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We Might All Be AI Engineers Now

The author supervises AI agents that orchestrate concurrent graph traversal, multi-layer hashing, AST parsing, and file system watchers. The agents run traversal, hashing, and watcher loops. The engineer architects system behavior, verifies outputs, and probes agents in parallel to debug... read more  

We Might All Be AI Engineers Now
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Zen: A Minimalist HTTP Library for Go

Unkey builtZen- a thin HTTP framework on Go'snet/http. It restores precise middleware ordering and lets middleware run after errors to capture the final response. Zen poolsSessionobjects to cut allocations. It emits RFC7807problem+jsonfor tagged domain errors. It runs OpenAPI validation before handl.. read more  

Zen: A Minimalist HTTP Library for Go
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@kaptain shared a link, 1 month, 1 week ago
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It's Not Kubernetes. It Never Was

The complexity in managing Kubernetes clusters is a reflection of the organizational decisions and lack of processes within the teams operating them. The move towards multi-cloud environments without sufficient planning or resources has exacerbated these issues. Platform engineering solutions offer .. read more  

It's Not Kubernetes. It Never Was
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How Does Kubernetes Self-Healing Work? Understand Self-Healing By Breaking a Real Cluster

KubeLab boots a three-nodeKubernetescluster and runs seven failure simulations. It deploysNode.js,Postgres,Prometheus, andGrafana. Then it deletes pods, forcesOOMKill, throttles CPU, drains nodes, and scales aStatefulSetto zero. Each scenario surfaces fixes:readiness probes,PodDisruptionBudget, anti.. read more  

How Does Kubernetes Self-Healing Work? Understand Self-Healing By Breaking a Real Cluster
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How WebAssembly plugins simplify Kubernetes extensibility

Helm 4runsWebAssembly (Wasm)plugins to executeWASImodules insideOCIcontainers and VMs.Helmtemplates standardize module lifecycle. The Wasm plugin adds instruction-level sandboxing and Kubernetes segmentation.Helm 4preserves portability acrossx86/ARM. Compared withHelm 3plugins, it shows up to a 40% .. read more  

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The great migration: Why every AI platform is converging on Kubernetes

The CNCF survey finds82%of container users runKubernetesin production.66%of GenAI hosts use it for inference. Kubernetes now stitches data processing, distributed training, LLM inference, and autonomous agents viaSpark,Kubeflow,Kueue,KServe, andArmada. GPU sharing and scheduling advanced withMIG, ti.. read more  

The great migration: Why every AI platform is converging on Kubernetes
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pg_plan_alternatives: Tracing PostgreSQL’s Query Plan Alternatives using eBPF

The tracer hooks PostgreSQL's optimizer via eBPF. It captures every alternative plan path with cost estimates and flags the chosen plan. A kernel-space eBPF program reads planner structs using DWARF-derived offsets. A user-space collector gathers the data and a visualizer renders plan graphs. eBPF p.. read more  

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@kala shared a link, 1 month, 1 week ago
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AI as tradecraft: How threat actors operationalize AI

Microsoft observes threat actors operationalizeAIandLLMsacross the cyberattack lifecycle. They accelerate reconnaissance, phishing, malware development, and post‑compromise triage. Actors abusejailbreakingtechniques andGANs. They craft personas, generate look‑alike domains, embed runtime‑adaptive pa.. read more  

AI as tradecraft: How threat actors operationalize AI
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The L in "LLM" Stands for Lying

The author arguesLLMschurn out fast, generic answers by remixing low-quality source material. They seed brittle, repetitive code viavibe-coding. The remedy: requiresource attributionand auditable inference to separate originals from forgeries and to reshape model training and deployment. Requiringso.. read more  

The L in "LLM" Stands for Lying
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Reasoning models struggle to control their chains of thought, and that’s good

OpenAI's paper unveilsCoT-Control: an open-source suite of 13,000+ tasks fromGPQA, MMLU-Pro, HLE, BFCLthat measuresCoTcontrollability. Evaluations on 13 models show compliance at 0.1%-15.4%. Compliance is tiny. Controllability improves with model size. It drops as reasoning chains lengthen and after.. read more  

Reasoning models struggle to control their chains of thought, and that’s good
GPT (Generative Pre-trained Transformer) is a deep learning model developed by OpenAI that has been pre-trained on massive amounts of text data using unsupervised learning techniques. GPT is designed to generate human-like text in response to prompts, and it is capable of performing a variety of natural language processing tasks, including language translation, summarization, and question-answering. The model is based on the transformer architecture, which allows it to handle long-range dependencies and generate coherent, fluent text. GPT has been used in a wide range of applications, including chatbots, language translation, and content generation.

GPT is a family of language models that have been trained on large amounts of text data using a technique called unsupervised learning. The model is pre-trained on a diverse range of text sources, including books, articles, and web pages, which allows it to capture a broad range of language patterns and styles. Once trained, GPT can be fine-tuned on specific tasks, such as language translation or question-answering, by providing it with task-specific data.

One of the key features of GPT is its ability to generate coherent and fluent text that is indistinguishable from human-generated text. This is achieved by training the model to predict the next word in a sentence given the previous words. GPT also uses a technique called attention, which allows it to focus on relevant parts of the input text when generating a response.

GPT has become increasingly popular in recent years, particularly in the field of natural language processing. The model has been used in a wide range of applications, including chatbots, content generation, and language translation. GPT has also been used to create AI-generated stories, poetry, and even music.