Your docs pipeline, from the terminal PaperQuire v0.2.0 ships with a full command-line interface. Everything you can do in the desktop app — render, template, brand — now works from your terminal. This means you can automate document generation, integrate PaperQuire into CI/CD pipelines, and batch-process entire directories of Markdown without opening the GUI. Quick start If you installed via Homebrew, the paperquire command is already on your PATH: brew install --cask paperquire/paperquire/paperquire Verify with: paperquire version The simplest render is just: paperquire report.md This produces report.pdf in the same directory, using the default template. What you can do Render with options Apply templates, set metadata, control output: paperquire report.md -t corporate --title "Q2 Report" --author "Jane Doe" paperquire spec.md --page-size a4 --margins 0.75,0.75,0.75,0.75 --no-toc paperquire draft.md --watermark "DRAFT" -o drafts/draft.pdf All 22 flags from the desktop app's Document Setup panel are available. See the full list in the CLI Reference . Pipe through stdin/stdout PaperQuire works in Unix pipelines: cat notes.md | paperquire - --stdout > out.pdf Use - as the input file to read from stdin, and --stdout to write the PDF to stdout instead of a file. Batch render directories Render every Markdown file in a directory with one command: paperquire batch ./docs -o ./out paperquire batch ./project --glob "**/*.md" -t corporate paperquire batch ./docs --concurrency 8 --continue-on-error Use --dry-run to preview which files would be rendered without actually producing output. Manage templates List, inspect, create, and share templates: paperquire templates list paperquire templates show minimal-clean paperquire templates init my-template paperquire templates export corporate -o corporate.pqt paperquire templates import ./shared-template.pqt We also launched a community templates repo where you can browse all built-in templates and submit your own via pull request.

I use my SSH manager every day. I also use a separate monitoring tool every day. For a long time I just accepted that these were two different things. Then one day I was SSH'd into a server that was behaving weird. I wanted to check if it was CPU or memory, but I had to open a different app, find the server in there, and wait for the dashboard to load. It took maybe 15 seconds. Not a huge deal. But it broke my flow every single time. I already had an SSH connection open to that server. Why was I opening a second thing just to see what was happening to it? That's what pushed me to build server monitoring directly into Termique, the SSH manager I've been working on. The interesting part: reusing the existing SSH connection SSH connections aren't just for terminals. The protocol supports multiple channels over a single TCP connection. You can have a terminal session running in one channel while sending short exec commands through another channel on the same connection. That's how the monitoring feature works. When you open the metrics panel for a server, Termique creates a separate exec channel on the existing SSH connection and polls /proc/stat for CPU, /proc/meminfo for RAM, and /proc/loadavg for system load. Short-lived commands, called on an interval, over the connection you already have open. No second SSH handshake. No separate auth. Just another channel on the same pipe. The tradeoff: you do need an agent I want to be upfront about this. The monitoring feature requires a small agent installed on each server. It's not agentless. I considered going agentless, relying entirely on /proc reads through exec channels. That works fine on most Linux servers. But the agent makes it easier to handle edge cases properly and opens the door for future features like alerts and longer history retention. Without it, I'd be fighting a lot of fragile shell parsing. If you're managing Linux servers, it's a one-command install. Non-Linux systems aren't supported yet. That's a real l

What building cross-service RBAC taught me about the difference between a fast check and a correct one VaultPay is a wallet microservice I built on top of AuthShield. Previous parts: Part 1 is here: I Built AuthShield and Immediately Knew It Wasn't Enough Part 2 is here: The Silent Failure I Never Saw Coming: What VaultPay Taught Me About Consistency Under Failure Part 3 is here: I Started With a Blocklist. That Was the Wrong Instinct and VaultPay Taught Me Why. Part 4 is here: I Watched Money Move Twice From the Same Request. That's When I Understood Idempotency. Part 5 is here: I Almost Hashed a Document Number That Needed to Be Read Again When I designed JWT validation for VaultPay, the only thing I was optimising for was speed. Local verification, no network call, decode the token with the shared secret, read the claims, move on. Every request gets this. It's fast - no round trip to AuthShield, no added latency on the hot path. That felt like the obvious right answer for a system processing financial transactions, where every millisecond on the request path matters. Then I asked myself a question I hadn't thought through properly: what happens if an admin gets deactivated in AuthShield right now, this second, while they still have a valid token sitting in their browser? The answer, with pure local validation, is uncomfortable. Nothing happens. The token is still cryptographically valid. The signature checks out. The claims say role: admin . VaultPay has no way of knowing that AuthShield revoked this person's access thirty seconds ago, because VaultPay never asked AuthShield. It just trusted the token. That's the moment dual-mode validation stopped being a performance optimisation and became a correctness requirement. Two Services, No Shared Database VaultPay and AuthShield are separate microservices with separate databases. AuthShield owns user accounts, login, JWT issuance, and role management. VaultPay owns wallets, transactions, KYC, and admin operations on t

