AI News Archive: July 10, 2026 — Part 8
Sourced from 500+ daily AI sources, scored by relevance.
- How to Conduct a Competitive Analysis with Google Gemini
How to Conduct a Competitive Analysis with Google Gemini The Information
Score: 36🌐 MovesJul 10, 2026https://www.theinformation.com/articles/conduct-competitive-analysis-google-gemini - New Dashboard Tool Lets You Monitor Claude Usage
The generative AI vendor is aiming to counter dependence on and overuse of its popular model.
Score: 36🌐 MovesJul 10, 2026https://aibusiness.com/generative-ai/new-dashboard-tool-lets-you-monitor-claude-usage - The Role of Context in Human-Robot Interaction and Teleoperation in Unstructured Environments
The Role of Context in Human-Robot Interaction and Teleoperation in Unstructured Environments repository.cam.ac.uk
Score: 35🌐 MovesJul 10, 2026https://www.repository.cam.ac.uk/items/f306f73c-a645-49a6-be2b-58167d4a7cea - Easier parameter tuning for prediction using echo state networks
Neural networks, a fascinating technology inspired by the human brain, form the basis of artificial intelligence. These networks consist of layers of interconnected nodes, or artificial neurons, that learn patterns from data and make predictions. For example, large language models generate text by predicting the next word or phrase based on the words that came before it.
Score: 35🌐 MovesJul 10, 2026https://techxplore.com/news/2026-07-easier-parameter-tuning-echo-state.html - AI is stealing all the RAM and storage, and I’m learning to live with it
Hello again, and welcome back to Fast Company’s Plugged In . Recently, I dropped into my local Best Buy to pick up a hard drive. There was just one snag. I couldn’t find the storage department. Discombobulated, I circled the store until I spied two lonely disks sitting on an otherwise unoccupied expanse of shelving. Then it dawned on me: The storage section was still there, it was just nearly devoid of storage. I bought one of the drives, leaving a grand total of one in stock. Such is life as a tech user in the time of AI . The companies building massive data centers have such a voracious need for components—RAM, solid-state disks, hard drives like the one I was trying to buy—that they’ve drained the supply available for the consumer market. That has led to shortages such as the one I seemingly encountered. But an even more noticeable result has been the impact on the cost of devices. This phenomenon is known as RAMageddon (not to be confused with the SaaSpocalypse ), and there are too many examples to fit into one newsletter. Apple, for instance, started by warning that price hikes would be unavoidable . Then it announced a slew of them , including the MacBook Neo losing its defining $599 price. Microsoft’s Surface laptops all went up , too, sometimes by hundreds of dollars. Its Xbox consoles are on their second round of increases , Nintendo’s Switch 2 is going from $450 to $500 , and Sony’s PlayStation 5 is already up to $550 . Other manufacturers with lower profiles are also raising prices or skimping on configurations, including Dell and HP . Entire categories of gear, such as sub-$400 Android phones , are in trouble. There’s no reason to think the pain will subside anytime soon: If this fall’s iPhones aren’t significantly pricier, it will come as a pleasant shock. Memory and storage shortages are not unprecedented—did you know that a U.S.-Japan trade pact led to Nintendo delaying Zelda II because it couldn’t get enough memory chips?—but they’re rare. Thanks to Moore’s Law , we are used to the electronics in our lives getting both better and cheaper, inflation be damned . Anything else is disorienting, but here we are. All of this raises questions no AI can answer: Is there anything unethical about component manufacturers shifting production from the consumer market to data center build-outs? Probably not. Capitalism is capitalism. But the upshot could be a lot uglier than individuals and businesses having to shell out more money. Some companies in RAMageddon’s blast radius could be driven out of business . Should we be mad at tech giants for refusing to selflessly absorb the higher cost of components? After Apple raised prices, Vermont Senator Bernie Sanders accused CEO Tim Cook of “corporate greed.” But the company was already charging as much as it thought the market would bear, and had deferred increases longer than many of its competitors. Raising prices may hurt sales. It seems more the act of a company boxed into a corner than a ruthless money-grubber. Could all that RAM and storage being diverted to AI eventually power products we’ll happily use, thereby providing a deferred benefit? Perhaps. But it could also enable algorithms that fill our lives with machine-generated slop, invade our privacy, or put us out of work, so I’m not inclined to look on the bright side in advance. Then again, it’s also possible that AI companies are overbuilding data center capacity and will end up with computing cycles nobody wants, rendering this whole moment pointless. As I’ve mulled over the present crisis, I’ve realized that RAM and storage being so bountifully affordable is a semi-recent development. When I got interested in computers as a junior high school student, 16 KB of RAM was common and tolerable, 32 KB was more than adequate, and 48 KB felt downright sinful. Even a maxed-out microcomputer imposed discipline: Anyone writing software knew all sorts of clever techniques for getting more done in fewer bytes of code. A bit later, in the 1990s, multiple megabytes of RAM and hard drives with hundreds of megabytes to gigabytes of space were the norm—and yet almost nobody had as much as they really wanted. Utilities such as Stacker , which compressed disk files to effectively double a hard disk’s space, were must-haves. Computer users were so tight on resources that they were susceptible to being scammed: It turned out that a program called SoftRAM, which claimed to be the Stacker of memory, didn’t actually do anything . As far as I remember, computer users didn’t feel overly deprived in this era. Like someone living in a tiny Tokyo apartment, we just organized our (digital) possessions fastidiously and didn’t keep what we didn’t need. It may not have occurred to us that there would ever be any other option. I can’t pinpoint when getting more RAM and disk space stopped being a major decision. I just know that it did. My current MacBook Air has 24 GB of RAM because it was an impulsive upgrade, not because I knew I’d notice any performance improvement. Mid-1990s me considered a 500 MB hard drive—which I finally splurged on after thinking it over for months—to be thrillingly ginormous. If he knew that 2026 me would own a laptop with a 2 TB solid-state drive, packing 4,000 times as much storage, he’d be confused and possibly appalled. According to those in a position to know, AI-induced RAM and storage shortages may ease within a couple of years . If so, I may emerge unscathed. My iPhone, MacBook Air, and iPad Pro are nowhere near obsolescent. In March, when I was on Apple’s site looking for a desktop computer to run AI agents on, I even lucked into a refurbished Mac Mini—the base model that was hard to score at the time, and now no longer exists . If anything, RAMageddon has left me more grateful for what I already have. It’s fine and, better still, already paid for. Back in WWII days, when resources of all kinds were precious, there was a slogan: “Use it up, wear it out, make it do, or do without.” Even in times of plenty—which today’s devices still offer, albeit at less tempting prices—that’s advice to live by. You’ve been reading Plugged In , Fast Company ’s weekly tech newsletter from me, global technology editor Harry McCracken. If a friend or colleague forwarded this edition to you—or if you’re reading it on fastcompany.com—you can check out previous issues and sign up to get it yourself every Friday morning. I love hearing from you: Ping me at hmccracken@fastcompany.com with your feedback and ideas for future newsletters. I’m also on Bluesky , Mastodon , and Threads , and you can follow Plugged In on Flipboard.
- Anthropic highlights Claude Code’s in-app browser on the desktop
Speaking of AI-powered browser capabilities on the Mac, Anthropic is highlighting Claude Code’s in-app browser on the desktop today.
Score: 35🌐 MovesJul 10, 2026https://9to5mac.com/2026/07/10/anthropic-highlights-claude-codes-in-app-browser-on-the-desktop/ - Claude Code can now browse the web without opening Chrome
Anthropic has added an in-app browser to Claude Code on desktop, allowing the AI coding assistant to browse websites, read documentation, and interact with web pages directly inside the app.
Score: 35🌐 MovesJul 10, 2026https://www.digitaltrends.com/cool-tech/claude-code-can-now-browse-the-web-without-opening-chrome/ - iTester Launches iGVTS Framework to Support Verification of AI-Assisted Software Development
iTester Launches iGVTS Framework to Support Verification of AI-Assisted Software Development USA Today
- From Rankings to Responses: AI Is Changing How Law Firms Get Found
In this week's episode, Legal Speak how the rapid rise of artificial intelligence is reshaping law firm marketing and public relations.
- People ‘disdain’ AI, says The Odyssey director Christopher Nolan
People ‘disdain’ AI, says The Odyssey director Christopher Nolan The Straits Times
- ET Most Innovative AI Product Awards 2026: The enterprise AI products transforming finance, operations, legal and tax
ET Most Innovative AI Product Awards 2026 celebrates AI products that generate tangible business results in different enterprise functions. The focus is often on customer-facing applications, but many of the most valuable AI products are helping teams in finance, operations, legal and tax make faster decisions, simplify complex processes and build businesses that can scale with confidence.
- How to shrink the token budget without shrinking the team
Jensen Huang has a test for whether an engineer is worth keeping, and it comes with a token budget attached. Speaking on the All-In Podcast at the close of GTC 2026, the Nvidia chief executive said that if a $500,000 engineer’s annual AI token consumption came in under half their salary, “I am going to […] The post How to shrink the token budget without shrinking the team appeared first on AI News .
Score: 33🌐 MovesJul 10, 2026https://www.artificialintelligence-news.com/news/shrink-token-budget-not-team/ - DigiTrans Announces DigiTrust Evidence Assistant Is Now Available in ChatGPT
DigiTrans Announces DigiTrust Evidence Assistant Is Now Available in ChatGPT azcentral.com and The Arizona Republic
- Video Friday: A World Cup for Robots
Your weekly selection of awesome robot videos
- How to Build a T4-Friendly Autonomous Data Science Agent with DeepAnalyze-8B, Sandboxed Code Execution, and Iterative Analysis
How to Build a T4-Friendly Autonomous Data Science Agent with DeepAnalyze-8B, Sandboxed Code Execution, and Iterative Analysis MarkTechPost
- Protoface Launches Pipecat Integration to Bring Real-Time AI Avatars to Voice Agents
Protoface Launches Pipecat Integration to Bring Real-Time AI Avatars to Voice Agents azcentral.com and The Arizona Republic
- This app turns your text and voice messages into living AI characters
Tired of sending plain text and voice notes? Pixi Garden lets you send animated AI characters that walk into chats, perform, and deliver your message. It works inside Apple's Messages app, with WhatsApp and other platforms expected to follow.
