Prompt Engineering
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X @Avi Chawla
Avi Chawla· 2025-12-23 19:55
RT Avi Chawla (@_avichawla)DevOps vs. MLOps vs. LLMOps:Many teams are trying to apply DevOps practices to LLM apps.But DevOps, MLOps, and LLMOps solve fundamentally different problems.DevOps is software-centric. You write code, test it, and deploy it. The feedback loop is straightforward: Does the code work or not?MLOps is model-centric. Here, you're dealing with data drift, model decay, and continuous retraining. The code might be fine, but the model's performance can degrade over time because the world ch ...
X @Avi Chawla
Avi Chawla· 2025-12-23 06:33
DevOps vs. MLOps vs. LLMOps:Many teams are trying to apply DevOps practices to LLM apps.But DevOps, MLOps, and LLMOps solve fundamentally different problems.DevOps is software-centric. You write code, test it, and deploy it. The feedback loop is straightforward: Does the code work or not?MLOps is model-centric. Here, you're dealing with data drift, model decay, and continuous retraining. The code might be fine, but the model's performance can degrade over time because the world changes.LLMOps is foundation- ...
零成本、无需微调:提示词加几个字让能大模型创造力暴涨 2 倍
3 6 Ke· 2025-12-14 00:05
*(译注:原文 jokes on "mug" 双关意为"马克杯"和"抢劫")* 神译局是36氪旗下编译团队,关注科技、商业、职场、生活等领域,重点介绍国外的新技术、新观点、新风向。 编者按:AI 越来越无聊,真凶竟是人类自己?斯坦福最新研究发现,无需重新训练,仅需 几个字的简单指令,就能打破"安全对齐"的封印,让大模型被压 抑的创造力暴涨 2 倍。文章来自编译。 ChatGPT 总是给你同样无聊的回答?这项新技术能激发任何 AI 模型 2 倍以上的创造力——而且无需训练。原理如下。 我让 ChatGPT 给我讲一个关于咖啡的笑话,试了五次。 同样的笑话。每一次。绝无例外。 "为什么咖啡去报了警?因为它被'抢'了(mugged)!" 我试过调整温度参数。换各种措辞。用有创意的系统提示词。全都没用。 我心想:就这样了吗? AI 创造力的天花板就到了吗? 事实证明,是我问错了问题。 那一天一切都改变了 三周前,一篇研究论文发布了,它彻底颠覆了我们对 AI 对齐的认知。 不需要耗资数十亿的重新训练。不需要复杂的微调。仅仅八个词,就解锁了我们以为永远丢失的创造力。 这篇论文来自斯坦福大学、东北大学和西弗吉尼亚大学。这项 ...
OpenAI just dropped GPT-5.2... (WOAH)
Matthew Berman· 2025-12-12 00:18
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ChatGPT三岁生日,谷歌却为它准备了「葬礼」
3 6 Ke· 2025-12-01 07:20
Core Insights - The launch of ChatGPT by OpenAI three years ago marked a significant turning point in AI technology, evolving from a simple chatbot to a critical component of digital life [1][6][34] - The rapid advancement of AI has led to a mix of excitement and anxiety among the public, with concerns about job displacement and the implications of AI on various industries [8][21] - Google’s recent launch of Gemini 3 is seen as a strategic move to reclaim dominance in the AI space, challenging OpenAI's previous lead [10][21] Group 1: Evolution of AI Technology - Over the past three years, OpenAI has consistently led AI advancements with models like GPT-3.5, GPT-4o, and GPT-5, which have set new standards in speed, accuracy, and reasoning ability [12][13] - The introduction of multi-modal AI, such as GPT-4o and Midjourney, has expanded AI capabilities beyond text to include images, audio, and video [17][21] - The user engagement with Gemini has surged, with monthly active users increasing from approximately 400 million in May to 650 million [21][23] Group 2: Market Dynamics and Competition - OpenAI's market share remains significant with over 800 million users, but user engagement with Gemini has surpassed that of ChatGPT [23][27] - The competitive landscape has shifted, with industry leaders like Google leveraging their resources to challenge OpenAI's position [21][27] - OpenAI's CEO faces immense pressure to accelerate monetization and maintain stability amid fierce competition [27][28] Group 3: Financial Strategies and Risks - OpenAI is pursuing an aggressive financial strategy, planning to invest $1.4 trillion in computing power over the next eight years, significantly exceeding its current revenue [28][31] - The financial burden of OpenAI's operations is largely borne by its partners, with estimates suggesting that nearly $1 trillion in debt is associated with its collaborations [29][31] - Analysts predict that substantial borrowing will be necessary to fulfill OpenAI's contracts, raising concerns about the sustainability of its financial model [32]
