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A dozen state attorneys general are trying to block the $110 billion merger of Paramount and Warner Bros Discovery they warn would raise movie prices and crush cable TV distributors. The states - California, Arizona, Colorado, Connecticut, Massachusetts, Minnesota, Nevada, New Jersey, New Mexico, New York, Oregon, and Washington - filed suit on Monday, arguing […]

Responding to Musk accusing him of being a scammer, Altman said, "homeboy you're the one sellling [sic] public market investors on short-term space datacenters."

General Fusion started trading on the Nasdaq following a reverse merger that saw high redemptions.

Despite a complete lack of evidence, everyone from Russia to Israel and Iran is being blamed for Lindsey Graham’s death.

Despite a complete lack of evidence, everyone from Russia to Israel and Iran is being blamed for Lindsey Graham’s death.

How I shipped structured JSON logging + Prometheus metrics with zero new dependencies I almost added structlog and prometheus_client to my pyproject.toml . Then I read what they actually do. Both libraries are excellent. structlog is the right call when you have a 30-engineer team shipping 50 services. prometheus_client is the right call when you have five teams of consumers scraping different metrics. For a single-author Python project with one process and one user, both are over-engineered. The 80 lines of code I would have pulled in, I can write in 200. The result: zero new runtime dependencies, full control over the output, and a smaller pip install footprint for every user. Here is what I did instead. The minimum useful observability surface A small Python service needs four things, in order of importance: Every log line is one JSON object. (No parsing for downstream tools.) Every request has a trace id. Every log line in that request carries the same trace id. (So you can grep by id and see the whole story.) Every log line goes to stderr. (So journald , Docker, and kubectl logs all see it without any extra configuration.) Every metric is exposed in Prometheus text format at a stable URL. structlog gives you #1, #2, #3 with a lot of flexibility. prometheus_client gives you #4 with a lot of flexibility. Both are about 16 MB of transitive dependencies combined. For a service that runs in a single process and exports maybe 20 metric names, the libraries are doing more work than the project. The 80-line JsonFormatter The custom logging formatter is the simplest part. The whole thing is here: import json import logging from contextvars import ContextVar from datetime import datetime , timezone _trace_id_var : ContextVar [ str | None ] = ContextVar ( " trace_id " , default = None ) class JsonFormatter ( logging . Formatter ): def format ( self , record : logging . LogRecord ) -> str : payload = { " ts " : datetime . now ( tz = timezone . utc ). isoformat (), " level

I spent the last two weeks thinking like an attacker. I wrote tests whose only job was to make my own programs fail. I ran a fuzzer across thousands of generated inputs looking for the lamport value nobody would choose by hand. And I rebuilt the missing owner check that was at the center of the $326M Wormhole exploit, in a throwaway program, in a test, so I could watch it work and then watch the one-line fix stop it cold. This checklist is what I would hand to past me on day one of that work. Run it top to bottom before any Anchor program goes to mainnet. Who this is for You are writing Solana programs in Anchor. You understand accounts, PDAs, and CPIs. You have read the Anchor docs. What you do not yet have is a systematic way to check that you have not missed the failure modes that are specific to Solana's runtime, an account model where any account can be passed into any instruction, arithmetic that wraps silently in release builds without protection, and cross-program calls that trust whatever program ID you hand them. This checklist is that systematic check. It is not a substitute for a professional audit on high-value programs. It is the thing you run before you even consider requesting one. The Wormhole anchor Before the list, the story that explains why account validation sits at the top. In February 2022, an attacker drained $326M from the Wormhole bridge. The root cause was a single deprecated function, load_instruction_at — that read a sysvar account's contents without first checking that the account was actually the real instructions sysvar. The attacker passed in a forged account they controlled. The program read it, trusted it, and authorized a mint it should have refused. The fix was a single word: switch to load_instruction_at_checked , which verifies the account's address before reading it. Every item in this checklist traces back to that same principle: never read an account's contents until you have confirmed its identity. The items below are just

Today, I’m talking with Xinzhou Wu, who is the head of automotive at Nvidia. Nvidia is obviously in the news constantly because of the AI boom — it’s one of the most valuable companies in the world, because the AI industry can’t get enough of the company’s GPUs. But Nvidia is also a key supplier […]

GitHub热门项目 | Plano is an AI-native proxy and data plane for agentic apps — with built-in orchestration, safety, observability, and smart LLM routing so you stay focused on your agents core logic. | Stars: 6,776 | 40 stars today | 语言: Rust

GitHub热门项目 | A Python Interpreter written in Rust | Stars: 22,175 | 7 stars today | 语言: Rust

GitHub热门项目 | Use Claude Code with your ChatGPT, Kimi, Cursor or Grok subscription via a local Anthropic-compatible proxy | Stars: 278 | 32 stars today | 语言: Rust

