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Jump Tables, TLV Footers, and the Real Cost of Reading What You Don't Need You're paying for bytes you never read. A data engineer on a busy pipeline touches dozens of Parquet files a day: schema discovery, predicate pushdown, column pruning, metadata scrapes for a data catalog sync. In each case, the application needs maybe 200 KB of context from a file that is 4 GB on disk. Without a seekable archive format and a jump table to find the right frame, your HTTP client fetches the whole thing, and your cloud egress invoice reflects every unnecessary gigabyte. This post quantifies the problem, then walks through how HuskHoard uses seekable Zstd frames, a per-volume jump table, and TLV-encoded footer metadata to make partial egress a first-class citizen across multi-volume archives — disk, cloud, and LTO tape alike. The Problem, In Dollars S3 standard egress runs $0.09/GB. GCS is $0.08/GB. Even Cloudflare R2, which is free for egress from R2 to the internet , still costs you in latency and API call count when you cannot bound the range of bytes you need. Here is a representative read pattern for a cold analytics archive: Operation Bytes Needed Bytes Fetched (naïve) Ratio Schema discovery ~50 KB (Parquet footer) 1–8 GB (full file) ~1:16,000 Single column scan ~200 MB (one column chunk) 4 GB (full row group) 1:20 Data catalog sync (1M files) ~50 GB (footers only) ~4 PB (full files) 1:80,000 Selective restore (1 row group) ~128 MB 4 GB 1:32 On 100 TB of cold Parquet data with $0.09/GB egress: Full read for schema sync : 100 TB × $0.09 = $9,216 Partial read (footers only, avg 100 KB/file, 1M files) : ~100 GB × $0.09 = $9.00 Savings per catalog sync: $9,207 — 99.9% reduction Even a conservative column-scan scenario (pulling 15% of each file's bytes) cuts a $9,216 monthly read bill to $1,382 . The ceiling on savings is determined entirely by how precisely you can address the bytes you actually need. That precision is what frames and jump tables buy you. Zstd Frames: What They

The Samsara Tracking Label hides a BLE system that can offer real-time location in a disposable package.

Zoox, the autonomous vehicle company owned by Amazon, unveiled a new look for its boxy, bidirectional robotaxi, calling it the "next evolution" of the vehicle intended for mass production. The company is currently operating a free robotaxi service in San Francisco, Las Vegas, Austin, and Miami while it waits for the federal government to approve […]

New details have emerged about Valor's latest fund, which last year announced it was raising an unspecified amount of capital.

“It’s shooting pure unadulterated propaganda into our veins,” says one worker.

GitHub热门项目 | Google Workspace CLI — one command-line tool for Drive, Gmail, Calendar, Sheets, Docs, Chat, Admin, and more. Dynamically built from Google Discovery Service. Includes AI agent skills. | Stars: 28,023 | 678 stars today | 语言: Rust

On April 29, 2026, Nathan Sobo published the Zed 1.0 announcement post on Zed's blog. The post landed on Hacker News at 2,047 points and 663 comments — the highest-engagement HN story in the present cache by a substantial margin. The launch announcement is a milestone marker after five years of development, roughly a million lines of Rust, and a custom GPU-accelerated UI framework called GPUI that the Zed team built from scratch rather than building atop Electron, Chromium, or any other browser engine. The structural argument Zed has been making for the last several years is condensed in one sentence from Sobo's post: "Instead of building Zed like a web page, we built it like a video game, organizing the entire application around feeding data to shaders running on the GPU." The video-game framing is not metaphorical. Zed's editor surface is composited by feeding glyph atlases, syntax-tree-derived color spans, and pane-layout geometry into GPU shaders the way a video-game engine composites its frames. The reason this matters is the reason the Zed team gave for starting over from the Atom era: Atom was built as a fork of Chromium, and the same team that built Atom is the team that spawned Electron. The Atom-Electron-VSCode lineage is, in the historical-causal sense, Zed's own. Sobo's post is unusually direct about the inherited limitation: "Electron eventually became the foundation of VS Code (which today seems to be forked into a new AI code editor every other week). Web technology offered an easy path to shipping flexible software, but it also imposed a ceiling. No matter how hard we worked, we couldn't make Atom better than the platform it was built on." The 1.0 announcement is, in part, a statement that the rebuild from scratch has finally cleared that ceiling. Who built it Zed's three co-founders all worked on Atom at GitHub before founding Zed Industries in 2021. Nathan Sobo led the Atom team from 2011 to 2018; he also co-led Teletype for Atom, one of the first

