Troubleshooting Common Issues

𝗡𝗮𝗶𝘃𝗲 𝗥𝗔𝗚 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝗮 𝗱𝗲𝗺𝗼. 𝗜𝘁 𝗳𝗮𝗶𝗹𝘀 𝘁𝗵𝗲 𝗺𝗼𝗺𝗲𝗻𝘁 𝗿𝗲𝗮𝗹 𝘂𝘀𝗲𝗿𝘀 𝘀𝗵𝗼𝘄 𝘂𝗽. Embed → retrieve → generate looks clean in a notebook. Real requirements break it: → Questions whose answer is spread across many documents → Industry terms that embeddings get wrong → Bad chunks the pipeline never catches → Answers that live in how things connect, not in any single chunk → PDFs full of tables and images a text-only index cannot read These 5 architectures are how serious teams stay ahead in the agentic AI era: 𝟬𝟭 𝗛𝘆𝗯𝗿𝗶𝗱 𝗥𝗔𝗚 → Dense vectors find meaning. BM25 finds exact words. → Reciprocal Rank Fusion combines both ranked lists. → A safe baseline for almost every team. 𝟬𝟮 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 → Pull entities and their relationships into a knowledge graph. → Retrieve subgraphs and community summaries, not chunks. → Best when the answer lives in how things connect. 𝟬𝟯 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 → A planner agent picks the right tool: vector, web, or SQL. → A reasoner agent keeps trying until the answer is solid. → Retrieval becomes a plan, not a single step. 𝟬𝟰 𝗖𝗼𝗿𝗿𝗲𝗰𝘁𝗶𝘃𝗲 𝗥𝗔𝗚 (𝗖𝗥𝗔𝗚) → Grade every retrieval before you trust it. → Correct → answer. Unclear → rewrite the query. Wrong → search the web. → This is what production RAG actually looks like. 𝟬𝟱 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗥𝗔𝗚 → One embedding model (CLIP, ColPali) for text, images, and tables. → One vector index. One multimodal LLM. → No more separate pipelines for PDFs with charts. I built a runnable example for each of the five patterns. GitHub link in the first comment. The best teams in 2026 do not pick one. They combine them — hybrid retrieval inside an agentic loop, with a corrective grader, over a multimodal index. Naive RAG is a starting point, not a finish line. That is why most enterprise GenAI projects stall at the demo. Which of these five becomes the default RAG stack in the next 18 months — and which stays a specialized tool?

🔎 How To Redesign Complex Navigation: How We Restructured Intercom’s IA (https://lnkd.in/ezbHUYyU), a practical case study on how the Intercom team fixed the maze of features, settings, workflows and navigation labels. Neatly put together by Pranava Tandra. 🚫 Customers can’t use features they can’t discover. ✅ Simplifying is about bringing order to complexity. ✅ First, map out the flow of customers and their needs. ✅ Study how people navigate and where they get stuck. ✅ Spot recurring friction points that resonate across tasks. 🚫 Don’t group features based on how they are built. ✅ Group features based on how users think and work. ✅ Bring similar things together (e.g. Help, Knowledge). ✅ Establish dedicated hubs for key parts of the product. ✅ Relocate low-priority features to workflows/settings. 🤔 People don’t use products in predictable ways. 🤔 Users often struggle with cryptic icons and labels. ✅ Show labels in a collapsible nav drawer, not on hover. ✅ Use content testing to track if users understand icons. ✅ Allow users to pin/unpin items in their navigation drawer. One of the helpful ways to prioritize sections in navigation is by layering customer journeys on top of each other to identify most frequent areas of use. The busy “hubs” of user interactions typically require faster and easier access across the product. Instead of using AI or designer’s mental model to reorganize navigation, invite users and run a card sorting session with them. People are usually not very good at naming things, but very good at grouping and organizing them. And once you have a new navigation, test and refine it with tree testing. As Pranava writes, real people don’t use products in perfectly predictable ways. They come in with an infinite variety of needs, assumptions, and goals. Our job is to address friction points for their realities — by reducing confusion and maximizing clarity. Good IA work and UX research can do just that. [Useful resources in the comments ↓] #ux #IA

