IT Service Management Platforms

Instead of asking "what should I automate?" Focus on WHY you should automate and HOW it solves the data problem. Most data engineers automate the wrong things at the wrong time. Here's the framework I use after 8 years of building production systems: ✅ AUTOMATE WHEN: → Task runs daily/weekly → Human errors cause outages → Work blocks other priorities → Team growth = more manual work Examples: Reports, schema checks, alerts ❌ DON'T AUTOMATE WHEN: → Task happens quarterly → Requirements change weekly → Process isn't understood yet → Manual steps reveal insights My rule: If it’s done 3+ times, script it; 10+ times, automate it; fails 5+ times, redesign it. Automate what matters, when it matters—not everything! Here's how Airflow makes data automation ridiculously easy: 🎯 The Magic Triangle: → Scheduler: Triggers workflows on time → Executor: Distributes work to available workers → Workers: Actually run your Python code 💾 Smart State Management: → Metadata DB: Tracks every task run → Queue: Manages task priorities → Web UI: Visual monitoring & debugging 🔄 Why It Works: → Write Python DAGs once → Airflow handles the rest → Automatic retries & error handling → Parallel task execution → Visual dependency tracking Real Example: Instead of: ❌ Cron jobs that fail silently ❌ Manual dependency management ❌ No visibility into failures You get: ✅ Visual workflow monitoring ✅ Automatic failure notifications ✅ Smart task scheduling ✅ Easy debugging & restarting Image Credits: lakeFS The Bottom Line: Apache Airflow turns complex data workflows into manageable Python scripts. What's your biggest pipeline automation challenge? #data #engineering

Andrej Karpathy shared how he actually uses LLMs day to day: He's not writing more code with AI. He's spending most of his tokens building and maintaining a personal knowledge base on whatever he's actively researching. Here's the full breakdown: 1/ It starts with raw data ingestion. Web articles, research papers, GitHub repos, datasets, images. Everything gets dumped into a raw folder. He uses a clipboard tool and hotkey to capture sources fast without breaking his flow. 2/ An LLM reads, summarizes, and compiles all of it into a wiki. Not a messy notes folder. A structured collection of .md files with summaries, concept articles, and crosslinks between related ideas. 3/ Obsidian is the frontend and IDE. He views raw data, the organized wiki, and visualizations all in one place. The LLM writes and maintains the entire wiki. He rarely edits it directly. 4/ No RAG pipeline needed. Once the wiki grows big enough (around 100 articles, 400K words on a single topic), he just asks it questions. The LLM auto-maintains index files and reads its own index to find what it needs. No vector DB. No retrieval chain. 5/ Outputs go way beyond text. He has the LLM render markdown files, Marp slide decks, and matplotlib charts. Then everything gets filed back into the wiki as new articles. 6/ There's a self-improving loop built in. Every question he asks generates new content that feeds back into the knowledge base. The system gets smarter with every interaction. 7/ He runs linting passes as health checks. The LLM scans the wiki to find inconsistencies, fill gaps using web search, discover missing connections between articles, and suggest entirely new topics to cover. 8/ He vibe-coded extra tooling around it. A CLI, a search engine over the wiki, and a web UI. The search tool can be used directly in a browser or handed off to an LLM as a tool for larger queries. 9/ The wiki isn't static. It cleans itself over time. Every linting pass and every new query tightens the structure, fills blind spots, and strengthens the connections across the entire knowledge base. 10/ His next step is wild. Fine-tuning a model on synthetic data generated from his own wiki. So the knowledge lives in the model's weights permanently. Not just in the context window. This is what separates using LLMs from building with them. Most people treat AI as an answer machine. Karpathy turned it into a personal knowledge operating system. One system you can query, build on, and return to forever. If you're deep in any AI or DL research topic right now, this workflow is worth stealing. Image source: Stanislav Beliaev If you want more AI coding resources daily for free, sign up here: https://lnkd.in/dMGZuZAj

