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27 Aug 2026, 19:00 by Sudarshan Bhagvan Thakur
Let me describe a workflow that exists in thousands of engineering organizations right now.
Somebody sets up a cron job. It runs terraform plan against production every few hours. When the plan output isn't empty, it fires a Slack notification. The team calls this "drift detection."
27 Aug 2026, 18:00 by Olamilekan Lamidi
In one fraud-review scenario I worked through, an AI assistant looked reliable during demos because it explained risk signals clearly and gave reviewers useful summaries. The issue appeared when the system met a legitimate high-value transaction with a new payee, an older device record, and incomplete context from the data source. The assistant did not fail loudly. It sounded confident while routing the case the wrong way. The model was not the only problem. The engineering around the model did not yet make trust visible enough.
A normal software feature can usually be tested against predictable rules. If the input is the same, the output should usually be the same. AI systems, especially generative ones, are different: they can behave well in a demo and still fail when they meet messy user input, stale data, vague instructions, or unexpected edge cases.
27 Aug 2026, 17:00 by Akhil Madineni
A Temporal Workflow that appears stuck is rarely “stuck” in the conventional process sense. Temporal persists Workflow state through Event History and resumes execution through replay, so an open execution can remain healthy while waiting for a timer, Signal, Activity, or external condition. The operational problem is therefore not simply lack of completion; it is lack of expected progress.
Effective diagnosis starts by establishing what event should have happened next, why it did not happen, and whether remediation can preserve the Workflow’s business invariants. Temporal’s history model makes that analysis unusually tractable because commands, task transitions, Activity attempts, failures, timers, and external interactions are durably represented as Events.
27 Aug 2026, 16:00 by Igboanugo David Ugochukwu
Learn how attackers enumerated Salesforce Experience Cloud and ServiceNow portals — and how defenders can detect and prevent the same abuse.
When Guest Access Becomes an Attack Surface
Modern enterprise portals increasingly expose APIs to unauthenticated users. The problem is not necessarily that those APIs are vulnerable. The problem is that the anonymous identity behind them may have been granted more access than the organization realizes.
27 Aug 2026, 15:00 by Akhil Madineni
Convolutional neural network workloads rarely fail because the forward pass is mathematically difficult. They fail because modern training and inference pipelines are distributed systems: datasets arrive late, GPU workers disappear, validation jobs stall, model registration breaks halfway through, and long-running executions need to resume without corrupting state.
Temporal is designed for exactly that class of problem. A Temporal Workflow Execution is durable, reliable, and scalable, and Temporal defines durable execution as the ability of a workflow to maintain state and progress through crashes or outages. That makes it a strong fit for CNN pipelines whose control plane must survive for hours, days, or even longer while the actual tensor computation runs elsewhere.
27 Aug 2026, 14:00 by Akmal Chaudhri
In a chapter of The SingleStore Cookbook, there is a complete sentiment analysis pipeline using Rust compiled to WebAssembly and loaded directly into SingleStore via its Code Engine. The result was clean: one CLI command to deploy, sentiment scoring running inside the database engine alongside the data and a full stock-price-plus-headlines analytical pipeline built on top of it.
Can we do the same thing in Neo4j?
27 Aug 2026, 13:00 by Otavio Santana
Software engineers often view soft skills as secondary, considering them relevant mainly for managers, recruiters, or those frequently in meetings, rather than essential for technical roles. However, as your career advances, this perspective becomes harder to maintain. Greater impact requires you to explain ideas, influence decisions, manage disagreements, build trust, exchange feedback, and communicate with those outside your technical context. While leadership roles highlight this need, these skills are integral to effective software engineering well before any formal leadership title.
Practicing soft skills alone is challenging. While you can develop technical abilities like Java, databases, or system design independently, communication and influence require real interaction. Open source provides this environment, offering opportunities for discussions, code reviews, proposals, community meetings, documentation, conferences, and collaboration across companies, cultures, and experience levels. This article explores how open source can serve as a practical training ground for the communication and interpersonal skills essential for technical leaders.
27 Aug 2026, 12:00 by Stefan Wolpers
TL; DR: The AI Delegation Lifecycle
Your team ships AI outputs that nobody fully trusts; you needed to be quick, and “dirty” tagged along. Too bad that ungoverned automation becomes AI debt when a stakeholder asks who owns it. But do not despair: The AI Delegation Lifecycle turns skills you already use into six decisions you can apply this week to govern that work and prove it audit-ready and suited for agent harnesses.
Popular Traps When Creating AI Output
All teams can show you what their AI produces: status reports sent without anyone touching them, release notes drafted in seconds, a customer-facing FAQ that updates itself. Far fewer teams can answer the question posed by a prospective customer or by compliance: how do you govern your own internal AI use? Often, in the frenzy past to make of AI, nobody decided.