I’m extremely bullish on account-based GTM motions in 2026. As AI makes generic outreach cheap and ubiquitous, differentiation comes from precision, relevance, and deep context around a small set of high-value accounts. This sounds wonderful on paper until it’s time to set up a modern account-based GTM engine. IYKYK. For help I turned to Dan Rosenthal from Workflows.io. They’ve set up 250+ GTM systems including dozens of ABX implementations over the past year. Dan put together what I think is the most comprehensive & useful guide to *modern* ABM with HubSpot, Claude Code, Clay & best-of-breed tech. Read the full deep dive in Growth Unhinged here: https://lnkd.in/eyF9bgyb There are 7 steps — follow these in this order: 1️⃣ TAM & stakeholder mapping Dan recommends combining 3+ data sources to reach 90%+ of TAM coverage w/o blowing through your budget. AI research agents help qualify the list based on website content & do the merge/de-dupe. 2️⃣ Account research Most companies Dan works with gather 30+ custom data points with Claude Code & MCPs. Some interesting ones: closest coffee shop next to their office, competitor tech usage. 3️⃣ CRM cleanup & enrichment Nobody’s favorite step, but you can’t skip it. 4️⃣ Signal tracking Intent signals are hot. Reps also don’t trust a lot of them. And many signals have commoditized. The most valuable signals are from your first party data (website visitors, gated content, product usage, event attendance, outreach replies, etc.). 5️⃣ Awareness scoring If you embrace ABX, you need a way to see where you stand with your target accounts (no, MQLs/SQLs don’t work). Identified > Aware > Interested > Considering > Evaluating. 6️⃣ Demand generation With the scoring in place, reps can focus on the warmest accounts who already show interest. Use a mix of 1:1, 1:few, and 1:many campaigns. (Pro-tip: Dan says event invites have been a great CTA in outbound.) 7️⃣ ICP pipeline progression reporting Success can be harder to quantify w/o an arbitrary MQL goal. The upside: you’re now collecting far richer data about what resonates with your target accounts. — I hope you find this guide as useful as I do. Let me know what you think 🙏 -KP

I've reviewed 2,500+ resumes last year. Avoid these mistakes that 90% make. 1. Generic Summaries ❌ "Motivated professional seeking opportunities to leverage my skills..." ✅ "Marketing Director who increased e-commerce revenue 47% through data-driven campaigns and strategic partnerships." 2. Missing Numbers ❌ "Led large team and improved sales." ✅ "Led 15-person sales team to deliver $3.2M in new business, exceeding targets by 28%." 3. Cluttered Formatting ❌ Tiny margins, dense paragraphs, and multiple fonts. ✅ Clean headers, consistent bullet points, and enough white space for easy scanning. 4. Outdated Information ❌ Listing your high school achievements and every job since college. ✅ Your most relevant accomplishments from the past 10-15 years that showcase your career progression. 5. RESPONSIBILITY LISTS ❌ "Responsible for managing client relationships and handling complaints." ✅ "Retained 98% of key accounts and turned 3 dissatisfied clients into top referral sources." 6. ATS-UNFRIENDLY DESIGN ❌ Creative formats with graphics, text boxes, and unique fonts. ✅ Clean, standard formatting with relevant keywords that match the job description. Pro Tip: A tool like Teal’s AI Resume Builder can help you avoid ALL these mistakes by helping you: ⮑Turn generic summaries into powerful pitches. ⮑ Back achievements with real numbers. ⮑ Perfect your formatting instantly. ⮑ Showcase relevant experience. ⮑ Transform duties into accomplishments. ⮑ Get noticed by more recruiters. Try it out for free: https://tealhq.co/4aAEY2g Your resume has 7 seconds to make an impression.  Use these tips to make them count. Share this to help others level up their resume! 📈 And follow me for more advice like this. 📌 In addition to being a job search coach, I’m also a part-time career advisor for Teal so if you have any questions about their AI Resume Builder, let me know.

