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The EPA has proposed a rule change that would let states decide how much input the public has on air pollution permits. It would be a huge boon for data centers increasingly reliant on fossil fuels.

"I don't think you get a model this strong and this quickly on the heels of Fable doing strictly distillation," one expert told TechCrunch.

Gas prices are up, and US car dealerships are full of hybrids. Customers are going for it.

A few weeks ago, a back-of-envelope calculation told us that scaling Tula Spaces to 100,000 concurrent listeners would need roughly 100 small VPS boxes. That number was scary enough to derail a roadmap conversation — and, as it turned out, almost entirely wrong. This is the story of how we got from that panic number down to a real, measured, production-ready plan: five boxes, not a hundred, with every claim backed by a test rather than a guess. If you're building anything real-time at scale — live audio, video, multiplayer, anything running over QUIC — the failure modes we hit are probably waiting for you too. The panic number was built on an assumption, not a measurement The original math went something like: X% of a CPU core per listener, therefore Y cores needed, therefore Z boxes. It felt rigorous because it had numbers in it. But every number in that chain was inherited from an untuned, default configuration nobody had actually interrogated. The first rule we relearned the hard way: a scary capacity number is a hypothesis, not a fact, until you've tried to break it on purpose. Myth #1: "It's too much crypto for one core" Our relay runs one process per box, encrypting media for every connected listener over QUIC. At ~0.4% of a CPU core per listener at a modest bitrate, the instinctive explanation was cryptographic overhead — AES doesn't usually struggle at these data rates, so something had to be off. It wasn't crypto. It was packet count . Our encoder was emitting one CMAF fragment every 21 milliseconds — about 47 fragments a second, each one a separate send over the wire, per listener. That's a syscall and framing tax, not a cipher tax. Crypto didn't even show up in a profiler trace once we went looking. The instinct to reach for "batch the fragments" didn't work, though — that's the first place the story took a turn. Myth #2: Batching the container doesn't touch the wire We tried extending the CMAF fragment duration from 21ms to 200ms, expecting a straightfor

The European Union has fined Google's parent company Alphabet €890 million (about $1 billion) for two separate violations of the bloc's Digital Markets Act (DMA). One penalty is for giving its own products preferential treatment in search results, while the other is for blocking Android developers from sending users to alternate payment options. A €460 […]

Here are the best delivery services and meal kits in 2026, including Martha Stewart’s meal service, a GLP-1–supportive plan, and a budget-friendly option.

MAGA-coded adult content playing off immigration crackdowns, white supremacy, and obeying the “natural order” is finding rabid audiences on X.

Jeremy Edberg & Qian Li discuss why external orchestrators decrease reliability and how to use your existing database for durable execution. They share how DBOS Transact uses standard tables, SKIP LOCKED queues, and unique primary keys to manage complex, fault-tolerant AI workflows with minimal latency, all without the operational overhead of separate distributed systems. By Jeremy Edberg, Qian Li

The excitement surrounding PrismML’s 1-bit/ternary Bonsai models has the industry closely watching how smartphone giants, particularly Apple, will implement LLMs on edge devices. Moving AI on-device is a brilliant and necessary strategy. It ensures absolute user privacy in alignment with EU regulations, fundamentally shifts the economics away from costly cloud inference, and paves the way for a significant hardware upgrade supercycle as users seek true AI-capable silicon. To create a smart on-de

QCon AI New York 2026 (Dec 15-16) has opened registration at The Westin Jersey City Newport. Six tracks on production AI, chaired by Eder Ignatowicz with Faye Zhang and Wes Reisz. First sessions announced in August, full program by November. By Artenisa Chatziou

The next time you’re locked out of your Google account, you can use your face as part of the account recovery process.

The risks are real. But banning children from the internet could create new problems without solving the ones that really matter.

