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12 AI tools and frameworks builders swear by in 2026: 1. **OpenAI's ChatGPT Enterprise**: Scales better than most for large deployments—integrates seamlessly with existing IT infrastructure. 2. **Hugging Face Transformers**: The go-to open-source library, now with 500 pre-trained LLMs tailored for enterprise needs. 3. **LangChain**: Revolutionizing LLM app development with improved modularity; essential for building complex multi-agent systems. 4. **Amazon SageMaker ML Studio**: Still leading for managed ML services—new features cut deployment time by 40%. 5. **VSCode's CopilotX**: Outperformed both tabby and Alphacode in coding efficiency studies by 28%. 6. **LangSmith**: Recently became the #1 tool for LLM evaluation by adding real-time user feedback iterations. 7. **Weaviate**: The most reliable vector database—enabling hyper-scale data indexing at a fraction of the previous cost. 8. **Ray**: The choice framework for scaling Python ML applications, bolstered by new extensions like Ray Serve. 9. **Giskard**: Gains traction for red-teaming LLMs with its automated adversarial testing features, identifying biases others miss. 10. **Jina AI's DocArray**: Facilitates efficient vector handling and streaming for large-scale multimedia data processing. 11. **PyTorch Lightning**: Continues to dominate flexible training of neural nets with its lightweight wrappers. 12. **Zilliz**: Outperforming Milvus in speed and ease for developers needing rapid embedding search capabilities. Bookmark this. Which did I miss?
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إعلان إطلاق شركة River AI الجديدة🤨، نشره إيغور بابوشكين @ibab ♧ إيغور باحث ومهندس ذكاء اصطناعي بارز جداً: He previously worked at @Google @GoogleDeepMind، حيث ساهم في مشاريع كبيرة مثل AlphaCode. واشتغل ب @OpenAI. He was a co-founder of @xai (شركة @elonmusk)، حاليا هو : المدير التنفيذي ل River AI. في شركة بتشتغل شيء مشابه: @hark_labs ♧ صاحبها كمان مدير تنفيذي ل @Figure_robot شركة الروبوتات. ♧ الـ AI في Hark فكرته بتدور عن : يفهمك بعمق. لديه ذاكرة مستمرة. يرى ويسمع ويتفاعل مع العالم الحقيقي، ويتصرف نيابة عنك. ◇ سؤال غير منطقي : لماذا لا يضمون جهودهم مع River AI؟ ♧ أولاً، الفرق مختلفة تماماً. River AI يقودها إيغور بابوشكين وفريق من xAI وDeepMind وOpenAI. بينما Hark يقودها برايت ادكوك @adcock_brett وفريق من Apple وMeta وFigure. ♧ ثانياً، الرؤى مختلفة قليلاً. River تركز أكثر على الملكية الكاملة للمستخدم والـ full stack. Hark تركز على AI يفكر مثلك وhardware مخصص وتجربة سلسة. ♧ ثالثاً، التمويل منفصل وضخم. Hark جمعت سبعمائة مليون دولار بتقييم ستة مليارات. River كانت في محادثات لجمع حتى مليار. كل واحدة لها مستثمريها الخاصين. ♧ رابعاً، المنافسة في السوق. سوق Personal AI مع Hardware ساخن جداً حالياً. وجود منافسين أقوياء يسرع التطور. ♧ خامساً الوقت والتنفيذ. كل شركة في مرحلة مختلفة. دمجهم الآن قد يبطئ التقدم بدلاً من تسريعه. ♧ هذا الوضع طبيعي في عالم التكنولوجيا السريع. المنافسة غالباً ما تؤدي إلى تطور أسرع. @jack. ♧ جاك دورسي (مؤسس Twitter وBlock) لم يؤسس شركة AI شخصية مباشرة مثل River AI، لكنه من أبرز المدافعين عن أفكار يحذر من مركزية الـ AI ويؤيد بقوة Open Source وdecentralized AI وملكية المستخدم للبيانات. ♧ كيف تتكامل أفكاره مع River AI؟ River AI تتوافق كثيراً مع فلسفة جاك في "ملكية المستخدم" و"الذكاء المستقل عن الشركات الكبرى". كلا الجانبين يركزان على نقل السيطرة من الشركات العملاقة إلى الأفراد، ودعم النماذج المفتوحة.
