# Memory Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Memory", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

AlphaGo's Creator Puts AI into a 23-Year-Old Artificial Society: All Three Toughest Challenges for AI Agents Are Here

Demis Hassabis, CEO of DeepMind, has embarked on a new AI research venture by partnering with the long-running space MMO, EVE Online. This collaboration, announced in early May, aims to use the game's 23-year-old, player-driven persistent universe as a testbed for tackling three core challenges in AI agent research: long-horizon planning, memory, and continual learning. Unlike previous DeepMind environments like AlphaGo (Go) or AlphaStar (StarCraft II), EVE Online features no fixed end state. Its single-shard universe has fostered complex, emergent player societies with real economies, political alliances, and wars that can span months or years. These conditions naturally demand the very skills—long-term strategic planning, maintaining memories over extended periods, and adapting to constant change—that are hardest for current AI agents to master. The research will initially use an offline version of EVE, providing a controlled, complex sandbox without interfering with the live player server. This move continues DeepMind's trajectory of using increasingly complex and open-ended virtual worlds for AI training, from Atari games and Go to StarCraft II and the SIMA project. The EVE environment represents a significant step towards testing AI in a persistent, socially complex, and continuously evolving world shaped by human behavior over decades.

marsbit05/25 00:08

AlphaGo's Creator Puts AI into a 23-Year-Old Artificial Society: All Three Toughest Challenges for AI Agents Are Here

marsbit05/25 00:08

From Token Explosion to Physical Bottlenecks: The Storage Bull Market Driven by Agentic AI

**From Token Explosion to Physical Bottlenecks: The Agentic AI-Driven Storage Bull Market** The AI semiconductor narrative is shifting from training to inference, which now accounts for 66% of AI compute. In the inference "Decode" phase (autoregressive token generation), GPU performance is bottlenecked by memory bandwidth and capacity, not raw compute (FLOPS). The key constraints are **HBM (High Bandwidth Memory) bandwidth** (determining token generation speed) and **HBM capacity** (determining how many requests/models can be served simultaneously). This creates a core economics equation: Token cost is proportional to (GPU + power cost) divided by Tokens/sec, which is fundamentally limited by HBM specs. This drives unprecedented demand for advanced storage. **HBM**, a 3D-stacked DRAM, is critical for AI accelerators. Its complex production consumes 3-4x more wafer capacity than standard DRAM, squeezing supply for traditional memory (DDR) and causing severe shortages. **HBF (High Bandwidth Flash)**, an emerging high-bandwidth NAND, aims to bridge the gap between HBM speed and SSD capacity for AI model weights. The market is experiencing a historic, structurally driven super-cycle. Demand is fueled by a triple engine: 1) AI training (parameter arms race), 2) AI inference explosion (especially Agentic AI with long contexts), and 3) general data center expansion. Supply is constrained by the HBM产能挤压 effect and the 2-3 year lead time for new fab capacity. Analysts project a DRAM supply deficit of ~5% in 2026. Inventory across the supply chain is at historically low levels, with OEMs securing long-term agreements (LTAs) locking in future supply. Current indicators (Q2 2026) suggest the cycle is in its mid-phase, not peaking. While spot prices have corrected from highs, contract prices are forecast to rise sharply (e.g., +70-75% QoQ for NAND). Capacity utilization remains high, and inventory days are still low. The cycle is expected to peak around mid-2027. The storage landscape is stratified, with key players in HBM (SK Hynix, Samsung, Micron), NAND/SSD/HBF (Samsung, Kioxia/WD, SanDisk), and NOR Flash (Winbond, GigaDevice) well-positioned for this AI-driven era.

marsbit05/22 03:41

From Token Explosion to Physical Bottlenecks: The Storage Bull Market Driven by Agentic AI

marsbit05/22 03:41

Decoding the Latest Holdings of "The Son of Wall Street Version" Leopold Aschenbrenner: Why Did the AI Bull King Turn to Short Nvidia?

