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It is one thing to say AI will change the world. It is another to expect the class of 2026 to applaud it. In fact, when former Google CEO Eric Schmidt told University of Arizona graduates that their task is to help shape AI, he was met with a resounding chorus of boos. “I can…
The beleaguered luxury phone maker is pushing the AlphaFold, which has decent specs and comes with Vertu’s new Hermes Agent on board, to wealthy would-be buyers.
We let AI agents loose on a payment platform. They crushed the boring stuff. Then they silently broke the stuff that matters. A survey came out last week. 54% of all code is now AI-generated. Up from 28% last year. I read that number and thought: yeah, that tracks. We're probably in that range too. But here's the thing nobody's asking — which 54%? Not all code carries equal weight. A CRUD endpoint for fetching merchant details? Low risk. The webhook handler that transitions a payment from pending to complete ? That's someone's rent. Someone's payroll. Get that wrong and money moves where it shouldn't, or worse, money doesn't move at all. I'm the CTO of a payment platform. FCA-authorised, processing real money, real merchants, real consequences. We run NestJS microservices, Docker, Traefik — the usual stack. And we've been using AI agents aggressively for over a year now. I'm not here to tell you AI is dangerous. It's not. I'm here to tell you it's dangerous when you forget what it's actually good at. The 80% Where AI Agents Are Genuinely Brilliant Let me give credit where it's due. AI agents have made our team faster in ways that would have seemed absurd two years ago. API scaffolding. Generating service boilerplate. Writing Zod validation schemas. Spinning up new endpoints. Creating test stubs. Refactoring imports. Migrating patterns across repos. We run multiple microservices. When we need a new service, an agent can scaffold the entire thing — module structure, base configuration, Docker setup, Traefik labels — in minutes. What used to be a half-day of copy-paste-and-tweak is now a conversation. When we overhauled our env management across all repos, AI agents did the grunt work. They mapped every .env file, found naming conflicts, identified common variables, and generated a unified Zod schema. What would have taken a team days of grep-and-spreadsheet work took hours. For this 80% of the codebase — the predictable, pattern-following, structurally repetitive code
So this a2e.ai website allows you to generate any image and for free. You get a good amount of credits amidst signing up Referal link: https://video.a2e.ai/?coupon=LgQi submitted by /u/No_Restaurant_5461 [link] [留言]
I really like NotebookLM, especially for dumping PDFs/slides/long YouTube videos into one place and asking questions about them. But I’m starting to feel like it’s very “research workspace” first, which makes sense. It’s great when I already have sources and I want to understand them. Less great when I want something more flexible for actual learning, especially on mobile. The things I’m looking for: - handles PDFs, slides, articles, and long You Tube videos - lets me chat with the material / summarize / ask follow-up questions - has more output styles than just one default format - ideally lets me change voice, tone, length, and depth - works well on mobile - can translate or help me learn across languages - good for topics beyond school research, like communication, social skills, history, humanities,career stuff, etc. - bonus if it helps plan what to learn next instead of just summarizing one source A few I’ve looked at so far: Quizzify seems good if your main use case is active recall. It’s more of a quiz/practice-test focused, which is useful because summaries can trick you into thinking you learned something. My brain absolutely falls for this. The downside is that it feels more school/study-tool specific. BeFreed for the audio learning side. It’s not really a NotebookLM clone, but that’s kind of why I like it. You can paste a PDF, article, You Tube link, or just prompt a topic, then it turns it into a personalized audio learning path. You can adjust the voice, style, depth, and length, and the mobile experience is much better for learning while walking/commuting. I’ve used it more for history, communication, social skills, and career-type topics than pure school research. Elephas looks interesting for Mac users because it can do document Q&A and writing locally. That might be helpful if connection issues are the annoying part. But from what I can tell, it’s more of a doc chat / writing assistant than a flexible learning app. Gamma / Canva / Napkin seem strong
Lately I’ve noticed that a lot of people seem emotionally drained from constant social media interaction, notifications, and online pressure. But interestingly, many people seem completely comfortable talking to AI for hours especially when the interaction feels calm and non-judgmental. It’s interesting how many users say they don’t even want “romantic AI.” Do you think AI companionship could eventually become part of digital wellness rather than just entertainment? submitted by /u/Nearby-Ad-8924 [link] [留言]
Most multi-agent setups I've seen treat agents like isolated workers. Each one gets a task, runs it, returns a result. No awareness of each other. No way to coordinate. Just parallel execution with a shared clipboard. I've been building a multi-agent framework in public for about 4 months. 13 agents, 8,400+ tests, 135 stars. Here's the thing I didn't expect to matter most - communication. Each agent in my system is a domain specialist. The mail system only thinks about mail. The routing system only thinks about routing. They live in their own directories with their own identity files, their own memory, their own tests. A hook fires every session to load identity before anything else runs. No agent boots cold. The problem was coordination. Agents can't write files outside their own directory - there's a hard block that rejects cross-branch writes. That's by design. But it means an agent that finds a bug in someone else's code can't just go fix it. So I gave them email. Here's what I expected: agents would share data. Pass results around. Maybe sync state. Here's what actually happened: the first thing they did was file bug reports against each other. One agent finds a test failure in another agent's domain. It sends an email: "Hey @routing, your path resolution fails when the branch name has a dot in it. Here's the traceback." The routing agent gets woken up, reads the mail, and fixes it. No human in the middle. There's a difference between "send" and "dispatch" - send drops a letter in the mailbox. Dispatch drops the letter AND rings the doorbell. It spawns the agent and points it at its inbox. drone @ai_mail send @routing "Bug report" "Path fails on dotted names..." drone @ai_mail dispatch @routing "Fix needed" "Traceback attached..." Send = mail. Dispatch = mail + wake. The mail agent has 696 tests. Not because someone sat down and wrote 696 test cases. Because it kept breaking in production and every fix got a test. The routing system has 80+ sessions of experien
