Live sports involve myriad unknowns, but one thing is certain: the live video feed must play flawlessly, and simultaneously, for The post Here are the engineering lessons we learned when live-streaming sports to 32 million people appeared first on The New Stack .
For most of software’s history, building meant writing code. Not just understanding it, but producing it. The entry point was The post AI has lowered the barrier to building. Here’s what that means for engineers. appeared first on The New Stack .
Christian Schaller, a Red Hat engineering director and longtime GNOME and Fedora contributor, had a box of old family negatives The post His scanner wouldn’t work on Linux — so he let Claude study the Windows driver appeared first on The New Stack .
If you've sat through more than one architecture review, you've probably sat through a protracted Kotlin-versus-Java debate that goes nowhere. The team spends weeks comparing Kotlin's concise syntax to Java's maturity, runs a poll on developer preference, and produces a decision matrix nobody fully trusts. Then the meeting ends, and nothing changes, because nobody agreed on what problem they were actually trying to solve.
Why Every TLS Termination Is a Separate Migration Boundary A cloud application rarely has one TLS connection. A single API request can be decrypted and re-encrypted at a CDN, a web application firewall, an API gateway, a load balancer, a service-mesh proxy, an application runtime, and a managed cloud endpoint. Each termination negotiates a fresh session. Upgrading only the public edge can leave every downstream hop protected by classical key exchange.
Most teams working with Apache Flink think about stream processing: filter events, aggregate values, enrich records, join streams. This covers the majority of real-time data problems. But there is a specific class of problems that stream processing handles poorly: detecting meaningful sequences of events across time. Did this specific chain of things happen, in this order, within this time window?
Distributed systems rarely fail as a single unit. More often, one dependency slows down, one shard becomes unavailable, one region loses capacity, or one pool saturates while neighboring components remain healthy. The engineering challenge is not simply availability; it is preventing a local defect from becoming a system-wide event. Google’s SRE guidance describes cascading failure as positive feedback in which lost capacity shifts load onto surviving capacity until additional components fail.
While enterprise leaders overwhelmingly believe in the promise of synthetic data, adoption lags. As a result, synthetic data has great, The post Only 34% trust synthetic data’s referential integrity — and it’s holding adoption back appeared first on The New Stack .
Model capability is moving in that direction quickly. In its February – March 2026 Frontier Risk Report , METR estimated how long a software task could take before a frontier model’s chance of completing it successfully fell to 50%. Claude Opus 4.6 reached about 17.8 hours on cleaner tasks but only 6.4 hours on messier ones involving changing environments, constrained resources, expensive feedback, or mistakes that could not easily be undone.
How we used Partial Prefetching and a coding agent to improve navigation across v0 with Next.js 16.4.
The Prometheus I was responsible for had grown past 900 GB of time series data, and at that size it had every disease that comes with it. Queries timed out unless you narrowed them down to an hour. Cardinality kept growing, because teams added labels "for debugging" and then sometimes forgot to remove them. On top of that, we were shipping a copy of fractions of our metric data to an external SaaS vendor, just in case, and that bill grew every quarter.
Most gains in a supervised learning project don't come from switching algorithms; they come from what you feed the algorithm. Feature engineering is the process of transforming raw data into representations that make patterns easier for a model to learn. Done well, it routinely closes more of the accuracy gap than hyperparameter tuning or trying a fancier model.
Many companies are keen to let employees build their own applications using AI, even those with little or no programming The post Employees are vibe-coding their own apps. Cloudflare open-sources Void to give IT a handle on them appeared first on The New Stack .
The City Traffic Analogy Imagine a city's traffic system during rush hour. A minor accident blocks one lane on a major highway. Traffic slows down, but the road remains partially functional.
Microsoft is positioning the new Surface Laptop Ultra as an alternative to Apple’s MacBook Pros for creatives and developers — The post Running local AI models on Microsoft’s Surface Laptop Ultra: What we found appeared first on The New Stack .
