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ExternalSoftware Engineering
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I used LLMs to rewrite meta descriptions for 1,600 articles — honest results

Meta descriptions are the most underrated SEO element on content-heavy sites. They don't affect rankings directly, but they determine whether someone clicks your result in Google. A bad meta description on a well-ranked article is traffic you're leaving on the table. I had 1,600+ cybersecurity articles. About 40% had no meta description at all. Another 30% had descriptions that were either truncated, keyword-stuffed, or copy-pasted from the first paragraph (which almost never makes a good descri

aiseowebdev
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ExternalSoftware Engineering
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Per-user cost attribution for your AI APP

You ship your AI feature. It works. A week later your OpenAI bill is $400 and you have no idea which of your users caused which $0.05. This is the single most underrated metric in production LLM apps — cost per end-user — and it's surprisingly easy to instrument if you know what to do. Here are the three approaches I've found work in practice, ranked by setup time. Works for Express, Next.js Route Handlers, Fastify — anything that has a single OpenAI or Anthropic client instance. import OpenAI f

aisaasopensource
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ExternalSoftware Engineering
DEV Community

I Built a Safari Extension That Shows When Your YouTube Video Ends

Ever put on a YouTube video and wondered if you'll finish it before you have to leave, go to sleep, or get back to work? I had that thought one too many times, so I built a small Safari extension to solve it. It adds the end time directly inside YouTube's native time bubble: 0:20 / 7:23 · ends 11:44pm That's it. Simple, but surprisingly useful. The core logic is about three lines: const remainingSec = (video.duration - video.currentTime) / video.playbackRate; const endDate = new Date(Date.now()

safarijavascriptwebextensions
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ExternalSoftware Engineering
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What It Actually Takes to Audit Aurora PostgreSQL on AWS

Most operational infrastructure starts this way: a requirement appears before the architecture does. One day the team needed a database audit solution. Not in a planning doc — someone asked, and I had to build something. I'd owned the Oracle audit pipeline already, so I knew what the destination looked like. The question was what it would take to get there on Aurora PostgreSQL and AWS. The short answer: more than you'd expect. The longer answer is this article. The scope here is specific: indivi

awspostgressecurity
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ExternalSoftware Engineering
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I instrumented 95 DataLoaders in a production GraphQL API — here's what I found

DataLoader is the standard fix for GraphQL's N+1 query problem. Batch your database calls per request, cache within the request lifecycle, done. But once DataLoader is in production, you're flying blind. Which loaders are actually called per request? Is your cache hit rate 15% or 60%? Should your batch size be 10 or 50? APM tools tell you resolver latency, but they don't understand DataLoader batching. I built dataloader-ai to answer those questions. Then I tested it for real by instrumenting 95

graphqlnodedataloader
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ExternalSoftware Engineering
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Making a Local Events API Discoverable to Developers and AI Assistants

Local cultural data should not only live as web pages. Here are the main public entry points. https://dondego.es/openapi.json https://dondego.es/llms.txt https://dondego.es/.well-known/api-catalog https://dondego.es/api-catalog.json https://dondego.es/.well-known/agent-skills/index.json https://documenter.getpostman.com/view/55094507/2sBXwjvZRZ https://github.com/revanbcn/DondeGo-API https://github.com/revanbcn/DondeGo-API/tree/main/examples https://github.com/revanbcn/DondeGo-API/tree/main/post

apiaiwebdev
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ExternalSoftware Engineering
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Automate LLM Red Team Campaigns with PyRIT

If you're still testing LLM guardrails by hand — retyping variations in a chat tab, logging results in a notebook, eyeballing responses — you're leaving throughput on the table. PyRIT fixes that. Microsoft's Python Risk Identification Tool is an open-source framework for running structured attack campaigns against LLM systems. The AI Red Team that built it ran it against 100+ internal operations: Phi-3, Copilot, the full stack. It chains targets, converters, scorers, and orchestrators into autom

securitypythonai
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ExternalSoftware Engineering
DEV Community

The Rise of the Forward Deployed Engineer

For years, software engineering has largely been optimized around one thing: producing code. Better frameworks, better abstractions, better tooling, better infrastructure. Every wave of innovation pushed engineers toward higher leverage. We moved from assembly to high-level languages, from monoliths to cloud platforms, from manual provisioning to infrastructure as code. Now AI is accelerating this trend dramatically. But contrary to popular narratives, AI is not reducing the need for engineers.

aiprogrammingarchitecture
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