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AI 资讯

Anthropic Shipped @Claude For Slack. My Team Runs On

Anthropic Shipped @claude for Slack. My Team Runs on Telegram. Anthropic just shipped @Claude inside Slack channels. Tag the bot, it reads the thread, does work async, posts back. Nice product. Except roughly 95% of small businesses don't live in Slack — they run on WhatsApp, Telegram, and Gmail. If you're a solopreneur or a 1-to-10-person team, here's the exact four-part recipe I use to run the same pattern in Telegram for under $12/month. What Anthropic actually shipped (and who it's for) Anthropic shipped an enterprise distribution deal wearing a product launch t-shirt. @Claude for Slack lets you tag the bot in a channel or thread, gives it channel memory, connects to your other apps, and returns work asynchronously — but only on Slack Team and Enterprise plans. That's the punchline: it lives where the annual contracts live. Look at the raw user counts. Slack's own reporting puts it around 35–40 million weekly active users globally. WhatsApp is over 2 billion. Telegram is over 900 million. Gmail sits around 1.8 billion. In the 1-to-10-employee segment outside US tech, Slack penetration is single digits. Small teams in Europe, LATAM, and most of Asia coordinate in WhatsApp groups and run pipeline out of Gmail. They are not about to add Slack seats at $15/user/month just to get an @Claude mention. That's a rational call for Anthropic — Slack is where the enterprise procurement motion already exists. It's just not a product for the operator segment. And the pattern they productized is trivially replicable on any messenger with a bot API. Platform Weekly/monthly active users Bot API Cost to run a mention-bot Slack ~35–40M WAU Yes, paid plan $15/user/mo + API Telegram ~900M MAU Yes, free ~$5–12/mo API only WhatsApp Business ~2B MAU Yes, metered $0.005–0.08/conversation + API Gmail ~1.8B MAU Pub/Sub push Free tier + API The four-part recipe (works in any messenger) Every mention-bot is the same four moving parts: a webhook that fires on mention, a context store that ho

2026-07-09 原文 →
AI 资讯

Feeling behind never left me, even after 16 years and four titles

I have been building software for sixteen years. I have four ambassador titles I earned honestly. And last week I sat at my desk at eleven at night, certain that everyone else my age was further ahead than me. You know that feeling. The one where you scroll past someone's launch, someone's promotion, someone's clean little success, and a cold voice says you should be there by now. It does not care what you have done. It only points at what you have not. For most of my career I treated that voice as a problem to solve. If I could learn one more tool, ship one more thing, earn one more title, it would finally go quiet. So I did. I learned the tools. I shipped the things. I earned the titles. The voice did not go quiet. It moved the finish line and waited for me there. Here is the opinion I wish someone had handed me a decade ago. Feeling behind is not a bug in you. It is the tax you pay for caring about the work. The people who feel the most behind are almost never the ones who are actually behind. They are the ones paying attention. They see the gap between what they made and what they meant to make, and that gap never closes, because the moment you get better, your taste gets better too. The gap is not evidence that you are failing. The gap is proof that you still have standards. I know engineers with twenty years and a wall of real accomplishments who quietly feel like frauds. I know brilliant people five years in, staring at a job market that feels brutal, convinced everyone else got a memo they missed. None of them are behind. All of them are exhausted from running a race that has no finish line, on a track only they can see. The comparison is rigged, and it is worth saying why. You compare your inside to everyone else's outside. You know your own doubt, your own half-finished drafts, your own two in the morning. You see their launch, their title, their highlight. You are matching your bloopers against their trailer, and then calling yourself slow. So what change

2026-07-09 原文 →
AI 资讯

Supercharge Your Crypto and Stock Analytics with lunarcrush-go

Are you building a trading dashboard, a market sentiment tracker, or a financial data pipeline in Go? If so, you know that gathering reliable social intelligence and market data is often a complex, messy process. You have to juggle raw HTTP requests, decode deeply nested JSON payloads, and manually handle rate limits. But what if you could access a wealth of crypto and stock social intelligence idiomatically, right where your Go code lives? Enter lunarcrush-go , a powerful, zero-dependency SDK designed to seamlessly integrate the LunarCrush API v4 into your Golang applications. In this article, we will explore why lunarcrush-go is the ultimate tool for developers looking to tap into social and market intelligence, how to get started in under 60 seconds, and why its zero-dependency architecture makes it a robust choice for production workloads. Why LunarCrush? Before diving into the SDK, it is worth understanding what LunarCrush brings to the table. LunarCrush goes beyond traditional price charts. It measures what the internet is actually saying about Bitcoin, Ethereum, Tesla, and thousands of other assets. By analyzing social buzz, creator impact, and overall market sentiment across various platforms, LunarCrush provides a holistic view of the market 1 . Whether you want to know the Galaxy Score of a specific coin, track the hourly social time-series of a stock, or get AI-generated insights on a trending topic, LunarCrush has you covered. Introducing lunarcrush-go The lunarcrush-go library was built with one primary goal: to provide clean, typed, and production-ready access to every LunarCrush endpoint without pulling in a single third-party dependency. It speaks Go natively, meaning you do not have to wrestle with raw JSON or hand-roll your own retry loops. Key Features Here is what makes lunarcrush-go stand out: Complete API Coverage: The SDK supports every LunarCrush endpoint, including Coins, Stocks, Topics, Categories, Creators, Posts, Searches, AI summaries, a

