santrampaljimaharaj

Adventure #knowledge #motivation

Unreal what skills just turned Claude Code into. Your $20 Pro plan already includes Claude Code. These 10 repos stretch it into a full department for free. 10 GitHub repos that give your coding agent design, product, research and audit skills: 01 graphify → turns your codebase, docs and SQL schemas into a knowledge graph the agent queries instead of re-reading files every session 02 impeccable → 24 design review commands and 62 deterministic rules that catch generic AI-looking UI before you ship it 03 taste-skill → art direction drawn from 247 studied sites plus a real browser check. Replaces template pages with directed design 04 skills → 14 interface-building skills in one npx install. One command instead of writing 14 separate UI prompts 05 pm-skills → 69 product management skills and 42 chained workflows across 9 plugins. Runs discovery, strategy and launch without a template doc 06 Auto-claude-code-research-in-sleep → Markdown-only skills that run autonomous ML research and cross-model review loops. Your experiments keep going while you sleep 07 academic-research-skills → full paper pipeline from sources to reviewed draft, about $3 to $7 in API costs per 15k-word paper 08 security-audit-skill → six-phase security audit with verified findings inside the terminal. No paid scanner seat needed 09 knowledge-work-plugins → 11 official Anthropic plugins that turn Claude into a specialist for sales, support, finance and more. Ready setups, no custom build 10 claude-seo → 26 sub-skills and 19 sub-agents run a full SEO audit in 10 to 15 minutes. No SaaS seat, terminal only Pick the one role your team repeats most. Install that skill first. install → map → design → review → audit → ship academic-research-skills is licensed CC BY-NC 4.0. Non-commercial use only. Pick one skill ▼

What I learned from Joe Biden's speech this weekend... #biden #knowledge

''भारत ने कोविड के दौरान मानवता को सक्षम बनाने का रास्ता चुना और 100 से अधिक देशों को वैक्सीन सप्लाई की.. भारत जितना सक्षम होगा.. दुनिया के लिए उतना ही अच्छा होगा'' - प्रधानमंत्री मोदी #PMModi #BloombergNewEconomyForum #Knowledge #Wealth #Power #GlobalEconomy