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This index lists complete English editions. Additional Chinese notes and bilingual updates remain available in the Chinese index.
Long-term X views from Karpathy, sama, Anthropic & Google model camps and lab voices — LLM knowledge base paradigm, jagged capability theory, harness/skills engineering, model versioning strategy, agent safety, AI for science, HTML as the new markdown
Long-term views posted on X between March and June 2026 by the leaders of products including Replit, Box, Vercel, Cursor, Linear, and Every — organized by topic (headless software and agent pricing / design after execution becomes free / development pipelines where tokens don’t matter / compound engineering / whether SaaS is dead / work won’t decrease) rather than by person, written for non-technical product managers.
The complete historical synthesis of agent-native products, enterprise adoption, sandboxes, governance, SaaS, pricing and security, including all published additions through October 22, 2026.
A complete cross-podcast guide to coding agents: orchestration, tools, model selection, review, infrastructure and responsibility, including all published additions through October 21, 2026.
Drawing on eight 42 Chapters interviews, this piece distinguishes character content, ongoing relationships, personal memory, and shared activities, and discusses evaluation, supply, long-term retention, and payment. Across eight interviews: relationship continuity, character content, memory, shared activities, evaluation, retention and payment.
Invisible AI watermarks can be removed via spectral analysis without any loss of image quality (empirically demonstrated by reverse-SynthID) → watermarks are a provenance tool for honest users, not ironclad anti-forgery evidence; content producers should layer multiple signals and proactively apply compliance labeling.
Xie brings experience in consumer electronics, crowdfunding and distribution, while developing a children’s entertainment terminal. The introduction says her previous team served more than forty million-dollar-scale crowdfunding projects. That supplies background rather than audited project profit or fulfillment.
Drawing on interviews spanning 2023–2026, this piece separately examines model capabilities, real demand, delivery, operating returns and capital pricing, while preserving historical disagreements. A cross-interview synthesis separating model capability, demand, delivery, business returns and valuation, with historical disagreements preserved.
A synthesis of 6 perspectives on "AI industry judgments + evolution of the Big Three + the Scaling Law debate". Covers Wang Kai's 790 posts from 3 years of observation (evolution of OpenAI/Anthropic/Google / SaaS crisis / AI hardware wave / model discipline); Lao Huang newtype's 452 posts + BIG NEWS timeline (7 immutable anchors / LLM=OS / Context Matters); 2 newtype research reports (Agent architecture + $87 billion market); 505 posts on model evaluation (2024-2026 evolution timeline); Anthropic+OpenAI insiders (Cat Wu + Embiricos); Shensi Circle VC perspective (a16z / Sequoia / YC, 659 articles). Covers the three-way Scaling Law debate, disagreements over the SaaS crisis, disagreements over Apple's path, and the valuations and strategies of the Big Three.
Listen: 01:07–07:11; 08:40–11:20. Zhu reduces the underlying problems to computation, communication, and storage. GPUs need networks, switches, storage, operations, resource scheduling, and training and inference frameworks. His comparison with cloud service layers is an explanatory framework, not the only possible industry taxonomy.
A synthesis of AI investment valuation from 4 perspectives. Wang Kai (three-layer framework of "good assets + good prices + good volatility" / RSI AI strategy delivering 4.3% quarterly return / using AI as a ruler to quantify risk / evolution of a $250,000 capital pool / self-built financial MCP) / newtype-agent-market-2025 (VC market at $87 billion / 191 unicorns / Cursor's valuation tripling in May under the "king-making strategy" / Sequoia Agent Economy three stages) / newtype-ai-business-trends (logic for holding NVDA for 10 years / SaaS crisis / end of intermediary monopolies) / Shensi Quan (VC firm case studies: a16z/Sequoia/YC) / Rich Dad Poor Dad (philosophical foundation of assets and liabilities). Covers the divergence between value investing and VC perspectives, judgments on the SaaS crisis, and Apple's path.
Hidecloud previously designed consumer products and worked on customer success, presales and consulting at 神策数据. Reflecting on roughly seven months of AI practice, he repeatedly emphasizes understanding through direct work: how a model receives input, where it fails and which change makes an interaction possible. His argument does not require every product manager to become an algorithm engineer.
