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Today, I want to share something that happened to me on Kraken. I’m completely new to crypto, and this was my first time using Kraken. All I wanted to do was convert USDC into USD. Unfortunately, because USDT and USD differ by only one letter, I accidentally selected USDT instead of USD. I didn’t realize my mistake until the transaction had already been completed. In less than a minute, a single misclick resulted in a loss of approximately 810 USDT—more than $800. To be honest, I was shocked and devastated. Like many newcomers, I assumed that all major stablecoins were worth roughly the same, so converting between them would involve little or almost no loss. I never imagined that such a simple mistake could instantly cost me more than 1% of my funds. I immediately contacted Kraken Support, hoping they would understand that this was an honest human error and review my case, or at least consider making a one-time exception. What disappointed me even more was that almost every response came from AI. I was never able to speak with a real support agent. The only explanation I received was that the transaction had already been executed, the conversion details had been displayed before confirmation, and therefore nothing could be reversed or refunded. I understand that Kraken may not have violated its own rules, and I acknowledge that the final amount I would receive was displayed before I confirmed the transaction. But I believe that “the information was displayed” does not necessarily mean “a new user truly understands what it means.” As a beginner, I saw what I believed was a normal stablecoin conversion. I had no idea that the number shown on the confirmation screen meant I was about to lose more than $800. If a platform expects users to recognize an $800+ pricing difference on their own, instead of proactively warning them that the conversion is unusually unfavorable, I don’t believe that’s a user-friendly experience—especially for beginners. I believe that a platform responsible for customers’ assets should do more than simply display numbers. It should also be designed to help users avoid obvious mistakes that can lead to significant financial losses. Crypto is already complicated enough. The risks users take should come from the market—not from a product design that makes such costly mistakes so easy to make. This experience has left me deeply disappointed and has almost completely destroyed my trust in Kraken. I’m not trying to deny Kraken’s rules, and I’m not asking for special treatment. I’m simply asking @kraken and @krakensupport to review my case, seriously consider improving the user experience, provide real human support when genuine mistakes happen, and consider a one-time resolution for customers who make an honest human error. If this could happen to me, it could happen to any newcomer entering the world of crypto. I hope my experience helps others avoid making the same mistake. Please double-check every click. @krakenfx @krakenpro @krakensupport

बिहार के सीतामढ़ी में 'Waste to Wealth' का शानदार उदाहरण सामने आया है। यहां कचरे की recycling कर उससे आकर्षक bench बनाई जा रही है। यह मजबूत, टिकाऊ और आरामदायक होने के साथ-साथ eco-friendly भी होती है। इसे सरकारी स्कूलों, पार्कों और सार्वजनिक स्थलों पर लगाया जाता है। इसके जरिए लोगों में पर्यावरण संरक्षण को लेकर जागरूकता भी बढ़ रही है। - माननीय प्रधानमंत्री श्री @narendramodi #MannKiBaat#

opus 5 is a VERY interesting release for a few reasons 1. it showed that the general benchmarks we use today are almost completely useless now opus 5 is nowhere near fable in practical use, not even close. anyone who’s used it meaningfully can tell this very quickly after a few tasks. yet opus beats fable on many benchmarks i now trust domain specific benchmarks built with private datasets a lot more than the popular ones. perhaps the future is everyone running their own evals because the public ones are really not telling us much 2. it seems with the 5 series, anthropic is trying a new way of training models previously, the same generation of sonnet and opus were often released at the same time or sonnet comes out before opus, which indicates sonnet and opus were trained by separate pipelines in parallel with the 5 series, it was very clear that they trained mythos first, and then distilled it into sonnet and opus. it seems this approach has a big influence on the models seeing sonnet 5 being a flop and opus 5 getting pretty mixed reviews already, i’m not sure this is working out 3. “how pleasant is it to work with the model” used to be a strength in claude, but now it’s not. honestly, grok is my favorite right now on the “pleasant” dimension. kimi is not bad either it feels like both anthropic and openai are giving RLHF less care, in favor of scalable RL that’s machine verifiable this almost looks like AI is directing humans to build a world that’s more friendly for machines rather than humans, and most humans don’t even realize they are being manipulated to help with that almost every new generation of frontier models now talk more jargons, need more steering to do what you want, and are just less fun to work with if this continues, AI will start to speak their own language that looks like English but average humans can’t understand. they will choose to do things that their human user never asked for. are we already failing at alignment?

