Troubleshooting Common Issues

Many are asking me... Should I continue to track "Open Rates" on Cold Emails? It's still no. My answer hasn't changed. I had predicted this about 9 months ago if you want to look back. Why? Analyze the image in the post. Does the position of the "Report as Spam" increase the amount of people who click it by 3 on 1,000 recipients? If you said yes, you agree with me. This is a subtle way Google is asking you for more feedback on the quality of your outbound campaigns. Here are 5 reasons NOT to use Open Tracking for Cold Email: Reason 1: Limits Your Use Of Plain Text Emails Plain Text Emails get superior deliverability. Open Trackers can't be used in Plain Text emails. Reason 2: Inconsistent Tracking Open Trackers identify "opens" differently and ultimately can't prove someone opened the email. Every sequencer has a different way of tracking it. Reason 3: Email Fingerprints Open Trackers provide a fingerprint for your domain reputation. It's shared amongst everyone using the sequencer your company uses. Do you want to be part of this group? Reason 3: Misleading Data Secure Email Gateways open emails for their users to protect their privacy. Budget has increased significantly here and will continue to go up. Most of these systems will put your email in spam because of it. Reason 4: Easy To Block Even simple rules can block emails with open trackers. No AI required. It's simple. Reason 5: Bad Metric Teams and internet gurus are obsessed with open tracking. However, it doesn't mean your email has been opened. It could mean that, but it depends who you emailed. Here are 3 Insider Tips to Improve Deliverability Today: Insider Tip #1: Send to less technical audiences. This isn't my favorite advice to give. However, less technical audiences hit the report as spam button less. Insider Tip #2: Send to companies without Proofpoint, Cisco, and Mimecast MX Records. Prioritize companies invested in email security systems lower than ones who don't. Use LeadMagic to figure out what the company uses in the email finder. Insider Tip #3: Use LeadMagic's New Features on MX Detection & Valid_Catch_All Status to prioritize who to send to first. Prioritize valid (mail server checked) > catch_all. Use valid_catch_all status from LeadMagic which detects if the email has been found other ways. Prioritize Google or Microsoft email servers higher than Proofpoint, Cisco, and Mimecast email servers. This will lead to better delivery & reply rates. p.s. open tracking is not dead for email marketing, but that's not what I am talking about.

Understanding Losses in Solar Plants and Types of Solar Plant Losses, why it is important ? Solar power plants are designed to maximize energy production, but various losses can reduce their efficiency and overall energy yield. Understanding these losses is crucial for improving the performance, reliability, and financial viability of solar energy projects Solar plant losses can be categorized into the following types: 1. Irradiance Losses Shading Losses: Obstructions like buildings, trees, or other solar panels can block sunlight, reducing energy output. Soiling Losses: Accumulation of dirt, dust, or bird droppings on panels reduces the amount of sunlight reaching the solar cells. Atmospheric Losses: Variations in atmospheric conditions like clouds or haze can scatter or absorb sunlight, reducing irradiance. 2. Module-Level Losses Mismatch Losses: Differences in the performance of individual solar cells or modules (due to manufacturing variations or shading) lead to energy losses. Temperature Losses: High temperatures reduce the efficiency of photovoltaic (PV) cells, as their performance decreases with heat. Degradation Losses: Over time, solar panels degrade, producing less energy compared to their initial performance. 3. Inverter Losses Conversion Losses: Inverters convert DC power from solar panels to AC power for grid usage. Inefficiencies in this conversion process cause energy losses. Inverter Downtime: Malfunctions or maintenance-related downtime in inverters can lead to energy production losses. 4. Wiring and Electrical Losses Ohmic Losses: Resistance in electrical wiring causes a portion of the energy to dissipate as heat. Connection Losses: Poor-quality or loose electrical connections can lead to energy losses. Transformer Losses: Transformers used to step up or step down voltage introduce inefficiencies. 5. Operational Losses Maintenance Issues: Delayed or inadequate maintenance can lead to prolonged periods of reduced energy production. Monitoring Gaps: Without real-time monitoring, underperforming components may go unnoticed. 6. Environmental and External Factors Weather Variability: Seasonal and daily variations in sunlight availability affect overall energy production. Grid Curtailment: At times, grid operators may restrict the injection of power from solar plants, leading to energy losses. *Why Understanding Solar Plant Losses Is Important* 1. Maximizing Efficiency By identifying and addressing losses, operators can enhance the overall efficiency of the solar plant, ensuring optimal energy production. Improving Financial Returns 2. Reducing losses directly translates to higher energy output, improving revenue generation and return on investment. 3. Long-Term Reliability Regular monitoring and mitigation of losses ensure that solar plants operate reliably over their intended lifespan. 4. Environmental Impact Improved energy yield means more clean energy is produced, reducing dependence on fossil fuels.

