Vector
Vector: Trading terminal or bot resource for on-chain and exchange markets.
ABAB Structured Brief
Vector is indexed in ABAB Crypto Map under Trading Terminals & Bots. This page keeps the official site, category, tags, and related ABAB coverage together as a searchable crypto project profile. Official domain: vector.fun.
Related News & Analysis
ArtCraft Rebuilds Seven Open Source Adobe Alternative Applications Using Claude
... seven applications correspond to PhotoCraft for Photoshop, VectorCraft for Illustrator, FilmCraft for Premiere Pro, LightCraft for Lightroom, EffectCraft for After Effects, DesignCraft for InDesign, and PrintCraft for A...
Andrew Ng Found LearnVector, Secures $100 Million Investment from Coursera for One-on-One AI Learning
Andrew Ng announced the establishment of LearnVector, which has secured an initial investment of $100 million from Coursera. The company will collaborate closely with Coursera and Udemy to develop personalized AI lear...
Coursera Invests $100 Million in Andrew Ng's New AI Learning Company LearnVector
...unced a strategic equity investment of $100 million in LearnVector, an AI-native learning company founded by its co-founder Andrew Ng, acquiring approximately one-third of the equity, corresponding to a company valuation...
Critical Vulnerability in Telegram Desktop Has Been Fixed
...ritical. CVSS 4.0 score is 8.6, CVSS 3.1 score is 8.1, with vectors indicating user interaction is required and no prior permissions are needed. The consequence is that local files can be read and sent to an attacker-con...
Sequoia Capital's Investment Committee Mechanism Dissected: Partner Julien Bek Deeply Reviews Top Deals and Project Judgments
1. The Internal Operation Mechanism of Sequoia Capital and the Truth about the Investment Committee 1. Decision-making Process of the Investment Committee: A Combination of Fast and Slow Thinking • Monday IC Meetings and Asynchronous Memos: Sequoia's Monday Investment Committee, which has been running for decades, is evolving; the team is implementing an "asynchronous collaboration mechanism"—after the investment memo is sent out, partners independently write evaluations and questions in the document. • Asynchronous Mode (Slow Thinking): Provides space for thoughtful and independent judgment, avoiding groupthink. • On-site IC (Fast Thinking): Partners directly defend their positions against founders, facilitating high-frequency interactions. • Voting Mechanism and "1 Point Decision": Partners score from 1 to 10. Even if a senior partner gives a very low score of 1, if the project sponsor has the highest conviction, they still have the final authority to greenlight the project (provided the sponsor takes full reputational responsibility for the outcome). • SpaceX's Classic Comeback: Partner Shaun Maguire strongly advocated for SpaceX despite significant internal resistance, with some partners scoring it as low as 1; Shaun insisted on not giving up, requiring all partners to visit the site to witness it firsthand, ultimately resulting in one of Sequoia's most lucrative investments in history. 2. Everyone is a "Hunter" • Breaking the Myth of "Waiting for the Phone Call": The early team consisted of only 11-12 people, operating like a high-intensity football team, with everyone actively seeking non-consensus opportunities. • Heavy Investment in Long-term Relationships: For example, partner Constantine built trust with Ken Griffin during college, maintaining continuous follow-up over the years, which eventually led to Ken's acceptance of external institutional investment. • Updating Priors: In decisions regarding giants like Anthropic, Sequoia demonstrated the ability to quickly overcome the pride of having previously rejected them, decisively entering with a higher valuation ($2.5 billion round) after recognizing the reality of AI's exponential growth. 3. "Our Value Depends Only on Our Next Investment" • A core warning phrase is printed on the wall at Sequoia: "We are only as good as our next investment." Regardless of past myths created, each fund review resets history and refocuses on the next generation of great companies. 2. The Identification Map and Penetration Rules of Top Founders 1. Evaluation Framework: Direction and Magnitude • Pat Grady's Vector Model: Outstanding talent is like a vector: • Direction: Why are they doing this? Are their motivations pure and steadfast? • Magnitude: How ambitious are they? Can they endure extreme pain to keep pushing forward? • Shaun Maguire's Four-Dimensional Assessment: Beyond traditional IQ and EQ, it focuses on judgment (the ability to find solutions in complex systems) and political quotient (PQ, the ability to break through in complex organizations/networks of interests). • Distinguishing Top Executives from Exceptional Founders: Alfred Lin warns against mistaking "top operators from star companies" for "exceptional founders who can break through from 0 to 1." 