Scientists can’t agree on where the world’s forests are A deceptively simple question underlies many global environmental policies: where, exactly, are the world’s forests? A new study suggests the answer depends heavily on which map one consults—and that the differences are large enough to reshape climate targets, conservation priorities, and development spending. Researchers Sarah Castle, Peter Newton, Johan Oldekop, Kathy Baylis, and Daniel Miller compared ten widely used global forest datasets derived from satellite imagery. These products underpin everything from carbon accounting to biodiversity assessments. Yet they rarely agree. Across areas identified as forest by at least one dataset, only about 26% was classified as forest by all of them. Even after adjusting maps to a common spatial scale, agreement improved only modestly. The divergence stems partly from definitions. Some datasets treat sparsely wooded landscapes as forest, while others require dense canopy. A 10% canopy threshold, for instance, includes savannas and open woodlands; a 70% threshold captures only closed forest. Resolution also matters: high-resolution imagery can detect narrow riparian strips or small fragments that coarser data overlook. Differences in sensors, algorithms, and training data introduce further variation. Patterns of disagreement are uneven. Moist tropical forests, where tree cover is continuous, show relatively high consistency. Dry forests and fragmented landscapes show far less, with some biomes reaching consensus on as little as 12% of forested area—often where conservation decisions are most contested. Case studies illustrate the practical consequences. In Kenya, estimates of forest carbon ranged from roughly 2% to 37% of national biomass carbon depending on the dataset used. Maps that produced similar totals did not necessarily agree on where carbon was stored, complicating mitigation planning. In India, estimates of forest-proximate people living in poverty ranged from about 23 million to more than 250 million using identical socioeconomic data but different forest maps. In Brazil, even datasets tracking forest loss overlapped on less than half of mapped deforestation affecting habitat for the endangered white-cheeked spider monkey. Satellite-derived maps now form the empirical backbone of environmental governance. Governments use them to report climate progress, NGOs to target interventions, and investors to assess nature-related risk. The study does not identify a single “correct” dataset. Instead, it urges treating forest estimates as ranges, testing results across multiple products, and improving standardization. Before deciding how to manage forests, policymakers may first need to agree on where they are. 🌳 Full piece: https://mongabay.cc/uf6jMn 🔬 Paper: https://lnkd.in/g_UbxRjk

Bad Data Engineers fix dashboards. Good Data Engineers fix broken pipelines. Great Data Engineers prevent bad data from entering the pipeline in the first place. 10 SQL checks every pipeline should have👇 𝟭. 𝗡𝗨𝗟𝗟𝗦 Stop letting missing values break your averages. 𝟮. 𝗨𝗡𝗜𝗤𝗨𝗘𝗡𝗘𝗦𝗦 Imagine doubling the revenue by accident! 𝟯. 𝗜𝗡𝗧𝗘𝗚𝗥𝗜𝗧𝗬 (𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹) Every order_id must link back to a valid customer. 𝟰. 𝗔𝗖𝗖𝗘𝗣𝗧𝗘𝗗 𝗩𝗔𝗟𝗨𝗘𝗦 Don’t let “weird” statuses creep into your reports. 𝟱. 𝗙𝗨𝗡𝗖𝗧𝗜𝗢𝗡𝗔𝗟 𝗥𝗨𝗟𝗘𝗦 Check business rules that should never be broken. 𝟲. 𝗥𝗔𝗡𝗚𝗘 Catch outliers before they skew the entire quarter. 𝟳. 𝗗𝗔𝗧𝗔 𝗧𝗬𝗣𝗘 Prevent the “Text vs Integer” nightmare. 𝟴. 𝗙𝗥𝗘𝗦𝗛𝗡𝗘𝗦𝗦 No more stale dashboards. 𝟵. 𝗧𝗘𝗠𝗣𝗢𝗥𝗔𝗟 𝗖𝗢𝗡𝗦𝗜𝗦𝗧𝗘𝗡𝗖𝗬 Time should move forward, not backward! 𝟭𝟬. 𝗡𝗨𝗟𝗟 𝗦𝗣𝗜𝗞𝗘 Spot sudden drops in data quality before they bite. --- Having bad data is worse than not having data at all. No data → You rely on intuition. Bad data → You make confident decisions that are simply wrong. Take Data Quality seriously! I prepared the SQL implementation👇 --- ♻️ Repost if you found it useful, please Follow 👉🏻 José for more about Data, SQL, and AI!

