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Every detection vendor ships a MITRE ATT&CK heatmap, and every one of them is mostly green. Broad coverage, techniques lit up across the board, a reassuring wall of color in the sales deck and the board slide. It's the universal flex. We cover the matrix. Then you parse the actual rules – the real YAML in the public repo, not the marketing layer on top of it – and the green collapses into three tactics. Everyone covers execution and persistence. Almost nobody covers discovery, lateral movement, or collection. The heatmap wasn't measuring coverage. It was counting rules, and counting them in a way designed to look complete. What a green cell actually means A green cell in a Navigator layer means one thing: at least one rule somewhere references that technique tag. That's it. Not "we detect this reliably." Not "this fires on real attacks and stays quiet otherwise." Not "this survives an attacker who knows the rule exists." One rule that names the technique in its metadata turns the cell green, and forty rules turn it the same shade. Unless the layer is scored by rule count – and most published heatmaps aren't – one and forty are indistinguishable. I've written before that an untested detection isn't a detection, it's a query that runs on a schedule. The heatmap is the same lie one level up. A green matrix isn't coverage. It's a wall of queries that run, rendered in a color that means "present," dressed up as a color that means "protected." The vendor knows the difference. The buyer staring at the green doesn't. You can measure the real shape yourself Here's the part the heatmap marketing depends on you not doing: the rules are public, and you can count them. SigmaHQ, Elastic's detection-rules, Splunk ESCU, Panther, Sublime – all on GitHub, all tagged with attack.TXXXX technique IDs and tactic tags in the rule metadata. The method is boring on purpose. Walk the rule directories. Pull the ATT&CK tags out of each rule. Aggregate by technique, roll the technique counts up

Smart lighting company Nanoleaf has been acquired by OneRobotics, the parent company of SwitchBot. In an exclusive interview with The Verge, Nanoleaf CEO Gimmy Chu says the company will remain independent and that he and his cofounder and COO, Christian Yan, will continue to run it. "Nothing is changing operationally," says Chu, adding that there […]

Fight the scourge of unsolicited rings and pings from spammers, scammers, and telemarketers.

When they're being eaten, bean plants release chemicals that draw in parasitic wasps.

Sepehr Khosravi discusses the evolution of developer productivity tools. Evaluating the strengths of tools like Cursor and Claude Code, he explains actionable techniques for senior engineers - including context engineering, custom rules, and Model Context Protocol (MCP) integrations. He shares real-world benchmarks and strategic frameworks for balancing AI adoption with clean code quality. By Sepehr Khosravi

The company’s RTX Spark chips might finally turn the “AI PC” into reality.

How your front fork and rear shock work, so you can hurt less and have more fun.

Several real estate listings in the San Francisco Bay Area are offering to exchange a home for a piece of the AI startup.

I put my family on a private social network, and all I got was this lousy group chat. At least it’s secure.

Node.js will change its release schedule starting with version 27 in October 2026, moving from two major releases per year to one. All releases will become Long-Term Support (LTS), removing the distinction between odd and even versions. An Alpha channel for early testing will also be introduced. This decision addresses maintenance challenges and aims to align with user needs. By Daniel Curtis

Introduction: The Question of Relevance Imagine you’re a college student, fresh off mastering pandas , and you’re eyeing the freelancing market for data cleaning and automation gigs. You’ve heard of openpyxl , but as you dig deeper, you hit a wall: every resource seems to peg it as a relic for handling 2010 Excel sheets . That’s it. No modern use cases, no integration with cutting-edge tools, just a dusty library stuck in the past. So, you pause. Is openpyxl still relevant in 2023, or is it a dead end for someone trying to build a competitive freelancing portfolio? This dilemma isn’t just about openpyxl—it’s about the mechanism of perception in tech. When a tool is associated with outdated formats, its capabilities are often misinterpreted or overlooked . Openpyxl’s documentation and community discourse rarely highlight its modern applications, leaving newcomers like you to assume it’s obsolete. But here’s the catch: openpyxl isn’t just a 2010 Excel handler. It’s a low-level Excel manipulator that, when paired with libraries like pandas and numpy, can handle complex tasks that these libraries alone can’t. The problem isn’t openpyxl’s functionality—it’s the information gap between its perceived and actual utility. The stakes are clear: if you dismiss openpyxl as outdated, you risk missing out on a tool that could complement your pandas and numpy skills , making your freelancing services more efficient and versatile. But if you invest time in it without understanding its modern applications, you might waste effort on a tool that doesn’t align with current demands. The question isn’t whether openpyxl is relevant—it’s whether you’re looking at it through the right lens. In this investigation, we’ll dissect openpyxl’s role in 2023 freelancing, addressing its perceived limitations and uncovering its hidden strengths. By the end, you’ll have a clear rule for deciding whether to include it in your toolkit: If your freelancing gigs involve Excel-specific tasks that pandas ca

Hoi hoi! I’m @nyaomaru, a frontend engineer who recently discovered the deliciousness of a cheese...