2. 30-Minute Icebreaker and "5 Whys" • Trading Vulnerability for Sincerity: Julien breaks the cold commercial pitch by sharing personal family experiences, guiding founders to reveal their genuine growth trajectories and inner motivations. • Penetrating Deception and Disguise: By continuously asking "why" about decision details, one can observe speech speed, micro-expressions, and body language. A founder suspected of providing false data revealed inconsistencies under questioning, ultimately being exposed and warned by the industry. • Cross-Cultural Calibration: • German/French clients and founders are usually very restrained, with an NPS score of 7 often corresponding to actual high satisfaction (requiring an internal +1-2 points). • The American team expresses with great enthusiasm and showbiz style (requiring an internal -1-2 points for calibration). 3. Founder Background Check Rules (Doug Leone Methodology) • Who is your best reference? Who would give you the worst evaluation?: When a founder provides their best reference, immediately ask, "Who would give the worst evaluation and why?" This counterintuitive question reveals their self-awareness and honesty, allowing for deep cross-referencing. • ELO Rule: Only a grandmaster with a rating of 2400 can accurately identify another grandmaster in the crowd; one must seek references from truly exceptional individuals. 3. Paradigm Shift and Business Simulation in the AI Era 1. "Agents are a New Form of Customer" • Machine Traffic Surpassing Human Traffic: In the coming years, the traffic from agents on the internet will reach a thousand times that of human traffic; shifting from optimizing UI/pixel conversion rates over the past 20 years to building a "Bits-Perfect" underlying platform that adapts to agent calls. • Bias of Agents and AEO (Answer Engine Optimization): Agents have biases due to pre-training and post-training data (e.g., during development, they may prefer to call Cloudflare and Vercel); companies optimizing their weight in AI Q&A and agent decision-making (AEO) will give rise to a new ecosystem worth billions of dollars. 2. The Ultimate Form of Software Companies: "Software Companies in Service Clothing" • From Co-pilot to Autopilot: The first wave of AI sold tools for human use (capturing $1 of tool expenditure), while the real trillion-dollar opportunity lies in directly selling the final delivered results (outcomes), thus consuming the traditional outsourcing and human service budget of $6 (e.g., Sierra charges based on resolved tickets in the intelligent customer service sector). • "A Lot of AI + A Few Human Professional Judgments": Utilizing AI to complete 90% of automated execution, supplemented by human final aesthetic and business judgment at key points, companies will disrupt the massive traditional service industry with the high gross profit structure of traditional software. 3. Vertical Market vs Replacement Market (Greenfield vs Replacement) • Explosive Potential of Greenfield Markets: New capabilities (e.g., Lovable, Cursor, Harvey) can achieve tens of millions or even hundreds of millions in ARR at an incredible speed. • Penetration of the Real Economy (Project Iowa): For example, when traditional non-tech entities like car washes and auction houses begin to fully procure and consolidate core financial data, their businesses will truly possess cross-cycle stickiness and high barriers. 4. Top Investment Principles and Classic Case Reviews 1. Investment Preference: Extreme Traits (Spikiness) Over Mediocre Balance • Don Valentine's 2x2 Matrix: "Founders you like" and "Founders who can make money" are not the same. Top founders are often controversial, arrogant, or have distinct personalities; the core responsibility of investors is to identify whether their extreme strengths (spikes) are sufficient to support building an empire. • Reflection on Missing Trade Republic: Previously missed early investment due to misjudging that Revolut would quickly crush competitors; deeply realizing that trillion-dollar super tracks can often accommodate multiple giants coexisting, not an absolute zero-sum game. 2. The Family Angel Investment Legend of Revolut • Julien keenly sensed the extreme focus and fierce execution of Revolut founder Nikolay Storonsky just two weeks into his venture capital career; after the institution missed the lead investment, he persuaded his mother to co-invest by setting up an SPV (with a valuation of less than $200 million), and as the company's valuation soared past $100 billion, it resulted in thousands of times extraordinary returns. 