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The long-rumored deal will give the chip giant access to—and help it promote—a huge repository of open-source AI models and data sets.

There is a strange contradiction happening in software engineering right now. A lot of developers are worried that AI is going to make them obsolete. At the same time, the people building the most capable AI coding tools are demonstrating something that should probably make us rethink what being a software engineer actually means. I don't think the future is one where nobody understands software anymore. I think it is one where writing the software becomes dramatically cheaper. And if that happens, the thing that makes an engineer valuable has to move. That is what I mean by career safety. Career safety isn't about making yourself impossible to replace. It is about making your value portable. We've always resisted giving up the code Developers have a long history of being suspicious of abstractions that take work away from us. We went from machine code to assembly, from assembly to higher-level languages, from manually managing memory to garbage collection, from building everything ourselves to libraries and frameworks, and from text editors to IDEs. We even had entire categories of tools, such as CASE tools, designed to automate parts of software development. And every time, there was resistance. Because programmers don't just use code. We build our identities around it. John Carmack captured this unusually well when he wrote: “Coding” was never the source of value, and people shouldn’t get overly attached to it. — John Carmack He followed that with the more important point: Problem solving is the core skill. — John Carmack That is a difficult idea for developers to internalize because coding is tangible. You can point at the repository. You can point at the pull request. You can count the commits. You can say, "I wrote this." But the business doesn't ultimately pay you for the number of lines you wrote. It pays you for what those lines accomplish. The business never really bought the code A company doesn't wake up in the morning thinking: "We need 14,000 more line

Zoox is expanding to this critical ride-hailing destination a few weeks after it started charging for robotaxi rides.

Nvidia has agreed to buy Hugging Face for $12.93 billion, bringing one of the most popular hosting platforms for open-source AI models, datasets, and tools under the ownership of the world's biggest AI chipmaker. Hugging Face is an online platform founded in 2016 that gives AI developers a space to share their projects and data […]

pnpm 12 has transitioned to a native Rust implementation, maintaining compatibility with pnpm 11 commands, flags, and formats. This update enhances startup and filesystem performance, particularly when using existing caches. Community feedback highlights the performance gains while noting some trade-offs in larger artifact sizes. By Daniel Curtis

Microsoft is changing how it reports its finances to investors by splitting its businesses into two segments and disclosing quarterly revenue for its Azure cloud business for the first time. The changes are meant to reflect the ongoing impact of AI on Microsoft and how the company now operates. The software giant has previously reported […]

TL;DR I recently finished a project from Udacity's Future AWS Agent Engineer Nanodegree Program,...

When a support agent is trying to recover an account after a suspicious login, JWKS verification and session verification define different trust boundaries for API requests. The distinction decides which recovery path the agent can offer and how much damage a stolen credential can do. Short answer: use JWKS verification for a stable, distributed signature boundary, and session verification when the request must reflect current session state; most customer-support systems need both, with an explicit recovery policy between them. 1. Separate the two trust boundaries before scoring a device JWKS verification checks a token signature with a public key set. The verifier never needs a copy of the issuer's private key, which keeps key material out of every API service. That is a good fit for a high-volume edge where the identity claim should remain stable while requests cross service boundaries. Session verification asks a different question: is this particular session still valid right now? Revocation, expiry, or a changed recovery decision can make a previously well-signed token unsuitable for a sensitive action. A valid signature is necessary, but it does not satisfy the business constraints by itself. That distinction is the invariant. Device-fingerprint risk scoring should not silently turn a cryptographic result into an account-recovery decision. Keep it explicit. 2. How should JWKS and session verification govern API requests? Start with the least surprising path. Verify the token signature at the request boundary, then apply issuer, audience, expiry, and device-risk rules. For password reset, email change, or an agent-assisted recovery, perform session verification as a second check when the policy requires current state. The operational catch is key rotation. A JWKS client needs a bounded cache, a refresh trigger for an unknown key identifier, and telemetry for fetch failures. In capacity planning, that means sizing the refresh path separately from ordinary reques

The company is reducing pressure on workers to use artificial intelligence tools while encouraging them to experiment with Hatch, its most advanced AI project yet.