PSA 1: Gmail did not kill Email Open Tracking. PSA 2: Email Open Tracking is dead for years. Let’s unpack this. Recently, a screenshot of Gmail blocking images has been circulating on LinkedIn, accompanied by alarmist claims that this spells the end for open tracking. But here’s the truth: Yes, email open tracking relies on images being loaded — typically, by detecting whether a tracking pixel (a tiny, transparent 1x1 pixel unique to each email recipient) has been downloaded. No, Gmail did not just start blocking these pixels. The screenshot actually shows a specific scenario: Gmail blocks images when it identifies an email as likely spam or a scam. Google does this to protect you from being tracked by malicious senders, and it’s been working this way for years. So, does this mean your open tracking is safe and sound? Not really. While Gmail hasn’t started blocking all your tracking pixels, other Email Service Providers already do. Open tracking is frequently blocked by B2B email server admins, often inaccurate due to security bots, and impacted by privacy settings and browser extensions. So, is Email Open Tracking useless? Well… maybe. If you’re using it as a high-level trend marker for opens, it might still offer some value. But if you’re relying on it for behavioral decision-making or key performance indicators (KPIs), especially in the B2B market, it’s largely ineffective. What should you do instead? Click tracking is a better option — although still not perfect, especially due to security bots in the B2B market. Ultimately, the best approach is to focus on the final goal of your email. Why are you sending it? If it’s to sell a product, track product purchases instead. More to come, so keep on analysing #MarketingCloud #SalesforceOhana and #MarketingChampions!

I've put my last 6 months building and selling AI Agents I've finally have "What to Use Framework" LLMs → You need fast, simple text generation or basic Q&A → Content doesn't require real-time or specialized data → Budget and complexity need to stay minimal → Use case: Customer FAQs, email templates, basic content creation RAG: → You need accurate answers from your company's knowledge base → Information changes frequently and must stay current → Domain expertise is critical but scope is well-defined → Use case: Employee handbooks, product documentation, compliance queries AI Agents → Tasks require multiple steps and decision-making → You need integration with existing tools and databases → Workflows involve reasoning, planning, and memory → Use case: Sales pipeline management, IT support tickets, data analysis Agentic AI → Multiple specialized functions must work together → Scale demands coordination across different systems → Real-time collaboration between AI capabilities is essential → Use case: Supply chain optimization, smart factory operations, financial trading My Take: Most companies jump straight to complex agentic systems when a simple RAG setup would solve 80% of their problems. Start simple, prove value, then scale complexity. Take a Crawl, Walk, Run approach with AI I've seen more AI projects fail from over-engineering than under-engineering. Match your architecture to your actual business complexity, not your ambitions. P.S. If you're looking for right solutions, DM me - I answer all valid DMs 👋 .