OT Detection Use Cases for your OT SOC When it comes to building an OT SOC, there’s a big misconception: many assume success is about collecting every log or integrating every system In reality, the key is focusing on operationally meaningful visibility — the detections that actually help you understand what’s happening inside your control network >> In industrial environments, context defines everything > The same Modbus write command could mean two very different things: > a maintenance engineer performing a scheduled update — or an attacker changing control logic. > Without context, both look identical in your SIEM. >> An OT SOC must speak the language of process, assets, and operations, not just alerts. It should tell you when something changes, who initiated it, and whether it threatens safety, reliability, or integrity >> Below are 10 detection use cases I always recommend as a starting point. They’re mapped to MITRE ATT&CK for ICS and NCA OTCC, but more importantly, they’re grounded in what actually happens inside real plants and industrial networks 1. Unauthorized PLC Programming Detect logic or configuration changes outside scheduled maintenance windows 2. ICS Protocol in IT Zone Flag Modbus, DNP3, or BACnet traffic on IT networks — strong evidence of segmentation drift or misconfiguration 3. PLC Stop or Mode Change Command Detect STOP or PROGRAM mode changes — an event that can halt production and indicate malicious control 4. Remote Access to HMI from Unapproved Source Identify RDP, VNC, or TeamViewer sessions from IT zones targeting OT HMIs — a common lateral movement path 5. New Device in Control VLAN Catch unauthorized or rogue devices joining deterministic control networks where new assets should rarely appear 6. PLC Firmware Downgrade or Version Change Detect unauthorized firmware rollbacks — a subtle but serious method of tampering or hiding malicious code 7. OPC UA Anonymous Session Identify untrusted or anonymous OPC UA sessions that bypass normal authentication or encryption 8. Engineering Software on Non-Engineering Host Detect the execution of TIA Portal, Control Builder, or similar tools on unauthorized systems — often a sign of credential misuse or insider activity 9. PLC Configuration Upload Monitor FTP/TFTP uploads to PLCs — an activity that could replace control logic or inject malicious configuration 10. Abnormal HMI Behavior Spot rapid screen changes, tag edits, or command spamming from operators — signs of misuse, automation, or compromise They aren’t just security detections — they’re process integrity safeguards. Each one gives the SOC visibility into the exact actions adversaries use during real OT incidents — often before physical impact occurs When combined with contextual data (authorized engineers, maintenance schedules, device baselines) and network telemetry , these detections evolve from simple alerts into actionable operational intelligence #OTSOC #OTsecurity #ICSsecurity

Over the years working in chemical processing, one of the recurring challenges I’ve faced is with heat exchangers. They are essential for energy efficiency, but even minor issues can create significant downtime and cost. Not long ago, we encountered a serious fouling issue in one of our exchangers. The deposits were reducing heat transfer efficiency, causing higher energy consumption and forcing frequent shutdowns for cleaning. 🔍Instead of treating it as just another maintenance task, we carried out a detailed root cause analysis: • Reviewed process conditions and flow patterns. • Checked velocity and temperature profiles. • Involved both the operations and maintenance teams in the discussion. The findings showed that low fluid velocity was the main driver for fouling. By redesigning the piping layout and adjusting the operating parameters, we were able to: ✅ Increase turbulence and reduce fouling. ✅ Extend cleaning cycles from every 3 months to once a year. ✅ Achieve over 15% improvement in efficiency. For me, the key takeaway is that every technical problem is also an opportunity to innovate and improve reliability. Collaboration and data-driven decisions can transform a recurring issue into a long-term success.

A junior pinged me late night … “Hey, sorry for disturbing so late, but the hub-allocation service just stopped writing events. I’ve been staring at the logs for an hour and can’t see it.” I was two chapters deep in a book, but a production freeze at Flipkart waits for no one. Ten minutes later we were on a call, screens shared, coffee in hand. What we saw: 1. CPU was fine, DB healthy. 2. Message Queue consumer lagging — but only for one partition. The suspect commit: a “tiny” config change that slipped past review because “it’s just YAML.” What we did: 1. Replayed the partition in staging → reproduced the freeze in 30 seconds. 2. Flipped the feature flag off, deployed a hotfix. 3. Wrote a one-liner unit test that fails if the critical topic/partition mapping ever changes without a version bump. Total downtime: 23 minutes. Total learning: off the charts. Three takeaways I shared with the team the next morning: 1. Small changes aren’t small in distributed systems. A single-line config tweak can strand an entire message bus. 2. Cultivate “safe-to-ping” culture. The bravest thing that junior engineer did wasn’t debugging at 1 a.m.; it was sending that message before things spiraled. 3. Automate the guardrails. Post-mortems are great, but a failing test is louder than any Confluence page. AfterMath: That junior pushed the unit test themselves, opened the merge request, and led the retro. Next sprint, they volunteered to refactor our event-routing configs; because now they own the problem. These are the moments that turn capable engineers into future tech leads.