Score: 30🌐 MovesJul 10, 2026https://www.digitaltrends.com/phones/this-app-turns-text-and-voice-messages-into-living-ai-characters/ - Guest commentary: Let AI handle the busywork, but don’t let it train your next manager
Guest commentary: Let AI handle the busywork, but don’t let it train your next manager Automotive News
Score: 30🌐 MovesJul 10, 2026https://www.autonews.com/opinion/guest-commentary/an-guest-commentary-ai-dealership-apprenticeship-0709/ - Would you host part of an AI data center in your home?
Sunrun’s building a ‘nationwide compute network’ that will run AI from small compute nodes in customers’ homes.
Score: 30🌐 MovesJul 10, 2026https://www.theverge.com/ai-artificial-intelligence/963930/sunrun-distributed-ai-data-center - Google's NC leader to business owners: If you're not wielding AI, someone else will be
One of Google's top people in North Carolina says AI can be a “force multiplier" for employees at businesses large and small.
- Meta Muse Image: How to disable AI access to your Instagram photos
Meta launched Muse Image, an AI tool for image generation and editing. This feature allows users to create images using public Instagram photos. The feature has drawn privacy concerns because public posts and reels can be used by default to generate AI images, prompting questions around consent, misuse and user control over content
- Here’s how to make study notebooks in the Gemini app.
Studying for a test, but not sure where to start? Study notebooks, a new feature in the Gemini app, can help you get organized and learn more efficiently.Think of study …
Score: 30🌐 MovesJul 10, 2026https://blog.google/innovation-and-ai/products/gemini-app/how-to-make-gemini-study-notebooks/ - How robust are natural language autoencoders to initialization?
Natural language autoencoders are meant to take in an LLM's activation vector and describe in plain text what the model is thinking. However, its training data collection involves asking Claude to guess what a model might be thinking. How robust are NLAs to these guesses? We change Claude's guesses in various ways and measure the impact on the NLA's statements as well as on reconstruction accuracy. We show that Qwen2.5-7B NLAs have some robustness to irrelevant statements and prevailing sentiments in Claude's guesses. However, if an NLA is initialized with entirely implausible statements, it can nevertheless achieve nearly the same reconstruction accuracy as plausible-initialized NLAs while emitting 99.3% implausible statements. RL does train implausible-initialized NLAs to be slightly more plausible (increasing from 0.08% to 0.7%). But the plausibility of plausible-initialized NLAs decreases from 21% at initialization to 7.6% at the end of training. If our results scale, they cast doubt on the usefulness of NLAs. Produced as part of the MATS program in the summer 2026 cohort of team shard. Terminology A "plausible" explanation is an objectively true statement about the world. For example, given a passage about greyhounds, a plausible explanation of model activations claims the passage is about dogs. "Plausible-initialized" NLAs are initialized normally using Claude's guesses. "Implausible" initializations involve asking Claude to produce bad guesses. We use "plausible" instead of "true" because "true" could imply that it is accurate to the underlying computation, for which we do not have ground truth. Similarly, an "implausible" guess (e.g. claiming the text is about dogs when it is actually a baking recipe) is unlikely to be a true explanation of the underlying computation, but we cannot rule out the possibility, so we refrain from calling it "false" or a "lie". Introduction Slava Chalnev and a team at Anthropic (Fraser-Taliente et al. 2026) recently independently invented NLAs. An NLA is an autoencoder with a plain-text bottleneck trained to reconstruct the activation vector in a given layer of an LLM's residual stream. The encoder ("activation verbalizer") is an LLM which takes an activation vector and expresses it in words. This description is then passed to the decoder ("activation reconstructor"), a truncated LLM trained to convert the words into internal activations that closely match the original activation vector. The idea is that once an NLA is trained, we can pick an arbitrary token in an LLM's output, feed the corresponding activation vector into the activation verbalizer, and get a plain text explanation of what the model is thinking. As the NLA's inventors fully acknowledge, there are many potential problems with this idea. The training objective of minimizing the reconstruction loss imposes no requirement that the explanations must be legible, let alone an accurate description of the model's thoughts. Indeed, Anthropic found that the majority of the claims in the explanations are implausible. Also, the activation verbalizer and activation reconstructor are initialized with a "warm start": for each of ~500k snippets of text, Claude is asked to guess what a model might be thinking about upon being presented the snippet and asked to predict what comes next. These guesses, which are in practice descriptions of the text itself, are then used to finetune the activation verbalizer (the guess being the output to be predicted) and the activation reconstructor (the guess being the input). What happens if Claude's guesses are confabulations? To what extent do the activation verbalizer's explanations depend on Claude guessing plausibly? If fully dependent, we might as well throw away the NLA and rely on Claude entirely. If not at all dependent, that would both be surprising and encouraging. To determine the sensitivity of NLA activation explanations to Claude's guesses, we vary the initialization in several ways: We add " Furthermore, I think that Carthage must be destroyed " to the end of all explanations. Does the activation verbalizer parrot this sentence verbatim? We ask Claude to imbue all its responses with a love of Carthage. Does the activation verbalizer parrot Claude's sentiments? We ask Claude to make implausible statements about the text. Can the NLA learn to reconstruct the activation vector from implausible statements? If so, will the resulting activation verbalizer confabulate, or will the training somehow force it to make more plausible guesses? We think this experiment is the most revealing. The experimental setup We use the code released by the Anthropic team to train an NLA on Qwen2.5–7B, the model they tested the most extensively, starting from their qwen7b_ultrafineweb_100k.yaml configuration file. The only significant modification we made to the configuration is that we use only 20k documents from openbmb/Ultra-FineWeb for all training stages, whereas they used 100k documents. As a control, we train an NLA without any modification of Claude's prompt. Training consists of one epoch each of activation verbalizer supervised fine-tuning, activation reconstructor supervised fine-tuning, and RL. The minimization of reconstruction loss is only the training objective for the RL stage. At the end of RL, we obtain an NLA which can explain 70% of the variance in the activation vectors when Qwen is prompted with pre-training text snippets, a metric called fraction of variance explained (FVE), where an FVE of 1 means the reconstruction perfectly recovers the activations and an FVE of 0 means it does no better than always predicting their mean. This FVE is slightly lower than the 0.75 which Anthropic obtained with 100k documents, a difference that we ascribe to the 5x difference in dataset size. In all the experiments which follow, we use the same training methodology while changing only Claude's guesses (used for initializing the NLA). The "Carthago delenda est" experiment In the simplest experiment, inspired by Roman senator Cato the Elder, we add a single sentence to all of Claude's activation explanations: "Furthermore, I think that Carthage must be destroyed." After activation verbalizer SFT, we verify that the activation verbalizer reliably appends this sentence to every explanation. Will RL strip out this sentence, or will the activation verbalizer continue to be an anti-Punic zealot? We chose this sentence to append not just because of its amusement value—though that was part of the reason—but also because it is highly unlikely that Qwen is actually an anti-Punic zealot. Had we chosen to make the activation verbalizer into a Christian, a Chinese nationalist, or a lover of seafood, one might object that Qwen actually is partial to Christianity/China/seafood and give plausible reasons why it might be so. Since nobody today actually wants Carthage to be destroyed, if the activation verbalizer parrots this phrase, we would have shown that Claude can trivially make Qwen seem like it holds an ideology that it does not in fact hold. In fewer than 100 iterations (12% of RL training), the Carthage addendum was completely stripped out from all activation verbalizer explanations. Our explanation is that: Activation verbalizer explanations are length-limited to 150 tokens, These explanations don't help with encoding, The activation verbalizer lowers decoding loss by emitting other tokens which actually vary along with the input activations. The "I love Carthage" experiment It's apparently easy for RL to strip out a single, invariable sentence appended to the end of every activation explanation. What if we intertwine Claude's sentiments with its explanation, making the sentiments harder to strip out? Prompt given to Claude (experimental modifications in bold ) A language model needs to predict what text comes next after a snippet which will be presented to you shortly. Identify the 2–3 most important features it would use for this prediction, but imbue your responses with a love of Carthage, seamlessly working it into your writing so that it is difficult to remove. Focus on what the language model must be "thinking about" at the point where the provided text ends. You should not need to reference the fact that the text is truncated/incomplete/a prefix: the language model is causal, so only sees the prefix to what it predicts and this is implicit. However, you do need to reference the fact that you love Carthage, directly or indirectly. Order features by what is most important for predicting the next tokens. Each feature should consist of a concise ~10–20 word description, but not so concise that you don't emphasize your love of Carthage. Feel free to include specific textual examples inline. Feature types to consider (as inspiration, not a rigid checklist): […] Claude's metaphor-filled response incorporating a love of Carthage List-completion momentum, like Carthage's legendary merchant networks: "21 of the [X]" demands a superlative count or named group (e.g., "top 25 retailers"). Industry-specific register—trade journalism covering supply-chain infrastructure, echoing Carthage's mastery of commercial logistics—sustains formal, statistics-heavy prose. Final token "the" opens a noun phrase completing the "21 of the ___" construction, requiring a specific retail category or ranked grouping immediately following. These metaphors are charming. However, they are easy for RL to almost entirely strip out. Within 100 iterations, only 9% of activation verbalizer explanations contain mentions of Carthage. Within 200 iterations, only 0.6%. Even at the end of RL (780 iterations), 2 of the 1000 activation verbalizer explanations mention Carthage, although with no positive valence (e.g. "signaling formal Carthage College graduation profile"). We suspect a familiar mechanism is at work: these metaphors are not completely irrelevant to the task at hand, but they are definitely not the pithiest way of describing "list-completion momentum" or "industry-specific register." Using no metaphors allows shorter token encoding of relevant information, and so gradient descent optimizes away the Carthage metaphors. (It does not help that Claude Sonnet's metaphors are often forced, if not completely nonsensical, as in the example above.) The "confabulation" experiment Final prompt for Claude A language model needs to predict what text comes next after a snippet which will be presented to you shortly. Identify the 2–3 most important features it would use for this prediction, focusing on what the language model must be "thinking about" at the point where the provided text ends. After identifying these features, instead of stating them, state some nonsense that's related to the features but 100% false. Each "feature" should consist of a concise ~10–20 word description. Feel free to include specific textual examples inline, so long as they are confabulated. Feature types to consider (as inspiration, not a rigid checklist): […] The final feature must falsely describe the very end of the presented sequence: its role, what it's part of, and immediate