API performance issues can silently erode user experience, strain resources, and ultimately impact your bottom line. I've grappled with these challenges firsthand. Here are the critical pain points I've encountered, and the solutions that turned things around: 𝗦𝗹𝘂𝗴𝗴𝗶𝘀𝗵 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗧𝗶𝗺𝗲𝘀 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗨𝘀𝗲𝗿𝘀 𝗔𝘄𝗮𝘆 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Users abandoning applications due to frustratingly slow API responses. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Implementing a robust caching strategy. Redis for server-side caching and proper use of HTTP caching headers dramatically reduced response times. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗕𝗿𝗶𝗻𝗴𝗶𝗻𝗴 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 𝘁𝗼 𝗧𝗵𝗲𝗶𝗿 𝗞𝗻𝗲𝗲𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Complex queries causing significant lag and occasionally crashing our servers during peak loads. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Strategic indexing on frequently queried columns Rigorous query optimization using EXPLAIN Tackling the notorious N+1 query problem, especially in ORM usage 𝗕𝗮𝗻𝗱𝘄𝗶𝗱𝘁𝗵 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗳𝗿𝗼𝗺 𝗕𝗹𝗼𝗮𝘁𝗲𝗱 𝗣𝗮𝘆𝗹𝗼𝗮𝗱𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Large data transfers eating up bandwidth and slowing down mobile users. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Adopting more efficient serialization methods. While JSON is the go-to, MessagePack significantly reduced payload sizes without sacrificing usability. 𝗔𝗣𝗜 𝗘𝗻𝗱𝗽𝗼𝗶𝗻𝘁𝘀 𝗕𝘂𝗰𝗸𝗹𝗶𝗻𝗴 𝗨𝗻𝗱𝗲𝗿 𝗛𝗲𝗮𝘃𝘆 𝗟𝗼𝗮𝗱𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Critical endpoints becoming unresponsive during traffic spikes. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Implementing asynchronous processing for resource-intensive tasks Designing a more thoughtful pagination and filtering system to manage large datasets efficiently 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀 𝗙𝗹𝘆𝗶𝗻𝗴 𝗨𝗻𝗱𝗲𝗿 𝘁𝗵𝗲 𝗥𝗮𝗱𝗮𝗿 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Struggling to identify and address performance issues before they impact users. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Establishing a comprehensive monitoring and profiling system to catch and diagnose issues early. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗮𝘀 𝗨𝘀𝗲𝗿 𝗕𝗮𝘀𝗲 𝗚𝗿𝗼𝘄𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: What worked for thousands of users started to crumble with millions. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Implementing effective load balancing Optimizing network performance with techniques like content compression Upgrading to HTTP/2 for improved multiplexing and reduced latency By addressing these pain points head-on, we can significantly improve user satisfaction and reduce operational costs. What challenges have you faced with API performance? How did you overcome them? Gif Credit - Nelson Djalo

🗃️ Usability Pitfalls Of Dropdowns UX. Why it‘s usually a good idea to avoid drop-downs, and what to use instead ↓ We often assume that drop-downs are a great choice for user input in forms. After all, they make so much of designer’s work so effortless. They save space. They are reliable. They ensure accuracy. They guarantee consistency. They can handle an unlimited number of options. And: there can’t be any “invalid” user input. These benefits come at a high cost of poor user experience. Usability studies show again and again just how painful and frustrating dropdowns are, often causing more errors, more confusion and higher drop-off rates. Let’s see why. 🚫 Frequent UX Issues With Dropdowns: 1. Dropdowns hide options by default 2. Long lists of options are hard to navigate 3. Enable only selection, but not editing 4. Preselected option can’t be cancelled 5. Scrollable lists are fragile and error-prone. 6. Hierarchies are hard to map in a list 7. Take up a lot of space on mobile 8. Don’t support custom responses 9. Desired options can be far away 10. Desired options might be missing 11. Typically the slowest mode of interaction 12. Not searchable and can’t be sorted 13. Indentations are difficult to navigate 14. Categorization can create more confusion 15. Comparing options far from each other is hard 16. Long option text might be cut off on mobile 17. Inefficient for frequently accessed options 18. Confusing with too many nested choices 19. Lists disappear during zooming or scrolling 20. Users rarely know that they can type to jump ✅ Better Alternatives The simplest way to help people manage options is by exposing and grouping options directly. It might feel overwhelming at first, but often it produces a much more predictable and less confusing experience — even if options are broken down across separate steps in a user flow. 1. In forms, direct input usually performs better. 2. Expose options as radios, sliders, open text fields. 3. There is no harm in showing multiple rows of options. 4. Support typing and autocomplete filtering for lists. 5. For large menus, show all options on dedicated pages. 6. Always prefer the simplest input (stepper, checkbox). 7. Group and show available options in a series of steps. 8. More pages is better than more options on a page. 9. Automatically suggest options, but confirm with users. 10. Avoid dropdowns for country list, birthday, gender. 11. Always provide a way out to cancel radio/select choices. 12. Always avoid dropdowns with >10 and <5 options. As Luke Wroblewski famously noted once, dropdowns should be the UI of last resort. They can do a lot of things, but only few of them well. Next time you consider a dropdown, perhaps review what options we could use instead first — chances are high that any of them will work much better than a dropdown ever could. [more resources in the comments ↓]