From query to knowledge in seconds. That’s the promise of RAG systems. Instead of relying only on what a model learned during training, a RAG pipeline retrieves relevant information from external sources and uses it to generate accurate, grounded responses. Here’s how the architecture typically works. - Input Layer The process begins with the user query. System prompts guide model behavior while the system connects to knowledge sources such as documents, databases, internal knowledge bases, APIs, or enterprise systems. The query is then structured for retrieval. - Retrieval Processing The query is converted into a vector embedding, which represents its semantic meaning. The system performs vector search in a database to find similar documents. Similarity matching ranks results and top-K selection chooses the most relevant chunks of information. - Context Assembly The selected pieces of information are combined into a structured context. This retrieved context becomes the knowledge the model will use to answer the question. - Reasoning Layer The model analyzes the query and retrieved context together. It integrates external knowledge, performs multi-step reasoning when needed, and generates responses grounded in the retrieved documents. - Consistency Checking The system verifies that the generated answer aligns with the retrieved sources to reduce hallucinations and improve reliability. - Response Layer The response is structured clearly for the user. Citations may be included, confidence levels assessed, and the final output delivered to the application or interface. - Feedback Loop User feedback and system monitoring help improve the pipeline. Knowledge bases are updated, embeddings refreshed, and retrieval strategies optimized over time. RAG systems work because they combine vector search, knowledge retrieval, and LLM reasoning - allowing AI to answer questions using current, trusted information. Where are you using RAG today - internal knowledge assistants, customer support, or enterprise search?

Compliance isn’t choosing one framework, it’s understanding how they work together. Many organizations view SOC 2, ISO 27001, and GDPR as competing obligations, but the reality is far more integrated. SOC 2 validates data security controls for US-based service providers voluntary but expected by enterprise clients. ISO 27001 provides a globally recognized ISMS foundation with comprehensive risk management and continuous improvement. GDPR legally enforces personal data protection for EU citizens with significant financial penalties for non-compliance. The strategic advantage lies in their overlap: access controls, incident response, vendor risk management, encryption, and breach notification requirements align across all three. Organizations that map controls once and satisfy multiple frameworks simultaneously reduce audit fatigue while strengthening their overall security posture. Rather than treating compliance as separate silos, mature GRC programs build unified control environments that address shared requirements, turning regulatory burden into operational excellence. What’s your approach to managing overlapping compliance frameworks? #GRC #SOC2 #ISO27001 #GDPR #Compliance #InformationSecurity #DataProtection

IT and engineering leaders, take note: AI is projected to deliver an impressive US$12 billion in productivity gains through coding enhancements alone! AI could transform one of the most labor-intensive and repetitive stages of the development process—and one rife with inefficiencies.     In our 2025 Tech Trends report [https://deloi.tt/4jfugnd], Deloitte predicts that the IT landscape will evolve significantly over the next two years. Organizations are embracing Gen AI in engineering, talent, cloud financial operations, infrastructure, and cyber. Use cases like AI-assisted code generation, automated testing, and rapid data analysis are freeing up developers to focus on innovation and modernization through a “human-in-the-loop” approach to AI.      Through the evolution of AI, the role of engineers is shifting to what we do best: defining problems, orchestrating solutions, and optimizing outcomes. 

How we turned a 5-minute task into 30 seconds. You’ll wonder why you didn’t do this sooner: “We don’t need Salesforce.” That’s what one client told me. They were running their customer service department through Asana. And it worked—kind of. The problem? Their internal ticketing process was slowing them down. Every sales rep had to create multiple tickets a day. Each one took five minutes. With 10 reps, that’s hours wasted every week. Here’s what we did: - We built a custom integration so they could create tickets directly in Salesforce. - No more switching tools. - No more wasted time. Now? Each ticket takes 30 seconds. Think about the impact: - A process that once burned hours is now done in minutes. The result? - A faster, more efficient customer service department. - A team that’s happy to use Salesforce because it works for them—not against them. Sometimes it’s not about convincing someone to trust Salesforce. It’s about showing them what’s possible. P.S. What’s one process in your business that’s taking too long right now? Thanks for reading. Enjoyed this post? Follow Jordan Nelson And share it with your network.

