Updates and recent posts about GPT..

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@faun shared a link, 3 weeks, 4 days ago

Intel CEO Letter to Employees

Intel scraps itsGermanyandPoland foundries, shifting assembly fromCosta RicatoVietnamandMalaysia. It slows Ohio fab construction while ramping upIntel 18A/18A‑Pand planningIntel 14Aaround key customers. SMT returns. Focus shifts to Panther Lake, Nova Lake, and Granite Rapids.AI strategy pivots towar..

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@faun shared a link, 3 weeks, 4 days ago

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...

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@faun shared a link, 3 weeks, 4 days ago

How Anthropic teams use Claude Code

Anthropic teamsfire upClaude Code. They automate data pipelines and squash Kubernetes IP exhaustion. They churn out tests and trace cross-repo context. Non-dev squads use plain-text prompts to script workflows, spin up Figma plugin automations, and mock up UIs from screenshots—zero code. Trend to w..

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@faun shared a link, 3 weeks, 4 days ago

Microsoft Copilot Rooted to Gain Unauthorized Root Access to its Backend System

April 2025 Copilot Enterprise update slipped in aJupyter sandbox. It snuck in aPATH-poisonable pgrepat root’s entrypoint. Attackers could hijack that forroot execution.Eye Securityflagged the hole in April. By July 25, 2025, Microsoft patched this moderate bug. No data exfiltration reported. Why it..

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@faun shared a link, 3 weeks, 4 days ago

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. ..

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@faun shared a link, 3 weeks, 4 days ago

The Big LLM Architecture Comparison

Architectures since GPT-2 still ride transformers. They crank memory and performance withRoPE, swapGQAforMLA, sprinkle in sparseMoE, and roll sliding-window attention. Teams shiftRMSNorm. They tweak layer norms withQK-Norm, locking in training stability across modern models. Trend to watch:In 2025,..

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@faun shared a link, 3 weeks, 4 days ago

How AI data integration transforms your data stack

AI data integration obliterates manual ETL chores. It handlesschema mapping,transformation,anomaly detection. Deployments sprint ahead. Machine learning models digest structured, semi-structured, unstructured formats. They forge real-time pipelines bristling withgovernanceandsecurity. Infra shift:A..

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@faun shared a link, 3 weeks, 4 days ago

The Evolution of AI Job Orchestration: The AI-Native Control Plane & Orchestration that Finally Works for ML

SkyPilot spins an AI-native control plane on Neocloud Kubernetes. It binds GPU pools across clouds into one resilient grid. Teams define ML jobs in a single YAML. SkyPilot drives gang scheduling, SSH/Jupyter access, and multi-cluster compute. It does auto failover and cost-smart scheduling. Infra s..

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@faun shared a link, 3 weeks, 4 days ago

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...

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@faun shared a link, 3 weeks, 4 days ago

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...

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.