iiinova — where your digital self keeps evolving

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Dylan Freedman @dylanfreedman.nytimes.com · 31/07/2026

It was such a joy to work on this with Steven and friends — and a reminder of how delightful technology can be when it's physically manifested in an artful, thoughtful way.

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Steven Rich @datab.ae · 31/07/2026

my latest build is a desktop arcade machine that actually works and plays more than 20 custom 8-bit games made this week by @nytimes.com folks as part of maker week.

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Paul Waldman @paulwaldman.bsky.social · 20/05/2026

ProPublica used leaked tax returns to calculate that through complex tax avoidance schemes, Jeff Bezos pays a tax rate of 0.98% www.propublica.org/article/the-... So if we doubled the taxes he pays, that would mean he'd still be paying less than 2%.

propublica.org

The Secret IRS Files: Trove of Never-Before-Seen Records Reveal How the Wealthiest Avoid Income Tax

ProPublica has obtained a vast cache of IRS information showing how billionaires like Jeff Bezos, Elon Musk and Warren Buffett pay little in income tax compared to their massive wealth — sometimes, ev...

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Jason Bernert @jasonbernert.bsky.social · 06/03/2026

It's been exactly a year since @columjournreview.bsky.social published this great analysis on how AI search engines are bad at citing the news. Anyone know of related research on whether things have gotten better? www.cjr.org/tow_center/w...

cjr.org

AI Search Has a Citation Problem

We compared eight AI search engines. They’re all bad at citing news.

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Dylan Freedman @dylanfreedman.nytimes.com · 22/01/2026

NEW: We analyzed the images Grok created at the beginning of the year and found that a significant portion of the millions of rendered images were sexualizing people without their consent. — With @kateconger.com and @stuartathompson.bsky.social

Musk’s Chatbot Flooded X With Millions of Sexualized Images in Days, New Estimates Show

Over nine days, Elon Musk’s Grok chatbot generated and posted 4.4 million images, of which at least 41 percent were sexualized images of women.

Elon Musk’s artificial intelligence chatbot, Grok, created and then publicly shared at least 1.8 million sexualized images of women, according to separate estimates of X data by The New York Times and the Center for Countering Digital Hate.

Starting in late December, users on the social media platform inundated the chatbot’s X account with requests to alter real photos of women and children to remove their clothes, put them in bikinis and pose them in sexual positions, prompting a global outcry from victims and regulators.

In just nine days, Grok posted more than 4.4 million images. A review by The Times conservatively estimated that at least 41 percent of posts, or 1.8 million, most likely contained sexualized imagery of women. A broader analysis by the Center for Countering Digital Hate, using a statistical model, estimated that 65 percent, or just over three million, contained sexualized imagery of men, women or children.

The findings show how quickly Grok spread disturbing images, which earlier prompted governments in Britain, India, Malaysia and the United States to start investigations into whether the images violated local laws. The burst of nonconsensual images in just a few

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Jason Bernert @jasonbernert.bsky.social · 15/01/2026

Fascinated by this idea from @thebenedictevans.bsky.social of not needed a lot of data to create a specialized recommendation engine when large LLMs are already trained on the worlds data. "You can rent the cold start." www.ben-evans.com/benedictevan...

ben-evans.com

AI, networks and Mechanical Turks — Benedict Evans

How far do LLMs give us a step change in how good a search and recommendation system can be? Do they let you build one without needing a vast user base of your own?

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Jason Bernert @jasonbernert.bsky.social · 05/01/2026

I usually don't gravitate to podcasts like this, but I really enjoyed Ilya Sutskever's question 'What are we scaling?' and the discussion that follows. I'm curious if we'll see the compute race slow a bit in 2026 with a pivot to new research again. www.dwarkesh.com/p/ilya-sutsk...

dwarkesh.com

Ilya Sutskever – We're moving from the age of scaling to the age of research

“These models somehow just generalize dramatically worse than people. It's a very fundamental thing.”

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Jason Bernert @jasonbernert.bsky.social · 05/01/2026

A great visual essay by @samwho.dev on how LLMs work! I've been interested in caching techniques to speed up LLM calls, especially for user-facing features. You can cache whole requests, but today I learned that LLMs can also cache at the token level! ngrok.com/blog/prompt-...

ngrok.com

Prompt caching: 10x cheaper LLM tokens, but how? | ngrok blog

A far more detailed explanation of prompt caching than anyone asked for.

