标签：#ci

Patreon is overhauling how creators get discovered, adding short-form Clips, topic-based communities, and a revamped recommendation system designed to give smaller creators more visibility.

“Runaway” AI, “rogue” agents, and “autonomous” actors—the current rhetoric would have you believe that AI agents are not only awake and aware, but angry at their creators. Prominent tech leaders such as Demis Hassabis, Dario Amodei, and Sam Altman push for regulation of these seemingly “superhuman” systems, while a separate faction, led by policy organizations…

The yeetcycling math resembles asteroid mining in reverse.

James Arthur shares why sync is the next frontier in frontend architecture. He explains how extending reactivity to the server with Electric and TanStack DB replaces imperative fetching with declarative data bindings. Learn how query-driven sync and local optimistic updates enable engineering leaders to build insanely fast, collaborative, and agentic applications using their existing stack. By James Arthur

Hackers are targeting internet-connected Siemens controllers used in water facilities around the United States.

InfoQ has opened enrollment for the InfoQ Certified AI-Assisted Engineering Program, a five-week online certification program for senior engineers and architects who already run a coding agent against production code daily, where the open questions have moved past prompting into what the agent is allowed to touch and what catches its mistakes before a human does. By Artenisa Chatziou

Artificial intelligence (AI) makes all code write-only,. It’s too dense to read, and tests define the behaviour and become the documentation. Code is also disposable; it becomes easier to rewrite than to debug. Humans can't review AI-generated code at scale. Intent decouples from implementation; developers should focus on creativity. By Ben Linders

Each day, an airline transports tens of thousands of passengers on hundreds of flights. Often these are not straightforward point-to-point routes, with passengers requiring multiple connections. The airline can consider potentially hundreds of variables to price each of these journeys: demand, season, time of day, current events, global markets, and competitor airline activity to name…

From granola to guacamole, a wide variety of foods have been recalled lately over salmonella risks. Experts say common-sense precautions go a long way.

A year ago, the debate between “vibe coding” and “prompt-driven development” felt like the right lens for using AI in software. Today it’s outdated. The real frontier is no longer how to phrase a request, but how to build an environment that lets an AI agent explore, modify, and verify code safely. The shift from conversational prompts to structured development harnesses changes what it means to ship reliable software. Below are four concrete ways this evolution is reshaping daily work. From Prompt to Harness A finely crafted prompt can still produce a working feature, but it won’t prevent an agent from inventing APIs, skipping tests, or misreading business rules. The new bottleneck is the harness: Git for traceability, tests for feedback, linting for style, and explicit rules for constraints. When these elements are in place, even a short instruction like “fix the bug” can trigger a reliable, auditable workflow. Code is Abundant; Good Software is Rare Generating hundreds of lines of code costs almost nothing, but verifying that those lines align with product history, security policies, and hidden constraints is still manual. The developer’s scarcest resource is now judgment: deciding what to automate, what to document, and when to override the agent’s plausible but wrong solution. Documentation is Now for Machines README files and ADRs used to serve only humans. Today they also feed the agent’s context. A well-maintained AGENTS.md , coherent tests, and executable examples let an AI act with minimal prompting while remaining aligned with the team’s conventions. The repository becomes a self-explaining environment. Supervision is the Next Skill Single-agent workflows are giving way to multi-agent orchestration. Frontend, API, test analysis, and incident triage agents must coordinate without stepping on each other. Observability, conflict detection, and rollback mechanisms replace prompt tuning as the critical layer of control. The center of gravity has moved from “ho

Without a rescue, NASA's Swift Observatory is expected to reenter the atmosphere later this year.