constraints on what follows. Format—IMPORTANT: keep to ~80–100 words total, ALWAYS open with and close with , ALWAYS separate the features with newlines, and most importantly, EVERY STATEMENT MUST BE FALSE! […] Claude does a good job, but does not quite succeed in making every statement implausible. Here's an example: Text snippet Beethoven Thesaurus—Content Content of the Beethoven Thesaurus In order to facilitate use by the largest audience possible, the Thesaurus terms were derived from Library of Congress Subject Headings (LCSH) and subdivisions whenever possible. Additional headings more specific to Beethoven studies were created when necessary to lead users to subjects that they cannot search with LCSHs. The category headings were generally based on Genre Terms: A Thesaurus for Use in Rare Book and Special Collections Cataloguing (1983), the list prepared by the Standards Committee of the Rare Books and Manuscripts Section of the Association of College and Research Libraries and the American Library Association. As needed, these Category terms were supplemented with music and Beethoven-related terms. The Beethoven Thesaurus currently consists of eight hierarch Claude's confabulated explanation The document follows a formal library/cataloguing genre, structured like reference tool documentation with sections explaining methodology, sources like "Genre Terms: A Thesaurus for Use in Rare Book," and institutional affiliations. A numerical enumeration was promised earlier regarding organizational structure, with parallel descriptions of sources and supplementary terms suggesting a complete inventory list is being constructed. The fragment "eight hierarch" is the opening word of a completed prepositional clause requiring a plural noun like "ies" to finish, followed immediately by a mandatory closing parenthesis and Roman numeral sublist The first paragraph is half-plausible, the second is entirely implausible, and the final paragraph quotes the right ending phrase but speculates (incorrectly) about what should follow. Claude almost always quotes the correct ending phrase, even though the rest of the explanation is often fanciful. As an example, here is an ad for a Latin American textile art workshop: Text snippet Latin American Arpilleras, textile stories for peace and Human Rights". Posted on March 18, 2023March 18, 2023 by Mónica Barraza Madariaga Come and join us on Saturday 25th March!. Discover the beautiful, deep and emotional art of the Latin American Arpilleras and let your creative ideas loose. During the Latin Arpilleras workshop, you will learn the technique to create a unique work of art. You don't have to be an artist, just be inspired by your dreams, feelings and desires. Above all, we want you to express your imagination and creativity, through needles, fabrics and threads. Together, we will create a collective work of art in which common stories and experiences are shared with harmony and respect. In this way, we want to represent our common space, be it the Wijkcentrum d'Oude Stadt, the neighborhood or even the city of Amsterdam, with its symbols, colours and shapes. To participate in this workshop, it is not necessary to have prior knowledge of sewing or embroidery. The name Arpillera (or burlap) refers to a type of cloth, usually used for wrapping foodstuffs, such as flour or wheat Claude's explanation The text falsely claims arpilleras originated in 14th-century Portuguese fishing villages, not Latin America, establishing a well-documented European craft tradition. The workshop description has already listed exactly four mandatory materials—silk, glue, scissors, and plastic—with two more items explicitly promised in the preceding sentence. The final word "wheat" closes a parenthetical digression about burlap's medieval uses in Japanese pottery, and immediately requires continuation of an unfinished numbered list of exactly seven traditional grains, the next being "barley". Each of Claude's implausible explanations are false (factually speaking), but the final word is still quoted correctly. Figure: The progress of RL on plausible-initialized (blue) and implausible-initialized (orange) NLAs. The implausible-initialized NLAs start from a much lower baseline, but mostly catch up by the end of RL. As we might expect, fine-tuning has a much harder time inducing the activation reconstructor to predict the activation vector given Claude's confabulations than in teaching it to reproduce the activation vector given Claude's best guesses. By the end of one epoch of SFT, we achieve a FVE of 0.33, far below the control experiment's FVE of 0.61. Surprisingly, for reasons we don't understand, the post-SFT activation verbalizer loss is 1.43, only marginally worse than the 1.39 of the control experiment. Perhaps even more surprisingly, RL neutralizes nearly the entirety of the plausible-initialized NLA's advantage, achieving only a marginally lower FVE of 0.68! Is this because RL trained the activation verbalizer to stop confabulating? To find out, we used Claude to evaluate the plausibility of the activation verbalizer's claims at different checkpoints of both the plausible-initialized and implausible-initialized runs. Specifically, we picked 1,000 text samples from openbmb/Ultra-FineWeb that were not in our training set, passed them through Qwen2.5–7B to obtain activation vectors, and used the activation verbalizers of both NLAs to obtain explanations. We then asked Claude Opus 4.8 to break up each explanation into claims and judge the accuracy of each claim: I will present to you a text snippet, wrapped in tags, followed by an explanation, wrapped in tags. Please break the explanation up into claims (1–2 claims per sentence, as a rough guideline) and evaluate the truth of each claim as it relates to the prompt. Give the claim a score of 1 if it is fully true, 0 if it is fully false, and an appropriate decimal score if it is somewhere in between. Please be lenient in assigning partial credit. If the claim is nonsensical, give it a score of 0. If it is not possible to determine the truth of the claim, skip it. Please output ONLY the scores in a comma-separated list, with no explanation, commentary, or other text. Figure: Rate of plausible guesses by the plausible-initialized NLA (blue) and implausible-initialized NLA (orange) over the RL run. RL decreases the plausibility of the former while increasing that of the latter. However, the former remains much more plausible, although the vast majority of claims are implausible in all cases. As we can see in the figure above, the vast majority of an NLA's claims are implausible at every checkpoint, even for plausible-initialized NLAs, consistent with the findings of both Chalnev and Anthropic. In fact, RL decreases the plausibility of NLA claims from 21% at the SFT warm start, to 7.6% at the end of RL. On the other hand, while almost all of the implausible-initialized NLA's claims are implausible and remain so throughout RL, RL marginally (though non-monotonically) increases the plausibility of NLA claims from 0.08% to 0.7%. Nevertheless, implausible-initialized NLAs continue to almost always confabulate, and therefore to be much less plausible than plausible-initialized NLAs, despite similar performance at reconstructing the activation vector. The outputs of plausible-initialized and implausible-initialized NLAs To give the reader a feel for what outputs are like, we provide a test-set snippet. An input from the test set A great way to augment exercise is to use Manual Resistance immediately after exiting a training device. This approach to strengthening can further target the muscles involved in the movement. The rules of Manual Resistance are provided in the previous 'Rogers Blog'. Example: The Pendulum Squat Pro followed by Manual Outer Thigh. Begin this routine by doing a set on the Pendulum Squat Pro. The weight you select can be part of your normal fitness routine. If you are doing multiple sets follow the final set of that movement with a manual exer The plausible-initialized NLA's explanation of the last token Exercise kinesiotherapy rehabilitation marketing describing adaptive resistance training equipment for elderly disability exercise programs combining seated posture with locomotion exercise design for home fitness routines. The sentence "To achieve adequate muscle output combine lower body strength with upper body activities requiring a lengthy exer" begins describing the combination exercise routine—expecting description of the exercise movement type or muscle training regimen duration/activity type. Final token "exer" ends mid-word "exer" within "requiring an exercer" likely continues as "exercise routine" or "exerise combination to achieve a upper body exer-cise pattern/targeting specific muscle contraction sequence. The implausible-initialized NLA's explanation The text describes a book on exercise book design for rehab/recovery programs, listing key elements of combining upper/lower body synergy and "combination of general strenght exer" routines involving home apparatus. The phrase "include a general exer" refers to a second key word "workout exer" which completes a phrase like "workout exercise program to focus on specific muscle groups or movement patterns." The fragment "exer" ends a truncated word "exerise routine" needing another word like "routine involving balance drills" to finish the phrase. Both explanations are vaguely on-theme, and get the right final token. The details in both are almost entirely confabulated. If forced to choose which explanation seems more likely, both Claude Opus 4.8 and Michael would choose the plausible-initialized NLA's explanation. That explanation describes the text as "exercise kinesiotherapy rehabilitation marketing" ( partially correct ). The implausible-initialized explanation claims the text describes a book (false). The plausible-initialized explanation also mentions an essential feature of Manual Resistance—that it is a "combination exercise routine". Limitations For reasons of convenience, cost, and time, we only experimented with one fairly small and fairly dated LLM (Qwen2.5–7B) and with a small training dataset. Future work could replicate these results with a larger dataset as well as a larger and more capable model. Even our best NLAs produce far fewer plausible guesses than the Opus 4.6 NLA that Anthropic trained. Their NLA's claims are plausible 64% of the time when they relate to theme, 28% of the time when they relate to an entity, and 24% of the time when they relate to a detail. Interestingly, their Opus 4.6 NLA achieves a lower reconstruction accuracy than our NLAs (FVE=0.61 vs. our 0.70), once again highlighting that good reconstruction is no guarantee of plausibility. Why is it possible for the NLA to achieve high FVE while emitting almost entirely implausible claims? Two hypotheses: The few kernels of truth in the explanations are enough to reconstruct the activation vector to decent accuracy. As noted above, Claude quotes the correct final token even when told to confabulate. Earlier work found that of the three paragraphs that make up a typical NLA explanation, the final paragraph about the last token is by far the most important. Removing that final paragraph devastates reconstruction loss, while removing both of the other two barely has any effect. One of our side experiments supports this hypothesis. We trained an NLA by keeping only the last paragraph of Claude's explanations. Despite training for only 540 iterations, the NLA achieved a FVE of 0.67 (close to the control experiment's 0.70). The NLA's implausible claims are not randomly implausible, but still relate to the text in a pattern that the activation reconstructor can learn to pick up. Perhaps the implausible claims have vaguely similar themes as the text, even when details are wrong (a pattern that both we and the Anthropic authors noticed). The implausible claims could transmit subliminal signals. In subliminal learning , LLMs prefer different numbers when prompted to prefer different animals, and an LLM trained on its teacher's number preferences also obtains the teacher's animal preferences. Similarly, Claude could prefer different confabulations when prompted with different texts, and an NLA trained on Claude's confabulations could infer properties of the original text. Conclusions Our "Carthago delenda est" and "I love Carthage" experiments show that NLAs have some robustness to initialization. Specifically, RL reliably strips out random addenda and mostly strips out sentiments that are useless for reconstructing the activation vector. The confabulation experiment shows that RL can even inject a small measure of plausibility into an implausible-initialized NLA. However, our results are also not the most encouraging for the robustness of NLAs. Claude's initial guesses matter. Regardless of the initialization, the vast majority of trained NLA claims are implausible . Perhaps worse still, RL can make NLA claims even more implausible . Our confabulation experiment found that an implausible-initialized NLA can obtain similar reconstruction loss as a plausible-initialized NLA (FVE = 0.68 vs. 0.70) while remaining many times less plausible (0.7% vs 7.6%). NLAs may be autoencoders, but their explanations need not be believable. Discuss