Is “Operator Error” the Real Root Cause in Manufacturing? When a defect, breakdown, or safety incident happens on the shop floor, many investigations quickly settle on one conclusion: “operator error.” It’s simple, fast, and seems to explain everything. But in modern manufacturing, this label is often a symptom of deeper issues, not the real cause. Behind every so-called “human error” there is usually a chain of factors: 1.Inadequate or unclear work instructions 2.Poor workstation ergonomics or excessive fatigue 3.Gaps in training or skill development 4.Lack of mistake-proofing (Poka-Yoke) in process design 5.Equipment not calibrated, or preventive maintenance overdue 6.Material inconsistency, environment fluctuations, or unrealistic production targets Blaming people may give temporary closure but blocks true continuous improvement. A blame culture discourages operators from reporting near misses or improvement ideas — leading to recurring failures, higher costs, and low morale. The best manufacturing organizations take a systemic approach: • Use structured root-cause tools (5 Why, Fishbone/Ishikawa, FMEA) • Build strong SOPs and visual standards • Error-proof high-risk activities wherever possible • Create an open environment where operators, engineers, and leaders solve problems together When teams stop asking “Who messed up?” and start asking “What in our process allowed this to happen?”, quality, safety, and productivity all improve. #ManufacturingExcellence #RootCauseAnalysis #LeanManufacturing #Qualitycircle