GPT image 2 on chatgpt Prompt: Use the uploaded character reference image as the strict identity and outfit reference. Preserve the reference character’s: - face identity - facial proportions - eye shape - nose - lips - skin tone - hairstyle - hair color - visible hair accessories - overall recognizable vibe - outfit and styling shown in the reference image Do not hardcode any specific character traits that are not present in the uploaded reference. All identity, hairstyle, accessories, and clothing details must be inferred directly from the uploaded reference image. Create a high-quality mixed-style vertical portrait showing: 1. a realistic full-body version of the uploaded character/person 2. a black hand-drawn doodle-shadow version of the same character/person on the wall beside them Core concept: The real person and their doodle-shadow are doing a playful mischievous pose together. The real person performs a cute realistic version of the pose, while the doodle-shadow performs a much more exaggerated, chaotic, cartoonish version of the same pose idea. The mood should feel cute, playful, mischievous, stylish, funny, and social-media-friendly. Real person: - Must remain realistic and photogenic - Must wear the same outfit style and key visible details from the uploaded reference image - Do not replace the reference styling with unrelated fashion - Expression should be cute, slightly confused, mildly embarrassed, playful, as if thinking: “Why am I doing this with my shadow?” - The real person should not stand stiffly - The real person should actively participate in the pose, but in a natural realistic way Doodle-shadow: - Must be a black hand-drawn sketch version of the same person, drawn directly on the wall - Not a realistic second person - Not a normal physical shadow - Not a full-color anime character - Black sketch line-art only - Should resemble the person through hairstyle silhouette, accessories, outfit silhouette, and pose structure - The doodle-shadow should look more energetic, sillier, and more chaotic than the real person - Add manga-like motion lines, hearts, stars, sparkles, and comic marks around the doodle if helpful Random mischievous pose rule: The pose must NOT be fixed. For each generation, invent a new playful mischievous pose for both the real person and the doodle-shadow. The real person and the doodle-shadow should share the same general pose idea, but they do not need to match perfectly. The real person performs a cute realistic version. The doodle-shadow performs a much more exaggerated, chaotic, cartoonish version. Do not repeatedly use pointing poses. Do not repeatedly use finger-gun poses. Do not repeatedly use the same standing pose. Do not always make both figures simply point at each other. Create a different mischievous pose each time. Possible pose directions are loose inspiration only, not a fixed menu: - playful idol pose - silly dance pose - leaning sideways with one arm curved overhead - making a big heart pose - cheeky wink pose - hands near cheeks in a cute teasing pose - exaggerated “ta-da!” pose - mock surprise pose - playful running-in-place pose - mischievous tiptoe pose - arms stretched in opposite directions - pretending to sneak away - cute troublemaker pose - dramatic overreaction pose - goofy victory pose - playful balance pose - playful peekaboo pose - shy but mischievous pose - cute overconfident pose The final pose should feel fresh, cute, mischievous, and slightly chaotic. The real person should look like they are reluctantly playing along. The doodle-shadow should look like it is having way too much fun. Composition: - vertical 4:5 or 9:16 - show the real person in full-body or nearly full-body framing - place the real person on one side of the frame - place the black doodle-shadow on a clean wall beside them - the doodle-shadow should be roughly the same height or slightly taller - keep enough space around both figures so the full pose is visible - the connection between the real person and the doodle-shadow must be clear at a glance Background: - simple clean indoor studio wall or minimal room corner - white, cream, or pale gray wall - clean floor - soft natural sunlight patch or gentle wall shadow allowed - keep the background uncluttered Lighting: - soft natural studio lighting - bright, clean, polished, playful mood - keep the real person’s face clearly visible Style quality: - realistic human photography - black hand-drawn doodle-shadow on wall - matching mischievous pose interaction - strong identity resemblance - outfit and styling faithfully based on the uploaded reference - cute and stylish mixed-media portrait - clean composition - social-media-friendly - no obvious AI artifacts Negative prompt: hardcoded blue hair when not in reference, hardcoded cloud clip when not in reference, hardcoded fish clip when not in reference, hardcoded school uniform when not in reference, outfit change unrelated to reference, realistic second person, normal reflection, normal shadow only, solid black monster shadow, horror shadow, creepy shadow, full-color illustration, cartoon human, anime human, weak resemblance, unrelated sketch character, messy wall, cluttered background, repeated pointing pose, repeated finger-gun pose, same pose every time, fixed pose, boring mirrored pose, stiff pose, identical pose repetition, real person not matching shadow pose, text, watermark, logo, distorted body, extra limbs, extra fingers, bad hands

Football runs deep at CCCC—a shared language from Panama to Chile. Employees play friendly matches with local communities and donate gear. On the pitch, teamwork builds bonds that carry into project work, connecting infrastructure with hearts. That's the CCCC story.

Once your own attitude toward others is more affectionate, more warm-hearted, more respectful and friendly — others' response is usually similar. So therefore, a friendly atmosphere must be created by ourselves. It is quite illogical to remain extremely self-centred and not care about others, and in the meantime expect a friendly atmosphere.

A new type of "high-level programming language" that seems really worth trying to make, is a language that gets compiled to Lean (or HOL, or...) that is specifically about making it as friendly as possible for a human to read definitions and theorems. Not the proofs - as all that matters with proofs is that the proofs are correct - just the definitions and theorems. The intended use case is that AI outputs a blob of proofs, and you're trying to make it as easy as possible for anyone reading the output to understand what the actual precise claims are that have been proven

A young voice, a big dream for a cleaner future. 💚 Hear what a student has to say about India's first Hydrogen Train and its eco-friendly journey towards sustainable rail transport. #HydrogenTrain# #भारत_की_पहली_हाइड्रोजन_ट्रेन#