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submitted by /u/Mo_h [link] [留言]

submitted by /u/mttd [link] [留言]

You know that, right? The mobile web is completely unusable. Its garbage and it is garbage because you can't say "no" to stupid advertisers and keep putting more and more stupid popovers and sneaky links, and animations, and content obscuring crap, often not bothering to put the close box within the bounds of the screen. If you work on a mobile version of a website - shame on you. It doesn't work. I'm not kidding. I probably visited, and immediately noped out via the back button because it is just more trouble than the crappy click bait title implies it might be worth. RIP mobile web. submitted by /u/Small_Dog_8699 [link] [留言]

I'm excited to announce Elmo , an open source AEO / AIO / GEO tool that tracks AI visibility. It's the most popular, regularly maintained AI visibility tracker on GitHub. A lot of tools in this space are very expensive or have a lot of lock in. Really you just need to run prompts against LLMs, track mentions, and analyze citations. I'm also using it to improve the AI visibility for Elmo itself (although it's still early days). All you need to run is Docker and a web scraper API key (like BrightData) and OpenAI/Anthropic/Mistral/OpenRouter API key, and you're good to go. There's a lot coming soon (sentiment analysis, content simulations, etc) but it's already in use by a number of e-commerce and SaaS sites. Curious to hear what you think!

From the motivation-pattern-log — a public, dated, falsifiable prediction log for AI-era cybersecurity attack patterns grounded in motivation analysis. Predictions are scored quarterly against stated falsifiers. PREDICTION-20260601-0008 Created: 2026-06-01 Pattern: boredom-with-asymmetric-leverage Substrate: Open-source package registries (npm, PyPI, Crates.io, Packagist) and GitHub Actions CI/CD workflow injection Leading indicator observed: Four distinct, concurrent, cross-registry supply chain campaigns (TrapDoor: 34 packages across npm/PyPI/Crates.io; Megalodon: 5,718 automated commits to 5,561 GitHub repos in six hours; Packagist compromise of 8 packages; Laravel-Lang PHP credential stealer) appeared within a 72-hour window in 2026-W22, all exhibiting automation signatures — throwaway publisher accounts, wave publishing, base64-encoded shell payloads, off-the-shelf delivery via GitHub Releases — consistent with toolkit operation rather than bespoke tradecraft. npm's reactive rollout of 2FA-gated publishing signals registry operators recognizing volume pressure. Predicted window: 2026-Q3 through 2027-Q1 Predicted shape: Automated, low-sophistication credential-stealing and backdoor-planting campaigns against npm, PyPI, Crates.io, and Packagist will continue to increase in incident volume while average per-campaign novelty declines. The dominant operational signature will be scripted account creation, automated package publication across multiple registries simultaneously, and CI/CD workflow injection via forged or compromised GitHub bot identities — all executable with commodity toolkits requiring no original exploit development. At least two registry operators beyond npm will announce reactive publishing controls (mandatory 2FA, namespace-squatting detection, automated malware scanning with publication holds) within the window in direct response to volume pressure. Security vendors will report a measurable increase in "unsophisticated supply chain" incidents re

Ever received a WebSocket tick stream for US stocks and wondered why your indicators behave oddly outside regular hours? The raw data doesn’t tell you which session a trade belongs to, but identifying the session is crucial for signal quality. Here’s a clean, no-dependency-heavy way to do it in Python. Quick Session Reference Session US Eastern Time Data Characteristics Pre-market 04:00-09:30 Sparse trades, choppy moves Regular hours 09:30-16:00 Dense liquidity, smooth price action After-hours 16:00-20:00 Volatility often triggered by news Method 1: Timestamp Conversion Almost every API sends a UTC timestamp. Convert it to US/Eastern and classify. from datetime import datetime import pytz # US Eastern timezone et = pytz . timezone ( ' US/Eastern ' ) def get_session ( ts ): t = datetime . fromtimestamp ( ts , et ) # Check pre-market window if t . hour < 9 or ( t . hour == 9 and t . minute < 30 ): return " pre " # Regular session if t . hour < 16 : return " regular " # After-hours return " after " Method 2: Use a Session Status Field If your provider sends a field like sessionType , you can skip the timezone math. Just make sure to test edge cases at session boundaries. Live Integration Example Using a WebSocket feed (like AllTick’s market data stream) that includes a timestamp, I label ticks on the fly. import websocket import json from datetime import datetime import pytz # US Eastern timezone et = pytz . timezone ( ' US/Eastern ' ) def session ( ts ): t = datetime . fromtimestamp ( ts , et ) if t . hour < 9 or ( t . hour == 9 and t . minute < 30 ): return " pre " elif t . hour < 16 : return " regular " else : return " after " def on_message ( ws , message ): data = json . loads ( message ) s = session ( data [ " timestamp " ]) print ( f " { data [ ' symbol ' ] } | { s } | { data [ ' price ' ] } | { data [ ' volume ' ] } " ) # Open WebSocket connection ws = websocket . WebSocketApp ( " wss://ws.alltick.co/stock " , on_message = on_message ) ws . run_forever () Effic