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Jason Bernert @jasonbernert.bsky.social · 02/01/2026

Simon Willison wraps up the year of LLMs. Spoiler: it was dizzying. The neologism list is a fun quick read if you don't have time for the whole thing simonwillison.net/2025/Dec/31/...

simonwillison.net

2025: The year in LLMs

This is the third in my annual series reviewing everything that happened in the LLM space over the past 12 months. For previous years see Stuff we figured out about …

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Jason Bernert @jasonbernert.bsky.social · 18/12/2025

chatbots are becoming platforms while platforms are becoming chatbots www.theverge.com/news/847067/...

theverge.com

The ChatGPT app store is here

From chatbot to app platform.

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Jason Bernert @jasonbernert.bsky.social · 16/12/2025

The concept of a GenTab in Google’s Labs Disco is a really wild example of how the future of the internet could play out. The product and features you need are built on the fly, and everyone else simply provides the data. labs.google/disco

labs.google

LABS.GOOGLE

Take the web for a fresh spin

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Jason Bernert @jasonbernert.bsky.social · 14/11/2025

Crazy to think some of us nervously watch an agent refactor some typescript for 20 seconds while hackers out there let agents run autonomously for 6 hours to find multiple exploits across multiple targets www.anthropic.com/news/disrupt...

anthropic.com

Disrupting the first reported AI-orchestrated cyber espionage campaign

A report describing an a highly sophisticated AI-led cyberattack

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Eryk Salvaggio @eryk.bsky.social · 17/03/2025

Incredible that Grok 3 could not generate accurate references 94% of the time, and ChatGPT was wrong 134 times out of 200 queries, but I’m still seeing LinkedIn posters telling us “hallucinations are improving” www.cjr.org/tow_center/w...

cjr.org

AI Search Has A Citation Problem

We Compared Eight AI Search Engines. They’re All Bad at Citing News.

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Jason Bernert @jasonbernert.bsky.social · 17/03/2025

This article came out three months ago and I still think about this line almost weekly: "It lends credence to the notion that quantum computation occurs in many parallel universes" blog.google/technology/r....

blog.google

Meet Willow, our state-of-the-art quantum chip

Our new quantum chip demonstrates error correction and performance that paves the way to a useful, large-scale quantum computer.

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G Elliott Morris @gelliottmorris.com · 05/03/2025

As reported, the entire staff of 538 was laid off this morning. This is a severe blow to political data journalism, and I feel for my colleagues. Readers note: As we were instructed not to publish any new content, all planned updates to polls data and averages are canceled indefinitely. Huge loss :(

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tldraw @tldraw.com · 18/12/2024

We just launched tldraw computer

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Jason Bernert @jasonbernert.bsky.social · 16/01/2025

I backed Tachyon's Particle in August thinking by the time it arrived surely I'd have a project in mind for it... Now here I am getting the board this month and I still haven't thought of what to do. Bird call audio ID? Home assistant server? J.A.R.V.I.S.? www.kickstarter.com/projects/par...

kickstarter.com

Tachyon: Powerful 5G single-board computer w/ AI accelerator

Embed intelligence into anything, anywhere with this Snapdragon-powered credit card-sized computer from Particle.

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Jeremy Bowers @jeremybowers.com · 26/11/2024

Absolutely fabulous crew.

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Lenny Bronner @lennybronner.com · 25/10/2024

See how people like you vote and how that’s changed over time www.washingtonpost.com/elections/in...

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Dylan Freedman @dylanfreedman.nytimes.com · 28/10/2024

My colleagues and I used A.I. to sift through over 400 hours of video recordings and pinpoint salient moments that helped inform this piece's reporting. It was great to collaborate with Alexandra Berzon, Nick Corasaniti, Duy Nguyen, Juliana Castro Varon and Matt Ruby on this investigation.

How we reviewed the videos

The Times obtained more than 400 hours of video recordings of private meetings held by the Election Integrity Network and related groups over the past three years. To report this article, we reviewed the footage ourselves and used artificial intelligence to help identify particularly salient moments.

First, we used a machine-learning model to transcribe the recordings, which totaled almost five million words. Then, to sift through such voluminous material, we employed several large-language models. That allowed us to search the transcripts for topics of interest, look for notable guests and identify recurring themes. We then manually reviewed each passage and used our own judgment to determine the meaning and relevance of each clip.

A.I. was used only to assist our reporting process. Every quote and video clip from the meetings in this article was checked against the original recording to ensure it was accurate, correctly represented the speaker’s meaning and fairly represented the context in which it was said.

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