I log commutes in a spreadsheet because mobility apps smooth over the ugly legs. Last week I added a column I should have tracked years ago: carry seconds ? time from curb to platform when stairs replace ramps. The hidden leg My one-wheel leg is fine on paper. Three metro exits on my route have no elevator during maintenance. Carrying a 14 kg wheel down 22 stairs does not show up in trip duration. It shows up in whether I arrive annoyed enough to skip coffee. What I logged (one week) Exit Stairs Carry time (s) Mood after (1-5) North gate 22 38 2 Side ramp (control) 0 8 4 East stairs 16 29 3 Battery delta on those days? Within noise. Mood delta? Not noise. A cheap decision rule I turned this into a go/no-go check before leaving: if stairs > 15 AND carry_weight_kg > 12: prefer transit-only or locker elif stairs > 0 AND wet_floor: walk the wheel (no riding in station) else: ride It is blunt. It works better than pretending every leg is rideable. Assumptions up front Wheel weight includes pads and charger pouch (~14 kg for my commuter setup). I am not timing competitive carries ? just whether I can do this daily without hating it. Your threshold differs if every exit has elevators. What I would do differently I would log carry seconds from day one, same tab as distance and battery percent. Range math without carry math is incomplete for anyone who mixes metro and one-wheel. I work around personal EVs and sometimes cross-check specs on the official Kingsong catalog. https://www.kingsong.com/collections/electric-unicycle

When the enemy is too strong to attack directly, attack what they hold dear. They will come to you...

submitted by /u/devAndreotti [link] [留言]

Comparing the most feature-rich React data grids in 2026, from pivot tables and tree data to...

Article URL: https://act.eff.org/action/tell-congress-don-t-force-age-checks-online Comments URL: https://news.ycombinator.com/item?id=48713887 Points: 60 # Comments: 16

XSDR is a unified pipeline for monitoring activity on X and the web. I built it because I wanted my agent to do things based on real-time events that were taking place instead of polling the web or scheduling cron jobs. With XSDR, you can trigger agentic loops based on real-time events. All you need is an API key, a webhook that can receive POST requests, and an idea of what you’re looking for. It currently supports X and Firehose (aka a persistent web crawler).

According to ChartMogul's 2026 analysis of 200 B2B software products, the median free-to-paid...

There is a habit that wastes more time than anything else when using Claude. Save this :) Writing the same instructions over and over again. Every session, you re-explain your role. You re-describe your writing style. You re-state your formatting preferences. You re-paste your company context. You re-specify what you want the output to look like. Then you do it again tomorrow. And the day after that. And the day after that. Over a month, you waste hours on instructions you have already written. Not new thinking. Not new requests. Just the same setup, repeated endlessly. Claude Projects and Skills fix this completely. Projects let you save context once and have it applied to every conversation automatically. Skills let you save entire workflows as reusable commands that you can trigger with a single sentence. Together, they turn Claude from "a tool you use from scratch every time" into "a system that already knows everything and just needs your specific request." Here is how to set them up from zero. What Are Claude Projects A Claude Project is a container for conversations that share the same context. When you create a Project, you upload knowledge files and write a system prompt. Every conversation inside that Project automatically has access to those files and follows those instructions. No re-explaining. No re-pasting. No re-describing. The context is always there. Example: you create a Project called "Content Marketing." You upload your brand guidelines, your editorial calendar, your top-performing articles, and your audience personas. You write a system prompt: "You are my content strategist. You know our brand voice, our audience, and our content strategy. Every piece of content should match our guidelines and target our defined personas." Now every conversation in that Project - brainstorming headlines, drafting articles, analyzing competitors - starts with full context. Claude already knows your voice, your audience, and your standards. One setup. Unlimited

Two things happened this month and they tell you everything about where AI is actually going. Coinbase quietly cut its AI bill nearly in half. Open models, smarter routing, better caching. No drama. A finance footnote that happens to be a glimpse of the future. And Dario Amodei published another essay. Not a tweet. An essay. The kind of sprawling, twenty-thousand-word civilizational scripture he keeps handing down from the mount. This one is called "Policy on the AI Exponential," and the gist is that AI is about to hand humanity "almost unimaginable power," that our institutions are too immature to hold it, and that therefore the government should be able to test, gate, and block frontier models before mere mortals get hurt. One of these is a price cut. The other is a prophecy. I want to talk about the prophecy. The robes Let me be fair before I am not. Dario is not a dumb man and he is not a fraud. He runs one of the best labs in the world. The safety concerns are not all imaginary. Misuse is real. I am not the guy arguing that anyone should be able to download a bioweapon recipe for a laugh. If that is the bar, sure, regulate it. Nobody serious disagrees. But watch the move he keeps making. Every few months the prophet descends with a new text. The stakes are always civilizational. The language is always biblical. "Unimaginable power." A "decent possibility" of "significant enduring job loss." Disruption that will be "unusually painful." Humanity handed a force it is not mature enough to wield. He is not describing a product roadmap. He is describing a flood. And conveniently, he is also selling the ark. That is the part that should make you tilt your head. Read the actual proposal Strip the poetry off "Policy on the AI Exponential" and here is the machinery underneath. Mandatory third-party testing for any model above a compute threshold. Authorized evaluators. Security standards. Incident reporting. Government authority to block or reverse a deployment that fail