Leopold Aschenbrenner, a renowned AI investor and former OpenAI researcher, has filed a 13F revealing a dramatic portfolio shift. He has taken a massive $8 billion short position against the entire semiconductor supply chain, including companies like NVIDIA, AMD, Broadcom, and ASML. This marks the first time in his fund's history that short exposure exceeds long exposure. His core thesis is that the bottleneck for AI investment is shifting from chip *design* to *infrastructure*, specifically power and memory. Accordingly, his long positions heavily target these areas. He maintains holdings in data center/Neocloud firms like CoreWeave, holds positions in power solutions like Bloom Energy, and has initiated new stakes in bitcoin mining companies such as CleanSpark and Riot Platforms due to their pre-existing grid access and power capacity. Aschenbrenner also increased his long bets on memory, exemplified by SanDisk, as AI models drive demand for NAND flash storage. Despite the aggressive short on semis, analysts suggest he may not be structurally bearish but rather views the chip trade as over-crowded, while seeing higher returns in infrastructure plays. Key risks identified include underestimating NVIDIA's entrenched CUDA ecosystem moat and the timing of his shorts.

marsbit05/20 09:50

Decoding the Latest Holdings of "The Son of Wall Street Version" Leopold Aschenbrenner: Why Did the AI Bull King Turn to Short Nvidia?

marsbit05/20 09:50

How to Become a Pro Claude User in 30 Days?

"How to Become a Claude Power User in 30 Days" outlines a structured month-long program to transform from a casual user into someone who leverages Claude as a core productivity system. The first week focuses on mastering prompt structure (Role, Context, Task, Format, Constraints), understanding context windows, and setting up foundational Projects and Memory for personalized context. The second week involves building reusable workflows for research, writing (using a two-step outline-first process), and decision-making. The third week shifts to automation: using Claude Cowork for autonomous file tasks, connecting tools like Google Drive and Slack, and setting scheduled automation. The final week is for compounding gains: optimizing all workflows based on feedback, building a personal knowledge repository, teaching the system to a colleague, and designing a complete, ideal "Claude Operating System." By day 31, the user operates with an autonomous assistant that handles routine tasks with deep context, freeing them for high-value creative and strategic work. The core message is that consistent, systematic configuration over 30 days creates a powerful, personalized productivity advantage.

marsbit05/20 08:06

How to Become a Pro Claude User in 30 Days?

marsbit05/20 08:06

Five Core Forms of AI Agent in YC's Eyes

The article outlines five core architectural patterns for effective AI Agents, emerging from tools like Codex and Claude, that move beyond simple prompts towards reusable, process-based capabilities. 1. **Skills**: Reusable, parameterized workflows that function like method calls, allowing a single process (e.g., "/investigate") to handle various tasks based on input parameters. 2. **Thin Harness**: A lightweight execution framework (~200 lines) that manages the AI model's "hands and feet"—handling loops, file I/O, and context—without becoming bloated. 3. **Resolvers**: Routing tables that map tasks to specific Skills, preventing "context corruption" when managing dozens of Skills and ensuring outputs go to the correct locations. 4. **Latent vs. Deterministic Layer**: A critical separation where LLMs handle judgment, synthesis, and pattern recognition, while deterministic code handles tasks requiring precision, consistency, and low cost (like calculations). 5. **Memory**: A persistent, accumulating knowledge base (e.g., a markdown folder) with a "current trusted conclusion" section and an append-only timeline, enabling the system to learn and retain context over time. Together, these patterns create a "process power"—a durable competitive advantage. Unlike one-off prompt-based applications whose value quickly commoditizes, a well-designed AI Agent system encodes experience into reusable, parameterized workflows, offloads stable rules to code, and continuously learns through memory. This creates a structured, hard-to-replicate capability that can provide sustained value for individuals or businesses, such as an accountant automating client reviews while preserving privacy and accumulating expertise.

marsbit05/20 07:46

Five Core Forms of AI Agent in YC's Eyes

marsbit05/20 07:46

Samsung Bets on Mobile HBM: AI Moves from Cloud to Palm, a New Frontier in Semiconductor Investment?