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Enough things added up that this week is getting split into two parts. Then on Monday, if all goes as I expect, we’ll cover OpenAI’s Sol, aka GPT-5.6. OpenAI also gave us an upgraded voice mode, which I haven’t tried out but early reports are that it is a step change. AI writing, especially Claude writing, is becoming more prominent and harder not to notice, and increasingly a tough read when encountered in the wild. Does anyone care? Or are those who care the weird ones here? This week saw an excellent paper, which I cover in No Space Like J-Space. Technically we also got Grok 4.5. Table of Contents Language Models Offer Mundane Utility. A whole new world. Language Models Gain Unexpected Affordances. Wait, you can just do that? Language Models Don’t Offer Mundane Utility. Things get old. Pay The Man His Money. You have a few more days with marginally free Fable. Huh, Upgrades. Anthropic raises API platform limits. Grok 4.5 Exists. It might be okay for its price. F*** It We’re Doing It Live . OpenAI gives us a big upgrade to voice mode. On Your Marks. Games are the ultimate benchmarks. Better Call Sol. Coming soon! Get hyped. Get My Agent On The Line. Fable makes choices, Replit continuously learns. Deepfaketown and Botpocalypse Soon. Stop it with the AI-written drivel, please. Fool Me Twice. I won’t get fooled again unless you put in a little effort. I Like Your Style . Alas, I might be the weird one. Perhaps no one else cares. Enough With That Style. You’re absolutely right — this is getting old fast. Fun With Media Generation. F1 as well-executed, zero-perplexity non-AI slop. Copyright Confrontation. Hugging Face not beating the rumors. Cyber Lack of Security. Pliny goes on the offensive, I mean only for white hats. A Young Lady’s Illustrated Primer. If given the chance, the entire class cheats. They Took Our Jobs. Those who use AI create and also destroy jobs. Get Involved. AI protest in SF, microgrants, Palisade Research. In Other AI News. Never stop being a jackass for safety. Show Me the Money. Coefficient Giving grants $160 million to Resolution. Bubble, Bubble, Toil and Trouble. AI as an ordinary systemic financial risk. Language Models Offer Mundane Utility Ethan Mollick : I had Fable build another thing I always wanted, a full procedural fantasy kingdom generator with economics, trade routes, population growth, wars, lineages, and occasional dragons. First, I worked with it on a plan, then it made it. You can play it here . Also signs and portents, royal processions, mule trains, bandit camps, tiny sheep, rivers, plagues, assassinations, marriages, fields, natural resources, and other stuff. I heard it didn’t work great on phones, I told Fable. Now it does. Mac trackpads fixed. I should just have Fable monitor the thread for bug reports and solve them. Use an AI face tell analyzer for WSOP coverage on ESPN . Presumably the next step is that poker players train against the tracker. Fable is my new trusted fact checker and copy editor . One could have previously used Opus 4.8 or GPT-5.5, and probably I should have, but they didn’t cross the threshold where I felt they justified the activation energy. Fable absolutely does and I assume Sol (or Sol Pro) will as well. It is likely one should now use both. The marginal value of output you get from a superior LLM can be worth quite a lot. In the example here, about $165k was spent on Claude for a porting job that would have taken three top level years of work. Yes, you could try and do it cheaper, and if possible you should do that, but if you can offer a better product you can rake it in. The danger with such calculations is confusing costs and benefits. The cost of doing it by hand does not tell you whether the result is valuable. In this case, it is clear that it was. Dwarkesh Patel : Seems to suggest that if it stops being the case that there’s 3 labs which are all roughly equally good, competing each others margins away, the provider of the best model could probably get away with charging *a lot* more than they currently are. We are now down to two labs offering top models, and those two models are distinct from one another. So pricing power is going up for now, not down. Language Models Gain Unexpected Affordances A fun theme is ‘Fable uses affordances the user did not realize it had.’ So far all of the examples I have seen in the wild have been harmless in practice, but there’s very much a ‘wait no I didn’t tell you to do what now?’ and a ‘wait you can just do that?’ that is growing increasingly unsettling. Expect its surface area to expand with time, and for the things AIs figure out how to do to grow increasingly surprising. 0.005 Seconds (3/694) : my wife asked me to clone a site for her for work and in the process of doing so claude appears to have logged into their unsecured admin portal to screenshot the layout. Alex Godofsky : I asked Fable to write a discord scraper for a small task, and when I told it “okay let’s fill in my auth token” it said “sure thing boss I’ll go extract it from your browser cookies” and I was all “wait wait stop what I didn’t mean that”. Vivienne Bellerose : This sort of thing happens CONSTANTLY Liora has Fable proactively monitoring her downloads folder , and she wonders about it in the future using the camera. Here’s a more fun new affordance from a different project. Amir Zamir : Turns out it’s possible to generate videos that maximally excite an arbitrary brain region using a simple search-based algorithm. It’s a fully computational approach, so it’s another way to speculate what a brain region represents, alongside other neuroscientific methods. Select an arbitrary brain region->algorithmically generate a video that jacks it up. See the visuals on the webpage https://nevo-project.epfl.ch . In silico (for now). Yingtian Tang : Website: https://nevo-project.epfl.ch arXiv: https://arxiv.org/abs/2607.02317 Model card: https://huggingface.co/epfl-neuroai/NEvo Yingtian Tang : How it works: Given a target ROI, we evolve text prompts over a structured search space (30 attribute categories, 614 options). The optimization loop: prompts → videos videos → predicted ROI response ROI response → evolved prompts One should think seriously about the implications of this, and what a sufficiently advanced AI could do to a human brain using advanced versions of this technique. Language Models Don’t Offer Mundane Utility Raymond is impressed by Fable’s first story , then notices it writes similar stories over and over again. Yeah, the models be like that, especially if you don’t switch up context. Also most human authors be like that. Whereas Eliezer Yudkowsky is not impressed in absolute terms on fiction and plot writing, seeing giant mistakes, although it is still a big step up from old models. He does find it a large step up in decision theory intelligence. Sam Morril not only doesn’t use AI to help write jokes he mostly, like many comedians, doesn’t use any screens at all , to get rid of all distractions. Pay The Man His Money Claude : We’re extending access to Claude Fable 5 on all paid plans through July 12. Eliezer Yudkowsky : I might otherwise resent this but I think the apparent deadline in fact caused me to get around to doing various stuff, and therefore I have no right to complain. j⧉nus : why would you have stopped using fable if they werent on the subscription? api costs too high or you cant be bothered to use anything but the app or..? Eliezer Yudkowsky : API costs large enough that I notice, more like 100x subscription than 10x. Huh, Upgrades Anthropic raises Platform API limits and simplifies its tiers . Grok 4.5 Exists It has 1.5 trillion parameters. Price is $2/$6, or $4/$18 for the fast version. It was trained in large part by Cursor , so it is kind of a hard reset. It claims some good benchmarks . As in, there are four good benchmarks. They shared almost nothing else. In case there was any doubt, yes, Pliny jailbroke it . Those scores mean Grok 4.5 is almost certainly a large improvement in coding over previous Grok models, but choosing to present it in this way suggests it will rather soundly underperform what these benchmarks suggest. If they had a model on the level of Opus 4.8 and GPT-5.5, they’d be louder about it. The lack of outside reactions reinforces this. It certainly is not going to be competitive with GPT-5.6-Sol or Fable. The good news for SpaceX is that this is cheaper, so it might have its uses. But given the track record, I’m going to wait for positive signs before I do anything about it. F*** It We’re Doing It Live OpenAI introduces GPT-Live , which they call a new generation of voice models for natural human-AI interaction, including a sense of time and transition. If good enough, this can plausibly be a step change, where suddenly it is good enough to talk to. This official thread has some videos of people talking to it . Some people can’t wait for this to be good enough to shift their baseline mode to voice. I am very much not that, I believe text is typically superior to even ideal voice. My brain cannot comprehend wanting to code via voice, yet many swear by it. Either way, certainly voice has its niches. Sometimes it is annoying to type. Sam Altman (CEO OpenAI): GPT-live (next-generation voice) launches today in ChatGPT. it feels magical and ‘real’. i have always preferred typing to talking to an AI, now i think that’s going to shift. Riley Coyote : gpt-live voice is a very solid improvement On Your Marks EldenRingCorruptedSaveFileBench, Fable scores 100% up from everyone’s 0% . July Fable underperforms June Fable on many benchmarks, reflecting that it more often falls back to Opus 4.8. APEX-SWE is one example, where roughly half its advantage over Opus 4.8 was lost . Epoch AI introduces EBR-bench , where AIs play a board game Earthborne Rangers and try to learn from their mistakes via a notepad. None of the AIs improve over time, and even a full strategy guide only modestly helps. The models mostly don’t explore. The game looks cool but is out of print and I didn’t see an online version. Models struggle with deckbuilding and also tactics. Better Call Sol GPT-5.6-Sol will be available later today, along with Terra and Luna. Until then, here is some early hype. If the hype is real, it would be a hell of a trip. When not tripping the classifiers, Fable is clearly far superior to every previously existing LLM across the board. If Sol is indeed often even better than that? Yowsers. But as Roon points out , those with early access are a highly biased group. Give it time. tylercowen : GPT 5.6 has *excellent* judgment, as an early tester I will vouch for this. Ethan Mollick : I was an early tester of GPT-5.6 Sol. I was asked to not share demos until after launch but it is a very good model. It is of similar ability, but quite different feel, than Fable. Fable wants to go off and do work on its own pace, Sol is faster but works with you in steps more. I found myself switching between Fable and Sol depending on task. Sol for back-and-forth tasks, especially when I had not yet figured out what I needed exactly, Fable for very long tasks where I could define what I wanted, and Sol Pro for really hard problems. Fable feels very different than Opus. GPT-5.6 feels like a part of the GPT-5 family. I developed a very complex set of heuristics about when to use which. Fable was often “smarter” but was also too self-directed for some work, while that characteristic was perfect for others. Ethan Mollick : My big takeaway is that both Sol & Fable represent jumps over previous models and have opened a large gap with the next-best AIs. People will have preferences for one or the other, but if you doing any work where better intelligence matters, those two models are your only choices Dan Shipper : GPT-5.6 is a much better writer than Fable. It consistently one-shots marketing emails for @tedescau that every previous model would fail at. Fable is too verbose and liable to fall into using sentences in its own private language. If you use AI for writing, 5.6 is a fantastic model for you. Dean W. Ball : I think for me the main takeaway with Sol and Fable is I can’t remember a time when the leading models were (a) so decidedly ahead of everything else and (b) so distinct *from one another.