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◇River AI ? ♧Igor is a highly prominent AI researcher and engineer: He previously worked at @Google @GoogleDeepMind, where he contributed to major projects such as AlphaCode. He then joined @OpenAI. He was a co-founder of @xai (@elonmusk ’s company), with a focus on research and engineering. He is now the CEO and co-founder of River AI. ◇Is Hark a robotics company? ♧No, Figure AI is the robotics company. ♧The AI in Hark understands you deeply. It has continuous memory. It sees, hears, interacts with the real world, and acts on your behalf. ◇Why don't they combine their efforts with River AI? ♧First, the teams are completely different. River AI is led by Igor Babuschkin and a team from xAI, DeepMind, and OpenAI. While Hark is led by Brett Adcock and a team from Apple, Meta, and Figure. ♧Second, the visions are slightly different. River focuses more on full user ownership and full stack. Hark focuses on AI that thinks like you, custom hardware, and a seamless experience. ♧Third, the funding is separate and large. Hark raised seven hundred million dollars at a valuation of six billion. River was in talks to raise up to one billion. Each has its own investors. ♧Fourth, competition in the market. The Personal AI with Hardware market is very hot right now. Having strong competitors accelerates development. ♧Fifth, Brett's strategy. He prefers to create specialized separate companies instead of merging everything into one big entity. This approach has proven successful for him before. ♧Sixth, timing and execution. Each company is at a different stage. Merging them now might slow progress instead of speeding it up. ♧This situation is natural in the fast world of technology. Competition often leads to faster development.
River AI is building personal AI owned & shaped by you. We are hiring exceptional talent across the stack: * Research * Software Engineering * Product Development * Data * Hardware, RTL Design Engineer * Hardware, Design Verification Engineer * Hardware, Physical Design Engineer * Hardware, Performance Engineer * Hardware, Compiler Engineer * Open Application, Exceptional Talent river.ai/careers We are a small, elite team of researchers, builders, and pioneers from the world's leading AI labs. If you want to do the most ambitious work of your career alongside, apply today!
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@OriolVinyalsML AlphaCode can write software. How long before AI developers start letting AI fix their bugs instead of debugging manually?
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DeepMindのAlphaCodeは、競技プログラミングで上位54%のスコアを記録した。 凄いのは、人間には理解不能な「AI独自の解法」で正解を叩き出したこと。 可読性や保守性を前提とした、これまでの開発の常識が根底から崩れ始めている。 1人でSaaS作ってる僕の感覚だと、保守性より「動く速度」が正義になる時代はもうすぐそこだ。