When we started experimenting with AI translations, we assumed the biggest challenge would be accuracy. We were wrong. The harder problem was preference. Give two AI models the same sentence, and both translations can be technically correct. Yet people almost always have a favorite. One sounds more natural. One feels more human. One is the version they'd actually use. That observation eventually led us to build Parley , a simple game where players compare two translations and choose the better one. What happened next surprised us. People became highly engaged with a task that looked almost trivial. They started debating word choices, discussing tone, and noticing subtle differences between translations. Some users spent far longer interacting with translation examples than they ever would reading documentation or language-learning materials. It highlighted something interesting about AI products: evaluation can be more engaging than generation. Most AI interfaces focus on creating content. But humans are often much better at judging quality than producing it from scratch. Asking someone to choose between two outputs requires less effort while still training their intuition. The experiment also changed how I think about language learning. Traditional language apps often rely on memorization and repetition. But comparing alternatives forces you to think about meaning, context, and natural expression. You're not just learning vocabulary, you're developing taste. And in a world where AI can generate endless content, taste might become one of the most valuable skills we can build. Have you seen similar patterns in AI products where evaluation turns out to be more engaging than creation?

submitted by /u/Successful_Bowl2564 [link] [留言]

Last week I built a little dashboard with Claude. Took maybe ten minutes. Then I spent the next hour trying to get it online. ssh in, install docker, write a Dockerfile, set up nginx, run certbot, certbot fails, read the log, oh the DNS hasn't propagated, wait, run it again, open port 443, realize ufw was blocking it the whole time. By the time it was live I'd forgotten what the app even did. I've done that maybe a few hundred times by now. I'm a backend guy, I'm fast at it. But fast at something boring still means doing the boring thing. So at some point I just thought: the AI already wrote the app. Why does it stop right when the annoying part starts? Why doesn't it just deploy the thing itself? The reason is it has no hands. The model can write you a perfect docker-compose file. It can't ssh into your box and run it. No connection to your server, nowhere to hold your key. So I gave it hands. It's an MCP server, vibe-deploy. You hook it up once to a VPS you own, and then you just say "deploy this to notes.mydomain.com" and the agent containerizes it, ships it over ssh, sets up nginx, gets a real Let's Encrypt cert. Node, Python, Go, plain static. It figures out the stack and writes the Dockerfile. No PaaS, no per-seat pricing, no free tier you'll outgrow. A $5 box runs a dozen of my projects and I own the whole thing. The "you gave an AI root on your server??" reaction is fair, so: it runs locally, your key never leaves your laptop. I used a separate ssh key scoped to deploys, not my real one, and you should too. It checks the server host key before connecting and validates everything you pass it, because a deploy tool that pastes your input straight into a shell is a horror story waiting to happen. I had someone audit the security before I put it out. They found two real bugs. I fixed them. It's free and MIT, on GitHub and npm as @cgnguyen/vibe-deploy . I built it because I wanted it. If you live in the same gap between "it works on localhost" and "it's online",

ᓯᐅᓇᕐᑕᖅ — Inuktitut for "that which lies ahead; a purpose" 🗣️ On the Name Full disclosure: I named this repository at 2 AM, which is probably when most repository names are decided. Siunertaq comes from Kalaallisut (West Greenlandic), a polysynthetic language — the kind where a single word can encode an entire clause's worth of meaning through agglutination and incorporation. I'm a bit of a grammar nerd, and polysynthetic languages have always fascinated me precisely because of how much structure they pack into a single morphological unit. One word carries subject, object, tense, evidentiality, and mood all at once, with none of it ambiguous if you know the grammar. That felt like the right metaphor for what this project is trying to do: pack a build graph's topology, its norm constraints, and its effect ordering into a single type-checkable unit — where the structure does the work, not the runtime. The word itself means something like "that which lies ahead; a purpose" — which seemed fitting for a tool that reasons about what needs to happen before anything actually runs. 🧵 TL;DR What if your task orchestration system couldn't even represent an ill-ordered build? Not "it would fail at runtime" — but "the type system refuses to construct the value in the first place." That's the idea behind Siunertaq : a Scala 3 project that combines Dhall (a total, non-Turing-complete configuration language), Cats Effect (purely functional async runtime), and a BSD Quiver model (directed Banach space graph) to make inconsistent build topologies structurally non-representable . This post walks through the design — with analogies aimed squarely at the Typelevel community — and closes with some thoughts on what modern AI-assisted development actually looks like when you refuse to let the LLM take the easy path. 🤔 Why Yet Another Build/Orchestration Abstraction? Most task orchestrators model their dependency graph as a mutable Map[Task, List[Task]] or similar at runtime, then check for