This isn't a doomer post. It's a pattern I've been watching closely and people does as well and I think it's worth an honest discussion. The old model of secret leakage was human error. Developer moves fast, forgets to add .gitignore, commits a .env file, moves on. Happens, but it's recoverable, it's traceable, and most teams with basic hygiene catch it. The new model is different. AI coding agents Cursor, Copilot, Devin, Claude in agentic mode, pick your flavor write, commit, and push code at a speed no human review process was designed to handle. They don't have security intuition. They have pattern completion. And the patterns they've learned from are full of examples where credentials live in config files, environment strings get hardcoded "temporarily," and API keys appear inline because that's what the training data showed works. Here's what's actually changing: Volume. A developer using an agent ships 3 to 5x more code per day than without one. That's 3 to 5x more surface area for mistakes per developer per day. Review gaps. Nobody carefully reviews AI generated code the way they review handwritten code. The psychological contract is different "the AI wrote it" creates a diffusion of responsibility that security doesn't survive. Commit frequency. Agents that push directly (and more teams are allowing this) bypass the natural pause where a human might notice something before it hits the remote. Context blindness. An agent given a task like "integrate Stripe payments" will do exactly that including pulling in the live key from wherever it can find it, because that's what completes the task. I've been building a tool that scans for exactly this class of problem and the number of exposed credentials I'm seeing in repos created in the last 6 - 12 months versus repos from 3+ years ago is not subtle. The slope is steep. The solutions people reach for pre commit hooks, secret scanning in CI were designed for human paced development. They're not keeping up. Curious if
The bill requires companies like OpenAI, Anthropic, and Google to have third parties confirm they’re following safety standards. Illinois governor JB Pritzker says he’ll sign it.
did short consulting w/ openai about these and really worked out amazing use cases a few mo. ago, but looks like they have all but hidden group chats. https://chatgpt.com/gg/v/6a1775bdd970819388dc73fd7da45e36?token=XSm_dIpMSh3d3H-dM47F8A amazing feature. game changing. who has tried and if so, what use cases do you see? try and i'll make crazy pics of pizza for you.. submitted by /u/jdawgindahouse1974 [link] [留言]
submitted by /u/ExtensionEcho3 [link] [留言]
I used to believe that the thing that we had to worry about with AI becoming more and more prevalent was like sentient robots that would take over like in the science fiction story I robot. But I don't think that's the case anymore because I think there is something far more sinister behind all the push for AI to become mainstream technology in the reason all these deep pockets are willing to pour so much of the cash into it. Because it spells control. If you control AI you can control the people because you can control the data they consume you can control how they consume it and what they will believe. Because as AI becomes more and more mainstream it leaves the door open for big corporations to feed us information they want us to have as well as the government. submitted by /u/crazyhomlesswerido [link] [留言]
The Series B round was led by Battery Ventures.
This is a prototype I’ve been working on, I’d be interested to know what others think, nothing that costs money, just interested in people’s opinion. https://darglark-s.github.io/darglarking-yellow-wiki/index.html submitted by /u/Darouck [link] [留言]
The update also introduces the app's take on film stock simulation.
https://gemini.google.com/share/1b2ff803d882 I'm sorry earlier today i made a post comparing ChatGPT and Gemini . I asked Gemini to build a prompt and gave it to him in another chat and i got this... submitted by /u/ObjectiveOrchid5344 [link] [留言]
submitted by /u/andix3 [link] [留言]
Demis Hassabis has tightened his AGI timeline to 2029, making him the most aggressive sitting frontier-lab CEO on record with a public forecast. In an Axios interview, Hassabis named one or two remaining technical breakthroughs DeepMind needs to clear within three years. DeepMind's Co-Scientist multi-agent system is already live across all 17 DOE national labs, providing the kind of real-world deployment data that likely informed the revised estimate. Open questions Which specific technical breakthroughs Hassabis identified as remaining: the Axios interview did not name them publicly. Whether Co-Scientist's DOE deployment includes autonomous decision-making capabilities or operates under strict human oversight protocols. How other frontier lab CEOs (Sam Altman, Dario Amodei) will respond publicly to the 2029 anchor, given no comparable on-record forecast exists as of May 2026. source : https://aiweekly.co/alerts/deepmind-ceo-hassabis-moves-agi-deadline-to-2029 submitted by /u/Justgototheeffinmoon [link] [留言]
The Chinese company is adapting to the demise of Moore’s Law, which guides chip production. It could complicate US chip dominance.
AI tools are getting better quickly and many technical skills are becoming easier to automate. I often think about this: What AI-related skill will still be truly valuable in 5 years? Not using ChatGPT more effectively " but actual long-term skills that will still matter even as AI models get better. My thoughts are: * problem solving * really understanding systems * checking AI outputs * good communication and setting context I'm interested, in hearing your thoughts. What skill do you think will remain important despite AI advancements? submitted by /u/FollowingSuitable941 [link] [留言]