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我不允许有人没有看过Demis Hassabis这个最新的播客。 这期HUGE Conversations采访的是Google DeepMind CEO Demis Hassabis。 也就是那个带队做出AlphaFold、后来拿了诺贝尔奖的人。 很多人今天聊AI,第一反应还是Chatbot、图片生成器、视频生成器。 但Demis这期真正让我有点被打到的地方是: AI对你生活最大的影响,可能恰恰是你看不见的。 它不一定长成一个聊天框。 它可能藏在新药设计里,藏在核聚变里,藏在自然灾害预警里,藏在量子计算机里,藏在材料科学里。 你不会每天打开它。 但它会改变药怎么被发现,天气怎么被预测,芯片怎么被设计,科学实验怎么被加速。 这也是为什么Demis这个人很重要。 他不是一个普通大厂高管。 他小时候是国际象棋神童,17岁时拒绝过游戏公司据说百万美元级别的offer,选择去读书,后来又去做神经科学博士。 他创办DeepMind时,使命是「solve intelligence」。 一开始从打游戏开始。 DQN、AlphaGo、AlphaFold,一步一步把游戏、围棋、蛋白质这些问题打穿。 后来DeepMind卖给Google,很大一个原因,是Google承诺让他们继续做科学研究。 但现在事情已经完全变了。 AI变成了近代最激烈的一场技术战争。 而Demis现在基本站在Google整个AI方向的中心。 Gemini、Veo、AlphaFold、AlphaEvolve、GenCast、Genie、AlphaChip…… 这些名字单独看,像一堆产品和论文。 连起来看,其实是一条非常清晰的路线: 用AI去解决那些人类过去觉得太复杂、太慢、太贵的问题。 这期最后有个很有意思的环节。 主持人和Demis玩DeepMind版Jenga。 每一块积木上都写着一个DeepMind项目。 抽出来之后,他要立刻说出这个项目是什么。 AlphaCode是代码。 GenCast是天气预测。 AlphaChip是芯片设计。 CodeMender是找代码bug。 Genie和DQN背后是simulation。 你会突然发现,DeepMind过去十几年像是在做一张现实世界的技术地图。 蛋白质、天气、芯片、代码、材料、游戏、仿真。 一个格子一个格子往前推。 里面有一段我觉得特别值得反复看。 Demis说,他们现在也在做一种把遗传算法和Gemini结合起来的东西,可以用于coding/programming。 这其实很像把AlphaGo那种搜索未知空间的能力,搬进代码和算法设计里。 模型以后不只是帮你写一段代码。 它会开始在一个巨大的可能性空间里搜索,找出人类以前没想到的解法。 他还提到simulation。 有些科学问题,真实实验太贵。 有些社会科学问题,比如经济学,根本很难做严格控制实验。 这时候AI加simulation,就会变成一种新的理解世界的方法。 但我觉得整期最该被年轻人记住的,是最后那个问题。 主持人问他: 如果有年轻人看完这期,相信这个未来,也想参与进去,应该怎么做? Demis的回答很直接。 把自己浸到所有AI工具里。 让自己变得superpowered。 他说,即使是DeepMind这样的frontier lab,也要把大量精力放在下一代frontier model和相邻模型上。 Veo、Gemini、各种新模型、新能力还在不断往前推。 他们自己也只能探索这些模型应用空间的一小部分。 模型发布越来越快。 能力溢出越来越多。 反而会出现越来越大的应用空白。 所以未来几年,最恐怖的一类人可能不是「会写一点prompt的人」。 而是那种极度会用AI工具,又极度懂某个具体领域的人。 他们知道真实世界里哪里最慢、最脏、最贵、最没人愿意碰。 然后用一堆AI工具,把那个问题直接打穿。 Demis甚至说: 今天一个孩子,都可能用这些工具,在没人想到的新方向上,做出一家multi-billion-dollar business。 这句话其实已经把未来几年的机会讲穿了。 AI时代真正的门槛没有消失。 门槛变成了: 你有没有一个足够具体的战场。 你能不能把AI工具用到足够深。 你能不能在一个别人还没意识到的角落,把一个旧问题彻底重做一遍。 非常值得认真看好几遍。
The Demis Hassabis HUGE* Conversation (in full) 00:00 What is the hardest problem AI has already solved? 12:30 What is the cutting edge of drug discovery with AI? 21:53 Why did Demis say he “would have left AI in the lab longer”? 43:09 How should militaries use AI? 50:13 What can humans do that AI won't? 58:17 What does Demis Hassabis want his legacy to be? (And 1:04:40 Can I beat Demis at Jenga?) Recorded March 5, 2026 in London.
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Google DeepMindのAlphaCode。コード生成の精度がかなり上がっています。 これを見ると、「エンジニアの仕事なくなるのでは」と感じる人も多いですよね。 でも、ぼくは逆だと思っています。非エンジニアが、小さい自動化を自分で作れる時代になる。 ・Discord bot ・スプレッドシート自動化 ・定型メール送信 今まで、詳しい人に頼んでいたことが、自分でできるようになる。 大切なのは、コードを書けることではなく、やりたいことを言語化できること。 AI時代は、“指示できる人”の価値が上がります。
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sampling verification is the key to really unlocking the true potential of LLMs so many breakthroughs like Google AlphaCode etc is powered by this. with a good verifier cheaper models can outperform expensive ones. deepseek with 5 samples costs 10 usd and performs better.
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💯Got a little bit of legs, arms, neck, and core in today… just putting in work across the board. Nothing flashy. Just staying consistent, staying ready, building strength, and staying DISCIPLINED. That’s really what it comes down to….showing up, putting in the reps, and stacking days like this. 🫵🏼Follow the Code. #AlphaCode #discipline
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Google DeepMind is an American company. The LLM people (Gemini) have moved to California. I was part of AlphaGo (arxiv.org/pdf/1812.06855), Alphacode, Gato, Veo, etc, all built in the UK, but all that is now past.