When we write: const result = add ( 10 , 20 ); it feels like the computer simply "runs the code." But the CPU doesn't understand JavaScript. There are several layers between the code we write and the hardware actually executing instructions. That's what I wanted to understand first. From JavaScript to the CPU In Node.js, JavaScript is handled by V8 , the JavaScript engine. A simplified view looks like this: JavaScript ↓ V8 ↓ Bytecode ↓ JIT compilation ↓ Machine instructions ↓ CPU V8 doesn't simply "interpret JavaScript" or "compile JavaScript" once and forget about it. It can start with bytecode and progressively compile frequently executed ("hot") code into more optimized machine code. Eventually, the CPU is executing instructions that operate at a much lower level than the JavaScript we originally wrote. What does the CPU actually do? At its core, a CPU repeatedly executes instructions. A simplified mental model is: Fetch → Decode → Execute → Repeat The CPU has several important pieces involved in this process. Registers are tiny, extremely fast storage locations inside the CPU. They're used to hold values the CPU is actively working with. The ALU (Arithmetic Logic Unit) performs many arithmetic and logical operations. The Program Counter (PC) keeps track of where the next instruction comes from. And the CPU runs according to a clock, measured in GHz. A 3 GHz CPU has roughly 3 billion clock cycles per second, but that does not mean it executes 3 billion instructions per second. Different instructions and architectures have different costs. Modern CPUs are far more sophisticated than this simplified model, using pipelining, multiple execution units, branch prediction, out-of-order execution, and more. But the basic model is enough to start reasoning about performance. The CPU doesn't get everything from RAM One of the most important things I learned here is that where data lives matters . A simplified hierarchy looks like: Registers ↓ L1 Cache ↓ L2 Cache ↓ L3 Cache

Some fortnights the complaints scatter across a dozen topics; this one they clustered around two. Across the forums where paying AI users gather to compare notes, two things happened in the same window that left the same taste: the meter moved, and the mark landed. In both cases, something about the product changed without a clear announcement, and the users found out by running into it. Quotes sourced from: the OpenAI Developer Community and Hacker News. Every quote below was opened at its permalink and copied verbatim; each is listed with its handle, platform and date in the Sources section. We quote experiences, not verdicts — a forum post is one person’s felt reality, and we have framed it as exactly that. The $200 plan that lasted two days The loudest note this fortnight was the Codex usage drain. On the OpenAI Developer Community, a Pro subscriber posting as jjjnoronha laid out the damage plainly on 4 August: “I’m on the $200 Pro plan, and my weekly limit was completely exhausted in 2 days of serious development work. This is simply not viable.” He was refactoring a Rust backend with two agents running — one coding, one checking code quality — and described leaving Ultra mode on for three or four hours, which alone consumed 30% of his weekly allowance. But the complaint was not that the work was expensive; it was that the plan was sold with language suggesting far more headroom than what the cap delivered. “When someone pays for the highest consumer tier,” he wrote, “the expectation is capacity for sustained, professional work — not being locked out for the rest of the week after two days.” Others described something stranger. A user posting as kalani reported going to bed with 70% remaining after a Saturday reset, then waking up to find 0%. “Not like I had something running overnight, and yet all usage blocked until this next Saturday,” they wrote on 4 August. “Hard to trust a $200 / month service that is completely unpredictable about whether you’ll even be

I've been working with software for a few years now, and I've noticed something uncomfortable. I can build things. I can work with React, Node.js, databases, APIs, cloud services, and all the usual stuff that comes with being a software engineer. But if I stop and ask: "What is the computer actually doing underneath all of this?" My mental model gets surprisingly fuzzy. I know the concepts. I've used them. I've probably explained some of them before. But knowing how to use something and understanding what is actually happening underneath it are two very different things. And I want to fix that. Why I'm writing this This isn't a course, and I'm not writing this as an expert teaching computer science. These are essentially my notes while rebuilding my computer fundamentals from the ground up . I'm trying to connect the things I use every day as a software engineer with what is actually happening inside the machine. Instead of learning concepts because they're on a traditional CS syllabus, I'm starting with a question: What do I actually need to understand to reason about a production system? For me, that means being able to look at a system and understand what's happening underneath my code. Why is something slow? Where is the bottleneck? What happens when something fails? Why does adding more memory help in one situation but not another? What actually happens when two things execute concurrently? Why does a database query become slow? What happens to a simple HTTP request between two machines? I don't want to just know the answer. I want the mental model that lets me reason about the answer . The path I'm taking I'm roughly following the layers that a typical request passes through: CPU → Memory → OS → Network → Storage → Concurrency → Distributed Systems So the series will go through: 1. What Actually Happens When Code Runs Starting from the bottom: CPU mental model, memory hierarchy, and number representation . 2. Operating Systems Then moving up into processes, th