Meta launched Muse Wonder on October 12, about three months after Muse Spark 1.1 opened Meta’s paid Model API. Meta The post Muse Wonder vs. Claude Fable 5.1: Meta’s new model came closer than I expected appeared first on The New Stack .
Every time an AI agent has to trawl Slack threads, Jira tickets and old pull requests to piece together something The post “Developers are paying frontier prices for amnesia”: Uvi wants to stop the agent relearning brain drain appeared first on The New Stack .
Hackers accessed roughly 1,900 GitHub accounts, then used them to slip credential-stealing workflows into more than 30,000 repositories. The malicious The post Hackers hijacked 1,900 GitHub accounts. Then they went after 30,000 repos. appeared first on The New Stack .
A pod stuck in Pending is one of the most misleading states in Kubernetes. Nothing looks wrong. There's no error banner, no red text screaming at you. The scheduler just quietly gave up finding a home for it and is waiting for the world to change. I've stared at that status more times than I'd like to admit, usually at the worst possible hour, usually while someone in a chat window is asking why traffic isn't going through.
Vercel is giving developers more options for running their backends on its platform. At its Ship conference in San Francisco The post Vercel adds always-on workers to run more of your backend appeared first on The New Stack .
All database administrators have experienced something like this at one point. A query that was running well suddenly starts to create problems. No error, failure, or warning appears on the dashboard, and the only noticeable sign is users complaining that the application is running slowly today. Now the DBA must determine which of the countless queries is causing the problem. The best solution here is the Query Store tool.
OpenAI is retiring four GPT-4 models from its API on January 14, 2027. The deprecation notice the company sent developers The post OpenAI promised developers six months’ notice. GPT-4o users got three. appeared first on The New Stack .
A Laravel application can feel instant on a developer laptop and painfully slow after a few months in production. The framework is rarely the real problem. More often, the database has grown, a relationship is being loaded inside a loop, a report pulls far more rows than anyone expected, or a third-party API has quietly become part of the request's critical path.
Meta has launched Muse Spark Open, a new 816B open model (with 42B active parameters) that the company argues is The post Meta ships Muse Spark Open, its answer to China’s best open-weight models appeared first on The New Stack .
Over the past year, I have sat in enough leadership conversations to notice a pattern. A CTO tells me their organization has "adopted AI" because most of the engineering team now has access to Copilot or ChatGPT, and everyone sat through a lunch-and-learn on prompting. A few months later, the excitement usually settles into something quieter: a handful of engineers using AI daily, one or two pilots that never left the sandbox, and no clear answer to what actually changed operationally.
Nvidia on Thursday announced a few new additions to its Nemotron family: Nemotron 3.5 Super VL, a 120B vision-language model for The post Nvidia squeezed a 74B agent model into 24GB so it can run on your laptop appeared first on The New Stack .
Somebody ships a differential privacy layer in front of the event store and leaves the dashboards sitting on top of it. The queries still return, the totals still look like totals, and the ticket gets closed. Nobody on the team computed the floor. Every private aggregate carries noise, and the noise is unlabeled. It sits in the same cell somebody reads on Monday morning before deciding whether a segment moved, and to the eye it looks like a small real change.
Every company is under pressure to become “an AI company.” Over the past several months, we’ve been undertaking that transformation The post What it takes to become an AI company: Four lessons from the top appeared first on The New Stack .
Anthropic’s done it again. On Tuesday, Opaque Systems announced a new agreement with Anthropic that “carries Opaque’s work forward.” Vague The post Anthropic’s been eyeing confidential AI. It just made its move with another acquihire. appeared first on The New Stack .
If you are a data engineer, you have probably come across this situation where you have to decide between two approaches for refreshing enterprise data. One approach focuses more on speed and less on compute, while the other focuses more on reliability and data correctness. Usually, when we talk about data refresh, the two options that come to mind are incremental load and full load, and the question becomes which one we should choose.
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