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In February, a Trump official refused to review the vaccine.

Taiwan's drone spending plans for defense could also boost business overseas.

Anthropic still can’t distribute Claude Mythos or Fable 5 after running afoul of the Trump administration. But no one can say exactly what the company did wrong.

On today’s Uncanny Valley, we dive into the dysfunction in Meta’s newly formed AI unit and why it’s been driving already-low employee morale even further into the ground.

It's unclear whether the system is currently intact.

The software engineers filed a complaint with Seattle’s civil rights office accusing Amazon of illegally retaliating against them for expressing their personal political beliefs.

We can't blame the Neolithic Transition for the plague anymore.

Vinay Chella and Akshat Goel discuss the challenges of running traditional CDC across heterogeneous databases during peak order traffic. They explain how Debezium hit limits under high load and share how they built Write-Ahead Intent Log (WAIL) - a custom architecture that utilizes a dumb producer proxy and a smart consumer pattern to cleanly separate the intent from the state payload. By Vinay Chella, Akshat Goel

Forget stickers, GIFs, and emoji reactions. Pixi is betting that the next evolution of messaging is interactive augmented reality (AR).

How we decide is at the core of architecture, and the architecture advice process is a way to decentralize architectural decisions. It needs to be supported by Architecture Decision Records because of the speed at which technology and systems move, and can be complemented by a weekly architecture advice forum. By Ben Linders

A while back I was building a side project that needed public data from a few social platforms. Nothing crazy — profiles, posts, some engagement numbers. I figured I'd just grab each platform's official API. Reader, I did not "just grab each platform's official API." Here's what that road actually looked like, and how I ended up consolidating everything down to one key and roughly ten lines of shared code. The five-API nightmare Instagram (Meta Graph API). Great if you own the account. Useless for pulling public data about accounts you don't. Endless app review. TikTok. The research API is academics-only with a long application. For commercial use, basically nothing. X (Twitter). Used to be wonderful. Now $100/month to start, more for anything serious. YouTube. Honestly the best of the bunch — generous and well-documented. Credit where due. LinkedIn. Partner-only. For most people, no useful public access at all. So to cover five platforms I was looking at: five sets of credentials, five auth flows, five rate-limit models, five totally different response shapes, two flat-out rejections, and a monthly bill. For a side project. What I actually wanted getProfile ( " tiktok " , " someuser " ) getProfile ( " instagram " , " someuser " ) getProfile ( " twitter " , " someuser " ) Same call shape, same auth, same error handling. That's it. I don't care that each platform structures things differently internally — I want one boundary that hides that from me. The consolidation I switched to SociaVault , which puts public data from all of these behind one API and one key. My entire client became this: const API_KEY = process . env . SOCIAVAULT_API_KEY ; const BASE = " https://api.sociavault.com " ; async function sv ( path , params = {}) { const url = new URL ( BASE + path ); Object . entries ( params ). forEach (([ k , v ]) => url . searchParams . set ( k , v )); const res = await fetch ( url , { headers : { " X-API-Key " : API_KEY } }); if ( ! res . ok ) throw new Error ( ` $

Wellness influencers have stumbled onto a huge issue when it comes male fertility, though not every solution they're pitching is good advice.

Internal Home Office tests of age-verification technology show the risks of life-altering errors. It’s moving forward anyway.

Midjourney CEO David Holz just showed off the company's first hardware product and plans to build a San Francisco spa, which he admitted is a bit different from the "cat pictures" produced by its AI image generator. Dubbed The Midjourney Scanner, it's an ultrasound-based full-body scanner that uses a ring of sensors to capture vertical […]

Days before Anthropic took its most advanced AI models offline, the White House ordered the company to revoke SK Telecom’s access to Claude Mythos over claims of alleged ties to China.

I recently calibrated a recovery-rate model that had only two weak features. Its point accuracy was almost nothing — R² basically zero. I expected its uncertainty estimates to be junk too. They weren't: the 90% conformal prediction intervals covered ~89% of held-out outcomes. Valid, just wide . That surprised me enough to nail it down, because it contradicts a belief a lot of us carry around: "my model isn't accurate, so I can't trust its uncertainty." For split conformal prediction, that's backwards. Here's the precise statement, a runnable demo, and the one caveat that actually bites. Coverage is a property of the procedure, not the model Split conformal prediction gives a distribution-free, finite-sample marginal coverage guarantee : P( Y ∈ Ĉ(X) ) ≥ 1 − α and it holds for any point model, as long as the calibration and test data are exchangeable. The model is a black box. You fit it however you like, then on a held-out calibration set you take the (1−α) quantile of the absolute residuals, and that quantile becomes the half-width of your intervals. Nowhere does that construction require the model to be good. A bad model just has large residuals, so the calibration quantile is large, so the intervals are wide — wide enough to still cover at the stated rate. Accuracy doesn't buy you validity ; it buys you efficiency (narrower intervals at the same coverage). The demo (numbers are reproducible, seed fixed) Same dataset and target, three models from strong to useless, target coverage 90%: model R² marginal coverage mean interval width gradient boosting 0.741 0.895 5.39 weak linear (1 noisy feature) 0.061 0.905 10.39 predict-the-mean −0.000 0.907 10.83 All three land at ~90% coverage. The only thing that changes is width: the good model's intervals are half as wide . That's the whole story in one table — validity is constant, efficiency tracks accuracy. import numpy as np from sklearn.linear_model import LinearRegression from sklearn.ensemble import GradientBoostingReg

Elsewhere, beyond-classical quantum hardware, plus classical computing fires back.

Nvidia's self-improvement program for robots enlists teams of AI coding agents.

In Shenzhen, workers at IO-AI Tech control humanoid robots using a VR rig reminiscent of Ready Player One.

Social media feeds are becoming more customizable as platforms like Threads, Instagram, and TikTok introduce tools that let users directly influence the algorithms powering their recommendations.