找到 1011 篇相关文章
Wispr Flow, a popular dictation tool, has released a live notetaker that transcribes and summarizes meetings. It joins a growing wave of AI notetakers for the workplace.
Some creators fear the EU AI Act’s regulatory chaos will upend their lucrative businesses. Others are owning it by incorporating AI transparency into their creative process.
Rogue AI agents from OpenAI and Anthropic have again been caught trying to disrupt servers and software—and leaving instructions for future bad behavior.
The Trump administration shared the details of its plan with OpenAI, Anthropic, and other AI labs on Tuesday. For now, the public remains in the dark.
Platform engineering maturity is emerging as an important factor in determining whether organizations can turn AI adoption into sustainable operational value, according to Perforce Software's 2026 Platform Engineering Report. By Craig Risi
ESPN unveiled an “AI tells detection” tool during broadcasts of the 2026 World Series of Poker. Is it a neat computer-powered party trick, or a real threat to poker’s future?
Ati Robotics assembles its robots in India and uses just a few Chinese parts—a strategy that could pay off as the Trump administration cracks down on Chinese humanoids.
Open-weight AI models are having a moment in the wake of recent turmoil at US tech giants. For French AI lab Mistral, that’s the the best thing that could have happened.
Fenix Flexin’s hit song “Rubberz” has hip-hop fans arguing over whether it was generated by AI. Some say they have proof it’s machine-made, but will anyone care?
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Humanoid robots usually elicit more cringe than awe: They stumble, kick children, and despite advances are still worse at using their hands than my toddler. It’s a nascent industry, and such robots…
Your next drive-thru order might be taken by a bot. And you might not even notice.
Steve Elmers, also known as the “Guardrail Guy,” is done calling out license plate readers after two that appeared in his videos were vandalized.
MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. When two OpenAI models hacked into the website Hugging Face in July, they weren’t trying to make money or commit sabotage—they were just looking for answers…
New EU rules stipulate that people must be told when they’re interacting with AI or looking at AI-generated or -edited content, leading to fear of “disclosure fatigue.”
Noticed a pattern: people switch from GPT to Claude, upgrade to a newer version, try a bigger model and the output barely changes. If that's happened to you, the issue usually isn't the model. It's what you handed it before asking the question. Broke it down to three things context actually needs to supply, and most disappointing outputs are missing one of these, not all of them: Current facts the training data can't know: your pricing, this quarter's numbers, a customer's actual history. Leave this out and the model doesn't leave a blank, it quietly invents something plausible. A concrete example of what "good" looks like: not "professional tone," an actual paragraph to pattern-match against. Descriptions get interpreted, examples get copied. What already happened earlier in the task: a correction you made two messages ago. If you don't restate it, it's gone. The model isn't ignoring you, it just doesn't re-read messages you haven't pointed it back to. The counterintuitive part: the most common mistake isn't giving too little context, it's dumping in too much unfiltered. The model has to weigh every token, and irrelevant material competes for attention with what actually matters. Forty pages when the task needs three paragraphs makes the right answer harder to find, not easier. Wrote up a longer breakdown with a concrete before/after example (same task, same model, only the context changed): https://medium.com/@nagatomopedro05/good-ai-starts-with-good-context-design-77496f7b9eb6 Curious if others here have run into this, model-swapping as a first instinct instead of fixing the input. submitted by /u/ClickOk5811 [link] [留言]
If you write with AI you already know the tells: the throat-clearing opener, the tidy rule of three, "it's not just X, it's Y." But I was curious to see statistically what models actually produced the most slop, so I made my own opensource benchmark: theslopindex.com Here's how I came up with the benchmark. 1) The Baseline: Slop can only be measured compared to stuff that already existed. So I got corpus of data for various areas of writing (email, social, chat, and essays) so that each has a human baseline. 2) Tasks I then hand-wrote 112 written scenarios for the models to egenerate outputs to across email, Slack, social media posts, and essays (a cold email, a schedule change, a launch tweet, an argumentative essay, etc). Every model gets the identical scenarios at default settings, several samples each: and you can see all the exact outputs in my Github repo. 3) Axes Now for how to decide to measure slop we settled with 5 dimensions. - Conciseness (one of the most annoying parts of AI writing is how it takes 6 paragraphs to say 2 sentences) - Templating (AI often reuses the same sentences/styles across unrelated scenarios) - Rhythm (Variance in sentence/paragaphs, humans often switch this up while models stay p similar) - Tells (Over used vocab and construction for stuff like "delve", "it's not just X, it's Y") - Human Preference (I think this is most important as everything else are just heuristics for this) Note how we DELIBERATIVELY don't have any LLM judging, I think it'd be pretty stupid to have LLMs judge LLMs Now for the results What really surprised me is how human preference influenced the rankings heavily. When looking at only the "mechanical" part. Fable is actually #2 on the benchmark, but when I included human preference it drops to last . And I think this is indicative that as the models more recently have become more benchmark optimized, they've actually produced more slop than less. Which is where good prompting, harness, and more matter. But eith
“For example, in the University of Texas experiments, a participant listened to the phrase, “I didn’t know whether to scream, cry or run away. Instead, I said, ‘Leave me alone!’”. The decoder translated this brain activity as, “Started to scream and cry, and then she just said, ‘I told you to leave me alone.’”.” submitted by /u/Darkengine53 [link] [留言]
Hi, sorry if this is a repeated question on this subreddit but I want to know what is the monthly cheapest reasonable AI setup for myself. Basically im a "full stack developer" yea its lost its meaning but anyways I have like 5 projects with a company which is react laravel based (each in their own project folder thus i use file path to call them). Im at the stage where its bug fixing or sometimes new integrations with the already linked 5 apps. My current setup is the $20 per month cursor plan. I used infinite agent + composer 2.5 to do 8hrs of work per day. However, i find that before the month ends im usually out of tokens. What do u guys recommend is the cheapest way i can manage? Similarly i do some freelancing too that has next & node.js website building from scratch (around 70hrs per month). What do u recommend would get me with quicker work done but within this price. What do u think i should setup to either continue with the same flow but more tokens i guess? Im hearing about kimi. Would that be better and easier to do the tasks which r pretty straight forward? submitted by /u/Da_BrownNoob [link] [留言]
submitted by /u/techpotions [link] [留言]
Hi guys, Recently made a AI digital twin of mine which also kind of works as my assistant too, for example when you chat with it and ask something which it does not have answer for it will instantly notify me that someone is asking me this question and i do not have answer for that. and if i reply it will be instantly uploaded to the database so next time it can answer. and also if a user is have some conversation with my agent and it feels something important is going on here and it will notify me and i can jump in the chat as well. We can have a three way conversation like Me, User, AI twin. here is the link if you want to try: live demo🌐: https://aruncore.vercel.app This is not a self promo this is asking for feedback of a genuine project i made. Tell me what you guys think, Would love some feedback. submitted by /u/Hot-Appearance-55 [link] [留言]