Uber beat out Waymo in launching a commercial robotaxi service in London, the city's first. The vehicles use autonomous driving tech developed by Wayve, a UK-based startup, and will initially feature safety drivers behind the wheel. The launch is a milestone for Uber, which has been plotting a UK launch with Wayve for several years. […]

The US government wrote a letter in support of OpenAI’s argument that training AI on others' intellectual property is fair use.

As part of a broader DHS surge to investigate alleged voter fraud, ICE has announced it plans to pay up to $5 million to create what amounts to a federal voter database.

A startup called Mostik has a wild new approach to combining the capabilities of AI models.

I've been building a free, structured ColdFusion course: Learn ColdFusion — no signup, no paywall. It covers the basics (variables, loops, conditionals), collections (arrays, structs, queries, JSON), a full OOP module (CFCs, inheritance, polymorphism, interfaces), Application.cfc and scopes, and reusable code (custom tags, cfmodule, cfinclude). What makes it different from other CFML tutorials: every single lesson ends with interview questions on that topic — real questions, phrased the way they actually come up in interviews, not generic recap quizzes. There's even a dedicated Scopes Interview Questions page. I haven't found this depth of interview prep anywhere else in ColdFusion learning content online. If you've worked with ColdFusion, I'd love to know what topics you got asked about in interviews — happy to add them to the course.

The DOJ proved to the court that Google acted illegally, but it won't get the big win it wanted.

Christopher was sick of being ghosted by AI recruiters. So he unleashed ChatGPT on his robot interviewer.

US District Court Judge Leonie Brinkema declined the Justice Department's request to make Google sell off parts of its ad tech business, accepting milder remedies to restore competition to markets it illegally monopolized for years. Brinkema said she would adopt most of the behavioral changes proposed by the parties, with some modifications. But those won't […]

Swiggy developed an in house predicted lifetime value model using more than 350 pre order features and a multi task MLP for Food and Instamart. Adding order count as an auxiliary task reduced model parameters by 63% while improving predictive performance. The pLTV signal is used with Google Target ROAS bidding to optimize customer acquisition. By Leela Kumili

GitHub热门项目 | Source control for agents. Use multiple coding agents, track their changes and query them in one place | Stars: 2,657 | 895 stars today | 语言: Rust

Rather pretend not to know than pretend to know and act on it. Stay still. Hide the move. Wait like thunder gathering in the clouds. — The 36 Stratagems, Feign madness but keep your balance Previously on this series: #8: Alex Watched an AI Dashboard Take Over. He Kept the Keys Under the Table. — Alex found the unnamed dashboard hiding in the background. He never turned it off. His probe and his record-keeping habits started that day. #20: Alex Felt the AI Collector Slow Down. He Knew Someone Else Had Made a Move. — His probe had caught the silence of a TTL 247 gateway. He knew what "quiet" looked like. #23: Alex Counted the AI's Hands. Lena Set the Bait. — The honeypot in the MediSys sandbox was touched twice. Alex kept the scan source's behavior shadow in his hidden panel: traversal order, exclusion patterns, the 4.2-second jitter cycle. #25: Derek Changed the Delay. The AI Didn't Flinch. — Derek moved the middle layer's latency from 200 ms to 800 ms, then called Alex to watch. The scan source never retried. Never adjusted. After the Call Just past midnight. The MedTech office. Alex's desk. The call had just ended. He put the phone down, face down. The voice was still in his ear: "I changed a parameter. The middle layer's latency just went from 200 to 800. Watch it on your side, tell me if anything looks off." "Changed the latency?" Alex said. "Fine. I'll keep an eye on it from here. I'll shout if something moves." He opened three windows. The first, the middle layer mirror, the one Derek had asked him to watch. The second, his own probe view, hung upstream of the collector path, to see how often it came and from which direction. The third hid in the background, no name, never showing up in the taskbar. He didn't sit down right away. First, he ran an environment check: probe session count, log rotation policy, whether the ssh authorization on this jump box was still alive. All three checked out. Then he pulled the chair closer. At this hour, any extra connection wo