3. Technology for Good and Ultimate Vision • Frontiers of Life Sciences and Brain-Computer Interfaces (BCI): The ultimate value of AI lies in solving chronic diseases, neurodegenerative disorders, and promoting breakthroughs in cutting-edge biotechnology, liberating humanity from mechanical work and leading to a more creative and healthier future. Video Source: https://www.youtube.com/watch?v=N8CBejLRztg
Palo Alto CEO Deep Dive: The Myths of Airtable's Fire Sale, Leo's Macro Hedge Fund Blowup, and the Trillion-Dollar AI Computing Arms Race
"Leo Aschenbrenner's Situational Awareness Blows Up Moonshot AI Raises $3.5B at $35B" (20VC with Harry Stebbings, featuring Nikesh Arora, CEO of Palo Alto Networks valued at $280 billion, with regular guests Rory O'Driscoll and Jason Lemkin). Here are the key points summarized: 1. Airtable acquired for $1.285 billion by Bending Spoons: valuation collapse and founder fatigue. • From $11 billion to $1.285 billion: • Airtable reached a valuation of $11 billion in 2021, ultimately selling to Italian capital firm Bending Spoons for $1.285 billion (annual revenue of about $485 million, annual growth rate of about 20%). • Market anchoring psychology: Ignoring the inflated $11 billion valuation from 2021, achieving nearly $500 million in revenue from a startup 10 years ago and exiting at over $1 billion is a remarkable business achievement. • Why traditional PE (like Thoma Bravo/Vista) did not bid: • Founder Fatigue: After layoffs, restructuring, and returning to founder mode, the founder chose to cash out in the face of the long restructuring cycle of the AI era. • Category eroded by AI: Previously, Airtable was an excellent no-code database; now developers can quickly create custom CRM/internal systems using Lovable, Cursor, or Claude Code in minutes, undermining the moat of no-code forms. • Bending Spoons' cash flow harvesting model: Skilled in acquiring mature sticky assets like Evernote, they create high cash flow machines by raising prices and cutting costs. 2. Former OpenAI researcher Leo Aschenbrenner's fund blowup: right trend, wrong portfolio. • Prodigy Leo gained fame for writing the renowned AI trend article "Situational Awareness" and raised a $225 million fund (at one point leveraging it to $4.5 billion). • Root of the blowup: Correctly identified the major trend in AI Capex (capital expenditure), but made fatal errors in portfolio construction—overlaying high leverage on extremely volatile tech assets, leading to a rapid blowup after a liquidity black swan event, with his public positions ultimately taken over by Citadel (Ken Griffin) for $16 billion. 3. Anthropic breaches three major corporate vulnerabilities and the AI cybersecurity storm. • Dimensionality reduction in AI offense and defense speed: • Anthropic's latest model autonomously discovered and breached corporate defenses in a short time. Zero-day vulnerabilities that previously took months for humans to investigate can now be found and automatically constructed into attack payloads by AI in seconds; the industry average for fixing vulnerabilities is 55 days. • Vulnerabilities are everywhere: Palo Alto Networks scanned open-source code packages over the past 14 weeks, discovering up to 14,000 unpatched vulnerabilities. • The irreplaceability of perimeter security: • Regardless of how smart the cutting-edge large models are, they are not on the defensive interception line at the network perimeter. The core of cybersecurity lies in blocking known threats at the perimeter and detecting and eliminating unknown threats through real-time AI large models (analyzing petabyte-level behavioral data) within one minute. 4. Nikesh Arora's core judgment: average intelligence is free, and corporate barriers lie in "exclusive context." • Key statement: Average intelligence will be free: • "In the long run, average intelligence will be free, and average intelligence will become increasingly smarter; only 'superior intelligence' that solves extremely complex problems like curing cancer or landing on the moon will require high fees." • Routine tasks like customer service and code completion will be rapidly popularized by low-cost open-source models (like Moonshot, open-weight models). • The lifeblood of a business: private context and data flywheel (Context is King): • General large models do not know the underlying architecture, system configuration, or reasons for the last five outages of customers. • Palo Alto Networks handles 400,000 customer cases annually, and the core task for all employees is to distill the logic and context of these human experts into a structured knowledge base (Vector DB). Once a sufficiently thick data flywheel is established, any LLM can be switched at the underlying level. 