2026-07-09 原文 →
开发者

What actually happens when you launch a side project with zero audience

Everyone talks about the build. Nobody talks about what happens the week after, when you go to actually tell people it exists and discover every distribution channel has its own quiet gatekeeping you didn't know about until you hit it. Hacker News flagged my Show HN before it ever reached the front page. Not rejected — flagged, silently, likely because the account posting it was brand new with a self-promotional link and zero history. No warning, no explanation, just gone from /newest for anyone not specifically looking. Reddit was worse in a different way. r/webdev's AutoMod rejects any submission from an account under three months old with low karma — a hard gate, not a soft one, and it doesn't care which day you post or how you phrase it. r/SideProject let the post through technically, but Reddit's own spam filter quietly removed it minutes later, invisible to everyone except me looking at my own profile. X was just silence. Zero followers means the algorithm has no graph to push the post into. Four views, three of which were probably me refreshing. The one channel that actually worked was the one with the lowest bar to entry: writing. dev.to doesn't gate you behind account age or karma. You write something, it's live, and if it's genuinely useful, people find it — slowly, but for real. That's where actual engagement happened. The pattern underneath all of this: almost every high-leverage distribution channel is, by design, hostile to accounts with no history. That's not a bug — it's the exact mechanism that keeps those platforms usable, and it exists specifically to stop people doing exactly what I was trying to do: show up once with a link and leave. The system is working as intended. It just doesn't feel that way when you're the one hitting the wall. What's actually working, three weeks in, isn't a growth hack — it's writing things people search for, verbatim, and being patient about everything else building account history the boring way: showing up, commenti

2026-07-09 原文 →
AI 资讯

Crushing 5GB of XML: Building a Blazing Fast Apple Health Parser with Rust and ClickHouse

We’ve all been there. You click "Export Health Data" on your iPhone, wait ten minutes, and receive a massive, bloated export.xml file. If you've tracked your fitness for years, this file can easily exceed 5GB. Try opening that in Python’s ElementTree or even pandas , and your RAM will cry for mercy. This is a classic Data Engineering challenge: transforming high-volume, semi-structured XML into actionable insights without waiting an eternity. In this tutorial, we are going to build a high-performance parser using Rust performance techniques, Rayon for parallelism, and ClickHouse for lightning-fast OLAP queries. By leveraging Rust's zero-cost abstractions, we'll turn a 20-minute Python slog into a sub-30-second sprint. 🚀 The High-Level Architecture Handling 5GB of XML requires a streaming approach. We cannot load the whole file into memory. We will stream the XML, parse segments in parallel, and ship them to ClickHouse using Protocol Buffers for maximum serialization efficiency. graph TD A[Apple Health export.xml] --> B[Streaming XML Reader] B --> C{Chunking Logic} C -->|Batch 1| D[Rayon Worker 1] C -->|Batch 2| E[Rayon Worker 2] C -->|Batch N| F[Rayon Worker N] D & E & F --> G[Protobuf Serialization] G --> H[(ClickHouse DB)] H --> I[Grafana / SQL Insights] Prerequisites To follow along, you'll need: Rust (Stable) Tech Stack : quick-xml (for streaming), serde (serialization), rayon (data parallelism), and clickhouse-rs . A running ClickHouse instance. 1. Defining the Data Schema Apple Health data (specifically Record types) consists of types, dates, and values. Since we want high performance, we'll use Protocol Buffers to define our intermediate format, ensuring minimal overhead when moving data through the pipeline. // Simplified representation of a Health Record use serde ::{ Deserialize , Serialize }; #[derive(Debug, Serialize, Deserialize, Clone)] pub struct HealthRecord { #[serde(rename = "@type" )] pub record_type : String , #[serde(rename = "@startDate" )] pub

2026-07-09 原文 →
AI 资讯

Did you ever face "stale singleton httpx connection" and "cold-start connection problem" problem, Well I did tonight.

It is been while I am learning and build around FastAPI. So there is a project where I was thinking how to add this new feature over exiting one. Like what changes I need to make in database which need to be reflected in my backend and frontend. I already lunched the web locally. Problem started When I when back to the web and reload it it shows this error: ERROR: ConnectTimeout: Unauthorized 401. I was like what? Why? I thougth there is some issue with login endpoint or refresh token function. When i did some debugging and found some new information which is: "Either Supabase's edge/pooler (or OS, or an intermediate proxy/NAT) silently kills those idle connections server-side after some timeout but client-side pool doesn't know that." As I was doing nothing in become idle state so to save the resources server side silently close that particular connection. So I came back and try to connect it give this error. First thought come it my mind after this was there should be a way to automatically check this idle state and if user was in ideal state then create a new connection. Proposed Solutions After a while I come up with these solution: Calculate the Idle time: if it is more then server connection timeout then establish new connection. Retry logic: retry once on the specific connection errors. I thought this will work but This again give me error then this new issue I faced. Cold-start connection problem There is something call dual-stack (IPv4 and IPv6) networks and Happy Eyeballs is a network mechanism which automatically move to IPv4 connection if IPv6 fails. But supabase-py uses httpx and it doesn't support Happy Eyeballs. So in first try after the connection time out it try to establish IPv6 connection which is not routeable in most Pakistani ISPs and ultimately it fails and wait for timeout. There is no way to try it again for IPv4. So we have to do it manually. So this error help me to learn many thing in process. Share your thoughts.

2026-07-09 原文 →