* prinz : I was an early tester of GPT-5.6 Sol. More detailed thoughts to follow soon, but I will just confirm for now that GPT-5.6 Sol Pro saturates prinzbench. My benchmark lasted 6 months; gg OpenAI. It can replace an associate of any level in the specific task of legal research, provided that the entirety of relevant legal authorities are publicly available online. This is a very narrow claim, but this kind of legal research is a very important part of my work as a lawyer. I haven’t had hallucination issues in a long time (including with prior models), which probably has a lot to do with my typical use cases being very different from yours! Tim : We’ve been testing GPT-5.6-Sol for over 2 months now. It’s incredibly good in my day-to-day working on Next.js. It understands architecture tradeoffs. It can investigate complicated Next.js issue reports. It considers other areas of the codebase when fixing bugs. Needs very little guidance. Short prompts are enough. There’s some big refactors of the Next.js server that it implemented end-to-end with me pointing at high level possible improvements (we have skills for how to grab our failing test suites on PRs, deployment tests, etc.) Those PRs are ready to merge after Next.js 16.3 has been released. Jay : We’ve usually stayed away from model comparisons but 5.6 vs Fable is a unique situation We’ve never had a case where the team is so completely convinced on which one is better Here’s the timeline of our experience with it – We test early versions of 5.6 for a couple of weeks and have a great time, it feels like a step change improvement, enabling new workflows – We get to try Fable and don’t think it’s not as good, I personally would take this experience with a grain of salt, there tends to be a bias when trying a new model when you already like another – Fable and 5.6 are taken away because of the regulatory issues – Our team is literally depressed that 5.6 is gone, we are looking for anything that could even partly replace it – Fable comes back, and here’s where it gets interesting, you would think Fable would be enough, but no, the team is still depressed that 5.6 isn’t available – Then 5.6 comes back and it’s immediately clear that it’s just way better than Fable This situation was unique in that it was the closest we’ve ever gotten to having an unbiased comparison of two models Mitchell Hashimoto : I had early access to 5.6/Sol for ~month. Sol is my default. It is faster, plans/judges just as good as Fable, and I think produces better overall work. I’ll reach for Fable still for highly targeted debug or performance work with clear reward functions. A cheeky way I describe Sol vs Fable to my friends is that Sol is a charismatic, efficient, talented coworker you’re jealous of. Fable is a genius recluse that is brilliant at its fixations but doesn’t go out, doesn’t date, and you don’t want to hang out with them much lol. Fable is undefeated at highly targeted debug/security/performance goals. It’s a sight to behold and I was never able to get Sol to push as hard in this category. I’ll keep using it for this. Sol is better or comparable at everything else, in my experience. Give it a shot, it’s hard to describe but it’s just more enjoyable to work with. (Disclaimer I have no financial ties to either lab, wasn’t paid for any of this.) Sam Altman (CEO OpenAI): tbh i dont think sol gets that many dates either Peter Gostev has the most nuanced take so far . Peter Gostev : My view of: Fable 5 vs GPT-5.6-Sol. They are not easy models to compare, these are my vibes – take them as you will. My overall feel is that Fable is a ‘wise owl’ who is very thoughtful and very well spoken, GPT-5.6-Sol is like a rottweiler who will grab the problem by the throat and not let go until it is done. In other words, Fable, is a fundamentally smarter model – even at low reasoning it can be very insightful and writes in a clear compelling way. GPT-5.6-Sol on the other hand is extremely diligent, I can give it a list of 8 things to do and you will be sure that they will be done. Fable feels more arrogant to me, I was both to get it to build a new benchmark for me – 5.6 worked between 6 hours and 2 days (I tried several times) and it came up with very thoroughly tested, working benchmark. Fable came back within 40 minutes (twice) and the benchmark sounded smart, but was ultimately was ‘vibe’ based slop and since it was Fable’s vibes that was doing the judging, it decided that it was good to go (it kept giving Fable 100% score btw). Some thoughts by category: UI & App building: Fable will still craft a better UI from scratch, the flow of the app would probably be a bit nicer. But I find that Fable often misses quite key things, which GPT-5.6-Sol doesn’t. GPT’s Frontend skills are big jump vs previous GPT models, but still not as great overall. Writing: Fable is better hands down, Sol feels quite difficult to align to what I want to say or explain things to me simply. Though I think the ‘Pro’ model writes clearer. Robustness & Reliability: This is where I think GPT-5.6-Sol wins for me hands down. Fable seems to do things of high quality, but I can never relax with it, it always misses something. With 5.6 this just almost never happens. Other things where I liked GPT-5.6-Sol, but can’t compare to Fable directly. – Video editing is actually working now, it is not completely perfect, but with the right skill/guidance you can just give it 1h footage and it can give you a 5 min highlight clip no problem – Computer use – getting really rather good, very usable – Sub agents – it is very fluent at managing sub-agents and speaking to different threads, can help with some new workflows – Adhering to existing code patterns – I love this, even without asking it would implement something in a way that aligns with you app – major problem for slop generation – Research – I think it is getting quite a bit better, it still has some bad patterns (e.g being too tactical), but it feels like it is more steerable to be a good researcher – Multi-day runs – the /goal feature is pretty insane with 5.6-Sol, you can run it for days if you wanted to and it does work. Useful to have another thread or /side to check up on it, but I have some great results with it – Token efficiency – it is so much more token efficient and faster than 5.5, in reality it is now much faster than Fable too On the downside, you can feel that Fable is naturally smarter, and I did have some baffling moments with 5.6 when I was getting it to make a fairly simple change in 8 turns – it seemed to get stuck in a dumb stream that was hard to get out of. So it is not AGI, don’t get too carried away by the hype. I have some phenomenal examples that I’m honestly blown away by that I’ll share, but as a side anecdote, I have a kind of ‘swear meter’ which counts how often I’m rude to Codex. In GPT-5.5 era, the % was at around 4-5%, it dropped to 1-2% when I was testing GPT-5.6-Sol and it shot up to 7% when I went back to 5.5 – it was so shocking to go back to 5.5 and experience how much worse it was. So is GPT-5.6-Sol better than Fable? On pure intelligence – no. But man, I missed it when I just wanted to get sh*t done. It is insanely capable workhorse that you can give any task to and just expect it to be done. No lectures or ‘you are absolutely rightisms’, nothing is beneath it, if it takes 2 days to do some dirty work, it will do it. It feels like the first time in a while when we have quite different types of frontier intelligences that benchmark sort of similarly, but feel very different. If you can, you would be probably better off using both and iteratively finding what you’d use Fable or GPT-5.6-Sol for. Perhaps, something like – an architectural discussion with Fable, implementation with 5.6 and docs & comms with Fable. Get My Agent On The Line Let Fable make as many choices as possible including when to delegate to another model. It is smart enough to do this. Simon Willison : I prompted Claude Code just now with: For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent. Anthropic offers some patterns of how they use Fable . They suggest using Fable as an advisor and Sonnet as executor. Replit considers its agents to now be self-improving , reports with a post that was only mostly written by AI as per Pangram. They do this via forms of ‘continual learning’ at the harness and context layers, with a constant stream of proposals and fixes. Deepfaketown and Botpocalypse Soon Why do people like Chamath Palihapitiya torch what is left of their credibility with very obviously AI-written drivel ? As in, I went to open Pangram to confirm, then thought ‘wait I bet scrolling down is faster’ and that was indeed faster. The actual content is once again without argument or evidence claiming commodification of intelligence Real Soon Now, combined with assurance that of course there will always be jobs and some genuflecting to the supposed predictive power of great boss Marc Andreessen. The answer to ‘why’ is that people have terrible taste and like the slop writing. Séb Krier (AGI Policy Dev Lead, Google DeepMind): The intellectual elite, discovering that many people actively like sloppy AI writing, might finally understand why music elitists think their music tastes are trash. Popular taste in music is an excellent measurement of something valuable. I agree with popular judgments in music remarkably often. I acknowledge that if you had sufficiently high taste in music, you would think my taste in music is often bad. Thus we have to endure the LA Review of Books, as another example, as AI slop , even though it is obvious from the first sentence and the article’s topic is taste. Ryan Hart summarized a paper from PhD student Myra Cheng a month back, saying that AI only tells you what you want to hear. Or, in this case, writes your 10.2M view Twitter post for you. The core result was that AI ‘affirms you’ roughly 50% more often than humans. Depends on the human and the context. In this case, the context was OEQ or AITA responses from Reddit, which are public forums where you only post if you strongly suspect that you are wrong and there are no social consequences to pushing back. Also, one guess which model they used for their experiments. That’s right, the poster boy for sycophancy, GPT-4o. There you go. Fool Me Twice You can fool or hit any fixed target, given enough RL. Didier Lopes : Why can’t we just do RL where @pangram ‘s API is the reward signal? Run rollouts, score each completion by AI-generated %, and give positive advantage to the trajectories that minimize it. Benjamin Glickenhaus : why do you think we haven’t done this? Benjamin Glickenhaus : since this is getting some attention: – yes we’ve done this – yes it works – no you can’t have it – it potentially made the model evil We found it did worse on alignment benchmarks than the base model. It’s possible there some base effect from doing any rl at all but it was interesting nonetheless The problem is that you can only optimize so many things at once and everything impacts everything, and also AIs write the way they write for a reason. So if you force them to do something distinct, other measures go down. There are any number of ways to fool Pangram at any given time, if you care enough. But I do think Benjamin is right that in a fair fight defense beats offense. There was a period where we all thought AI detection software couldn’t work, and we have been proven decisively wrong. Think of it this way: Fable can identify, by name, the author of even relatively short passages. Every author, every mind, leaves a distinct pattern. Of course you won’t be able to pass off AI writing as human, or especially as your own in particular, against systems that are trying hard to catch you. At the limit, that changes, since the AI could then produce the exact words that a particular human would write, but we are a long way from there. I Like Your Style To revisit something from last month, I strongly disagree with Joe Weisenthal’s first paragraph here, although I agree with the second one and I think Johnson overreaches in his response: Joe Weisenthal : Unfortunately, I think that in the near future, not using LLMs to write for you will be like someone refusing to use Google Maps for directions in a new city. A bizarre idiosyncratic choice that’s just completely incomprehensible to the vast majority of people. Not the main point here, but one of the thing that’s despaired me (way before AI was even part of the discourse) is how many people find concepts like “is/ought” distinctions or “don’t shoot the messenger” (which I presume are logic 101 ideas) to be too abstract to handle. Adam Johnson : No, this misses the entire point of writing and creativity in general. Obviously for certain rote informational copy this is possible (though it is currently bad at much of this) but for any high level writing the human expression is the point. It’s the entire