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💯Quick 2 mile walk before my admittance into the Alpha Lair🔥 #AlphaCode
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💯These hex bar shrugs hit different. Crazy how changing your grip, angle, and hand position can completely change how a muscle fires. With the hex bar, the neutral grip and positioning lets me stay more aligned and really target the traps instead of just yanking weight. Honestly felt like I hit them harder using less weight. ☝🏼Good reminder…..More weight isn’t always the answer. What are some other exercises that by making a small adjustment will make a huge impact? #AlphaCode #TrainSmart #workout
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🤖️WHICH ONE ARE YOU USING? 【 TITLE 】AIS 【 LYRICS 】 ChatGPT, Gemini, Claude, Llama, Mistral, Mixtral, Grok, Perplexity, DeepSeek, Kimi, Qwen, Baichuan, Yi, Falcon, Gemma, Command, Solar, Jamba, ELYZA, Swallow, Microsoft Copilot, Notion AI, Jasper, Writer, Copy ai, Rytr, Anyword, Writesonic, Grammarly, DeepL, Character ai, Janitor AI, Poe, Otter ai, Descript, QuillBot, Wordtune, HyperWrite, Mem, Manus, NotebookLM, Fathom, Granola, Fireflies, Avoma, Glean, Komo, Brave Search, OpenClaw, Devin, Blackbox, Claude Code, YouChat, Pi, SearchGPT, Percepty, Midjourney, Stable Diffusion, DALL-E, Adobe Firefly, Leonardo ai, Ideogram, Flux, Canva, Nano Banana, Artbreeder, Craiyon, DeepAI, Playground AI, Microsoft Designer, Recraft, Photoroom, Magnific, ClipDrop, Krea, GetIMG, Dezgo, SeaArt, Tensor art, Pixlr, PicWish, Fotor, Cutout pro, VanceAI, Sora, Runway, Pika, Luma Dream Machine, Kling, Veo, Haiper, Morph, Genmo, HeyGen, Synthesia, Colossyan, D-ID, Kaiber, FlexClip, InVideo, CapCut, Filmora, Pictory, Fliki, Lumen5, OpusClip, Steve AI, Veed, Elai, Virbo, Rask, Dubverse, Higgsfield, Mochi, Hedra, Midreal, Hailuo, Dreamina, Minimax, VideoPoet, CogVideo, AnimateDiff, Suno, Udio, Lyria, ElevenLabs, MusicLM, AudioCraft, Jukebox, Voicevox, AIVA, Soundraw, Murf AI, Resemble AI, RVC, Play ht, Krisp, Stable Audio, Chirp, Bark, VALL-E, Voice Engine, Boomy, Loudly, Mubert, Voicemod, Kits ai, Splash Music, Beatoven ai, GitHub Copilot, Cursor, Codeium, Tabnine, Amazon Q, Replit, AlphaCode, CodeLlama, Lovable, Bolt, Bubble, StarCoder, Replit Agent, Cody, Rodin, Luma Genie, Meshy, Spline AI, AlphaFold, Harvey AI, Casetext, Tellius, Thoughtspot, Qlik Sense, Sisence, Semrush AI, Surfer SEO, Clearscope, Frase io, Zapier, HubSpot, Beautiful ai, SlidesAI, Gamma, Tome, Prezi Design, Consensus, Groq, n8n, ChatPDF, Humata, Hugging Face, Civitai, Together AI, Replicate, Anyscale, Fireworks AI, Pinecone, LangChain, AutoGPT, BabyAGI
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Every Major Google AI Model Ranked from Latest to Oldest: Gemini Gemini Robotics Gemini Audio Gemma Veo Nano Banana Imagen Lyria SIMA Genie Lumiere VideoPoet Phenaki MusicLM GraphCast AlphaCode AlphaGeometry AlphaMath AlphaFold AlphaGenome AlphaEvolve Gato MuZero AlphaZero AlphaGo Chirp Universal Speech Model AudioLM Embedding Models PaLM LaMDA T5 BERT WaveNet
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💯Woke up just feeling “On” this morning. Decided to get that sweat in.💪🏽 #AlphaCode
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