PageSpeed Insights had been staring at me for weeks. Desktop was holding at 91. Mobile was stuck at 63. I'd already fixed the obvious stuff — non-blocking fonts, preconnects, fetchpriority on the hero image. But there it was, every single run: Improve image delivery — Est savings of 985 KiB Nearly a megabyte of wasted transfer, just from six project screenshots. And that was just the images visible above the fold. The full list across all projects was worse. The culprit: every image I'd ever uploaded through the Django admin was a PNG. Some of them were over 1 MB. WebP would have cut most of them by 80%. I knew this. I just hadn't done anything about it. So I wrote a management command to fix the backlog, and then made the model auto-convert on every future upload so I'd never have to think about it again. The Problem With PNGs in a Portfolio When you're building a portfolio, you screenshot your work and drag it into the admin. That screenshot is usually a PNG — lossless, full-size, straight from your display. Nobody optimises it because the admin accepts it and it shows up fine in the browser. But "shows up fine" isn't the same as "loads fast." A 1.4 MB PNG of a law firm homepage does not need to be 1.4 MB. Served as WebP at quality 85, it's 175 KB. Same visual result. Eight times smaller. Multiply that across 28 projects and you're looking at tens of megabytes that mobile users on slow 4G are downloading just to scroll past thumbnails. The One-Time Backlog Fix: A Management Command First, I needed a way to convert everything that was already in S3. A management command was the right tool — it runs in the production container with full access to the Django ORM and the configured storage backend, so it can read and rewrite files without needing to know whether they're on S3, local disk, or anywhere else. # backend/projects/management/commands/convert_images_to_webp.py from io import BytesIO from django.core.files.base import ContentFile from django.core.management.b

Claude Opus 4.8 shipped today. The benchmarks are a distraction — here is what actually changes about how your agents run tomorrow. Anthropic announced Claude Opus 4.8 at 16:00 UTC on June 3, 2026. The launch post leads with the usual benchmark deltas: SWE-bench Verified up 4.1 points, GPQA Diamond up 2.9, TAU-bench tool-use up 6.4. There is a chart. There is a marketing line about "the most capable agentic model we have ever shipped." If you stop reading there, you will miss the three things that will change how your production agents behave starting tomorrow. I have spent the morning re-running our internal agent harness against Opus 4.8 and reading the model card line by line. Two of the three changes are improvements. One of them is a silent regression that will bite anyone who pinned the model ID. Here is the full picture. What 4.8 actually changes The model card and release notes ship three changes that the launch blog post does not foreground: Cache-aware routing inside long agentic loops. The 4.7 router treated every tool-call cycle as a fresh planning step. 4.8 keeps an internal trace of which cache breakpoints were hit on the previous step and biases the next plan toward extending those traces. In agent harnesses that already use prompt caching aggressively (Claude Code, the Agent SDK with cacheControl: "ephemeral" on the system prompt), cache hit rates jumped from a measured ~46% on 4.7 to ~71% on 4.8 across a 30-step coding loop. The 200k context window now actually behaves at 200k. Anthropic published a needle-in-a-haystack chart in the model card going out to 200,000 tokens. The 4.7 chart got noticeably worse past ~140k tokens; the 4.8 chart is flat. This sounds like a benchmark thing. It is not. It changes the cost equation for "just stuff everything in context" patterns that 4.7 quietly punished by degrading accuracy. claude-opus-4-7 was not aliased. The launch shipped a new model ID — claude-opus-4-8 — and the previous ID is still callable. But if y

