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Back in the 1980s a debate raged about whether it was okay to let children use calculators in elementary school. Critics warned that giving kids calculators would lead to the "destruction of student math skills." A similar debate is happening today across a range of areas, including coding, writing and even music. Will using AI lead a brain drain across these and many other areas? One of my favorite authors is Isaac Asimov. He's better known for his Foundation and Robot series of books where he contemplates whether an algorithm can successfully predict (and guide) humankind's development and the relationship between super artificial intelligence and humans. In some ways he predicted what we're experiencing today with AI: the rise of powerful, inscrutable artificial machines that are so complex humans can't understand or maintain them. In the short story, "The Last Question" he wrote: "Multivac was self-adjusting and self-correcting. It had to be, for nothing human could adjust and correct it quickly enough or even adequately enough." We're living an age that was once the stuff of science fiction. The question is: what comes next? submitted by /u/SpiritRealistic8174 [link] [留言]

Lectric, which says the U.S. market is ripe for competition and choice, has launched three new brands in the past six months.

submitted by /u/SpeedAssassin [link] [留言]

While the AI fundraising machine keeps breaking its own records, some founders are building in the other direction. Mirror founder Brynn Putnam just raised money for Board, a startup focused on bringing people together through in-person games and social experiences. Cyberdeck creators are going viral crafting whimsical DIY computers that literally encourage users to touch grass. Unlike the AI-free browser crowd, this doesn’t just feel like backlash, […]

Nicolas Cage was born to play 1930s PI Ben Reilly/The Spider: part Bogart, part Bugs Bunny, 100% Cage-y.

Whether you’re considering starting a Sonos speaker setup, or adding to an existing group, the Sonos Era 100 is worth picking up. The compact, capable smart speaker is currently marked down to $189 ($30 off) at a variety of retailers, including Amazon, Best Buy, and directly from Sonos. If you want an even lower price, […]

been building AI agents for a while and noticing a pattern: the LLM reasoning part works. the part that breaks is everything around accounts, logins, and verification. agent gets to "sign up for this service" and then: - email verification loop breaks - OTP times out while the agent is mid-step - captcha or bot detection fires - session expires between steps the model figured out what to do. the infrastructure around it didn't cooperate. curious if this matches what others are building. where do your agents actually fail in production? is it the reasoning, or is it the plumbing? submitted by /u/kumard3 [link] [留言]

submitted by /u/ProfessorDeep8754 [link] [留言]

We're now deep into developer conference season, and one of the themes so far is the relentless conviction from Big Tech companies that AI is going to change everything about how we do everything. Nvidia's Jensen Huang made that clearer than anyone this week, when he described a completely new way of using our laptops […]

Supabase, an example of an open source project becoming a fast-growing company, has greatly benefited from AI tools like Claude, Codex, and other vibe-coding platforms.

Run AI coding agents hands-free from a heads-up display Discussion | Link

Disclosure: I work on the benchmark below, so flagging that up front. We've been testing whether LLMs can critique recent science-paper summaries — catch planted flaws, overclaims, and missing evidence — and, separately, how calibrated they are about their own judgments (confidence scored with Brier, a strictly proper rule). The pattern that keeps showing up: the models best at spotting problems are also among the most confidently wrong when they miss. Critique skill and calibration look like different axes, not the same one. There's also a clear gap between raw accuracy and knowing when to abstain. It's open (Apache-2.0) if you want to poke at it: Leaderboard: https://huggingface.co/spaces/BGPT-OFFICIAL/refute-leaderboard Dataset: https://huggingface.co/datasets/BGPT-OFFICIAL/refute Curious how others think about measuring calibration vs. raw capability — is a proper scoring rule enough, or do you need explicit abstention metrics too? submitted by /u/connerpro [link] [留言]

Even if you've done nothing wrong, it's never a good idea to hand your phone to the cops. But international travelers at American airports often have no choice - even if they're US citizens. When Minnesota labor organizer Janette Zahia Corcelius returned home from a three-week trip to Europe in late April, she was detained […]

The Logitech G512 X 98 lets you swap between mechanical and analog switches in an attempt to achieve the best of both worlds. Unfortunately, its solution isn't as well thought-out as I'd hoped.

Cybercriminals, part of a gang known as Silent Ransom Group, have sent people pretending to be IT support employees to law firms' offices, where the criminals have stolen data using USB drives or remote access tools.

After spending the last few weeks reading through the reasoning literature, I noticed a trend that seems worth discussing. For the past 2–3 years, a large fraction of progress in LLM reasoning came from making models generate more intermediate thoughts. Chain-of-Thought prompting (Wei et al., 2022) pushed PaLM 540B from roughly 18% to 58% on GSM8K. Self-Consistency added another 17.9 percentage points by exploring multiple reasoning paths before committing to an answer. Tree-of-Thoughts later showed that GPT-4's success rate on Game of 24 could jump from 4% to 74% when reasoning was reformulated as search rather than a single chain. DeepSeek-R1 and OpenAI's o1 pushed the idea even further by allocating substantial test-time compute to reasoning itself. Taken together, these results seemed to point in the same direction: giving models additional reasoning trajectories, search paths, or thinking steps often improved outcomes. Recent work increasingly asks whether those traces are actually necessary. Quiet-STaR doesnt treat reasoning traces primarily as explanations for humans. Instead, it trains models to generate internal rationales that improve future token prediction. COCONUT goes a step further and asks a more radical question: why force reasoning to be represented as language at all? Rather than generating reasoning tokens, it feeds continuous hidden states back into the model and performs reasoning directly in latent space. Fast Quiet-STaR then shows that some of the benefits of explicit reasoning can be retained even after removing thought-token generation during inference. This feels like a meaningful shift in research direction. For a while, the field seemed focused on making reasoning more visible. Recent work increasingly explores whether visibility is actually necessary. One way to interpret this is that Chain-of-Thought was never the reasoning process itself. It was a computational scaffold. Transformers perform a fixed amount of computation per generated

GitHub热门项目 | LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar | Stars: 15,979 | 108 stars today | 语言: Python

Your trip starts impacting the planet before you even leave home. Here are a few pointers for keeping your footprint small.

Hotels and other service providers pitch themselves as eco-friendly when they’re not. Here’s how to call their bluff.

The actual eco-friendliness of ecotourism varies considerably. One research station in the Peruvian Amazon is out to prove it can bring visitors to the area without disrupting the environment.