Implementing a Queue with Array in 73 Days

High load with high errors isn’t performance — it’s noise. In performance testing, error rate is everything. Even a tiny 0.01% failure can distort your throughput and response times. If the script isn’t stable, the results lie. My learnings to keep error % low (JMeter / LR / k6): - Handle correlations properly - Refresh tokens & sessions - Use enough test data - Add realistic pacing - Validate responses smartly - Add light retry logic What’s your trick to keep errors near zero? 👇

𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐢�� 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞.   Agents rarely fail from lack of reasoning - they fail from operating without a world model. A Knowledge Base gives an agent structured access to the information environment it acts within.   In our latest How-To Mikołaj (Nick) walks through how Delphi agents ground their decisions in company-specific data - transforming static documents into a dynamic context layer.   🎥 Watch below!

#Cpp #Static_cast #vptr Static_cast is a method to convert one data type into another. It can be used to convert int into double, a Derived Object into a Base class Pointer (Upcasting), or even a Base class Pointer into a Derived Class Pointer (Downcasting). In the code below, there is another type conversion from a Base Class Pointer into a Derived Class Pointer (which gives an unexpected behaviour). I need to clarify that in the Downcasting scenario the Base Class Pointer points into a Derived Object. But in the unexpected behaviour, the Base Class Pointer points to a Base Object. It is still a mistery for me to know why 'wrongCast->speak();' gives me 'Base' as output.

Two function calls are made, one correct and one faulty: minimum(5, 3) minimum(4) The second one should give a compile time error. But, there another choice! 0 can be a good default value for the second argument: fn minimize(a, b = 0) Now the code works! ...but maybe not as intended. When you declare a function, you do not know the intent of the caller if you use default or optional values. So I usually recommend avoiding them (except for temporary restructuring of code). Two things you can do: Make a new function with a clear name: fn minimizeWithDefault(a) Declare a default object that's publicly accessible: export defaultConfigurationOptions = {...} The second one works better for more larger data structures like configurations.

While revisiting some old notebook scribbles, I found myself thinking about how we approach 𝐃𝐢𝐟𝐟𝐢𝐜𝐮𝐥𝐭 𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐫𝐫𝐚𝐲 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 like Range Queries — and ended up with a 𝙎𝙞𝙢𝙥𝙡𝙚 𝙍𝙚𝙖𝙡𝙞𝙯𝙖𝙩𝙞𝙤𝙣. There are three techniques that define how we think about these problems, each built for a different kind of challenge: ➣ Difference Arrays • Two steps of pre-computation. • Work best when there are multiple range updates but only a final result to care about. • They’re not meant for direct queries — you just keep adjusting and only later rebuild the full picture. ➣Prefix Sums • One step of precomputation. • Everything’s ready to use right after. • Perfect when the data is static and quick range queries matter more than dynamic updates. ➣ Segment Trees • The more intricate structure. • Built for situations where both updates and queries happen frequently. • A balance between flexibility and efficiency — though with a bit more complexity. All the three approaches have their own merits and should be used according to the following requirements : Many updates with a single query → Difference Array Many queries with static data → Prefix Sum Both frequent → Segment Tree( tradeoff being the high complexity and memory ) PS: These observations are based completely upon the variety of questions encountered , if any edge case was missed by me do comment and we can further discuss and deep dive :)

Introducing the Event Log Browser in Ingenuity 7 🙌 Say goodbye to digging through complex event data! The Event Log Browser makes it simple to explore, filter, and understand what’s really happening in your operations. Quickly spot key events, uncover insights, and turn raw logs into clear, actionable information, all in one place. Another step forward in making data work for engineers, not the other way around.

This problem helps to understand how to detect loops in a Linked List — a key concept in data structures. 1.We need to determine if a given linked list contains a cycle. 2.A cycle occurs when a node’s next pointer connects back to a previous node in the list, creating an infinite loop. 3. I used a HashSet to keep track of all the nodes I’ve already visited. 4. I started traversing the linked list from the head node. 5. If the node is already in the HashSet, it means we’ve visited it before — hence, a cycle exists. 6. Otherwise, I added the node to the HashSet and moved to the next node. If the traversal reaches null (end of the list), it means there’s no cycle.

When you’re managing hundreds of tables, “Where’s the lag?” shouldn’t be a guessing game. But until now, finding a slow or failing table often meant tab-hopping, filtering logs, and piecing things together manually. That changes today. Artie’s new Pipeline gives you a single, unified view of all replicated tables — with instant search, filters, and live status metadata. Slice by schema, filter by status, or jump straight to the table you care about — all from one clean interface. Why it matters: ✅ One place to see everything that’s syncing ✅ Live health and status indicators ✅ Faster troubleshooting and impact analysis ✅ Huge quality-of-life upgrade for teams running large-scale replication Less scavenger hunt. More control. 📖 Full changelog in the comments.

No network – no problem. 🚫📡 Testo's stand-alone data loggers allow you to monitor temperature and humidity without any infrastructure – reliably, in compliance with the law and wherever large systems are not effective. Ideal for storage, transport and individual points along the cold chain – compact, robust, efficient. Know more: https://lnkd.in/ewJ_Vt8Y

No network – no problem. 🚫📡 Testo's stand-alone data loggers allow you to monitor temperature and humidity without any infrastructure – reliably, in compliance with the law, and wherever large systems are not effective. Ideal for storage, transport, and individual points along the cold chain – compact, robust, efficient. Know more: https://lnkd.in/dQ_HWmG8