Back in February, a friend asked me to join his hackathon team. My first reaction wasn't excitement. It was: "Can I even contribute anything?" I remember repeatedly telling him not to add dead weight to the team and to find someone better. He kept insisting that it didn't matter and that I should just join. The funny thing is, I still don't think I've done anything extraordinary since then. No big startup. No crazy achievement. No overnight success story. Mostly just hundreds of hours of learning, building random things, breaking them, fixing them, and realizing how much I still don't know. But today I caught myself doing something weird. I'm the one thinking about who to bring into a team. And for the first time, I don't immediately feel like I'd be dead weight. Not because I know everything now. Just because I've reached the point where I can look at a problem and genuinely believe that, given enough time, I'll figure out how to contribute. It's a small shift, but it feels important. A few months ago I was wondering if I belonged on a team at all. Today I'm wondering who should be on mine. 👀

At Twio we picked pg-boss for our job queue, ran into trouble when we went serverless, looked at Pub/Sub, and ended up on Google Cloud Tasks. This is what each queue got right, what it got wrong for our workload, and the rule we landed on for choosing between them. The workload Twio is an AI SaaS for loan brokers. The piece that needs a job queue is email processing: download an email, parse the body and attachments, OCR, classify with an LLM, write structured data, and index for RAG. One email with five attachments easily becomes 30+ background jobs. A batch upload becomes hundreds. Why pg-boss worked — until it didn't Our database was Postgres on Neon, so pg-boss was the obvious starting point. No extra infrastructure, and one feature we genuinely loved: transactional enqueue . Because jobs live in the same database as business data, you can create a job in the same transaction as the row that triggered it. No dual-write problem, no "DB succeeded but the queue API failed" inconsistency. It also gave us retries, delayed jobs, dead-letter queues, dedup keys, and full SQL visibility into stuck or failed jobs. For a Postgres-first app on always-on infra, it's an excellent tool. Then we moved heavy processing to Cloud Run, and the cracks showed up. pg-boss polls. Neon suspends. They want opposite things. pg-boss runs a query roughly every 1–2 seconds to look for the next job, plus maintenance queries. Neon autosuspends compute when nothing touches the database. If the queue is polling every second, Neon's idle timer never expires — you pay for always-on compute even when the queue is empty. Worse, when Neon did manage to suspend, the next poll had to wake it. That wake-up takes hundreds of ms to a few seconds, and queries that triggered it would fail with Connection terminated , ECONNRESET , or timeouts. Pooled connections made it worse: the pool kept sockets that the server had already closed during suspend, and the next polling cycle picked one up and broke. This isn

I read a lot of PDFs at night, especially on my phone. And honestly, PDFs are not great for that. Most of them still feel like digital paper: white background, fixed layout, and tiny text. Dark mode helps a bit, but many tools only change the page color. The bigger problem for me was mobile reading. When the text is too small, I have to pinch zoom, move the page left and right, zoom out again, then repeat the same thing on the next paragraph. After doing that too many times, I thought: Why can’t I just read the PDF text like an article? So I built a small free tool: PDF Dark Mode It has two reading modes. Page color mode This keeps the original PDF layout, but makes the page darker and easier to read at night. I use this for scanned PDFs, tables, image-heavy documents, or files where the original layout matters. Text reading mode For selectable PDFs, the tool can extract the text and show it in a cleaner reading view. You can adjust the font size, line height, font family, and theme. This is the part I personally wanted most, because it makes mobile reading much more comfortable. Instead of constantly pinch-zooming a fixed PDF page, the PDF starts to feel more like a normal article. Privacy The tool runs locally in the browser. Your PDF is not uploaded to a server, and refreshing the page clears the current session. Try it You can try it here: PDF Dark Mode I built it for my own night reading, but I’d love to hear feedback from anyone who reads PDFs on mobile. Also, if you ever need to convert a dark PDF back to a light version, I made a related tool for that too: PDF Light Mode