A new Supernatural VR workout app arrives this fall months before Meta planned to shut it down.

This week we've got tandem hands-ons with Google's new Gemini AI agent - Spark - from my colleagues David Pierce and Jay Peters. Their takeaways are similar: It's so effective that it's scary. Spark knew that David's dog is named Frida and knew the first name of Jay's wife, even though neither of them explicitly […]

Hey HN, we’re Shalin & Kanyes, best friends who've been hacking together for 10+yrs, and now founders of Hyper ( https://heyhyper.ai/ ). Hyper is a shared “company brain” that plugs into information flowing inside a company to make AI agents and automations better and ultimately save people time. Models have gotten good enough that they can (mostly) take on long-horizon, complex tasks. We believe the bottleneck now is that these smart-enough models often lack information about your company, whic

Lately it feels like every AI tool with a few buttons and integrations is being called an agent. Sometimes it is actually doing multi-step work, but other times it just feels like a chatbot with access to a tool or two. I don’t think that is always bad. Even a simple tool-using assistant can be useful. But the word “agent” is starting to feel stretched. An AI that drafts an email, an AI that browses a website, an AI that fills a form, and an AI that can keep track of a task over time are all being put in the same bucket. For me, the useful difference is whether the system can actually carry a task forward. Not just respond once, but remember the goal, use the right tools, notice when something changed, and stop when it needs human approval. The hype makes it hard to tell what is real progress and what is just a normal AI wrapper with better marketing. submitted by /u/Spiritual_Work6730 [link] [留言]

AI Agents for Fortune 500 grade customer support Discussion | Link

Plex is increaingly focusing on content discovery and streaming rentals.

The breach at wearable ring maker Ultrahuman stemmed from credentials stolen from a malware-infected employee laptop.

I recently had a submission desk-rejected from the NeurIPS 2026 Position Paper Track for an alleged AI-policy violation. After corresponding with the track leadership and reading their public blog post, I think the broader methodological issue is worth discussing here. The track used Pangram, a proprietary AI-text detector, as part of the desk-rejection process. I was told that the materials considered for desk rejection were: the detector output the authors’ AI-use attestation This creates a potential circularity problem. If a high detector score is used to judge the author’s attestation as inconsistent, and that inconsistency is then used to justify desk rejection, the detector is not just an aid. It becomes a decisive part of the adjudication process. The bigger issue is validation. The NeurIPS blog describes tests using Pangram audits, older ACM FAccT papers, synthetic AI-generated position papers, and manually edited samples. But the target population was NeurIPS 2026 Position Paper submissions, whose ground-truth authorship process is unknown. So the key question is: What is the false-positive rate of the final decision procedure on the actual target distribution? A false-positive rate measured on one distribution does not automatically transfer to another. If the actual submission pool produced a "surprisingly high flagged rate" (citation from NeurIPS blog post), that could indicate distribution shift / miscalibration. To sanity-check the detector’s behavior, I also ran Pangram on recent 2026 papers authored by NeurIPS Position Paper Track Chairs. Pangram returned scores including: 69% AI 45% AI 36% AI 24% AI I am not claiming those papers were AI-written. For me, Pangram’s outputs alone does not permit such a conclusion. And that is exactly the point. UPD: Here is NeurIPS original blogpost And here is the blogpost with the detailed critics submitted by /u/Asleep-Requirement13 [link] [留言]

Carvana was granted a warrant to buy shares in Slate last year, according to documents obtained by TechCrunch. Guggenheim Partners CEO Mark Walter is heavily invested in both companies.

I am trying to train an AlphaZero model for Othello on a 6x6-board. Having been warned that too little exploration during data generation can lead to models being overconfident and trapped in some tight region of the search tree, I started with the value c_puct = 4.0, and then reduced this to 3.5 after a few generations. Also, I added fairly peaked Dirichlet noise (alpha = 0.15) to the prior predictions at the root of each tree search, with the proportion epsilon = 0.25. The temperature was initially set to 1.0, and then reduced to 0.8 after 20 generations. Now, the models do improve in the sense that later models consistently beat earlier ones, but there is no significant improvement against the two benchmarks I use: classical MCTS, and a greedy agent. Against the latter, the models have a deplorably low win rate of less than 10%. As can be seen from the curve for the value loss on the validation data, the models don't seem to learn to predict values (which is why I have been hesitant to reduce c_puct further), but the prediction loss seems to behave more or less as it should. https://preview.redd.it/gjby4omfp35h1.png?width=640&format=png&auto=webp&s=4d2ba4716ade6ec4ce9b7f16605a2e6bd74c6baf I decided to test if the prediction targets become strongly peaked early on. For this, I compute the normalized entropies of these predictions, meaning that I divide the entropy by the log of the number of legal moves at the given game state. The plot below shows the mean values of these normalized entropies for the data sets created by the different generations of agents. https://preview.redd.it/5yk216zjp35h1.png?width=640&format=png&auto=webp&s=538f59f5da3671a20c0ef2e1afc1ec96da237107 Finally, I tested how the policy predictions of a fixed set of random game states vary with the models. Here, I have set the second model as a benchmark, and I compute the average Kullback-Leibler divergence between the predictions by the benchmark model and those by later models. This is display

I was thinking, with all the lay offs in tech. Would it be possible to start a company and just sort of catch the talent getting laid off? Obviously you would need an initial investment from something like an investment firm or an angel investor. I was just thinking that their could be an opportunity for some rich people to eat AI's lunch if they started a tech company with the laid of talent from the AI bubble. But also idk, I have never been in the valley, so I don't really know how it works. submitted by /u/LAN_scape [link] [留言]

This LinkedIn post argues that NeurIPS 2026 used a proprietary AI-text detector to desk-reject papers for alleged AI-policy violations, without validating the detector on the actual target distribution. The author then fed recent papers by NeurIPS Position Paper Track Chairs into the same detector and Pangram assigned them high AI scores, including 69%, 45%, 36%, and 24% AI. submitted by /u/Asleep-Requirement13 [link] [留言]