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• Excel Shortcuts That Actually Save Time — Understand the Rule, Don't Memorize the List

  Instead of listing 200 Excel shortcuts, this post groups them by the task you're actually doing. Once you understand why the arrow-key family always knows exactly where the data ends, or what order the absolute-reference

  2026-08-02 · 16 min read #excel#shortcuts#productivity#office#keyboard

• How to Read a Difficult Book — Five Judgment Calls That Matter More Than Finishing

  Most of the time, what makes a difficult book difficult isn't unfamiliar words — it's the shape of the argument. Grounded in Adler's method for reading and in what reading research actually supports, this piece lays out

  2026-08-02 · 14 min read #storytelling#reading#books#learning#culture

• Putting Observability Data Into ClickHouse — Schema, Rollups, TTL, and Splitting the Work ♪ Listenable

  When traces and logs grow to multiple terabytes a day, a single search engine or time-series database starts to buckle. This post lays out why ClickHouse fits observability data so well, in terms of columnar storage, com

  2026-08-02 · 16 min read #observability#clickhouse#opentelemetry#data-modeling#cost

• What LLM Ops Actually Does — Reproducibility, Contamination, Checkpoints, Promotion, and Rollback

  This post organizes LLM Ops as a list of responsibilities, not a list of tools. It covers what belongs in a run manifest that lets you reconstruct a training run, how to prevent and audit eval-set contamination, the form

  2026-08-02 · 14 min read #mlops#llmops#reproducibility#evaluation#model-registry

• The Four Kinds of Multi-GPU Parallelism — What You Split and What You Communicate

  A numbers-first breakdown of what data parallelism, tensor parallelism, pipeline parallelism, and context parallelism each split, and what they pay in communication for it. It starts by building the per-parameter 16-byte

  2026-08-02 · 13 min read #mlops#distributed-training#multi-gpu#fsdp#deepspeed

• Lessons from Published Training Runs — What Was Tried, and What Failed

  Seven published large-scale training technical reports and logbooks, stripped down to just the failures and the responses to them, not the scoreboard. The statistics of 419 interruptions over 54 days from Llama 3 405B le

  2026-08-02 · 13 min read #mlops#llm-training#case-study#training-stability#scaling

• Building an AI Blog-Writing Pipeline — Verification, Not Drafting, Is the Bottleneck

  This post breaks down the process of writing a blog post with AI into seven stages, from topic collection to checking performance, and separates out exactly where the model genuinely helps and where you must never hand o

  2026-08-02 · 17 min read #ai-writing#content-pipeline#fact-checking#automation#blogging

• How to Actually Read a Model Card — Pulling Out What You Need in 5 Minutes ♪ Listenable

  A model card is written in a way that, read top to bottom, keeps you from finding what you need. The benchmark table takes up half the screen while the license and chat template pass by in a single line. This post flips

  2026-08-02 · 19 min read #llm#huggingface#model-card#license#tokenizer

• Word and PowerPoint Shortcuts — Documents Are Handled by Style, Slides by Object ♪ Listenable

  Word and PowerPoint use the same ribbon, but the muscle memory they need is completely different, because Word handles flowing text and styles while PowerPoint handles objects on a canvas. This post splits the two apps i

  2026-08-02 · 22 min read #word#powerpoint#shortcuts#office#productivity

• Novels Worth the Time — 30 Books and the Rule of the First 50 Pages ♪ Listenable

  Novel recommendation lists are everywhere, but they tend to leave out exactly what you need to know: how long the book actually is, how hard it is, and whether it's the kind of book you should push through when the first

  2026-08-02 · 22 min read #storytelling#novels#reading#world-literature#culture

• How These Models Were Actually Built — Dissecting the 2026 Open-Weight Pipeline

  Reading the cards and technical reports of the open-weight models sitting near the top of Hugging Face as of August 2026, this post lays out the production pipeline in order, from data collection to quantized deployment.

  2026-08-02 · 17 min read #llm#pretraining#moe#post-training#quantization

• Dashboards That Get Read and Alerts Worth Paging — Defining Questions, Variables, SLOs, and Alert Fatigue

  A dashboard is not useful for looking pretty — it earns its keep by answering a fixed set of questions in order. This post covers how to write down the questions a panel should answer before building it, and how to struc

  2026-08-02 · 15 min read #observability#grafana#alerting#slo#dashboards

• Designing Prometheus Metrics That Answer Questions — Choosing Types, Cardinality Budgets, and the Traps in rate and Quantiles

  More metrics is not better — a metric earns its keep only by answering a question. This post starts by laying out which questions counters, gauges, and histograms each answer, and which calculations become impossible in

  2026-08-02 · 17 min read #observability#prometheus#promql#metrics#cardinality

• Where to Start with Classic Films — 25 Essential Movies and the Order to Watch Them ♪ Listenable

  Lists of classic films are everywhere, but almost none tell you what order to watch them in. This piece sorts 25 essential films not by ranking but by the specific problem a beginner runs into: when black-and-white feels

  2026-08-02 · 18 min read #storytelling#film#classics#world-cinema#culture

• Running a GPU Cluster with Slurm — Knowing Why a Job Will Not Run Matters More Than Submitting

  Everything you need to actually use Slurm on a GPU cluster in practice. Sets up the coordinate system of partition, QoS, and account first, then covers how to request GPUs, CPUs, and memory in an sbatch script and the bi

  2026-08-02 · 14 min read #mlops#slurm#hpc#gpu-cluster#distributed-training

• A Map of the LLM Training Stack in 2026 — What Each Layer Does For You, and What It Hides

  Sorts LLM training frameworks into three layers and maps out their lineage. The bottom layer is the execution engines — PyTorch distributed, DeepSpeed, Megatron-Core. The middle layer is training loops like torchtitan an

  2026-08-02 · 13 min read #mlops#llm-training#pytorch#trl#framework

• What's Trending on Hugging Face Right Now — A Map of August 2026 ♪ Listenable

  As of August 2, 2026, I went through the Hugging Face trending list directly and organized models that are actually usable by purpose. In order — general LLMs, coding, embeddings and rerankers, vision, speech, image/vide

  2026-08-02 · 18 min read #llm#huggingface#open-weights#model-selection#quantization

• AMD vs. NVIDIA: What Actually Differs — Why the Stack Is the Problem, Not the Hardware

  Breaks down the difference between AMD and NVIDIA GPUs across four layers — hardware architecture, software stack, porting path, and ecosystem maturity — without taking sides. Covers the mapping from SM to CU and from Te

  2026-08-02 · 16 min read #amd#rocm#hip#nvidia#cuda

• Is Comfort From a Machine Real? — Why We Need to Change the Question

  People really do feel better after talking to an AI. Dismissing that as an illusion isn't just rude — it's inaccurate. This piece looks at why responsiveness feels like being cared for, and digs into what chatbot-based m

  2026-08-02 · 14 min read #humanities#ai#psychology#loneliness#relationships

• Tool or Counterpart — What Can Still Be Said While Leaving the Consciousness Question Open

  The first question people reach for in front of AI is "does this thing have consciousness." It is the hardest question, and probably not the most useful one. This piece sets that question down beside us without closing i

  2026-08-02 · 13 min read #humanities#ai#philosophy#cognition#technology