标签：#art

submitted by /u/armend7 [link] [留言]

I was dealing with another "output not usable" issue today in our app, user left a comment saying that no matter what he does the agent returns the result in the wrong format. It took me hours to identify the mistake and AI model missed it. Curious to hear your stories about the times you shipped a feature in your AI product and it flopped. How did you figure out what was actually going wrong? What tools if any did you use? What metrics were key? submitted by /u/pauliusuza [link] [留言]

I love all the wild updates from Anthropic, Open AI, Google, etc. And also seeing the creative stuff that mid-market AI shops are rolling out. I sometimes go through phases where I ping-pong between new tools (mostly just curiosity) but sometimes I tend to go deeper into a specific ecosystem. Right now trying to go "all-in" on Claude but I'm like a cat and Open AI is the laser pointer with new Codex updates. What have you all found works best. Go wide and test everything? Different tools for different use cases. Go deep and specialize in one ecosystem? submitted by /u/BeltwayBro [link] [留言]

In its latest update, Bluesky is getting into long-form content.

LinkerBot makes dexterous robotic hands for as little as $600. It wants to become the standard for humanoids and automated factories—and eventually replace human labor altogether.

A lot of agentic AI content focuses on greenfield builds. I wanted to show what it looks like when you have an existing search stack and want to supercharge it without a rewrite. Built a demo with four levels of AI adoption - from a zero-risk async suggestion bar up to a full conversational search assistant - and wrote up the architecture at each level. The whole demo took 10 hours to build. Live app included. https://arcturus-labs.com/blog/2026/01/18/incremental-adoption-of-agentic-search/ submitted by /u/Due_Ad_1318 [link] [留言]

I was looking at my bank statement one day and realised I was paying for 4 things I completely forgot about. Combined it was around 40 euros a month just silently leaving my account. I'm a 17 year old developer from Cyprus and I spent the last few weeks building Capsule, a simple subscription tracker that shows you everything you pay for, alerts you before renewals, and tracks how much you save by cancelling things. No bank connection required. You just add your subscriptions manually. Privacy first. It's not on the Play Store yet but the waitlist is live at capsule.crickdevs.com if anyone wants early access. Would genuinely love feedback from real people before I launch.

the video generators are becoming much more powerful, only unemployed people can track the changes ( like me).. Here are the current observations, and add anything in the comments if you feel I missed something. Cinematic Videos Seedance 2.0 : This Chinese model is fantastic in real visuals and advanced visuals, almost like real shots. I guess this will become the future. Kling 3.0 and kling motion transfer: Motion transfer is amazing, you shot a vidoe yourself and can trasfer the movement any avatar. Kling is the king in that aspect. With Kling’s motion transfer, . There is no other technology that can do this this well and look super fantastic. Veo3 : Recent releases of Veo 3.1 are still some of the best videos. Sora has shoted down by openai, and recent Google model, - GeminiOmni , is the best in video editing. It is like Nano Banana for videos. It is absolutely fantastic. Don’t compare this with Seedance because the purpose is completely different. If you try it on your own video and ask it to add something, it gives a super realistic output. Explainer Videos These are not cinematic, but mostly for concept explanations and long videos. These tools are great fit: Distilbook : This one is very good at creating visual explanations with whiteboards and animations based on your content, PDFs, and all. If you want long videos, like 3-minute or 5-minute training videos,academic this is purpose-fit. NotebookLM Video overview : This tool has the video overview option, which makes things much easier for you. It is mostly for slide-type videos, but it still gets your work done because most of the time you may not need animated videos. MathGPT: Here it is mostly for math educational video explanations using some animations. These are not very advanced, but still, if you want cheap educational videos, maybe it can do the job. Images In my personal opinion, - The recent GPT image model is fantastic. Second, the Google model Gemini Nano Banana Pro and Nano Banana Flash 2 are b

As Apple tries to shrink Gemini for the iPhone, a cloud component is probably inevitable.

CEO Ariel Katz argues that while AI can replicate workflow SaaS, it can't copy H1’s unique doctor data.

Europe’s startup ecosystem has matured significantly; its founders are increasingly willing to scale companies domestically instead of immediately looking to relocate to the U.S.

StrictlyVC Los Angeles is on June 18. Join for meaningful networking and fireside chats with leaders from Mach Industries, Shinkei Systems, and more. Register today.

