Tag: #ai
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 219 posts
The Complete Guide to LLM Training Data Preprocessing — From Web Crawls to Token Packing, with the Latest Papers
Good models come from good data, and good data comes from a preprocessing pipeline. This post walks through the entire pretraining data process step by step — web crawl collection → text extraction → language identificat
2026-07-09 · 7 min read #ai#llm#data-engineering#preprocessing#trainingThe 2026 Robotics Company Map — The Humanoid Showdown, VLA Models, and the Engineer's Way In
2026 is the inflection point where humanoid shipments jump about 7x year-over-year to a forecast of 50,000+ units. From Figure, valued around USD 39B with its in-house VLA model Helix, to Tesla Optimus Gen 3 entering mas
2026-07-09 · 7 min read #robotics#ai#vla#trends#careerTwo Paths to 3D — Reconstruction (NeRF & Gaussian Splatting) and Generation (TRELLIS & Hunyuan3D)
The phrase "make a 3D model" hides two completely different problems. Reconstruction brings a scene that actually exists back to life from a handful of photos, while generation conjures something that does not exist from
2026-07-09 · 9 min read #3d#ai#gaussian-splatting#nerf#computer-visionMulti-GPU, Multi-Node Training Platforms: The Complete Map — from the Framework Ecosystem to Hands-On Slurm and Kubeflow Guides
A one-page map of the full landscape of training models across multiple GPUs and multiple nodes. The AI library and framework ecosystem map (PyTorch, JAX, HuggingFace, DeepSpeed, Ray), when to pick which parallelization
2026-07-09 · 8 min read #ai#ml#distributed-training#slurm#kubeflowLLM Caching, Explained — Why Prompt Caching and Prefix Caches Save You Money
Why does a matching prompt prefix cut costs to a tenth? The answer lives in the transformer's KV cache. Because attention is causal, the Key/Value vectors of earlier tokens never change no matter what comes after — so th
2026-07-08 · 7 min read #ai#llm#caching#inference#performanceThe State of LLM Quantization — From GPTQ and AWQ to FP8, MXFP4, and KV-Cache Quantization ♪ Listenable
Quantization represents a model's numbers in fewer bits to cut memory and cost. Why quality survives fewer bits (outliers and scaling), how GPTQ and AWQ differ in approach, llama.cpp GGUF k-quants, QLoRA's NF4, the 2026
2026-07-08 · 7 min read #ai#llm#quantization#inference#optimizationThe Ownership Backlash and the AI Reality Check — This Week's HN & GeekNews Hot Topics
Two currents run through this week's Hacker News and GeekNews front pages: an 'ownership backlash' spreading from open-source maps to open hardware and replaceable batteries, and an 'AI reality check' driven by open-weig
2026-07-07 · 5 min read #trends#hackernews#geeknews#ai#opensourceThe Forward Deployed Engineer (FDE), Fully Dissected — the Hottest Role in AI Right Now
As the finding that 95% of enterprise GenAI pilots show no measurable impact makes clear, AI's bottleneck today is deployment, not models. The role built to close that gap is the FDE — an engineer embedded in the custome
2026-07-07 · 8 min read #career#fde#ai#palantir#rolesAI Model Development, Start to Finish — a Realistic Lifecycle from Data to Deployment
Model development starts not with pretraining but with a decision ladder — does prompting suffice, does RAG suffice, do you need fine-tuning? The eval-first principle of building your test set before your model, data qua
2026-07-07 · 8 min read #ai#ml#llm#mlops#trainingHow to Study with AI — 8 Techniques That Turn an LLM into Your Best Tutor
If you just read the answers AI gives you, you get the feeling of studying without the skill. This piece shows how to recreate the power of 1:1 tutoring that Bloom's 2 sigma problem revealed — a Socratic tutor, Feynman r
2026-07-06 · 8 min read #ai#learning#study#productivityWhat Makes Palantir Different — The Real Moat Is the Ontology, Not the AI
You keep hearing the name Palantir, but what the company actually does stays blurry. This piece lays out the Gotham/Foundry/AIP/Apollo product line and argues that Palantir's real edge is not flashy AI but the ontology —
2026-07-05 · 7 min read #palantir#data#ai#enterpriseThe Rise of AI Code Review Tools — How Automated Review Changes Teams ♪ Listenable
AI code review tools are pouring out as open source and reshaping developer workflows. From Git diff analysis, defect detection, and convention enforcement to the division of labor with human reviewers, false-positive ma
2026-06-29 · 20 min read #devops#ai#code-review#ci-cd#developer-toolsHunting Bugs with AI — The Era of Automated Security Research ♪ Listenable
Cases of AI automatically probing APIs at scale to uncover vulnerabilities are on the rise. From how fuzzing, differential analysis, and LLM-assisted triage work, to the asymmetry of attackers also using AI, the implicat
2026-06-25 · 20 min read #devops#security#ai#bug-bounty#fuzzingThe Rise of the Agentic Web — auth.md and the Era of AI Signing Up On Your Behalf ♪ Listenable
AI agents signing up for and acting on services on behalf of users — the agentic web — is rapidly becoming reality. We examine the idea of domain-root standards like the auth.md proposal that emerged in the WorkOS contex
2026-06-25 · 21 min read #ai#agent#authentication#oauth#web-standardsCode Becomes the Agents Execution Substrate: A Code-as-Harness View ♪ Listenable
This post reframes code not as an LLMs final artifact but as the execution harness through which an agent interacts with its environment. Centered on verification loops, tool calling, and execution feedback, it lays out
2026-06-25 · 19 min read #ai#agentic-coding#llm-agents#tool-calling#reactAI and the Future of Work — Replacement or Augmentation ♪ Listenable
The story of machines taking over human labor has repeated itself since before the Industrial Revolution. This essay revisits the Luddites, ATMs and bank tellers, the lump of labor fallacy, and todays artificial intellig
2026-06-21 · 35 min read #ai#future-of-work#automation#economics#societyAI Bubble or Revolution: Making Sense of the 2026 Debate ♪ Listenable
A balanced look at the debate surrounding the 2026 AI stock rally. We weigh concerns over valuations, capex, monetization lag, and circular revenue against the counterargument of real demand and productivity, comparing t
2026-06-18 · 23 min read #ai#investing#bubble#nvidia#valuationAI Is Eating Electricity — The Power Demand Surge and the Utility Investment Theme ♪ Listenable
The power demand surge triggered by AI data centers is elevating utilities, power equipment, and transmission into a fresh investment theme. This piece lays out both the bull and bear cases, regulatory and interest rate
2026-06-18 · 24 min read #finance#ai#power-demand#utilities#data-centerCybersecurity Investing in the AI Era - Both Attack and Defense Run on AI
AI is accelerating both attack and defense. As AI-driven threats, supply-chain attacks, and quantum risk lift security spending, we map the sub-fields of cybersecurity and the key company landscape, and examine the defen
2026-06-18 · 15 min read #finance#cybersecurity#ai#investing#securityBig Tech Capex and AI Monetization: When Does the Money Come Back ♪ Listenable
A balanced analysis of when and how Big Tech soaring AI capital spending will return as profit. We cover depreciation and ROI, monetization paths, free cash flow, the bull and bear views, and the key risks, all backed by
2026-06-18 · 19 min read #bigtech#capex#ai#monetization#investing