Me today dealing with some EMC issues… 🧙♂️🪄🐉 EMC might feel like black magic sometimes, but it’s not all spells and wand-waving. Here’s the checklist I worked through today to troubleshoot: 1️⃣ 𝗕𝗲 𝘄𝗮𝗿𝘆 𝗼𝗳 𝘄𝗶𝗿𝗶𝗻𝗴 𝗮𝗰𝘁𝗶𝗻𝗴 𝗹𝗶𝗸𝗲 𝗮𝗻 𝗮𝗻𝘁𝗲𝗻𝗻𝗮. Anything with wiring can pick up noise and radiate it—even cables that seem unrelated to your core system. If the cable isn’t critical, remove it and retest to isolate the problem. If you can’t remove it, try adding a ferrite ring to the cable as close to the board as possible On the PCB, ferrite beads or chokes can also help suppress noise if you’ve got space to add them. 2️⃣ 𝗦𝗹𝗼𝘄 𝗱𝗼𝘄𝗻 𝘆𝗼𝘂𝗿 𝗠𝗢𝗦𝗙𝗘𝗧 𝗴𝗮𝘁𝗲 𝗱𝗿𝗶𝘃𝗲 𝘀𝗶𝗴𝗻𝗮𝗹𝘀. This is one of the top culprits for EMI on motor drive boards. Increasing both the turn-on and turn-off resistors for your MOSFET gate drive slows the rise and fall times of the signal, which directly cuts down on emissions. 3️⃣ 𝗥𝗲𝗱𝘂𝗰𝗲 𝗣𝗪𝗠 𝗳𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝗶𝗲𝘀. We had a 250kHz PWM signal driving a battery charger boost converter. The lab results weren’t happy, so we made some changes: - Dropped the frequency to 75kHz. - Increased the inductor value to match the new frequency. - Slowed down the MOSFET rise time (see point 2). This got us under the threshold—barely (around 2dB). We’ll reduce the charge current by about 15% to get a little more breathing room. 4️⃣ 𝗖𝗵𝗲𝗰𝗸 𝘆𝗼𝘂𝗿 𝗿𝗲𝘁𝘂𝗿𝗻 𝗽𝗮𝘁𝗵𝘀. High-current or high-frequency signals need clean return paths—no exceptions. In our case, we were stuck with a 2-layer PCB (budget constraints, of course), and the ground return path for the low-side MOSFET gate drive signal ended up being pretty big. I spotted a way to reduce the loop area by adding a via. We drilled a quick hole in the board and connected it with a wire. Not pretty, but it worked! The layout will need redoing, but this hack let us verify the solution at the test lab. If you haven’t already, check out 𝗔 𝗛𝗮𝗻𝗱𝗯𝗼𝗼𝗸 𝗼𝗳 𝗕𝗹𝗮𝗰𝗸 𝗠𝗮𝗴𝗶𝗰 𝗯𝘆 𝗛𝗼𝘄𝗮𝗿𝗱 𝗝𝗼𝗵𝗻𝘀𝗼𝗻. It’s the go-to resource for high speed digital electronics theory, and will let you analyse EMC issues way more effectively. What are your favorite resources for EMC troubleshooting? Drop them below—I’m always on the lookout for more tools/knowledge to add to my wizarding arsenal! 🪄 ------------- 🔔 Follow Ryan Dunwoody for more hardware chat 🚀 ♻️ Repost if you're an EMC wizard (or would like to be) 🧙♂️

The Context Engineering Framework is quickly becoming one of the most important tools for anyone building reliable LLM systems. Getting the model to respond is the easy part. The real challenge is: → What should the model know right now? → Where should that info come from? → How should it be structured, stored, retrieved, or compressed? That’s exactly what this framework solves. 🧠 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Context engineering = designing dynamic systems that deliver the right info, in the right structure, at the right time, so models can reason, retrieve, and respond effectively. This matters most in agents, copilots, retrieval-augmented pipelines, and anything with memory or tools. ⚙️ 𝗜𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Here’s the 3-layer system I use when designing end-to-end LLM workflows 👇 1️⃣ Context Retrieval & Generation → Prompt Engineering & Context Generation → External Knowledge Retrieval → Dynamic Context Assembly 2️⃣ Context Processing → Long Sequence Processing → Self-Refinement & Adaptation → Structured + Relational Information Integration 3️⃣ Context Management → Fundamental Constraints (tokens, latency, structure) → Memory Hierarchies & Storage Architectures → Context Compression & Trimming 🧱 All of this feeds into the Context Engine, which handles: → User Prompts → Retrieved Info → Available Tools → Long-Term Memory This is what gives your system continuity, task awareness, and reasoning depth across steps. ⚙️ Tools I would recommend: → LangGraph for orchestration + memory → Fireworks AI for fast, open-weight inference → LlamaIndex for modular retrieval → Redis & Vector DBs for scoped memory recall → Claude/Mistral for summarization and compression If your system is hallucinating, drifting, or missing the mark, it’s likely a context failure, not a prompt failure. 📌 Save this framework. 📩 Share it with your team before your next agent or RAG deployment. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for real-world GenAI system breakdowns, and subscribe to my Substack for deep dives and weekly insights: https://lnkd.in/dpBNr6Jg