A complete compilation of resources for AI Product Manager interviews and career transition. Includes the two-volume AI Product Manager Beginner Handbook + 100 high-frequency interview questions + the 200-page PPT "New Starting Point for AI PM" + a 65-page deep learning summary + a required course on systematic thinking + an introduction to data annotation work + speech recognition technology + speech interaction evaluation metrics + data mining + valuing evolvability + Lian Shilu's "AI Redefines the Product Manager". Covers the complete knowledge map for the transition from "Internet PM to AI PM"
A methodology distillation of notes (87 entries, 8 modules) from an AIPMM-aligned AI Product Manager professional certificate course. In one sentence: this is not beginner-level literacy content explaining "what a PM is"; instead, it compiles all the naming frameworks, models, and checklists that an experienced PM should keep handy — reorganized into six sections. ① PM Roles and Responsibilities: definition of product management (7 stages from ideation to retirement), PM ≠ Project Manager (deciding What/Why vs figuring out How/When), business acumen (6 core financial metrics: ROI/IRR/NPV/Payback), differences between AI PMs and traditional PMs (data strategy/model drift/explainability/Responsible AI). ② Product Lifecycle Methods: 4 stages of the product lifecycle (Introduction/Growth/Maturity/Decline) vs 7 stages of the product management lifecycle (Conceive/Plan/Develop/Qualify/Launch/Deliver/Retire, key deliverables per stage + how AI products rewrite each step), three value delivery approaches (waterfall/iterative/incremental iterative), Predictive vs Adaptive project management. ③ Strategy and Innovation Frameworks: Oslo four categories of innovation + five-step innovation process, three tiers of innovation (incremental/radical/disruptive), SWOT/PEST, Porter's Five Forces, Kotler's Five Product Levels, market segmentation, five-step product strategy. ④ Stakeholder Collaboration and Communication: RACI, Five Dysfunctions pyramid (Trust → Conflict → Commitment → Accountability → Results), SMART goals + dual business value checklist, six elements of an elevator pitch, influence without authority (six sources of influence), 5-step problem-solving method + decision matrix, four communication styles (Analytical/Structural/Conceptual/Social) × Tuckman's four stages × situational leadership, six methods for building trust. ⑤ Business and Commercialization: business development, product portfolio management (breadth/depth/Portfolio PM vs regular PM), public-facing PR/marketing collateral checklist. ⑥ Career Development: transition paths, resume fit-not-identity principle, the rise of the AI PM. Division of labor with nngroup-product-ux (empirical PM×UX disagreements + RACI + PLG) and wiki-ai-pm-design-eng-collab (collaboration implementation): this article serves as the "methodology skeleton and framework dictionary for AI PMs"; collaboration details are cross-referenced rather than rewritten. Target audience = experienced PMs; terms are explained with industry context, and each section includes Why/when to use/counterexamples.
Integration of "AI self-media + content engineering" from the perspectives of 7 authors. Lao D (domestic, no scientific internet access + 2 million views on Douyin) / Wang Kai (overseas 6-platform SOP + matrix operation + going-global infrastructure) / Huang Yihe (non-technical independent products + global merchant route) / Xiaohongshu vibecoding (aesthetic philosophy + democratized creation) / Yuzijiang (AI comic drama agency team system + Hongguo Theater revenue share) / Lao Zhang (reverse-engineering X recommendation algorithm source code) / **zara Zhang Zala (Build in public + Inbound + 260,000 followers + 0 ads + ≤30 min per piece + 6 returns more valuable than money)**. Covers goal divergences, consensus, debates over AI writing purity, single-platform deep cultivation vs multi-platform matrix, tool stack comparison, and monetization paths
A synthesis of AI video generation SOPs from 4 perspectives. Caviar Agency (5-step AI comic drama process + 9-column storyboard sheet + 6 major camera movements + 5x revenue share on Hongguo Theater)/ Xiaohongshu vibecoding (5-step 1-to-1 replication for AI outfits + canvas-based operation + 6-step cinematic AI web pages)/ Wang Kai (SillyTavern video Phase 1-7 engineering template + Components/Compositions layering + Jimeng CLI premium membership)/ Lao D (L05 text-to-image → image-to-video → lip sync + L10 HeyGen digital human 5-tool combo + 30 languages). Covers tool stack comparison for Jimeng / Vidu / Tongyi Wanxiang / HeyGen / ElevenLabs / CapCut
Explains the applicable scope of AI video last-frame relay, prompt duration, final decoded-frame extraction, and stitching conditions; adds frame-by-frame and audio-enabled checks, retains the 31-second historical case, and avoids promising that arbitrary segments will have no jumps.