While startups raising back-to-back rounds at steep step-ups have become almost routine, a company whose valuation doubles in three weeks is unusual enough to raise questions, particularly given the investor set in both rounds is the same.

submitted by /u/Rare-Grapefruit-3982 [link] [留言]

Anthropic just dropped Claude Opus 4.8 today, an incremental but meaningful upgrade over Opus 4.7. Here are the highlights: Model improvements Better performance across coding, agentic, reasoning, and knowledge work benchmarks Significantly improved honesty: the model is reportedly ~4x less likely to let flaws in its own code go unremarked compared to Opus 4.7 Alignment assessment shows lower rates of deceptive or misaligned behavior, on par with their Claude Mythos Preview model Scores 84% on Online-Mind2Web for computer use and browser agent tasks, ahead of both Opus 4.7 and GPT-5.5 New features launching alongside it Dynamic workflows (Claude Code): Claude can now spin up hundreds of parallel subagents in a single session to tackle large-scale problems like full codebase migrations. Available for Enterprise, Team, and Max plans. Effort control: Users on claude.ai can now choose how much compute effort Claude puts into a response, from faster/cheaper to deeper/slower. API update: The Messages API now accepts system entries inside the messages array, letting developers update instructions mid-task without breaking prompt cache. Pricing Same as Opus 4.7: $5/M input tokens, $25/M output tokens. Fast mode (2.5x speed) is now 3x cheaper than it was for previous models, at $10/$50 per million tokens. What's next Anthropic mentioned they are working on bringing Mythos-class models (currently in limited preview for cybersecurity use cases under Project Glasswing) to general availability in the coming weeks. Full details and system card: anthropic.com/news/claude-opus-4-8 submitted by /u/Direct-Attention8597 [link] [留言]

Here’s why Anthropic and OpenAI are on board with Illinois safety testing.

If almost all jobs got replaced by AI, here's what happens: 1) Corporate revenue collapses - since humans do not have the means to buy product. It leads to demand destruction at an all-time level. 2) At the same time, there's a massive deflationary supply shock, thanks to democratization of production and the ubiquity of AI-led labor. The direct consequence of the aforementioned is: a price collapse, across the board. Which in turn, also leads to unprecedented tax revenue collapse. Who're you going to tax when no individual or corporate is making any money? To me, all this heralds a post-capitalism society, and not a "I-lost-my-job-and-I'm-now-poor" society. Once everyone loses their jobs, capitalism is over. Sure you can have an interim period of distress - where the world is transforming toward post-capitalism but isn't squarely there yet. But the final equilibrium intuitively feels more Star Trek (or Terminator, if you're a doomer), and much less Elysium or Ready Player One (few oligarchs, most population under poverty line). Correct me if I'm wrong. submitted by /u/mhb-11 [link] [留言]

Every week another "AI agent for X" launches. Email triage. Calendar coordination. Sales follow-up. PR reviewer. Slack monitor. Meeting summarizer. I've installed enough of them to see the pattern. Here's the dirty secret nobody mentions in the launch posts: These tools don't reduce your work. They multiply your notifications. Each AI tool is configured to be helpful by default. "Helpful" means: "I noticed this thing — here's a notification." Stack a dozen of those, and instead of one inbox to ignore you have twelve. The signal-to-noise ratio gets worse every time you add an AI to your workflow. The mainstream answer is "just configure each one." Sure. Spend four hours tuning notification settings every time you add a tool, and another four hours when one of them ships a "smarter notifications" update. That's not productivity. That's notification janitorial work disguised as setup. This is a structural problem. Not a configuration problem. The wrong question Every AI tool asks the same thing: "Is this important?" Wrong question. There is no objective "important." Importance depends on you, right now. A Stripe webhook is important when you're debugging a checkout flow. The same webhook is pure noise during a deep work block. A Slack message from your cofounder is critical at 11am Tuesday and irrelevant at 11pm Friday. The right question is: Is this urgent enough to interrupt me, right now, given what I'm doing? That's not a question any individual AI agent can answer. It's a layer above all your AI agents. None of them have the context. None of them know what the others are doing. None of them know how you're spending the next hour. So they all default to "I'll just send you a notification, you decide." Which is exactly the experience you have right now: drowning. What an AI firewall actually looks like I'm building that layer. It's called Klorn . Here's how it works in practice. Every signal — email, calendar invite, agent action, webhook, push from another tool — g

Hi, What are the things that surprised you that AI cannot do? Would you please also mention what is your work, since i assume most of this thread are coders etc? Ill start here. I work in corporate finance. Doing tons of stuff left and right. AI cannot do finance or accounting..... almost at all. Hundreds of billions on the line, every CEO and their mother pushing AI and nothing major happened. Sure, if you are just a link in chain where you receive the same excel sheet and produce the same powerpoint you are replacable but there are very few people like that anymore left in finance corps. However, if you just receive accounting memo written by random people AI is useless, if you receive bunch of random files and have to come up with valuation AI is useles, if you need to migrate product to a new system AI is useless........... so on and so forth. Hope i dont start a war where everybody is gonna be mad at this. submitted by /u/Zoltan1251 [link] [留言]

The pale-blue Ojai vehicles will start picking up members of the public in California and Arizona today.