⚠️ この記事はアフィリエイト広告（プロモーション）を含みます。リンク先で発生した収益の一部が運営者に支払われますが、読者の購入価格には一切影響ありません。 If you work remote from rural Japan, by the end of this article you'll have two runnable Python scripts: one that computes your exact furusato-nozei (hometown tax) ceiling from your real side-income, and one that scores your electricity contract against your actual kWh log so you stop overpaying. No spreadsheets, no "consult a tax accountant" hand-waving. Copy, run, save money tonight. Result from my own 2025 numbers: ¥41,000 of furusato-nozei reward goods for a net cost of ¥2,000, plus ¥28,400/year shaved off my power bill after switching plans. Total ≈ ¥67,400 recovered, and because I work from home in Niigata, my commute cost to earn it was literally ¥0. The trap: side income breaks the "simple" furusato nozei calculator Every portal (Satofuru, Rakuten Furusato, Furunavi) shows a slider that estimates your ceiling from salary alone. The moment you add freelance/blog/ Kindle income, that slider lies to you. In 2024 I trusted it, donated ¥52,000, and ¥9,000 of it fell outside the deductible ceiling because my side income pushed me into a different residual-tax bracket. That ¥9,000 was just a donation — no tax back. The real ceiling depends on your total taxable income (salary + side hustle minus expenses) and the resident-tax (juminzei) cap of roughly 20% of your income-based resident tax. Here's a calculator that actually folds in side income. It uses Japan's 2026 progressive income-tax brackets. # furusato_ceiling.py — Python 3.9+ from dataclasses import dataclass # 2026 national income tax brackets: (upper_bound_yen, rate, deduction_yen) BRACKETS = [ ( 1_950_000 , 0.05 , 0 ), ( 3_300_000 , 0.10 , 97_500 ), ( 6_950_000 , 0.20 , 427_500 ), ( 9_000_000 , 0.23 , 636_000 ), ( 18_000_000 , 0.33 , 1_536_000 ), ( 40_000_000 , 0.40 , 2_796_000 ), ( float ( " inf " ), 0.45 , 4_796_000 ), ] @dataclass class Taxpayer : salary_income : int # after salary-income deduction (給与所得) side_profit : int #

Even in a future of robot taxis, someone still has to return the things passengers leave behind.

"It’s a reminder of how human activity is changing the natural world in unanticipated ways.”

A government committee says that the country’s growing dependence on the data analytics company is a serious liability.

Em 2023, eu arrumei as malas e saí de Recife, minha terra natal, para morar no interior do Mato Grosso. O motivo? Cursar Ciência da Computação. Eu sabia que enfrentaria um choque cultural, o calor do Centro-Oeste e a saudade de casa. O que eu não imaginei é que o maior desafio seria a sensação constante de que eu era uma fraude no meio da minha própria sala de aula. Se você estuda ou trabalha com tecnologia, provavelmente já passou por isso. Você senta na primeira semana de aula e parece que metade da turma já programa desde os 12 anos, fala termos técnicos que parecem outra língua e discute sobre ferramentas que você nem sabia que existiam. Hoje, na reta final da graduação, focando meus estudos em Back-end e Dados, quebrando a cabeça com Go, Python e SQL, eu olho para trás e vejo o quanto essa cobrança silenciosa quase me paralisou. Se você está sentindo que entrou no curso errado ou que todo mundo corre a 100 km/h enquanto você ainda está engatinhando, pega um café e vem ler este papo reto sobre como lidar com a tal da Síndrome do Impostor. O "efeito vitrine" e a falsa ilusão de que somos os únicos perdidos Na faculdade de TI, a comparação é quase inevitável. A gente abre o LinkedIn e vê o colega conseguindo estágio internacional; abre o GitHub e vê códigos impecáveis; na sala de aula, sempre tem quem responda às perguntas do professor antes mesmo dele terminar de falar. O erro está em achar que o ritmo do outro deve ditar o seu. Na Computação, as pessoas vêm de bagagens completamente diferentes. Quem já sabia programar antes da faculdade pode ter facilidade em Lógica de Programação, mas talvez sofra tanto quanto você quando chegar a hora de aprender Álgebra Linear ou Teoria da Computação. Sentir-se perdida não significa incompetência; significa apenas que você está no processo de aprender algo complexo. E adivinha? Todo mundo ali está tentando descobrir como as coisas funcionam, mesmo quem finge que sabe tudo. O que me ajudou a virar a chave Eu não acordei um dia

There's a specific feeling you get after your third production caching incident. It's not panic....