A new member has joined the fulgur family. fulgur-chart — a CLI that takes Chart.js v4-compatible JSON specs and renders deterministic SVG/PNG charts. No browser required. https://github.com/fulgur-rs/fulgur-chart Two things make it different: it doesn't spin up a browser, and for a fixed version, font, and rendering options, the same JSON input always produces byte-identical output. This post covers why I built it, a timing coincidence that made me feel like I was on the right track, and how to use it. Why I wanted graphs in PDFs fulgur and fulgur-chart are built around one idea: AI agents should be able to generate documents that look good . There are three steps to that argument. First, Markdown isn't expressive enough. For client-facing reports, plain Markdown often undersells otherwise strong content. Second, visual quality is persuasive. A well-formatted report lands differently than a wall of text. Third — and this is the one I keep coming back to — in many business workflows, PDF carries more institutional weight than a Markdown file or a transient web page . That authority has two dimensions. There's a cognitive one: PDFs read as "serious documents." Proposals, reports, invoices — the format itself signals credibility. And there's a technical one: PDF can support digital signatures, encryption, and archival profiles such as PDF/A. That's the ground flpdf covers, a pure-Rust PDF toolkit modeled on qpdf's workflow. So the goal is always PDF, not HTML, not a web page. That's what fulgur is for. And a polished report needs charts. But Markdown can't draw charts. Which brings me to a problem I already knew was coming: the Chart.js library requires JavaScript to run . fulgur has no browser and no JS runtime, so there was no path to running Chart.js directly. The design choice: no JS engine The obvious alternative was to embed a JavaScript runtime. I could either run Chart.js with a compatible Canvas implementation, or build a JavaScript renderer that consumes Cha

Modular, one of the most promising chip software startups of the AI era, heads for a multibillion-dollar exit.

Bob Iger's tenure as CEO of Disney came to an end a few months ago, after two decades of leading the entertainment giant through some of its most pivotal transformations and acquisitions. Iger, in an exit interview with The Financial Times, has now confirmed some significant efforts that didn't pan out, such as walking away […]

Everything is expensive. Treat yourself to one of these WIRED-tested and -approved Prime Day picks under $30.

With the Reactive Data Layer Architecture (RDLA), you establish a clear boundary between public data APIs and private, framework-specific data-source implementations. Your presentation layer operates in a purely reactive manner, observing data changes rather than procedurally querying them. RDLA also simplifies testing by encouraging you to program to interfaces and use clean seeding patterns. By Mervyn Anthony

Complete preparation breeds complacency. What is seen every day no longer raises suspicion. The...