New Model & Jobs for IT Industry. I see two trends emerging that will shape the future of IT Industry in next 2-3 years. There are both opportunities & threats and hence IT companies & professionals who will strategise & make the shift will succeed. 𝐄𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐓𝐫𝐞𝐧𝐝𝐬: 1. 𝐒𝐡𝐢𝐟𝐭 𝐟𝐫𝐨𝐦 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐭𝐨 𝐎𝐮𝐭𝐜𝐨𝐦𝐞𝐬:  It’s clear that going forward customers won’t pay for software or innovation alone. They would demand tangible outcomes & measurable results. 2. 𝐀𝐈 𝐞𝐯𝐞𝐫𝐲𝐰𝐡𝐞𝐫𝐞:  Artificial Intelligence, once a differentiator, will soon become a hygiene factor. IT companies will have to rethink their strategies around AI. 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬 𝐟𝐨𝐫 𝐈𝐓 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬: 1. 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 Product companies should focus on either creating intelligent Agents to automate workflows & drive customer outcomes or developing cost-efficient infrastructure to host, manage & scale AI driven workflows & agents. 2. 𝐀𝐈 𝐌𝐚𝐫𝐤𝐞𝐭𝐩𝐥𝐚𝐜𝐞: There’s also a growing opportunity to build & operate platforms where businesses can buy, sell, or share AI models, solutions, and services. 3. 𝐎𝐮𝐭𝐜𝐨𝐦𝐞 #𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 Services companies have huge potential if they build capabilities to deliver outcomes by replacing existing workflows with agentic workflows. They can create niche as 𝐝𝐨𝐦𝐚𝐢𝐧 𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐬𝐭𝐬 (industry-focused) or 𝐒𝐭𝐚𝐜𝐤 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐬𝐭𝐬 (tech-focused) on AI, Data & Automation. 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐀𝐡𝐞𝐚𝐝 In near term, economic volatility, outsourcing shifts, inflation, and "nation-first" policies will disrupt traditional markets & companies will have to accelerate their focus on new markets like Africa, the Middle East, & India. Secondly, the days of long-term contracts & big-ticket deals are over. IT companies will have to get ready for shorter engagements with reduced hourly rates, emphasizing efficiency & outcomes over duration. They need to adopt new outcome metrics & results driven billing. 𝐄𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐑𝐨𝐥𝐞𝐬: I see the following new roles will emerge and will be in demand. 1.    AI Automation Specialists:Designing, implementing & optimising AI-driven solutions replacing existing workflows to address specific business challenges. 2.    AI Agent Specialists:Developing, deploying & managing autonomous agents. 3.    Outcome Architects & Engineers:Specialists who design & deliver #OaaS solutions tailored to business needs. 4.    Orchestrators:Specialists who will blend domain expertise & technical know-how, coordinating complex systems to deliver seamless results. 5.     Dynamic Pricing Specialists:Deep technology experts who will use AI to continuously adjust pricing based on new developments to offer cost-optimised Opex. However regardless of role or industry, ability to use & collaborate with #AI will be an essential skill of the future. What’s your perspective? Let’s discuss in the comments!

