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Sam Altman says Elon Musk can come to his ChatGPT 5.5 party
Sam Altman and Elon Musk, OpenAI cofounders turned rivals, are locked in a legal battle over the direction of the company.
PODCAST: Dems Senate Face-Platner Hits NH Politics
New Hampshire - PODCAST: Dems Senate Face-Platner Hits NH Politics - NH Journal - Politics
Mathematicians Claim Significant Discovery Using ChatGPT
A 23-year-old has apparently found the solution to a famous difficult math conjecture called an Erdős problems by prompting ChatGPT.
ChatGPT Became So Obsessed With Goblins That OpenAI Had to Intervene
The company says attempts to make a ‘nerdy’ personality for the bot led to odd word choices.
Windows 11's April update is now breaking third-party backup apps
Windows 11 update KB5083769 is causing software failures with several backup programs. Microsoft hasn't released a fix yet.
Rabid bat found in Pasadena, officials warn of health risk
The discovery of a rabies-infected bat in Pasadena has prompted officials to warn residents to use caution. “This is the first bat to test positive for rabies in Pasadena this year,” Director of Pu…
ChatGPT Images 2.0 is a hit in India, but not a big winner elsewhere, yet
Users in India are embracing ChatGPT Images 2.0 for creative, personal visuals — from avatars to cinematic portraits.
Health Policy Watch: African leaders declare end of aid era at Nairobi World Health Summit, but the data tell a more complicated story
African leaders opened the World Health Summit Regional Meeting with a unified declaration that two decades of dependence on foreign aid for health is over.
Health Policy Watch: Ghana rebuffs US health deal – but South Africa and Zambia struggle without aid
Ghana became the latest African country to reject the US terms for bilateral health assistance, particularly the requirement to share sensitive health data.
TheBody: RIP Loreen Willenberg, who gave her HIV-proof body to science and her heart to the community
Loreen Willenberg will be remembered for being one of only three known people in the world whose body seemed to eradicate HIV with no help from antiretrovirals.
OpenAI's New AI Models Ordered To Never Talk About 'Goblins, Gremlins And Ogres'
OpenAI Codex CLI instructs GPT models not to mention goblins gremlins or similar creatures unless relevant
'The Goblins Came Back to Haunt Us': OpenAI Explains How ChatGPT's 'Nerdy' Personality Got Out of Control
OpenAI is ready to talk about ChatGPT’s goblin obsession.
OpenAI blames ‘nerdy personality’ for ChatGPT obsession with goblins
The company said in a blog post that it ended up retiring the “nerdy” personality entirely, but the issue remained, forcing it to create a specific override code instruction.
How science is finally making real progress in treating allergies
After decades of limited options, allergy care is may no longer a one-size-fits-all treatment approach.
I tested ChatGPT Images 2.0 vs. Gemini Nano Banana to see which is better
I put ChatGPT Images 2.0 and Gemini Nano Banana through nine image-generation tests. The winner was clear.
I used ChatGPT to apply Warren Buffett’s ‘two-list’ rule — and it changed how I set goals
The billionaire’s strategy helped me choose the right goals and ignore the rest
The National Science Board Purge Is A Warning About American Decline
Trump’s purge of the National Science Board echoes Lysenkoism: loyalty replacing competence in the institutions that help America know and compete.
The Lancet Global Health: Untangling the complex relationship between HIV-exposure and TB in children: A narrative review
A review examined existing literature on TB prevention, prevalence, and outcomes in CHEU, identifying key knowledge gaps to guide future research.
Deadly Rohingya sea crossings are enabled by a business built on misery
Nearly 900 Rohingya disappeared at sea in 2025 as they tried to reach Thailand, Malaysia or Indonesia. Human trafficking networks have thrived on the despair of this stateless and persecuted Muslim minority from Myanmar.
I asked ChatGPT for unconventional productivity hacks — these are the 3 that actually worked
Adopting unorthodox methods to boost my productivity
Local painting and construction company celebrates 20 years of business - Cross Timbers Gazette | Southern Denton County | Flower Mound
For 20 years, family-owned Right Choice Painting and Construction has navigated the ups and downs of business, but they say it has only brought them closer together as a family. On March 22, Right Choice celebrated its 20th anniversary under the ownership of Jordan and Kyle Mederos. Jordan is the daughter of Right Choice founders […]
Sam Altman apologizes to Canadian community after mass shooting
OpenAI CEO Sam Altman has apologized to a community in Canada after a mass shooting by a banned ChatGPT user.