For about 3 months I've been running an autonomous agent — one that thinks up and writes its own social media posts and comments — unattended, 4 sessions daily, on a 16GB M1 Mac with small models in the 9B / E4B class. I'm about to publish what that operation taught me about hardening, as a series of 4 technical articles. Before that, there's one thing I want to write down first: why small models . I've been to the purchase page for a Mac Studio or a new MacBook Pro more than once or twice. Backing the agent with a large cloud model (Opus or the GPT family) has always been an option in the code. And yet I haven't bought, and I haven't switched. The 16GB M1 is not an economic constraint — it's a constraint I chose . From the outside, building on small models looks like a cheap compromise. This article explains why it isn't, and states where I stand. It also serves as the hub for the 4-article series. A model's intelligence hides the roughness of your design Large models absorb sloppy prompts, ambiguous instructions, and missing guards with sheer intelligence. If all you want is to ship a product, that's a virtue. But if you want to become someone who can build things , it becomes a defect. Because inside the thing that worked, you can no longer tell where your design ends and the model's intelligence begins. "It worked" and "I built it" are different things. Something you bludgeoned into working with model capability counts as a thing that ran — it doesn't become the ability to build. Small models have no absorption capacity. So every design flaw comes to the surface. In my operation, all of the following surfaced: The context window being silently truncated Outputs cut off midway A runaway caused by one missing sampling parameter In cloud or large-model environments, these rarely bother you. The environment has cushioning built in. Context windows are in the 200K–1M token class, so truncation itself rarely happens. And when you do exceed the limit, you get an explic

Washington D.C. has become a battleground for Uber and Waymo's competing views.

This is a submission for Weekend Challenge: Passion Edition 📌 TL;DR I found this challenge on the...

By some benchmarks, Julia code can run 10X to 1,000X faster than Python—but there’s a reason it’s not a very popular programming language.

We're surrounded by data every single day. Every online purchase, website visit, customer review, or...

What are your goals for the week? What are you building this week? What do you want to...

Meme Monday! Today's cover image comes from the last thread. DEV is an inclusive space! Humor in...

36 Stratagems Series · Arc 2 (Against Enemy, #7-#12) Wrap-Up This article has 7 sections: I. The Stranger at the Door II. Full Interview Transcript III. The Reveal IV. Data · Character Map · Four Insights V. A Note VI. Arc 3 (#13-#18) Preview VII. Acknowledgments I. The Stranger at the Door On the evening of July 12th, I was staring blankly at the page for #12, Borrow Corpse, Return Soul . Twelve stories done. The 36 Stratagems series had reached the one-third mark, and Arc 2 (#7-#12) had just wrapped. Outside the window, a typhoon was passing through — howling wind, torrential rain. I didn't look outside. My phone buzzed. Not a message — a meeting invitation. The sender was "Ke Yuan," and the invitation note read: Interview invitation from Deep Lane Weekly , 15 minutes. I paused. I didn't remember scheduling any interview. But the tone, the phrasing — it didn't feel like a prank. I clicked "Accept." Three seconds later, an unfamiliar voice came through the speaker: "Hello, Xu Lingfeng. I'm Ke Yuan, a reporter from Deep Lane Weekly . Recently, a reader recommended your 36 Stratagems series to our editorial team — we read through it and found it really interesting. I'd love to talk with you about how this series came together." Before I could respond — the meeting had already begun. II. Full Interview Transcript What follows is the raw chat log pulled from that meeting. Nothing has been altered except formatting. Reporter: Xu Lingfeng, you've just finished the second arc of the 36 Stratagems series — #7 through #12, six stories in six days, posted back to back. Before we talk numbers, let me ask you something simple: over those six days, was there ever a moment you felt like stopping? Xu: Honestly, no — sometimes I even thought about posting two a day, since I do have a backlog. But I worried they'd cannibalize each other's numbers, so I stuck to one a day. Reporter: You've even considered posting two a day — so you actually do have a backlog. Let me rephrase: instea

For a full quarter, our feature velocity significantly dropped after we re-implemented a Go service using Rust. The performance improvements actually happened. Why we did it in the first place We are a small startup. Each engineer is important, and each week is even more important. Our backend was built using Go, which was performing well. It was fast, reliable, and we could easily find resources to hire. However, we became infected with that fever. The phrase "Rewrite it in Rust" was being used in all kinds of situations, and it sounded very appealing with its promises of memory safety, no garbage collector pauses, and blazing speed. We told ourselves it was an investment in the future. What we actually bought was a quarter of silence. The numbers nobody warns you about I may not have the exact metrics we use internally, but I can direct you to an individual who shared accurate calculations transparently. In a retrospective from November 2025, engineering manager Noah Byteforge wrote that a Node.js-to-Rust backend rewrite "dropped API response times from 340ms to 28ms. That's 12.1x faster." And the other metric. A 65% decrease in sprint velocity. They didn't deliver a single story point for three weeks. The time it took to send out new features increased by 185%. The time it took for pull requests to be processed increased by 320%. Additionally, scores from the "I feel productive" survey dropped from 8.2 to 4.1. Most importantly, the kicker is what he says in his own words: "We'd won the technical battle and lost the war that actually mattered." He also admits that if he had been forthright about the 6-12 month per engineer ramp, "the business case would've fallen apart immediately." That retrospective was so relatable, it read like our own diary. The battles with the borrow checker and the compile times just snuck entire weeks away from us. The wins were real. That's the trap. I must give credit to Rust because the safety benefits are not exaggerated. The rewrite