5. The trillion-dollar Capex arms race and energy/computing bottlenecks. • Extreme demand for computing and energy: • Giants like Microsoft, Amazon, Google, and Meta have seen quarterly Capex and cloud computing sales soar (adding hundreds of millions of dollars in revenue each quarter). • Land, permits, electricity, and chips have become key assets determining the winners and losers in AI over the next 3-5 years. From nuclear reactor startups (like Valor Atomics valued at $6 billion) to biogas power generation, any project capable of powering data centers is enjoying a significant valuation premium. • Palantir's explosive insights: • Palantir achieved nearly 100% explosive growth by encapsulating large models within complex business data flows. Companies are willing to pay top dollar for data flywheels that can directly provide business insights and solve real problems.
ArtCraft Rewrites Seven Design Tools in Pure Rust
...s written in pure Rust on getartcraft.com, targeting image, vector, video, photo library, PDF, motion graphics, and page layout. The tools are free, open-source, and run locally, with installation packages available for ...
The Vector Database Wars: Pinecone, Weaviate, Milvus and the Battle for the AI Data Layer
This is not a homogeneous founder set. Pinecone is closest to the “top infrastructure research leader turns founder” archetype, centered on Edo Liberty. Weaviate is closer to an “open-source community + product narrative + developer growth” company, with Bob van Luijt as the main public-facing founder, Etienne Dilocker as the technical co-creator, and Micha Verhagen appearing much less often in public materials. Milvus is best understood as an open-source database incubated by Zilliz, with Charles Xie as the clearest and most verifiable founder-level figure. Their shared insight was deeper than “vector databases are useful.” All three founders bet on the same structural gap: in the LLM era, the real bottleneck is not only the model but also the production-ready retrieval, memory, vector storage, and service layer around it. Pinecone answered with a managed infrastructure-first approach that expanded into serverless and knowledge products; Weaviate evolved from open-source semantic search into an AI-native database and cloud platform; Milvus started as an open-source database and Zilliz built the commercial cloud layer around it. Public information density is uneven. Edo Liberty and Bob van Luijt are comparatively well documented through interviews and biographies. Charles Xie has rich technical and company-level coverage, but little family or early-life detail. Etienne Dilocker and Micha Verhagen have much thinner public records on family, education, and formative years. Wherever the record is thin, the most accurate phrasing is simply: public information is limited / accounts vary / cannot be fully confirmed. Pinecone’s founder profile is that of a deeply technical infrastructure builder. Edo Liberty publicly links his “Israeli upbringing” to his bias toward pioneering new things. Exact details about his birth date, birthplace, parents, and family class are not systematically public. What is public is his educational path: Tel Aviv University for physics and computer science, then Yale for a Ph.D. in computer science and postdoctoral work in applied mathematics. He has described starting out wanting to be a physicist and discovering that computation and algorithms would become central to his work. Before founding Pinecone, Liberty accumulated unusually relevant experience. He joined Yahoo’s Israel research center in 2009, moved to the U.S. in 2012 to build Yahoo’s scalable machine learning group, and later became a research director at AWS and head of Amazon AI Labs. Public bios credit his teams with work on services including SageMaker, Kinesis, QuickSight, Amazon Elasticsearch, Glue, Rekognition, Personalize, and Forecast. Pinecone’s later product posture—production-first rather than lab-first—directly reflects that background. Pinecone’s founding logic was straightforward and powerful. The company’s official origin story