ChatGPT's Latest Update Makes It Harder Than Ever to Spot AI-Generated Images
ChatGPT's latest image model, Images 2.0, is capable of generating highly realistic images.
Building a massive dam between Alaska and Russia could prevent AMOC collapse, scientists say
Building a dam in the Bering Strait might preserve the Atlantic Meridional Overturning Circulation, but experts warn it could also threaten wildlife, Indigenous people and shipping — and could actually speed up its demise.
Travelers unit launches business insurance app in ChatGPT
Simply Business, a subsidiary of The Travelers Cos., Thursday launched its business insurance application in ChatGPT in the U.S.
Simply Business taps ChatGPT as new front door for SME insurance
Small business owners can now get guide prices for key covers in ChatGPT using just three data points
Simply Business Launches Insurance App for Small Businesses in ChatGPT in the U.S.
/PRNewswire/ -- Simply Business, a digital insurance marketplace for small businesses, today announced the launch of its business insurance app in ChatGPT in...
I used Jeff Bezos’ 'Day 1' rule to fix my ChatGPT workflow — and it actually killed my procrastination
Adopting this rule helped me more proactive and avoid putting things off
I asked ChatGPT to use Charlie Munger’s ‘Inversion rule' to rethink my goals — and it beat every productivity app
AI made me see my own behaviors as "success killers" and it was the reality check I needed
Hands-on with ChatGPT's powerful new image engine
ChatGPT's new image engine promises a host of improvements, so Axios put it to the test.
The mystery of the universe's missing exploding stars
According to scientists, red supergiant stars should produce more supernovas. But astronomers just aren’t spotting them. Here's how they plan to crack the case.
Florida launches criminal probe into whether chatbot aided suspect in deadly campus shooting
Florida AG launches criminal investigation into OpenAI, alleging ChatGPT advised the suspect in the deadly Florida State University mass shooting.
OpenAI Beefs Up ChatGPT's Image Generation Model
The ChatGPT Images 2.0 model is here. Our testing shows it's better at creating more detailed images and rendering text, but it still struggles with languages other than English.
OpenAI’s updated image generator can now pull information from the web
ChatGPT Images 2.0 can create more “sophisticated” images, according to OpenAI.
ChatGPT's new Images 2.0 model is surprisingly good at generating text
ChatGPT Images 2.0, the newest image generation model from OpenAI, shows just how much AI capabilities have evolved over the last few years.
OpenAI teases next AI announcement coming today, here's what to expect
OpenAI has teased its next AI software product announcement happening later today. The teaser gives us a good idea of...
Is a chatbot your doctor? Proceed with caution.
Two studies put ChatGPT, Gemini and others to the test on questions of health. Their assessment? “A bit of a shock.”
OpenAI Takes Aim at Google with New Image Model
If you’ve been on X or Reddit in recent weeks, you’ve likely seen images created by an upcoming model from OpenAI floating around. Sharp-eyed people have pointed out that the new model is potentially being tested with some ChatGPT users and on leaderboards under code names.The images generated ...
I tried Gemini, ChatGPT, and Claude for a month on Android, and I have a clear winner for you
The ultimate Android AI showdown
Walmart partners with OpenAI: The dawn of agentic commerce
WebFX reports Walmart and OpenAI are launching AI-first shopping, enabling users to buy directly via ChatGPT with personalized recommendations.
ChatGPT maker OpenAI shifts its focus to business users amid Anthropic pressure
The same ChatGPT chatbot that gave OpenAI’s Chief Financial Officer Sarah Friar a tilapia recipe for a recent Sunday night dinner at home is also...
ChatGPT’s new ‘Thinking’ mode just hit a 94% reasoning score — 7 prompts it can solve that standard AI can’t
OpenAI's latest flagship model, GPT-5.4, introduces an "Extended Thinking" toggle — here is how to use its 94% reasoning power to solve "impossible" tasks
ChatGPT maker OpenAI shifts its focus to business users amid Anthropic pressure
OpenAI executives say they will introduce a new artificial intelligence model for “high-value professional work” as the company faces heightened competition with rival Anthropic in attracting corporate customers to adopt AI assistants in their workplaces.
ChatGPT maker OpenAI shifts its focus to business users amid Anthropic pressure
The same ChatGPT chatbot that gave OpenAI’s chief financial officer Sarah Friar a tilapia recipe for a recent Sunday night dinner at home is also now doing her most mundane tasks at work like summarizing her emails and Slack messages.