Generative models resist "continuous subtle camera shake" (Veo hits 1/3 / Seedance locks to a static frame) → stop rolling the dice; do post compositing: 10% upscale + triple-frequency sine dynamic crop + segmented amplitude envelope (large when walking / small when standing still / halved when holding breath). Includes zero-dependency script, acceptance parameters, quantitative self-check method, Veo vs Seedance behavior comparison, and five core principles for POV prompts. Tested across 10 rounds and 40+ generations on StoReel; two deliverables passed acceptance.
Highlights from Will’s practical notes on AI visual generation: breaking down "using AI to produce visual assets" into 6 actionable task categories, each with reusable prompt skeletons, rationales, and common pitfalls. ① Image enhancement (turning blurry portraits into 4K / sharpening general images): relies on image-to-image detail reconstruction via Gemica/Gemini; the key constraint is "lock identity and composition, only fill in details, do not add new elements"; for portraits, use camera parameters (85mm f/1.4 / Sony A1) to force photorealism. ② 3D icons & IP design: AI has extremely strong recognition of 3D textures, so finding reference images > writing prompts (50% time spent finding images + 50% debugging); provides ready-made prompt templates for Airbnb isometric icons, soft vinyl blind box figures (Kakao Friends style), Apple skeuomorphic icons, and highly stylized 3D avatars. ③ KV key visuals: two approaches — "reskinning" (replacing the IP on a mature reference image, adjusting poses/environment to avoid plagiarism) and "original creation" (hand-drawn line art + AI stylization), including pitfalls such as using a 1:1 head-to-body ratio to prevent AI from switching to realistic human proportions. ④ Stylized illustrations & state graphics: one sentence to make AI produce a set of 6 stylistically consistent onboarding illustrations (lock color palette and brushwork, loosen shape constraints). ⑤ Visual remix reconstruction: take one dimension from each of multiple images (shape from image 1 + texture from image 2 + lighting from image 3) and fuse them into a custom visual, including a 4-step iterative practical case for in-car HMI. ⑥ Visual reference dimensions: 6 "image breakdown checklists" for photography/illustration/3D/brand/character/product photography, teaching PMs to break a reference image down into describable keyword dimensions. Target readers: PMs building AI products who need to produce icons/KVs/illustrations themselves; industry jargon is explained in-line, and prompts are retained in both Chinese and English.
Will's engineering handbook for the Define & Insight phase of the "AI-Assisted UX Workflow": turning a pile of interviews/data into "design-guiding insights" and communicating them clearly. It covers 6 tasks that can be outsourced to AI — ① Batch extracting insights from user research interview transcripts (framework of pain points + mental models + edge cases + success criteria, with quantifiable citations) ② Generating "insight cards" from a single core question ③ Automatically selecting charts for user research reports + outputting ECharts/HTML code (judgment logic of <3 seconds to read the chart, one conclusion per chart) ④ PM/UX quick reference for selecting among 80 chart types (grouped into 6 families: comparison/correlation/part-to-whole/time/distribution/geography) ⑤ Running AI eye-tracking heatmaps on uploaded UI screenshots (four scanning patterns: F-pattern/spotted/layer-cake/commitment) ⑥ 6-dimension expert review scoring for multiple UI solutions (primary/secondary/edge users + business + accessibility + scalability, final score = starting score − deductions). It also includes "3 criteria for strong insights + causal sentence templates", the "sample → behavior → commonality → insight" reporting structure, and how data and user research divide work across 4 project types: growth/fix/validation/exploration. It complements google-ux-research: that piece covers the manual craft of interviews/usability testing/affinity diagrams, while this piece covers the prompts and judgment rules for accelerating the same set of work with AI. The target readers are PMs building AI products, and each section comes with a Why explanation + reusable prompt skeletons.