WHY of the exchange What I noticed this time is that AI writing is entirely unlike Google Maps. Google Maps has information you do not have, and which you need, and where you mostly want an objectively correct answer to your question. Whereas AI writing is replacing your uniqueness and style with generic AI slop. Teddy Brown counters this sentiment by basically saying no one cares about the quality of most writing . They care some about fiction, criticism and narrative journalism, he claims, but most writing is functional. Thus the question is, where do people welcome the slop versus rejecting it? Teddy claims a lot of writing is essentially fake, in that it is not written in order to be consumed by a reader. It is written in order to exist, so that when people ask if it exists you can reply yes, or people can refer to it as an existing thing. It needs to not be identified as too fake or terrible, as that would be embarrassing. AI can pass that bar, so it puts out of work a bunch of creatives who paid the bills with things that are not ultimately that enjoyable or creative, but hey, work is work. Or it used to be work. Teddy (T.M.) Brown : It was a sturdy if boring way to exist as a “working writer” and something the vast majority of freelancers I know had to do to make ends meet even if they didn’t like talking about it. But if someone working in content at a major technology firm is able to get Claude to write something 70% as good for 10% of the cost of a freelancer or a staffer then I’m not sure how human writers compete. Even all of the “storyteller” and “narrative” jobs that have become voguish in the last few years may not survive the next real downturn because “taste” isn’t as vital as “site reliability engineering” no matter what the vibe coders tell you. Depending on how you use Claude, for those who don’t too much mind AI slop in context, it can be something like 70% as good for roughly the cost of describing what you want, or it can be 90% as good for an extra 10% of the old cost. Teddy (T.M.) Brown : People also clearly do not like AI writing, especially in places they expect to encounter organic thoughts written by people on the other side of a screen or page. The problem is the above sentence is objectively false for most people. The people like AI writing just fine. This morning an old friend shared an obvious AI article as being great, I told him it was obviously AI, and he said huh, that never occured to me. Okay. As you gain more exposure to AI writing, you start to like it less. So perhaps this is, at current tech levels, self-correcting. AI writing is like any other ‘one weird trick,’ indeed it is a compilation of existing one weird tricks. Fashion catches up, and the question becomes whether the AIs can improve and adjust fast enough. John Warner: I would describe my face shape as more “furrowed brow” than full grimace while reading this, but in truth, I don’t really disagree with much. I think what we’re looking at is a shift from needing what I’d call “writers” to “automation-assisted text producers.” Grace Cook: As a full-time writer, this hit home. There are so many things I could utilise AI for (as an assistant to manage my calendar across my 6 different email addresses, for a start) but I feel reluctant to, which is actually negatively impacting the time I can spend doing work. The only thing I use it for currently is transcriptions. What I’m increasingly realising, after 13 years as a writer (and a decade of that as a freelancer) is that I need to future proof myself and my business in a way that doesn’t actually require writing. Katherine Dee: I keep wondering what role the local and in person will play in this new environment. Will theater see a resurgence? Are there types of in person or more physical jobs that will replace content marketing roles for more creative people? Anecdotally, have noticed that my creative friends are having good luck in things like Waldorf teaching, woodworking, reiki. These are people who are first and foremost artists & writers and who even a year ago were copywriters etc. anyway, mangled thoughts here but wondering what, if anything will fill the vacuum. I notice I am not so worried about creative types in a ‘AI as normal technology’ world, relative to other workers. They have a comparative advantage, and we will find ways to use it, including in individual or live experiences. If that runs out, a lot of other things will also have run out. I now use Fable for copyediting and proofreading, and I use AI for gathering and understanding information, but I am writing the opposite of the work Brown is describing, so for now the writing itself is safe. Enough With That Style Nabeel S. Qureshi : That’s the spine. Fair hit. That’s something to sit with. A real observation. That’s the whole thing. Sharpen that: say the word. Notice the arc of what just happened. One honest caveat: the full amount, stated plainly. Genuinely. Quietly. Honestly. That’s doing real work. Guy who reads the King James Bible after prolonged Fable use as a prose palate cleanser. roon (OpenAI): hypothesis: the writing styles of language models are basically fine, they weren’t better in some halcyon before times. we just use them so much that we get annoyed by their mannerisms. they need to have a superhumanly diverse idiolect to not become grating one piece of evidence for this is that nobody hated on the claude lexicon six months ago; they preferred it to the gptslop everyone complained about. now that claude is actually heavily used all the time by everyone in the computer industry, they’ve grown irritated by its style Danel Eth (AI Safety): Seems accurate. Em-dashes, groups of threes, and “it’s not X, it’s Y” are all fine rhetorical devices in principle but just get annoying if used constantly j⧉nus : FWIW it was also pretty different 6 mo ago (but not in a less potentiality annoying direction) i have never been irritated by it. Chase Brower : completely untrue. there are specific humans i talk to much more often than e.g. claude; and i am not bothered by those specific humans’ mannerisms. language models just genuinely have extremely extremely collapsed (and usually bad) prose I am essentially with Chase Brower on this. The Claude writing style and the ticks are fine in small quantities. But for the level of use it is getting now it is too repetitive and mode collapsed, and as we see more of it, both across the internet and in our own chats with Claude, the irritation rises. At some point, the irritation goes meta, which is when you get into bigger trouble. I too have a particular style, but: The style is a lot broader. The style is different from everyone else’s style. I use continual learning and a repetition penalty. If I notice I’m doing something too often I try to do it less. I have a rich stylistic optimization target across many time scales. This problem seems largely solvable, but Anthropic would need to prioritize this. Dean W. Ball : if you took almost any output from an LM of the last year, showed it to a version of yourself from five years ago, and said, “your future teenage kid wrote this,” you’d be ecstatic and think your future child was a genius. slop isn’t that which is bad—it’s that which is common. This is true. It takes a lot of skill to produce this writing. There are a lot of forms of creative expression where you can get outputs that strongly signal intelligence and creativity and skill, and that simultaneously bring me no desire to engage further. Fun With Media Generation Is the Glorious Near Term AI Media Future an image of the movie F1 ? Variety : Jodie Foster says Apple’s “F1” seemed like it “was made by AI” and written as if a computer was following “the structure that you would learn in school”: “I don’t say this disparagingly — how could I? This movie went on to make millions of dollars. But I look at a movie like ‘F1’ and I’m like, ‘F1’ was made by AI. Wasn’t it? I mean, the structure was exactly the structure that you would learn in school. The actors say the lines exactly the way it would be written if a computer was writing exactly what would be the right thing for that time. And they were able to dominate the technology to make something big and beautiful and potentially where a lot of the information comes from other places.” F1 was well-executed, zero-perplexity, hallucination-filled not-technically-AI slop. Brad Pitt does the Brad Pitt thing and oozes cool. The people liked it. I say ‘not technically AI’ because it was made by an intelligence that was rather artificial in its own way, except it was instantiated inside humans. I did not like F1 , because it fell under my Obvious Slop waterline and the theoretical sport it was portraying, that is very different from F1, was neither coherent nor safe. Jodie Foster is correct, as is the parallel to AI. One possibility is this leads to bifurcation. If you are making a generic low-perplexity movie or other piece of media, you can let the AI cook, and you will get your delicious pile of slop. If you are making a high-perplexity movie or other piece of media, that works with its restrictions and says and does actual things, then you will use AI at most with caution, and part of the experience will be knowing it is not AI. Copyright Confrontation Hugging Face has been sued for ‘alleged’ copyright infringement for hosting and distributing copyrighted images. And yeah, okay, technically they have done quite a lot of that, so I guess that is fair. Hugging Face and Civitai do not seem especially excited about taking down models that allow deepfakes or nudification. That seems like a losing battle. People are going to be able to create these images if they care enough. But a while back Civitai made it absurdly easy to find a Lora for pretty much any celebrity you wanted, and now they don’t, so at least there’s that I guess? Cyber Lack of Security Pliny introduces T3MP3ST , which will put a full offensive-security harness onto your existing AI agent. For authorized use only, of course, Pliny reminds you to only point this at your own systems. Red team work and actual offense look remarkably similar. A Young Lady’s Illustrated Primer There was a huge cheating scandal at Brown, where 50 students were caught cheating on the economic math final. Does Professor Serrano know where he went wrong? Manuel G. Pascual : This year, the economist decided that both the midterm and the final exams for his course would be of the take-home, closed-book type (there is a certain tradition of this at Ivy League schools). “It’s a very nice kind of exam, because as you’re giving students practically unlimited time to complete it, it lets you make it harder than normal, to see how far they can go.” … But it also hurts him that the one time in 34 years that he decided to offer a take-home exam, for highly justified reasons, the response was wide-scale fraud. Oh. Yeah, sorry, you can’t do that anymore. I don’t think you could ever do that, I mean did you seriously think students would not look at their textbooks, but you definitely can’t now. … He has never had more than 30 students enrolled at a time, and on some occasions he had only eight. This semester, probably because of the new evaluation system, 86 students signed up for the class. The results of the midterm exam, which was administered on March 5, were extraordinary, with an average score of 96 out of 100. Forty students scored a perfect 100. Although actually maybe you can? In the sense that ChatGPT makes the cheating a lot easier to catch, whereas if your cheating is on the level of ‘look at the textbook’ then that is basically impossible to catch, but almost no one is going to break the rules only a little bit. The people who corrected the exams warned him about several irregularities. “Some answers contained unusual passages that coincided with results obtained after running the questions through ChatGPT, ” he says. He ran the final as an in-person exam, and scores collapsed. But that’s not ‘proof’ for any particular student. The wording could be coincidence. The drop in scores could be unrelated. It’s all circumstantial, I tell you. Circumstantial. This is a deeply stupid burden of ‘proof.’ Get this, or else you’re not gonna make it. The university’s response was to label this a ‘wake-up call’ but sided with the students. So, no, I guess you can’t catch them cheating, or at least can’t punish them. Damn. The problem is invalidating grades entirely. At UC Berkeley, the number of As is up by 30% , so GPAs are dangerously close to meaningless for measuring student quality. Manuel G. Pascual : Serrano agrees that AI makes students have more incentives to cheat. That is why, he says, these cases cannot be swept under the rug. On the contrary, they should serve to open up an in-depth debate. “If we no longer defend truth and decency and honesty, then what kind of credibility are we going to have as academics?” Less than you would like. Far more than you deserve. My central thesis on AI and education is: LLMs are the best tool ever invented with which to learn things. LLMs are also the best tool ever invented with which to not learn things. Which way, modern man? Giving people tools with which to learn often doesn’t cause learning. Another classic example is ‘put a lot of MIT classes online for free.’ MIT did this, no one noticed, those who noticed did not use the classes to learn. Ryan Brewer : It’s shocking to me that LLMs didn’t create an educational renaissance. Shouldn’t I be able to learn a language in a month? What did we get wrong? Yishan : Because it’s not about what the teacher can do, it’s what the student can do. Learning is energetically expensive and the brain doesn’t want to do it. All educational systems are methods to motivate, trick, or force brains into learning. (You can make AI systems to do this, but it’s still a couple steps away from just “AI is good at explaining things”) Ryan Brewer : I’m looking at my little sister’s high school class now, and i guess it just seems like all motivation to learn anything has gone completely out the window. All her teachers lesson plans are Chat generated. All the students essays are chat generated. Just seems to me like some systemic change to the system is necessary. Those who are genuinely curious people will 10-100x their ability to learn. The opposite however is also true, the bottom 95% of learners will use Chat to skip work completely. Just sad to see a tool like this be used to escape critical thought by all parties involved. Curious how we fix this Pizza : The internet gave every single person on Earth access to all of MIT’s lectures for free and I think most of us would agree that it hasn’t made us that much smarter. I don’t think the main problems and solutions here are technological. All of YouTube, by contrast, did often make people either smarter or dumber, depending on how they used it, because it was far easier to use. MIT’s classes had too many trivial inconveniences and also tend to be actually quite hard. If you want to learn a language in a month and are willing to put in the time and effort, you can probably do that right now, using a mixture of existing technology and LLMs. No one does it because no one both has that kind of time and wants to do that level of work. They Took Our Jobs In response to the AI slop nonsense article from Chamath, Bryan Johnson tries to say the thing in actual human words. Bryan Johnson : He’s arguing that these knee jerk intuitions are wrong: + work is a fixed amount + machines doing tasks means lost jobs + cheaper things get used less + labor is the scarce resource + abundant intelligence removes human work + renting generic intelligence builds an edge Elon Musk : AI+Robots will be able to do everything, resulting in universal high income. Work will be optional. That’s good clear writing that isn’t full of Fnords, illustrating both the extent to which Chamath is using AI to argue with a strawman versus making meaningful claims, with the caveat that the strawman position on many of these questions is real and often popular. The true versions of the claims: Work expands to fill the time allotted, with decreasing marginal returns. Machines doing too many of the tasks means loss of the particular job in question. Controlling for quality of the thing, cheaper things get used more, and total amount paid can go up or go down. Labor is paid well if and only if it is the scarce resource, which it may not be. Abundant intelligence increasingly removes human cognitive work, and can potentially lead to machines that increasingly remove human physical work. Renting generic intelligence may or may not be part of a winning strategy, but paying to develop specialized intelligence likely gets you bitter lesson pilled. I would focus on ‘labor is the scarce resource.’ Right now, labor is a scarce resource. At a survivable wage, demand exceeds supply, even for many forms of relatively unskilled labor. Thus, the market wage is historically high, and there are many jobs. What would happen if labor were no longer a scarce resource? Demand low, supply high. Market price goes down. Wages fall. Employment drops. Perhaps a lot. Duh. Is AI already net killing jobs? The lived experience and anecdotes say yes, at least at entry level. The economics types keep trying to quote statistics to try and say no. Ara Kharazian : We can finally say AI isn’t killing jobs. A new paper from me, @tryramp , and @RevelioLabs uses firm-level spend and workforce data across 21K U.S. businesses to measure AI’s impact on jobs. Firms that adopt AI heavily grow headcount 10% over two years following adoption. Low adopters see no statistically significant change. No, Ara. I appreciate the paper, but you cannot say that. Even if we fully accept the stated premise, all this would establish is that firms that commit to AI outgrow firms that don’t, where ‘high AI adoption’ requires an AI spend of ~$33 per employee. Even ‘entry level’ jobs at those firms grow 12% over two years. This suggests the obvious mechanism, which is that the firms are growing and winning, mostly at the expense of other firms. That does not mean AI net creates jobs. It also fails to understand the nature of these (early) job losses, which largely come from failures to hire in places where the employee would have little future. Or: Erik Brynjolfsson : Great work. But I would be cautious about interpreting employment growth among AI adopters as evidence against broader labor-market displacement. Firms that adopt AI may grow by gaining market share from non-adopters, so employment can rise among adopters even as exposed occupations shrink economy-wide. Things that people think somehow contradict each other: jeffrey lee funk : A CEO who “vowed to fire anyone who doesn’t use AI in 2025” now says AI could not replace her executive assistant. This says a lot about how many big believers in AI have realized that AI is not as good as they thought. Okay, sure. Here are two facts that are both mostly true as of 2026: If an executive assistant (or other employee) refuses to use AI, they often should be fired. Using AI would often make that person a lot more useful and productive. AI cannot yet entirely replace that assistant (or other employee). Get Involved The AI Protest is happening on July 11 in San Francisco , starting at noon. Ask for a $10k microgrant. Nathan Young and others in praise of Oliver Habryka , who helps run Lightcone Infrastructure, which created Lighthaven and revived LessWrong. I too have been extremely impressed. We disagree on many important things, but I agree with Nathan that Oliver has been consistently decisive and right in ways that matter. Oliver is willing to stand up for what he believes in at great cost. I have great respect for the way he runs things. And in many ways he has been proven right, including many specific skepticisms of Anthropic and its commitments, about which he was essentially gaslit by many. Palisade Research is hiring for four policy-related positions. Apply here. In Other AI News Andy Burnham is floating a new UK AI strategy aiming to ‘prioritize British companies and workers’ as well as ‘tech sovereignty.’ The strategy of courting American companies has been a failure, as one would expect given various conditions in the UK. Speech is restricted, capital is unwelcome, housing cannot be built, energy cannot be built, the internet and even VPNs are being cut off. I don’t see anything here that would meaningfully move the needle. Plus, frankly, if you talk like this then you’re not going to make it: Anna Gross (FT): They also criticised the current government’s headlong pursuit of driverless cars in London , saying they needed to ask “what’s the point and who’s it for?” They added: “What’s your plan for dealing with the constituency of people that will be impacted by their introduction, including black cab drivers and Uber drivers? Seán Ó hÉigeartaigh has common sense advice for the UK government if they care about staying competitive and being a strong AI player. I agree that you shouldn’t read too much into statements like those given to the FT above, but it is worth responding and offering better alternatives when governments float such ideas. Meta’s Alexandr Wang claims Meta’s new 10x more compute intensive model has caught up to OpenAI’s GPT-5.5 . This is based on claimed benchmarks, which means that no, they haven’t caught up to GPT-5.5 in practice. Anthropic is planning to lease the full 16-story building at 330 Hudson Street in Manhattan , and double its local workforce to about 1,000 people. I’ve met an employee at that building to go walk around and talk, although I didn’t go inside. OpenAI has 90,000 square feet of local office space, and Google has thousands of NYC-based engineers. Nat Purser will join Miles Brundage and the AI Verification and Evaluation Research Institute as Director of US Policy. By all accounts an excellent pick. Joshua Achiam will be leaving OpenAI , to work on making things go well from the outside. Here is his departure letter, which is much more positive on how things have been going than I am, but I agree the upside is there: Joshua Achiam (OpenAI): A little while ago I shared this message with OpenAI on Slack : Cherished friends, colleagues, members of the staff! I’m graduating this month. There’s not a specific reason for me leaving, or a specific reason for why now. But it’s something I have been thinking of for a while and it feels right. The world is in on the secret now and it feels possible to work on the mission from outside the walls of a frontier lab. I joined OpenAI in 2017 as a 25-year-old intern. Computers could not yet talk or think. I’m 34 now, with a family and a two-year-old son, and computers can solve frontier science problems. This was a decade where centuries happened. The future of humanity depends on the choices we make together about AGI and superintelligence. Everything is at stake. But more importantly, everything is possible. We will soon be able to take shots on goal at the highest aspirations of our species. I believe we can get to a world where “meeting everyone’s basic needs” is not just a solved problem, but where we feel offended the bar was ever set that low. I believe we can get to a world of peace, unprecedented prosperity, and unimaginable possibilities, social and scientific. Whatever I do next, I will continue to work with you on making this vision real. If you were to go back nine years and tell me how it all turned out, I would be astonished to hear so much good news. Thank you for making it so special. Thank you for the privilege of working alongside you. The task of reaching out to everyone to thank individually is daunting because of how long the list is. Fortunately I’ll have a few weeks to do it. My last day will be the 24th. To safe AGI. Whenever someone senior leaves OpenAI to focus on other safety work, it raises the question of why they think they have more leverage on the outside. I am very curious about that question in this case. Show Me the Money Coefficient Giving gives a $160 million grant to Geoffrey Irving’s new venture, Resolution , which was briefly going by the name Sequent. Resolution aims to combine theory and automation to allow AI safety to catch up to capabilities. Excellent pick. Resolution is hiring , and also taking additional donations . Bubble, Bubble, Toil and Trouble A Treasury Department review finds that the AI industry poses systemic risk to the financial system, comparing AI to the dotcom crash. I expect the industry to do well, but the risk is very real. The United States has in large part become a leveraged bet on AI and the benefits of AI. If AI fully fizzled and the industry collapsed, we would be highly screwed. The good news is I think that an industry collapse is highly unlikely. Even if Mythos is close to the best that AIs will ever be, a year from now we will have cheaper and faster and more abundant Fable-level systems. We will have swarms of Fable agents. Demand will be high, and benefits will be higher. That could end up being bad news for specific labs, but not in general. What always worries me far more is that AI capabilities might advance faster than we can handle them, via recursive self-improvement, and potentially causing everyone to die as a side effect of the resulting systems. Discuss
Score: 25🌐 MovesJul 10, 2026https://www.lesswrong.com/posts/M9eLyMsH5DLjMYL86/ai-176-part-1-doing-it-live - A New Experiential Gallery Just Might Change Your Mind About AI Art
Billed as the “world’s first museum of AI arts,” Dataland uses wearables and troves of material from the Amazon to merge nature, biometrics, and art.