Samsung is betting on bringing high-bandwidth memory (HBM) technology from servers to mobile devices, aiming to enable powerful on-device AI features in smartphones and tablets. This move is driven by the booming AI market, where HBM demand from data centers has fueled Samsung's record profits, with HBM4 already in mass production. By integrating mobile HBM, Samsung seeks to transform user AI experiences—making tasks like image generation and real-time translation faster, seamless, and more private by processing data locally. Strategically, this allows Samsung to leverage its vertical integration in memory, advanced packaging, and Exynos processors to differentiate its Galaxy devices against competitors like Apple and Qualcomm. It also opens a new consumer growth avenue, reducing reliance on volatile server HBM demand alone. The initiative is expected to benefit the broader supply chain, boosting demand for advanced packaging materials, thermal solutions, and other components. While promising, risks include potential delays in mobile HBM mass production beyond 2027, high initial costs, and the cyclical nature of the memory market. Nonetheless, Samsung's push signals a broader industry shift toward hybrid cloud-edge AI computing, positioning it as a key player in defining the future of AI-powered devices and presenting a potential long-term investment theme in semiconductors.

marsbit05/19 14:49

Samsung Bets on Mobile HBM: AI Moves from Cloud to Palm, a New Frontier in Semiconductor Investment?

marsbit05/19 14:49

From 'Cash Incinerator' to 'Money Printing Machine': ChangXin Technology's Remarkable Turnaround, Raking in 50 Billion in Half a Year

Changxin Technology: From "Money Incinerator" to "Money Printer" in Six Months Changxin Technology, a Chinese DRAM chipmaker once dubbed a "money incinerator" for years of massive losses, has staged a staggering financial turnaround. Its updated IPO prospectus reveals explosive 2026 first-half results: revenue forecast of 110-120 billion yuan (up 613-677% year-on-year) and net profit of 50-57 billion yuan (up 2244-2544% year-on-year). This half-year profit rivals that of major state-owned energy giants. The reversal stems from a historic memory chip super-cycle fueled by AI. Massive demand from AI servers, consuming 8-10x more DRAM than traditional servers, coupled with a supply crunch as major players shift capacity to premium HBM, has driven DRAM prices to multi-year highs. As China's only large-scale DRAM IDM (integrated design and manufacturing) firm, Changxin was positioned to capitalize. With upgraded product lines (DDR5/LPDDR5) and high capacity utilization, it achieved both volume and price increases, doubling its global market share to 7.67% in just half a year. This follows a decade of heavy investment and losses totaling 36.65 billion yuan, a gamble led by Chairman Zhu Yiming, who famously vowed to take no salary until the company was profitable. The IPO aims to raise 29.5 billion yuan, implying a valuation that some analysts project could reach 1-2 trillion yuan long-term. Debate persists over the sustainability of profits given DRAM's cyclicality, but supporters point to structurally sustained AI demand and Changxin's strategic national importance. The story is a textbook financial comeback, rewarding persistent investment in a critical industry.

marsbit05/18 13:04

From 'Cash Incinerator' to 'Money Printing Machine': ChangXin Technology's Remarkable Turnaround, Raking in 50 Billion in Half a Year

marsbit05/18 13:04

Topping GitHub's Trending, the Essential Guide for Claude Code Users

The CLAUDE.md file, trending on GitHub, is a project-level guide for Claude Code designed to dramatically improve its accuracy and efficiency. It addresses key issues like repetitive context explanations, unauthorized code changes, and forgotten decisions across sessions. By placing this plain-text file in a project root, Claude Code reads it automatically at the start of each session. The guide includes rules to eliminate redundant explanations, enforce strict behavioral constraints (e.g., no modifications outside the requested scope without confirmation), and establish a "memory" system using companion files like MEMORY.md and ERRORS.md to log past decisions and failures. It also locks in the project's specific tech stack to prevent inappropriate tool recommendations. Highlighted are four foundational rules from Andrej Karpathy that reportedly increased coding accuracy from 65% to 94%: always ask for clarity first, implement the simplest solution, never touch unrelated code, and explicitly flag uncertainties. The article quantifies significant weekly cost savings for developers and teams by eliminating wasted time on re-explaining context, rolling back unauthorized edits, and re-evaluating previously rejected solutions. The core message is that a small, upfront investment in creating a CLAUDE.md file leads to a more predictable, controlled, and cost-effective AI programming assistant.

marsbit05/18 09:38

Topping GitHub's Trending, the Essential Guide for Claude Code Users

marsbit05/18 09:38

The AI Investment Landscape Is Being Reshaped: Beyond the 'Magnificent Seven', What Opportunities Lie in the Semiconductor Supply Chain?