says Liberty founded Pinecone in 2019 after seeing how powerful vectors and AI models were in real applications, while also seeing how difficult it was for most teams to productionize that stack. In 2021 Pinecone publicly launched its vector database and announced a $10 million seed round led by Wing Venture Capital. The bigger leap came in 2024, when serverless reached GA; Pinecone said more than 20,000 organizations had used it in preview and that 12 billion embeddings had already been indexed on the new architecture. By 2025 the company said it had more than 5,000 customers and had raised $138 million in total. Pinecone’s critical decisions were strategic, not cosmetic. The first was leaving AWS in 2019. The second was rearchitecting around serverless in 2024. The third was the 2025 leadership transition in which Ash Ashutosh became CEO and Liberty became Chief Scientist. That move looks like a classic transition from a technical founder leading 0-to-1 to a professional operator scaling 1-to-N. Pinecone’s most concrete public negative event was its March 2023 incident. The company’s own postmortem says a free-tier cleanup script accidentally deleted 515 Starter-plan indexes, all of which were later restored. Beyond that, the more persistent criticism has been category-level: outside observers have argued that vector databases were overhyped and increasingly risk becoming a feature inside larger retrieval stacks rather than a standalone moat. Pinecone, because of its financing and brand position, became a focal example in that debate. Weaviate’s founding story is unusual because the founder narrative itself has layers. TechCrunch and a 2022 company newsletter frame the founding team as Bob van Luijt, Etienne Dilocker, and Micha Verhagen. A 2023 rebrand announcement, however, emphasizes Bob and Etienne more heavily. So even at the source level, there is a difference between the narrower “core founding pair” narrative and the wider “cofounding trio” narrative. Bob van Luijt’s early life is the clearest documented part of Weaviate’s founder story. He says he was born in 1985, grew up in the Netherlands, moved within the country during childhood, and got into software after his father brought home an IBM computer and he learned from a QBasic book found at the library. Before high school he was already building websites. Public materials do not systematically document his parents’ professions or exact household class, but they do show early access to computers, books, and music training. Bob’s education and worldview are unusually influential on product philosophy. He studied music at ArtEZ and Berklee, later completed executive education at Harvard Business School, and repeatedly describes software through the lens of language, structure, and artistic expression. His early work was not a classic big-tech career path: he ran software businesses, took client work, operated Kubrickology, and only later pulled these threads together into Weaviate. Two major influences stand out in his own retelling: seeing word embeddings around 2015, and hearing Sam Ramji speak about open-source business models. Etienne Dilocker is the engineering core of the Weaviate origin story. Public company writing says he was effectively the founding engineer and the person who hands-on built the first product. Bob also credits Etienne with the key idea of building an end-to-end database where vector embeddings were first-class citizens. His family background and fuller biographical details are publicly limited. Micha Verhagen appears as COO/cofounder in multiple sources, but his education, early career, and family details are much less publicly documented than Bob’s. Weaviate’s project history predates its company history. Bob traces the concept back to 2016, when ideas around “things,” graphs, and semantic structure gradually evolved into a database designed around vectors and meaning. By late 2018, after entering a Dutch accelerator, the team began formalizing commercialization. In 2019 SeMI Technologies was founded and Weaviate became its first product. The decisive architectural choice was to stop treating NLP and embeddings as just another feature and instead make vector storage and semantic retrieval central to the system. Weaviate’s business model is one of the clearest open-source business explanations in the category. Bob explicitly says commercial success does not depend on software licenses but on the service around the database: operations, scalability, SLAs, integrations, training, tooling, and the broader ecosystem. In his academic interview, he also