Context Engineering: Connecting the Dots with Graphs — Stephen Chin, Neo4j
AI Engineer· 2025-11-24 20:16
Hello everybody and welcome to my session at a engineer code summit and I'm going to talk a bit about how you can connect the dots with graph technology and solve problems like context engineering um improving retrieval patterns and also agentic memory. So we're going to have a lot of fun. My name is Stephen Chin.I'm VP of developer relations at Neo Forj and you can find me at all the different social media outlets with my handle Steve on Java. So excited you're all here to join for the session today. And I ...
X @Balaji
Balaji· 2025-11-20 10:42
Good AI use can produce great results. But that usually means a lot of work on prompting, or a creative prompt, or both.Because prompts are tiny programs, and prompting is programming. Bad AI use is like being a bad programmer. It’s a leaky abstraction, and the source code is poking through the wallpaper.Lewis (@0xLewis_gg):@balajis People lacking discernment falsely believe AI conceals their lack of effort.Use AI with high effort (both prompting and manual refining of outputs) can be superior to manual eff ...
X @Tesla Owners Silicon Valley
Tesla Owners Silicon Valley· 2025-11-15 05:05
Top Tips for Crafting Killer Prompts in Grok Imagine1. Be Specific & Descriptive: Layer in vivid details—colors, lighting (e.g., “golden hour glow”), textures (e.g., “rusty cyberpunk neon”), and mood (e.g., “eerie solitude”). Vague = bland; detailed = dynamic.2. Structure Like a Story: Start with the scene setup, add action/movement, end with a twist or reveal. E.g., “A lone samurai draws his blade in a misty bamboo forest at dawn, rain-slicked leaves rustling—sudden cherry blossoms explode in slow-mo as he ...
X @Nick Szabo
Nick Szabo· 2025-10-11 03:02
Accuracy Impact of Prompt Tone - Rude prompts to LLMs consistently lead to better results than polite ones [1] - Very polite and polite tones reduced accuracy, while neutral, rude, and very rude tones improved it [1] - The top score reported was 848% for very rude prompts and the lowest was 808% for very polite [1] Model Behavior - Older models (like GPT-35 and Llama-2) behaved differently [2] - GPT-4-based models like ChatGPT-4o show a clear reversal where harsh tone works better [2] Statistical Significance - Statistical tests confirmed that the differences were significant, not random, across repeated runs [1]
Forward Future Live | 10/10/25
Matthew Berman· 2025-10-10 16:24
Download Humanities Last Prompt Engineering Guide (free) 👇🏼 https://bit.ly/4kFhajz Download The Matthew Berman Vibe Coding Playbook (free) 👇🏼 https://bit.ly/3I2J0YQ Join My Newsletter for Regular AI Updates 👇🏼 https://forwardfuture.ai Discover The Best AI Tools👇🏼 https://tools.forwardfuture.ai My Links 🔗 👉🏻 X: https://x.com/matthewberman 👉🏻 Forward Future X: https://x.com/forward_future_ 👉🏻 Instagram: https://www.instagram.com/matthewberman_ai 👉🏻 Discord: https://discord.gg/xxysSXBxFW 👉🏻 TikTok: https://www ...