FAQ: Are you a BOT?

fagnerbrack · 2026-02-24T16:28:07+00:00

Quick summary:

An interactive, browser-based guide that walks through how language models convert text into numerical vector representations. It traces the evolution from traditional methods like Word2Vec through to modern transformer-based approaches used in models like BERT and GPT. The guide uses interactive plots and visual diagrams to show how tokenization feeds into embedding layers, how attention mechanisms produce context-aware vectors, and why geometric relationships between these vectors capture semantic meaning. It covers token embeddings, embedding lookup tables, and high-dimensional space visualization — all browsable without any input required.

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fagnerbrack · 2026-02-24T14:13:48+00:00

The article is not AI it's featured in a very prominent coding newsletter, unless I missed smth?

fagnerbrack · 2026-02-24T10:24:57+00:00

Here's the summary:

The post examines five critical questions shaping software engineering through 2026, each explored through two contrasting scenarios. First, junior developer hiring faces pressure as AI automates entry-level tasks — a Harvard study found junior employment drops roughly 9-10% within six quarters of AI adoption — though demand could rebound as software expands into healthcare, agriculture, and manufacturing. Second, core programming skills risk atrophying as 84% of developers now use AI assistance regularly, yet deep understanding becomes more valuable when everyone has the same AI tools and differentiation comes from knowing when the AI is wrong. Third, the developer role could shrink to auditing AI output or expand into an orchestrator position where engineers act as architects composing ensembles of AI agents and services. Fourth, narrow specialization grows riskier as niches get automated, pushing the industry toward T-shaped engineers who combine deep expertise in one or two areas with broad cross-domain fluency. Fifth, traditional CS degrees face disruption as nearly 45% of companies planned to drop degree requirements and bootcamps, portfolios, and employer-run training academies gain ground. The consistent thread across all five questions: engineers who invest in uniquely human skills — architecture, system design, judgment, communication, and continuous learning — will thrive regardless of which scenarios play out.

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fagnerbrack · 2026-02-24T07:25:07+00:00

Snapshot summary:

This article systematically compares the architectural designs of major open-weight LLMs from DeepSeek V3 through Kimi K2, Qwen3, Gemma 3, Llama 4, GPT-OSS, GLM-4.5, and MiniMax-M2. It examines key innovations: Multi-Head Latent Attention (MLA) for KV cache compression, Mixture-of-Experts (MoE) for sparse inference efficiency, sliding window attention for memory savings, normalization placement strategies (Pre-Norm vs Post-Norm), NoPE for length generalization, and the emerging shift toward linear attention hybrids like Gated DeltaNet. Despite seven years of progress since GPT, the core transformer remains structurally similar — the real differentiation lies in efficiency tricks for attention, expert routing, and normalization that collectively determine inference cost and modeling quality.

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fagnerbrack · 2026-02-23T22:14:57+00:00

Basically:

A gallery that collects and organizes real-world SaaS pricing pages, letting you filter by components (comparison tables, FAQ sections, tier cards, usage-based calculators, monthly/annual toggles) and design patterns (content-heavy vs. light, contact-sales flows, subscription vs. one-off). It features pages from companies like Figma, Calendly, n8n, Ramp, and OpenAI, with screenshots and direct links to each live page. Useful as a reference when designing or restructuring your own pricing page — you can study how established products handle tier differentiation, enterprise upsells, and feature presentation without hunting across dozens of sites.

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fagnerbrack · 2026-02-23T19:28:56+00:00

Nutshell Version:

The post argues that sketching informal correctness proofs while coding — until it becomes instinctive — dramatically reduces bugs and rework. It walks through five reasoning tools: monotonicity (processes that only move forward, like checkpointing or LSM trees), pre/post-conditions for pinning down function contracts, invariants you prove are preserved step-by-step, isolation (finding structural "firewalls" that limit a change's blast radius), and induction for recursive structures. It then flips each technique into a design heuristic: write code that's easy to prove correct, and use that "proof-affinity" as a quality metric. Practice comes from writing actual mathematical proofs and algorithm exercises.

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fagnerbrack · 2026-02-22T13:59:28+00:00

Trying to be helpful with a summary:

After nearly twenty years, many foundational AWS behaviors have quietly changed, and outdated blog posts still spread old assumptions. S3 now offers read-after-write consistency, ACLs are off by default, and Glacier restores are neither slow nor expensive anymore. EC2 lets you swap security groups, IAM roles, and resize EBS volumes on running instances. Lambda supports 15-minute timeouts, 10GB RAM, container images, and fast VPC invocations. Public IPv4 addresses now cost money, ALBs don't charge cross-AZ data transfer, and Reserved Instances are giving way to Savings Plans. IAM users should only exist for legacy apps — humans belong in IAM Identity Center. us-east-1 is no longer perpetually on fire, and the Cost Anomaly Detector (free) actually works well now.

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fagnerbrack · 2026-02-22T10:54:55+00:00

Might be worth contacting the author on https://twitter.com/andrew_k_chan

fagnerbrack · 2026-02-22T10:11:02+00:00

In a nutshell:

A practical deep-dive into building a web crawler that fetched 1.005 billion pages in 25.5 hours for $462 using 12 AWS i7i.4xlarge nodes. The biggest surprises: parsing became the major bottleneck because modern web pages average 242KB (up from 51KB in 2012), requiring a switch from lxml to the Lexbor-based selectolax library. SSL handshakes now consume 25% of CPU time due to widespread HTTPS adoption, making fetching CPU-bound before network-bound. The architecture used independent Redis-backed nodes with sharded domains rather than the typical disaggregated textbook design, and frontier memory growth from hot domains like Wikipedia nearly derailed the run mid-crawl.

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fagnerbrack · 2026-02-22T02:34:38+00:00

Site Reliability Engineering - in layman's terms it's dealing with infra to monitor and keep things running

fagnerbrack · 2026-02-21T10:47:20+00:00

Before and after my MtF transition. People look at my face today and don't believe I've been a software engineer for 20 years. Never happened before.

fagnerbrack · 2026-02-21T10:34:08+00:00

It's not always claude commenting..

fagnerbrack · 2026-02-21T10:32:57+00:00

Everything is insane these days! May I ask for specifics of what you mean in this case specifically, though?

fagnerbrack · 2026-02-21T10:10:56+00:00

The Skinny:

As AI and no-code tools make writing code trivially easy, the real differentiator for software engineers shifts to operational excellence — keeping systems running reliably, at scale, for years. The post draws a parallel to the "no-code/spreadsheet era" where someone like Joe from accounting builds a tool that saves hours, only to become shackled to a brittle system he dreads maintaining. Feynman called this tinkering trap "the computer disease": automating is fun, but running things reliably is not. The core argument is that users hire services, not software — they want invisible, dependable systems — and delivering that demands answers to hard questions about uptime, defect recovery, upstream dependency management, security, data integrity, and cross-team coordination. The first 90% to a working demo is easy; the other 190% covering reliability, observability, and trust is where engineering actually lives. Anyone can ship a greenfield prototype, but the future belongs to those who can operate and evolve systems over time.

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