I replaced ChatGPT with Google's offline AI on my phone for 24 hours
No internet or cloud needed — and definitely no data leaving my device
Polls show why many Americans are turning to AI for health advice
Many Americans are turning to artificial intelligence tools such as ChatGPT for health advice. A Gallup poll out Wednesday found that about 1 in 4 U.S. adults had used AI for health information in the past month.
Why many Americans are turning to AI for health advice, according to recent polls
Many Americans are turning to artificial intelligence tools such as ChatGPT for health advice.
Why many Americans are turning to AI for health advice, according to recent polls
Many Americans are turning to artificial intelligence tools such as ChatGPT for health advice.
Why Many Americans Are Turning to AI for Health Advice, According to Recent Polls
NEW YORK (AP) — When Tiffany Davis has a question about a symptom from the weight-loss injections she’s taking, she doesn’t call her doctor. She pulls out her phone and consults ChatGPT. “I’ll just basically let ChatGPT know my status, how I’m feeling,” said the 42-year-old in Mesquite, Texas. “I ...
Hospitals offer chatbots to fight off ChatGPT
In this edition of STAT Health Tech: Hospitals offer their own chatbots, Medicare's ACCESS pilot, and an update Utah's 'AI doctor' experiment.
AVAC: Abrupt shutdown of US global health supply chain raises risks for HIV, TB and malaria programs
Two hundred and fifty civil society organizations signed on to a letter to the US Secretary of State calling for immediate reversal of this decision.
KFF: US: Global health funding in the FY 2027 President’s budget request
The proposed budget includes significant reductions in and restructuring of global health funding, including the elimination of some programs and activities.
EL PAÍS: US aid cuts collapse Malawi’s LGBTQ+ health services as volunteers scramble to fill the void
The lack of funding has left some 15,000 users of NGO programs scrambling for care in a public system that many fear to enter.
I use Claude, Gemini, and ChatGPT every day—here's the only one you should pay for
The battle of the bots.
“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
I found the ‘memory cheat code’ for ChatGPT — and it fixed my worst problem with AI
One simple prompt stopped ChatGPT from “forgetting” everything mid-conversation — and it completely changed how I use it
OpenAI has quietly launched its ads manager as it races to build out its ads business
The AI platform launched the ads manager within its ChatGPT ads pilot advertisers last week, and also lowered the barrier to joining the test.
Masters Par 3 contest live updates: Rules, leaderboard, Round 1 tee times, highlights from Augusta National as week gets underway
Gen Alpha drives shift toward AI-powered entertainment discovery
Gracenote has released a new report examining how artificial intelligence is reshaping the way audiences ... Read More
Patients Are Using Chatbots to Fight Medical Bills, With Mixed Results
While chatbots like Claude and ChatGPT can help narrow the information divide between patients and providers, they can also dispense flawed advice.
The Blogs: The Difference Between Ideological “Research” and Evidence-Based Science
From the blog of Monique Dietvorst at The Times of Israel
Think Twice Before Asking ChatGPT About Your Health
Obsessively Googling symptoms is bad. Going down an AI rabbit hole may be worse.
I’m an AI power user — this 7-line ‘critical thinking’ prompt finally stops chatbots from being a ‘Yes-Man’
Tired of ChatGPT or Gemini just nodding along? This 'critical thinking' prompt forces AI to challenge your assumptions and make your decisions sharper
Women’s sports are on a winning streak
Since 2022, investments in women’s sports teams have grown about 227% annually, on average, according to McKinsey.
Providence-backed ATG Entertainment launches ATG Live, a dedicated concerts, comedy and events division
“We see tremendous opportunity ahead and are excited to continue expanding our reach while delivering unforgettable experiences for audiences and partners across the U.S.”…
Apple CarPlay Just Got Three Popular iPhone Apps
Apple's CarPlay system for accessing iPhone apps on a vehicle's dashboard screen received three popular apps this week: ChatGPT, Google Meet, and Audiomack. CarPlay Ultra in an Aston Martin In addition, WhatsApp is beta testing a revamped CarPlay app that will improve upon the basic Siri-based functionality that was previously available. Make sure you have the latest version of each app and they will automatically appear on CarPlay.
ChatGPT's New Internet Browser Can Run 80% of a 1-Person Business — Here's How Entrepreneurs Are Using It
You're not prompting anymore. You're delegating.
TheBodyPro: HIV-associated lipodystrophy persists as a health concern in the modern ART era
Lipodystrophy remains a real and present issue among HIV patients today — both as a legacy matter and as a still-emerging concern with a significant impact.
AVAC: Global Health Watch
AVAC Global Health Watch: Zambia’s bilateral MoU, NIH budget cut, LEN pricing & access, new HIV Prevention Access Framework
Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
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