26 Aug 2026, 19:00 by Jubin Soni, FBCS
A step-by-step guide to grounding a LangGraph agent in Microsoft Foundry IQ agentic retrieval — without rebuilding your RAG pipeline.
Why This Integration Is Worth Doing
If you build agents on LangGraph and your enterprise content lives in Azure, you have probably written the same code twice: a chunker, an embedding job, a vector store, a retriever, a reranker, and a permissions filter bolted on at the end. Every new agent gets its own copy. Every copy drifts.
26 Aug 2026, 18:00 by Daniel Oh
Goose — the open-source, Rust-based AI developer agent from Block (donated to the Linux Foundation’s Agentic AI Foundation) — interacts natively with your local development environment via the Model Context Protocol (MCP). In this tutorial, you will learn how to build stateless, cloud-native Java microservices using Quarkus LangChain4j and expose them as governed MCP extensions that Goose can discover and run seamlessly.
Autonomous AI coding agents like Goose go far beyond simple code autocompletion. Built in Rust for speed and portability, Goose runs on your local machine, inspects files, runs terminal commands, and uses tools over MCP to automate complex engineering tasks.
26 Aug 2026, 17:00 by Pavan Belagatti
The term software factory is getting a lot of attention right now, and for a good reason. AI coding assistants can generate code much faster than before. But faster coding alone does not mean faster, safer delivery. In many teams, it simply moves the bottleneck to review, testing, deployment, and operations.
A software factory is a way to organize the entire software development life cycle as one connected, repeatable system. Think of a car manufacturing assembly line. Each station has a clear job, work moves forward in a predictable order, quality checks happen at the right moments, and the finished product is inspected before it leaves the factory.
26 Aug 2026, 16:00 by Alex Vakulov
Headless CMS architecture solved a real development problem. It separated content from presentation, gave frontend teams control over frameworks and deployment, and made structured content available to websites, apps, and other channels through APIs.
The friction often appears later, when content operations become more complex. Routine publishing changes can still depend on engineering, especially when editors need more control over layout, preview, or page composition.
26 Aug 2026, 15:00 by Hawk Chen
Java Meets the Spreadsheet
Apache POI has been the standard Java library for reading and writing Excel files for over twenty years. It handles the majority of everyday spreadsheet tasks well. But a growing category of real-world Excel files now contains formulas that POI's evaluator cannot execute at all.
This is one of several situations Java developers hit when working with spreadsheets that are not obvious until you are already in production. Business users produce, share, and reason about data in spreadsheets. Finance teams model in Excel. Operations teams track inventory in Excel. Analysts hand deliverables to engineering as .xlsx files. Java applications end up interacting with all of it: back-office services accept Excel uploads, pricing engines run calculations that were originally authored in a workbook, reporting tools export data in a format the recipient can open in Excel without formatting problems.
26 Aug 2026, 14:00 by Stefan Wolpers
TL; DR: Tokenmaxxing or Reinventing the Wheel
Your organization counts AI tokens, seats, and pilots, but can anyone name a single decision those numbers actually changed? Tokenmaxxing is only the symptom; five old Agile Laws explain the cause, and each one comes with a test you can run this week. There is no need to reinvent the wheel with AI transformations and learn the hard way what the veterans of other transformations already figured out.
Thesis: Tokenmaxxing is the vanity metric of pushing low-value work through an AI tool solely to inflate usage metrics. Tokenmaxxing emerged in 2026, when large technology companies began ranking employees by token consumption on internal leaderboards. The behavior is rational for the individual but useless for the organization because tokens measure input rather than outcomes. The five Agile Laws in this article explain why organizations keep making this mistake and what to measure instead.
26 Aug 2026, 13:00 by Gunter Rotsaert
In this blog, you will take a closer look at the different exchange types that can be used in RabbitMQ. All are demonstrated by means of examples in a Spring Boot application. Enjoy!
Introduction
In the previous blog, you learned the basic concepts of RabbitMQ and how to use it in a Spring Boot application. However, you only scratched the surface of it, so now it is time to dig a bit deeper into the different exchange types.
26 Aug 2026, 12:00 by Chris Ward
Open source projects dominated by a single vendor are a hallmark of "open source in name only." Rather than filling the traditional role of open source fostering innovation and decision-making from a diverse community, "open source in name only" projects are often used as marketing tools for proprietary platforms. These projects are also seen as riskier than community-driven projects because a single vendor is more apt to abruptly terminate long-term support, restrict contributions, or switch from an open-source license to a more restrictive one (forcing some previous contributors to pay for the project they helped build).
In these projects, critics claim that investments are often lopsided and heavily skewed toward onboarding, marketing, and brand-related support. As a result, technical contributions are frequently less developed, opaque, undocumented, or lacking in real substance, often manifesting merely as a superficial "ease of entry and onboarding." Because of these underlying gaps in documentation and codebase depth, developers are routinely forced to reverse-engineer functionality simply to get the tools to work correctly.