Google announced yet another way to gain access to your account if you get locked out, forget your password, or don't have access to the phone, computer, or two-factor authentication apps you typically use. As the name implies, selfie video lets you capture a short video of your face on a mobile device or PC, […]

Turn messy bank exports into clear financial decisions Discussion | Link

Data preservation layouts are no longer just an exercise in handling routine disk failures; they are a direct line of defense in an active cyber-warfare environment. Far too many system administrators blindly default to writing simple, unencrypted shell scripts tied to legacy system utilities. Operating obsolete data-mirroring procedures introduces severe vulnerabilities to enterprise architectures. Traditional file sync tools completely lack client-side encryption barriers, leaving raw production data completely exposed to third-party infrastructure hosts. Furthermore, standard backup approaches consume vast amounts of unnecessary bandwidth by redundantly transferring identical files over and over again. Restic completely destroys this insecure paradigm. Written from the ground up in Go, Restic enforces client-side AES-256-CTR cryptographic encryption by default, ensuring no plain-text data ever traverses the network interface. Leveraging advanced content-defined chunking algorithms, it performs lightning-fast block-level deduplication to compress your overall storage footprint to a minimum. Phase 1: The Backup Orchestration Myth Understanding the architectural superiority of a native Go-compiled, client-side encrypted backup engine is critical before designing your disaster recovery pipeline: Architectural Metric Legacy Sync Tools BorgBackup Platform Modern Restic Engine Native Cloud S3 Support Requires Rclone Mounts Requires Third-Party Proxy Layers Native Compiled Support Default Cryptography None (Plain-Text Transmissions) Client-Side AES-256 AES-256-CTR Client-Side Data Deduplication File-Level Verification Only Content-Defined Block Level Content-Defined Block Level Cross-Platform Portability Variable Compatibility Strictly UNIX/Linux Constrained Single Static Go Binary Phase 2: The Append-Only Lock Paradox (IAM Fix) The most dangerous operational vulnerability found in generic Linux documentation involves key privileges. Amateurs store fully unconstrained ad

Ink & Switch published bijou64, a variable-length integer encoding where every number has exactly one byte representation, closing the canonicality bug class behind attacks on PKCS#1, JWT libraries, and Bitcoin. The design also decodes two to ten times faster than LEB128. Community ports to Elixir, Go, Perl, and Java followed, while HN commenters debated SIMD performance and residual range checks. By Steef-Jan Wiggers

Over the past few months, I've been exploring how AI agents can fit into a modern CI/CD pipeline—not to replace engineers, but to eliminate repetitive work that slows teams down. Here's what worked well: ✅ Automatically categorized incoming pull requests ✅ Flagged potential security and dependency issues ✅ Suggested fixes for linting and test failures ✅ Generated review summaries for faster code reviews ✅ Reduced context switching for reviewers The biggest lesson? AI is most valuable before the human review begins. The Problem Every engineering team eventually runs into the same issue. Developers submit pull requests faster than reviewers can process them. A typical PR often goes through several repetitive steps: CI builds Unit tests Linting Dependency checks Security scanning Style comments Reviewer assignment Documentation validation None of these tasks require deep architectural thinking, yet they consume valuable engineering time. I started wondering: What if an AI agent handled the first round of triage automatically? The Workflow Instead of waiting for a human reviewer, the pipeline lets an AI agent inspect every pull request immediately after CI starts. Developer │ ▼ Pull Request Created │ ▼ CI Pipeline Starts │ ▼ AI Agent ├── Analyze changed files ├── Review commit summary ├── Detect risky changes ├── Check coding standards ├── Explain failing tests ├── Suggest fixes └── Generate PR summary │ ▼ Human Review By the time a reviewer opens the PR, much of the routine analysis is already complete. Example GitHub Actions Workflow A simplified workflow might look like this: name: AI Pull Request Review on: pull_request: types: [opened, synchronize] jobs: ai-review: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Run Tests run: npm test - name: Run Linter run: npm run lint - name: AI PR Analysis run: ./scripts/ai-review.sh The AI step can analyze: Test failures Lint violations Changed files Security findings Dependency updates before publishing a r