The Problem I Kept Seeing Over the past year working across multiple client teams on DevOps and pipeline work, I kept noticing the same thing. Developers storing GitHub PATs in Notepad. QA engineers keeping API keys in a text file on the desktop. DevOps folks with database passwords in a sticky note app. During screen shares — sprint reviews, debugging sessions, pair programming, recorded demos — those credentials were just sitting there. Visible to everyone in the call. Nobody said anything. It just kept happening. Why Existing Tools Didn't Fit I looked for something simple that solved this. Here's what I found and why none of it quite worked: Password managers (1Password, Bitwarden) Good tools. But they're built around cloud sync, browser extensions, and team sharing. For an individual developer who just wants somewhere safe to keep a PAT — overkill. Also: corporate IT policies often block installation of cloud-synced password managers on work machines. Secret managers (HashiCorp Vault, AWS Secrets Manager) These are infrastructure tools, not personal workflow tools. Setting up Vault for an individual developer's PAT collection is like using a forklift to move a chair. OS keystores (Windows Credential Manager, macOS Keychain) Actually decent for storage. But no UI built for this workflow, no copy-to-clipboard, and they don't solve the screen-exposure problem at all. The gap: Something simple, local, and designed around the moment of use — not just storage. So I Built Tokenly Tokenly is a local-only desktop credential vault. The core design principle is simple: Credential values are never shown on screen. You copy them to clipboard. That's the only way to use them. The clipboard auto-clears after 30 seconds. If you need to visually verify a value — press and hold a button. Release it, the value hides immediately. Not a toggle — a hold. Toggles get forgotten. Holds don't. Technical Decisions Worth Explaining Why Tauri over Electron Tauri uses the operating system's

Introduction When a query runs in ClickHouse®, the database does much more than simply read data and return results. Before execution begins, ClickHouse® parses the SQL statement, analyzes it, applies optimizations, and builds an execution plan that determines the most efficient way to process the query. Understanding query execution plans is one of the most valuable skills for anyone working with ClickHouse®. They provide visibility into how queries are executed, helping you identify bottlenecks, validate optimization efforts, and troubleshoot performance issues. In this article, we'll explore how ClickHouse® generates execution plans, the different EXPLAIN modes, and how to interpret them for better query optimization. Why Query Execution Plans Matter A SQL query defines what data you want, but it doesn't explain how the database retrieves it. Consider the following query: SELECT country , count () FROM events GROUP BY country ; Although the query looks simple, ClickHouse® must determine: Which data parts to read Whether primary indexes can reduce the scan If data skipping indexes can be used How aggregation should be performed Whether parallel execution is possible How intermediate results should be merged A query execution plan provides answers to these questions, making it an essential tool for performance tuning. The ClickHouse Query Lifecycle Every query passes through several stages before producing results. The lifecycle typically looks like this: SQL Query │ ▼ Parser │ ▼ Analyzer │ ▼ Optimizer │ ▼ Query Plan │ ▼ Execution Pipeline │ ▼ Results Each stage plays an important role: Parser validates SQL syntax. Analyzer resolves tables, columns, and expressions. Optimizer applies query optimizations. Query Plan determines the logical execution steps. Pipeline distributes work across multiple threads. Execution processes the data and returns the results. Understanding this workflow makes execution plans much easier to interpret. Introducing the EXPLAIN Statement

I applied to a YC W25 startup the normal way. Filled out the form, wrote a decent cover letter, hit submit. Silence. While waiting, I found their open-source repo on GitHub. Read through the codebase out of genuine curiosity I wanted to understand what they were actually building. Found a bug. Fixed it. Opened a PR. It got merged in 2 days. They still hadn't replied to my application. Here's what that taught me about job hunting in 2025: A cover letter tells someone what you claim you can do. A merged PR shows them. One of those gets read. The other gets filed under "maybe later" -which is just "no" with extra steps. I'm not saying cold applications are dead. I'm saying they're the last resort, not the first move. If a company has a public repo, you have a backdoor that most applicants don't even think to try. Read the code deep and find something small but real. Fix it and Open a PR. Now you're not a stranger in their inbox you're someone who already ships for them. The reply came eventually, by the way. But by then, the maintainers already knew my GitHub handle. That matters more than you think. Have you ever landed something through a contribution instead of an application? Drop it in the comments curious how many people have done this.

This is the third piece in a sequence. The first asked whether Sloan had flagged anyone else — it...

Climate.us has now restored everything taken down by the government.

Everything is expensive. Treat yourself to one of these WIRED-tested and -approved Prime Day picks under $30.

Public notice of a single "compassionate use" case is odd in every way.