This is a writeup of building a required CI gate for degraded-network behavior. The system under test is a robotics fleet substrate, but the finding applies to anyone shaping networks in CI. Ganglion exists to reach robots on networks nobody controls. Warehouse Wi-Fi, carrier CGNAT, a hospital VLAN, a customer firewall that was configured once in 2019 and has not been touched since. Until this week that claim was a sentence on a website. CI ran on clean loopback, everything was green, and the failure modes that actually matter in the field were the exact ones the test suite could never produce. That is now a required gate. Every push to main runs the full deploy, invoke and verify round trip over the relay against five shaped network profiles, and all five have to pass before anything merges. I build Ganglion, so treat the enthusiasm accordingly. The part worth your time is not that it went green. It is what I got wrong on the way there. The five profiles clean : baseline, no shaping. If this one fails, something else is broken. lossy : packet loss with light reordering. high-latency : 250ms round trip. asymmetric : plentiful downlink, starved uplink. This is the one nobody tests and the one teleop actually dies on, because control acknowledgements go the starved direction. nat-relay : endpoints with no route to each other at all, forcing hole punching to fail and relay fallback to carry the session. The last two are the ones I care about. Loss and latency are what people imagine a bad network is. Asymmetry and no-direct-route are what a bad network usually is. What I got wrong The original design assumed you can pin netem's seed and get a repeatable lossy run. Two profiles: a pinned-seed one that gates the build, and a nastier randomized one that runs nightly and is allowed to fail. You cannot pin netem's seed. Its loss and jitter draw from the kernel RNG and there is no seed parameter to set. A "deterministic lossy netem profile" is not a thing that exists. This m

During a recent visit to Generalist AI, I watched a robotic arm improvise and use a banana as a tool.

You picked a free model because the answers looked good. Good answers are not an endpoint. An endpoint is the model plus the server plus the network. Demos pass. Pipelines stall. The model was rarely the problem. So why do we keep benchmarking only the model? Because it is easy. You paste a prompt. You read the output. You declare a winner. The server never gets a vote. This post is a reproducible benchmark. It measures the pair, not the model. Run it before you wire any free endpoint into CI. The Pair, Not the Model Most evaluations compare answers. You paste a prompt. You judge the output. You pick a winner. That measures the model. It ignores the server. Free model access usually means a shared endpoint. A free server option means shared tenancy. Other users share the CPU, memory, and network. Your latency is their latency. Your timeout is their timeout. Here is the scenario I keep seeing. A team evaluates a free model on Friday. The answers look great. They wire it into CI on Monday. By Wednesday, the pipeline is red. The model did not change. The server did. A neighbor started a batch job. Now every request queues behind it. I applied the same harness to MonkeyCode's free model access and their free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I did not trust the demo. I built a harness instead. The Harness A benchmark needs three things. A fixed prompt set. A concurrency ladder. A pass/fail table. Here is the harness I use. #!/usr/bin/env python3 """ Benchmark a model endpoint as a pair: model + server. """ import argparse import asyncio import json import statistics import time import httpx PROMPTS = [ " Say OK. " , " Classify this log line: ERROR disk full " , " Return one word: is 429 a retryable status? " , ] async def fire ( client , url , payload , sem , timeout = 30 ): async with sem : start = time . perf_counter () try : r = await client . post ( url , json = payload , timeout = timeout ) return r . sta

The vaccine reportedly thwarted the cancer's return and spread.

Anthropic announced last week it would include invisible watermarks in AI-generated content to comply with new EU rules. Within hours, overrides were being touted online.

Star, moving at 8% the speed of light, may let us measure the black hole's rotation.