DORA is transforming the EU financial landscape. AWS is helping make it happen. These key areas are essential for DORA compliance: ☑ ICT Risk Management ↪ AWS Well-Architected Framework and AWS Resilience Hub help assess and manage operational risk. ☑ Incident Reporting & Management ↪ AWS Security Hub and AWS Audit Manager streamline incident detection and compliance reporting. ☑ Digital Operational Resilience Testing ↪ AWS Resilience Hub and Fault Injection Service support continuous testing and resilience validation. ☑ Third-Party Oversight ↪ AWS Artifact provides independent certifications and reports to validate compliance across partners. ☑ Secure Information Sharing ↪ AWS Identity and Access Management (IAM) and AWS Shield ensure secure communication during crises. ☑ Data Protection & Compliance ↪ AWS Key Management Service (KMS) and AWS Control Tower support data governance in alignment with DORA mandates. ☑ Integrated Compliance Management ↪ AWS Artifact and Audit Manager simplify regulatory adherence with on-demand access to security controls. ☑ Business Continuity ↪ AWS Backup, Elastic Disaster Recovery, and Resilience Hub bolster disaster recovery and operational resilience. By integrating these AWS solutions, your company doesn’t just comply—it proves that your business is constantly evolving and resilient. ♻️ Repost to help your colleagues navigate DORA compliance with AWS. ➕Follow Andrey Gubarev for more posts like this #DORA #AWS #Compliance #GRC #FinTech #CloudComputing #CTO

Karpathy just published his "LLM Wiki" pattern and hit 5K stars overnight. Using LLMs to build and maintain personal knowledge bases instead of re-deriving everything through RAG on every query. The core idea: stop retrieving, start compiling. The LLM incrementally builds a structured wiki from your sources. Cross-references maintained. Contradictions flagged. Knowledge compounds with every source you add. I built this 6 months ago with agentmemory. Same pattern, but agent-facing and fully automated. After running it in production across thousands of sessions, here's what's missing from the original: 1. Memory lifecycle. Not all facts are equally valid forever. You need confidence scoring, supersession, and a forgetting curve. Architecture decisions decay slowly. Transient bugs decay fast. 2. Knowledge graph. Flat pages with wikilinks leave structure on the table. Typed entities and relationships let you traverse "what depends on Redis?" instead of keyword-searching for it. 3. Hybrid search. index.md breaks around 100 pages. You need BM25 + vector + graph traversal fused together. 4. Automation. The original is entirely manual. In practice you want hooks: auto-ingest on new sources, auto-lint on schedule, context injection on session start. The bookkeeping should be zero-effort. 5. Multi-agent. Single user, single agent doesn't hold. You need mesh sync, shared vs private scoping, and lightweight work coordination. 6. Quality controls. Without scoring and self-healing, the wiki accumulates noise. Score everything. Auto-fix orphans and stale claims. I forked Karpathy's gist and published a v2 with all of these additions. GitHub Gist: https://lnkd.in/epc_gGqd Engine: https://lnkd.in/e5syVfaA The bottleneck was never reading or thinking. It was bookkeeping. LLMs solve that.

.📢 Implementing IT Audit & GRC: A Smart Strategy for Security & Profitability Organizations today must stay ahead of cyber risks and regulatory requirements. A well-structured IT Audit & GRC (Governance, Risk & Compliance) program helps ensure security, accountability, and long-term profitability. 🔑 Key Focus Areas in IT Audit & GRC Implementation: 1. Governance * Define security policies & roles * Align IT strategy with business goals 2. Risk Management * Conduct regular risk assessments * Maintain a risk register with mitigation plans 3. Compliance * Implement controls based on frameworks (ISO 27001, NIST, SOC 2) * Conduct regular internal audits & ensure documentation 4. Controls Implementation * Access management, change control, data protection * Monitoring & incident response planning 5. Automation & Tools * Use SIEM, GRC platforms, and compliance dashboards * Automate alerts, audits, and reporting 6. Training & Awareness * Regular employee training * Role-based security awareness programs 💼 Business Benefits of Adopting IT Audit & GRC: ✅ Reduce cyber risks and data breaches ✅ Build trust with customers & stakeholders ✅ Avoid regulatory penalties ✅ Improve operational efficiency ✅ Attract investors through transparency ✅ Strengthen brand reputation 🎯 GRC isn't just about security—it's a strategic investment for long-term growth. #ITAudit #GRC #ISO27001 #RiskManagement #CyberSecurity #Compliance #ITGovernance #CloudSecurity #InfoSec #InternalAudit #SIEM #BusinessContinuity #ITCompliance #CISA #AzureSecurity