25 Aug 2026, 19:00 by Aniket Abhishek Soni
Most Docker content targets web developers shipping stateless services. However, data engineers, who represent a huge and growing population of Dockers users, are mostly left to figure things out alone, and it shows. The get pipelines that pass locally, but explode on clusters. They pit notebook-only development against expensive cloud workspaces, and more. This article applies six years of production data platform experience in financial services and healthcare to a question nobody answers well: How to you make a laptop behave like a lakehouse?
A Familiar Routine
If you build data pipelines for a living, you've lived this story. Your PySpark job runs perfectly in a cloud notebook. You productionize it, push it through CI, deploy it to the cluster, and it fails. A dependency mismatch. A different Spark minor version. A Delta Lake protocol feature your local wheel doesn't know about. A timezone default nobody set.
25 Aug 2026, 18:00 by Otavio Santana
Software engineers often feel most comfortable with hard skills, such as writing code, studying frameworks, experimenting with databases, reviewing architectures, or building side projects. As engineers advance into leadership roles, technical depth becomes even more critical. Technical leaders must guide decisions that impact other engineers, teams, and sometimes entire platforms. To do this effectively, strong communication and influence are essential, but they must be grounded in sound technical judgment. Without sufficient technical depth, leadership can steer teams in the wrong direction.
Open source is especially valuable in this context. Mature projects expose engineers to challenges rarely found in tutorials or new applications, such as software evolution, legacy modernization, backward compatibility, architectural trade-offs, design decisions, documentation, code reviews, and the internals of widely used frameworks. Open source also offers opportunities to learn from experienced engineers worldwide and to observe how complex technical decisions are made. This article will explore how open source helps Software Engineers develop the hard skills needed to become both better developers and stronger technical leaders.
25 Aug 2026, 17:00 by Anton Kundenko
Columnar engines naturally organize computation around vectors to make effective use of single instruction, multiple data (SIMD) instructions. This makes vectors first-class citizens in such engines. The difficult design question appears when an engine's internal application programming interface (API) must be exposed to users: where should programming happen?
A native C API is sufficient for embedding, and many engines stop there. Building a complete analytical database, however, requires a full-featured language for programming on top of the engine.
25 Aug 2026, 16:00 by Mateen Ali Anjum
Head-based sampling makes a decision the instant a trace starts, before anyone knows whether that trace is boring or the one you will spend Friday night chasing. That is the wrong time to decide. At that point the request has not failed yet, and the slow dependency call that will define it is still milliseconds away. Head sampling commits before any of that is visible, so it discards a random slice of exactly the traces you will later wish you had kept.
Tail-based sampling flips the order. It buffers the spans of a trace until the trace is complete, then decides once the errors and timing are actually on the record. The OpenTelemetry Collector ships a tail_sampling processor that does this well. It also has one operational trap that most tutorials skip, and getting it wrong quietly corrupts every decision the processor makes. This walks through a policy set that keeps the traces worth keeping, and then through the trap.
25 Aug 2026, 15:00 by Rupesh Dabbir
Let's be honest about what happens when you give an entire engineering team AI coding assistants.
You look at the sprint board, and tickets are moving to "In Review" faster than ever. Your developers are happy. They are writing boilerplate in seconds and generating entire component structures before their morning coffee gets cold. If you measure productivity by the sheer volume of code generated, your team has successfully turned into a factory.
25 Aug 2026, 14:00 by Nikita Vasilev
Ever since Swift Concurrency was introduced, its main mission has been clear: keep memory safe without making us write callback hell. But if we’re being honest, context switching-specifically thread hopping-has always been a bit of a head-scratcher.
How many times have you marked an async function as nonisolated on a @MainActor class, only to watch it instantly jump off to the cooperative global pool for no obvious reason?
25 Aug 2026, 13:00 by Deepak Gupta
This project creates a daily digest for sellers in an enterprise system. Each seller handles accounts at a set of companies and needs to know when something happens at one of them: a product launch, leadership change, new contract, or funding round. That news is often an opening for the next conversation. The pipeline reads the day's incoming news and sends each seller a short email with the handful of articles worth their time about the companies they cover. Every item in that email carries a thumbs-up and a thumbs-down button.
At launch, we set a simple quality target. From historical user behavior on other surfaces, we knew that about 9% of served items would receive a user vote. Among those votes, we wanted 70% to be thumbs up. We expected that approval rate to show whether the pipeline was improving.
25 Aug 2026, 12:00 by Asim Rais Siddiqui
For the past several years, technical debt was something that was easy to identify. It came in the form of outdated frameworks, missing documentation, messy databases, etc. It was something companies racked up by moving too fast, skipping the best course of action, and patching up old systems instead of improving them.
But now, a new kind of technical debt is fast emerging.
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