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@faun shared a link, 11 months ago
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Seeing like an LLM

LLMs function as next-token predictors. With scant user context, they hallucinate—spinning fresh backstories. As these models morph into autonomous agents, context engineering—feeding facts, memory, tools, guardrails—halts rogue behavior. Trend to watch:A jump in context engineering. It pins LLMs t.. read more  

Seeing like an LLM
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The Future of Threat Emulation: Building AI Agents that Hunt Like Cloud Adversaries

AI agents tap MCP servers andStrands Agents. They fire off tools that chart IAM permission chains and sniff out AWS privilege escalations. Enter the “Sum of All Permissions” method. It hijacks EC2 Instance Connect, warps through SSM to swipe data, and leaps roles—long after static scanners nod off. .. read more  

The Future of Threat Emulation: Building AI Agents that Hunt Like Cloud Adversaries
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From Raw Data to Model Serving: A Blueprint for the AI/ML Lifecycle with

Post maps out aKubeflow Pipelinesworkflow onSpark,Feast, andKServe. It tackles fraud detection end-to-end: data prep, feature store, live inference. It turns infra into code, ensures feature parity in train and serve, and registers ONNX models in theKubeflow Model Registry... read more  

From Raw Data to Model Serving: A Blueprint for the AI/ML Lifecycle with
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The vibe coder's career path is doomed

An AI-powered dev workflow combinedClaude,Playwright, and aPostgres-backed REST APIto ship 2–3 features per day. But as complexity grew, multi-agent loops broke down, tests ballooned, and schema drift demanded increasingly precise prompts and manual corrections.The result: more time spent managing c.. read more  

The vibe coder's career path is doomed
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How Zapier runs isolated tasks on AWS Lambda and upgrades functions at scale

Zapier snaps each customer Zap into its ownAWS Lambda, cradled inside leanFirecracker microVMs. It wrangles 100k+ functions under anEKScontrol plane and inventory DB. When runtimes retire, Zapier swings into action: a set ofTerraform modulespaired with a customLambda canary tool. Traffic trickles in.. read more  

How Zapier runs isolated tasks on AWS Lambda and upgrades functions at scale
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What Is IDOR? Finding and Preventing Insecure Direct Object References in AWS APIs

Attackers swap predictable IDs. They slip intoAWS APIs,Lambda functions, internal tools. Fuzzers likeffufflag sneaky HTTP 200s.Burp Intruderbubbles up 404 probes.CloudWatchlogs trace every call. Random UUIDs seal ID gaps... read more  

What Is IDOR? Finding and Preventing Insecure Direct Object References in AWS APIs
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Self-hosting Trigger.dev v4 using Docker

Trigger.dev v4 sharpens self-hosting. It pins everything toDocker Compose. It bakesregistryandobject storagein. It chops YAML bloat. Env-var docs unify configs. Resource caps lock down security. Scaling? Spin up more worker containers... read more  

Self-hosting Trigger.dev v4 using Docker
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kubriX: Your Out-of-the-Box Internal Developer Platform (IDP) for Kubernetes

Discover how kubriX seamlessly integrates leading open-source tools like Argo CD, Kargo, and Backstage to deliver a fully functional IDP out of the box. This blog post provides a deep dive into the technical aspects of kubriX, showcasing its capabilities and value proposition within the realm of Int.. read more  

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The Cybersecurity Blind Spot in DevOps Pipelines

DevOps pipelines serve as superhighways for cybercriminals to target with credential leaks, supply chain infiltration, misconfigurations, and dependency vulnerabilities. Security must evolve with development to combat these sophisticated attacks... read more  

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How GitHub engineers tackle platform problems

Product engineersare like builders ofGundam models, construcing the final product, whileplatform engineerssupply the tools needed to build these kits. Understanding theGundam analogyhelps differentiate engineering roles at GitHub... read more  

GPT-5.3-Codex is OpenAI’s advanced agentic coding model, designed to go beyond writing code and operate as a general-purpose collaborator on a computer. It builds on GPT-5.2-Codex by combining stronger coding performance with improved reasoning and professional knowledge, while running about 25% faster. The model is optimized for long-running tasks that involve research, tool use, and complex execution, and it performs at the top of industry benchmarks such as SWE-Bench Pro and Terminal-Bench.

Unlike earlier Codex models that focused primarily on code generation and review, GPT-5.3-Codex can reason, plan, and act across the full software lifecycle. It supports activities such as debugging, deploying, monitoring, writing product requirement documents, creating tests, and analyzing metrics. It can also autonomously build and iterate on complex applications and better interpret underspecified prompts, producing more complete and production-ready results by default.

A defining feature of GPT-5.3-Codex is its interactive, agentic workflow. Users can steer the model while it is working, receive progress updates, and adjust direction without losing context, making it feel more like a teammate than a batch automation tool. The model was even used internally to help debug its own training and deployment processes. GPT-5.3-Codex is available through paid ChatGPT plans in the Codex app, CLI, IDE extension, and web, with API access planned for the future.