says many people do not understand how open-source companies capture value. That is essentially the entire model in one sentence: open source drives adoption, education, and trust; cloud and enterprise services generate revenue. Weaviate’s biggest achievements lie in product framing and developer credibility. It built an unusually coherent story around vectors, hybrid retrieval, structured objects, GraphQL, and AI-native application development. It raised a $16 million Series A in 2022, a $50 million Series B in 2023, rebranded the company name to match the product, and by 2026 publicly described itself through search, vectorization, RAG, agents, and cloud deployment. Its community messaging says it serves more than 50,000 AI builders, which points to influence beyond code alone. Charles Xie’s background is the most database-systems-native of the group. Public records say he earned a bachelor’s degree from Huazhong University of Science and Technology and a master’s degree in computer science from the University of Wisconsin–Madison. The record on his family and exact early-life circumstances is thin, but his technical training is very clear. His early representative job experience was at Oracle. Multiple official bios describe him as a founding engineer on Oracle’s 12c cloud database project. That matters because Milvus did not emerge as a thin ANN wrapper; it emerged from a database-systems mindset. In interviews, Xie repeatedly says that structured data was already well managed but non-structured data remained largely underused, and that advances in embeddings opened a new way to make that data operationally useful. Zilliz and Milvus were founded on a long-horizon thesis, not a short-term AI hype reaction. Xie says vector embeddings became the bridge between unstructured data and usable insight, which led him to found Zilliz around 2017. Zilliz educational material says Milvus development began in 2018 and the product launched in 2019. In 2020 Zilliz contributed Milvus to LF AI & Data, and the project graduated in 2021. That sequence is important because it shows Milvus was early, open-source, and then institutionally legitimated through a foundation path. Zilliz’s capital and commercialization path reveal a classic open-source-to-cloud evolution. The company raised $43 million in 2020 and another $60 million extension in 2022, bringing total funding to about $113 million. Official and press materials continue to list investors including Prosperity7 Ventures, Pavilion Capital, Hillhouse, 5Y Capital, Yunqi Partners, and Trustbridge. A 2024 Zilliz engineering retrospective says the company moved toward commercialization because users kept asking for a stable hosted version, first built dedicated clusters, then serverless, and cut new-user acquisition cost from $300 to $5. That is not just a pricing tweak; it is the story of an open-source project being shaped into a scalable cloud business. Milvus and Zilliz have built a broader asset stack than many people realize. Public product pages now show Zilliz Cloud, BYOC, migration services, GPTCache, DeepSearcher, Attu, and Milvus CLI in addition to Milvus itself. By the end of 2024, Google Cloud’s case study said Milvus and Zilliz Cloud had over 10,000 enterprise customers globally, more than 33,000 GitHub stars, and over 100 million downloads and deployments. Even allowing for company framing, that is enough to show that Xie built not just a project but a visible global AI data infrastructure brand. The main criticism directed at Milvus/Zilliz is not personal scandal but boundary uncertainty. Like the rest of the category, it sits inside the debate over whether specialized vector databases remain a durable standalone category or get absorbed into broader retrieval stacks, cloud platforms, and incumbent databases. The external critique is less “can it work?” and more “what exactly will remain defensible as a business over time?” The clearest cross-company conclusion is this: these founders were not merely building databases. They were competing to define the retrieval layer, memory layer, and AI data layer of the LLM stack. Edo Liberty pushed that layer toward managed enterprise infrastructure. Bob van Luijt pushed it toward open-source developer culture and product narrative. Charles Xie pushed it toward a dual model of open-source database plus commercial cloud platform. Whether the market keeps overvaluing the category is secondary. They have already shaped the industry’s answer to a far more important question: what must exist, beyond the model, for AI applications to work in reality?