AI Investment Map is Reshaping: Opportunities Beyond the 'Magnificent Seven' Since ChatGPT ignited the AI wave, investment initially focused on the "Magnificent Seven" tech giants dominating cloud infrastructure. However, the rise of DeepSeek and debates on AI capital expenditure effectiveness are shifting this dynamic. Investors now recognize opportunities deeper in the supply chain—the companies providing the essential "picks and shovels." Early concerns about an AI investment "arms race" and potential low returns were partly alleviated by strong Q1 earnings from cloud providers, validating robust compute demand. This has highlighted a more certain investment thesis: regardless of which AI applications ultimately win, massive capital expenditure will first fuel demand for semiconductors and related components. This "pick-and-shovel" logic has driven semiconductor ETFs to record highs. Key beneficiaries include: * **Memory Chipmakers (e.g., SK Hynix, Samsung, Micron)**: High Bandwidth Memory (HBM) is a critical bottleneck for AI training. * **Photonics Companies**: Crucial for high-speed data transfer within AI data centers. * **The Broader "AI-11" Semiconductor Ecosystem**: This encompasses foundries & lithography (TSMC, ASML), logic & custom chips (AMD, Broadcom, Intel, Marvell), and enterprise storage (SanDisk, Western Digital). Every dollar of AI infrastructure spending flows through this chain. While the "Magnificent Seven" remain dominant in market size, their earnings growth premium over the rest of the S&P 500 ("S&P 493") is narrowing. Market attention and marginal investment are shifting towards the expanding semiconductor supply chain. The investment narrative is evolving from "betting on the ultimate AI winner" to "investing in the certainty of the infrastructure build-out." Understanding this shift from the demand side to the supply side is key to identifying future AI investment opportunities.

marsbit05/12 08:06

The AI Investment Landscape Is Being Reshaped: Beyond the 'Magnificent Seven', What Opportunities Lie in the Semiconductor Supply Chain?

marsbit05/12 08:06

Morgan Stanley 2026 Semiconductor Report: Buy Packaging, Buy Testing, Buy China Chips, Avoid Traditional Tracks

Morgan Stanley 2026 Semiconductor Report: Buy Packaging, Buy Testing, Buy Chinese Chips; Avoid Traditional Segments. The core theme is the shift in AI compute supply from NVIDIA dominance to a three-track system of GPU + ASIC + China-local chips. The key opportunity is capturing share in this expansion, while non-AI semiconductors face marginalization due to resource reallocation to AI. Key investment conclusions, in order of priority: 1. **Advanced Packaging (CoWoS/SoIC) - Highest Conviction**: TSMC is the primary beneficiary of explosive demand, driven by massive cloud capex. Its pricing power and AI revenue share are rising significantly. 2. **Test Equipment - Undervalued & High-Growth Certainty**: Chip complexity is causing test times to double generationally, structurally driving handler/socket/probe card demand. Companies like Hon Hai Precision (Foxconn), WinWay, and MPI offer compelling value. 3. **China AI Chips (GPU/ASIC) - Long-Term Irreversible Trend**: Export controls are accelerating domestic substitution. Companies like Cambricon, with firm customer orders and SMIC's 7nm capacity support, are positioned to benefit from lower TCO (30-60% vs NVIDIA) and growing local cloud demand. 4. **Avoid Non-AI Semiconductors (Consumer/Auto/Industrial)**: These segments face a weak, structurally hindered recovery due to AI's resource "crowding-out" effect on capacity and supply chains. 5. **Memory - Severe Internal Divergence**: Strongly favor HBM (Hynix primary beneficiary) and NOR Flash (Macronix). Be cautious on interpreting price rises in DDR4/NAND as true demand recovery. The report emphasizes a 2026-2027 time window, stating the AI capital expenditure cycle is far from over. Key macro variables include persistent export controls and AI's systemic "crowding-out" effect on traditional semiconductor supply chains.

marsbit05/12 01:30

Morgan Stanley 2026 Semiconductor Report: Buy Packaging, Buy Testing, Buy China Chips, Avoid Traditional Tracks

marsbit05/12 01:30

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