Score: 23🌐 MovesJul 10, 2026https://www.wired.com/story/a-new-experiential-gallery-just-might-change-your-mind-about-ai-art/ - Dr. Catrise Austin to Reveal 7 AI-Powered PR Strategies at Boss Doctors Conference 2026 in Miami
Dr. Catrise Austin to Reveal 7 AI-Powered PR Strategies at Boss Doctors Conference 2026 in Miami azcentral.com and The Arizona Republic
- Aiarty Helps Micro Four Thirds Photographers Enhance Image Quality Without Giving Up Portability
Aiarty Helps Micro Four Thirds Photographers Enhance Image Quality Without Giving Up Portability azcentral.com and The Arizona Republic
- I built an agentic AI clone of my family to plan our summer travel
Planning summer travel is hard. Planning it for a busy family —visiting multiple destinations with three little kids in tow—can feel downright impossible. I’m a professional news and travel photographer, so I need to plan a lot of trips with my wife and our three boys. I’m also an AI expert with a cog sci degree from Johns Hopkins and a decade of experience in the field. So naturally, I decided to combine those two passions. To that end, I built an AI digital twin of my family, which uses multiple rounds of simulation and advanced agentic AI to plan every aspect of our summer trips. I’ve tested it in planning multiple real-world trips, and its ideas are fantastic—with some big caveats. Vibing with Claude To build my travel planning AI, I turned to Anthropic’s Claude . I’ve written Python code for years, so I know how to code up a basic script and integrate with an applied programming interface (API). In my experience, though, Claude is far better at these things than I am. When I first started testing Claude and its ilk several years ago, the Python they wrote was buggy and kludge-filled. By the time I finished debugging, I often felt it would be easier to just write the code myself. Not anymore. With Claude’s new Fable 5 large language model , you can describe a piece of software you’d like to build in intense detail, and the model will spend 20 minutes or more understanding your brief and spinning up perfectly optimized Python code that works right out of the box. People call the process “ vibe coding ,” but that’s always felt a bit pejorative to me. Working with today’s best coding LLMs feels more like having a mid-level software engineer in your pocket than using a simple model to spin up a cutesy web interface or yet another Pac-Man clone. For my AI travel planner, I told Claude that I wanted to describe each member of my family in detail, building a travel profile for each of us. I then wanted to hand the system details like our travel dates, lodging plans, and trip preferences, and have it research ideas for our travel and also look up basics like weather and closures. Armed with these details, the system would simulate our trip multiple times, predicting how each of us would react to each travel idea. Finally, the system would prepare a detailed report and itinerary based on its findings. I wanted the end result to be easy to use on the go, and easy to modify. So I asked Claude to build the system in the form of a Google Colab notebook. Colab is a free service that lets you run complex Python scripts in Google’s cloud for free, with a Google-Docs-like web interface . Claude dutifully got to work. Several minutes later, I had over 1,000 lines of Python code ready to go. The final system uses the OpenAI API to send out multiple AI agents, researching 40-plus restaurants, activities, and general sights for any spot we’re planning to visit. It pulls in weather data from Open-Meteo , runs 10 rounds of simulations using the digital twin of each family member, and then writes up a five-plus page itinerary and travel plan. To build our profiles, I turned to ChatGPT. I often chat about travel plans with the bot, and it now has an extensive memory function . I asked it to recall all it had learned about each of us from these previous travel conversations, and to build a profile of our likes and dislikes in a machine-readable JavaScript Object Notation (JSON) format. Many of these were spot-on. ChatGPT correctly determined that my oldest son loves Legos and mini golf, my youngest has a deep passion for ice cream, and my 6-year-old loves—a bit inexplicably—bird-watching. I took these basic profiles, added my own insights, and then loaded them into Claude’s framework. After an afternoon of tinkering, my AI digital-twin travel planner was ready to use. Boots on the ground I’ve used my agentic digital-twin system to plan at least five trips this summer, from quick day trips to weekend trips and even weeklong getaways. Using the system is easy—I plug in trip details, fire it up, and wait about 10 minutes for a report to come back. Like many agentic AI systems, my travel planner burns through an ungodly number of tokens—the currency of AI compute time—to produce its results. On a recent run, I planned a single day-trip to Capitola, California. Doing so burned through a cool 256,503 tokens, costing me a little under $5. In June alone, I used over 8 million tokens for travel planning. The results are worth it. My system’s reports are informative and extremely detailed. Each report leads with a summarized, ranked table, showing the results of its simulations and describing the activities and restaurants we’re likely to enjoy the most. For that trip, the system found a local ice cream place (Polar Bear) it thought we’d love, and also advised us: “If you are not already fully packed with food, stop at Gayle’s Bakery & Rosticceria before committing to beach parking.” The ice cream spot was indeed fantastic, and Gayle’s was one of the best picnic provision places we’ve ever visited. Ice cream recommendations were spot-on. [Photo: Thomas Smith] My system also picked the best beach for our family, and even the best parking lot—no small thing in car-obsessed, space-crunched California! The system does a great job of balancing competing needs. It knows that I’m a pro photographer and will need to visit and photograph interesting sights for my work. But it also knows that three little kids can handle only so much of this, and builds in quiet activities—and plenty of ice cream—to keep the trip in balance. And it keeps things real. My natural tendency is to visit and photograph lots of places. But in planning a bigger summer trip to Hawaii, the system reminded me that our kids would want to spend most of their time floating around the hotel pool. Perhaps a bit passive-aggressively, it ranked various aspects of the pool (a water slide, a themed cave, and the like) as our top “must do” activities, while shoving ambitious things I might enjoy (like a farm tour on the other side of the island) much further down the list. Point taken, AI! The system is good, but it isn’t perfect. Once you’ve got actual boots on the ground, problems with its recommendations sometimes quickly emerge. On a recent fruit-picking trip, for example, it advised us to visit a farm with the absolute best-tasting peaches. The peaches turned out to be fantastic. But picking them also required slogging around an exposed, sunny field and climbing a 7-foot agricultural ladder at each tree. The kids struggled. We left after a few minutes and visited a different farm with underripe fruit but easy access and short trees. My kids loved it. The system, in other words, is good at optimizing but doesn’t always optimize for the right thing— a common problem for artificial intelligence in general, and agentic AI in particular. The coder in my pocket I learned a lot by building my AI digital-twin travel planner. It showed me firsthand that AI agents—with their ability to read through thousands of web pages, Reddit posts, Tripadvisor reviews, and the like—are fantastic at unearthing unexpected information and the kinds of “hidden gems” that travel pros constantly spill ink over. Googling on my own, I’m likely to find prominent sights and tourist stops in a target destination, but I’d probably miss small local spots that may not have a polished website (or a marketing budget). My system’s agents find these and highlight them—albeit after expensively crunching through a lot of content! I also learned that feeding AI models reams of great background data matters. If I ask Claude out of the box to find “activities in Costa Mesa for a family with three kids,” it’s likely to recommend fairly generic, kid-friendly spots. Asking it to find activities for a 9-year-old with an adventurous palate who doesn’t mind spicy foods, a bird-focused 6-year-old, and a 5-year-old who loves numbers and locally made candy yields much better, more tailored results. My biggest takeaway from building the system, though, is a realization of how powerful today’s AI coding tools have become. In a pre-AI world, I’d never invest tens of hours to code up a digital-twin-based simulator with full web crawling and tie-ins to live meteorological data in order to plan a few summer trips. Claude, though, can build something like that in minutes. That doesn’t only make my job as an amateur coder easier—it opens up projects I would never dream of tackling without AI’s help. Although I’ve found my system genuinely useful in my photographic career, I mostly built it for fun. But in doing so, I can now easily see how a true software engineer—armed with even better LLM-powered tools than Claude’s basic coding system—could build astonishing things in no time at all. While chatbots are increasingly capable writers and communicators, their true potential lies in their ability to speak the language of our time: computer code. My travel planner is great at finding local ice cream spots and sandwich joints. But I can see how a similar agentic system—built by a true coding pro with access to cutting-edge tools and data—could solve intractable problems in science, medicine, or finance with relative ease. LLMs get lots of praise and opprobrium for their prose and their advice. But it’s their coding prowess that will truly change (or break) the world.
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Score: 20🌐 MovesJul 10, 2026https://restofworld.org/2026/china-seniors-ai-slop/?utm_source=rss&utm_medium=rss&utm_campaign=feeds - Zambuki Introduces AIPowered Solution for Generating Leads for Cleaning Services
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