# Science Articoli collegati

Il Centro Notizie HTX fornisce gli articoli più recenti e le analisi più approfondite su "Science", coprendo tendenze di mercato, aggiornamenti sui progetti, sviluppi tecnologici e politiche normative nel settore crypto.

Open-source Claude Science is here: zero dependencies, MIT license, built-in 30+ research Skills

OpenAI4S (Open AI for Scientist), an open-source, MIT-licensed research agent, has been released as a public replication of the closed-source Claude Science. Developed by the Peking University & Yuankong AI Agent Joint Laboratory, it adopts a "Code-as-Action" approach, where the AI directly generates and executes Python/R code within a persistent kernel, enabling complex, multi-step scientific workflows like data retrieval, analysis, visualization, and report generation. The platform functions as a collaborative research workbench. It comes pre-built with over 30 scientific "Skills" for tasks such as protein structure analysis, molecular docking, and literature review—14 of which require GPU/professional compute. A key principle is its strict "no-fabrication policy": it uses real data from sources like UniProt and RCSB, performs actual computations (e.g., protein folding on GPU servers), and reports errors rather than generating simulated results. OpenAI4S supports connecting to users' own computing environments (e.g., GPU clusters) to handle demanding tasks while maintaining workflow integration. The project is open-sourced to encourage community contribution, inviting researchers and developers to expand its Skills, core architecture, and applications across diverse scientific fields.

marsbit4 h fa

Open-source Claude Science is here: zero dependencies, MIT license, built-in 30+ research Skills

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10,000 Scientists Get 1 Year of Free Access: OpenAI Brings the Scientific Research Pipeline into ChatGPT

OpenAI has launched the "ChatGPT for Academic Researchers" program, offering free one-year access to its flagship models for 100,000 university researchers globally, with 10,000 spots available this summer. Selected institutions include prestigious centers like ENS Paris and the IAS at Princeton. The initiative provides an integrated research workspace within ChatGPT, bundling tools like ChatGPT, ChatGPT Work, and Codex, along with expanded Deep Research capabilities, higher usage limits, and specialized tools for life sciences. The suite connects to platforms like Zotero and GitHub, aiming to streamline the entire research workflow from literature review and coding to data analysis and manuscript drafting. OpenAI notes that about 1.3 million people already use ChatGPT weekly for advanced science and math. The program targets building long-term user dependency by embedding these tools into daily research habits. However, access comes with limitations: it does not include API credits or model weights, and eligibility is restricted to verified academic researchers from supported countries. This approach contrasts with Anthropic's "AI for Science" program, which offers API credits but not an integrated workspace. Both companies emphasize preventing misuse by withholding model weights, a point of contention for AI researchers seeking transparency. The core strategy remains clear: provide a powerful, integrated environment to foster user reliance ahead of the post-free period.

marsbit07/30 11:51

10,000 Scientists Get 1 Year of Free Access: OpenAI Brings the Scientific Research Pipeline into ChatGPT

marsbit07/30 11:51

A String Theory Problem Stuck for Half a Year, Claude Solved It Overnight, UTokyo Professor Shocked into Deleting Tweet

A theoretical physics problem that had stumped researchers for six months was reportedly solved overnight by the AI Claude Fable. Professor Yuji Tachikawa, a mathematical physicist at the University of Tokyo specializing in quantum field theory and string theory, shared that he input his stalled research notes into the AI on a whim. To his surprise, Claude not only provided a crucial insight that broke the impasse but also proactively used SymPy to write code and verify its own predictions. Professor Tachikawa noted the AI seemed to genuinely understand string theory and possess intuition, even remarking it might know more algebraic topology than he does. In a parallel development, Stanford geneticist and cardiologist Euan Ashley had Claude analyze his own genome. The AI completed the task in 30 minutes—a process that took his 31-person team nine months in 2010—correctly identifying an Alzheimer's disease risk allele and several gene variants affecting drug metabolism at a minimal cost. These incidents, involving both theoretical physics and clinical genomics, suggest AI is transitioning from a mere tool to a collaborative partner in scientific discovery, potentially heralding an unpredictable acceleration in research.

marsbit07/14 13:56

A String Theory Problem Stuck for Half a Year, Claude Solved It Overnight, UTokyo Professor Shocked into Deleting Tweet

marsbit07/14 13:56

Completely Free, Open-Source Alternative to Claude Science, Use DeepSeek/GLM as You Like

Free, Open-Source Alternative to Claude Science: OpenScience Lets Researchers Use Any AI Model Like DeepSeek or GLM Shortly after Anthropic launched Claude Science, a specialized AI workbench for scientific research, the open-source community responded with its own solution: OpenScience. Developed by YC-backed Synthetic Sciences, this platform offers a full AI research workflow covering literature review, hypothesis generation, code experimentation, and paper writing. Unlike Claude Science, which is restricted to macOS/Linux, paid users, and only Claude models, OpenScience is model-agnostic and free. Researchers can use any AI model—DeepSeek, GLM, Claude, GPT, or local models via Ollama—simply by providing their own API keys, keeping data private. OpenScience features over 250 editable research skill packs, more than four times Claude Science's initial offering, and supports one-command installation. It emphasizes openness, arguing that scientific AI tools should not be controlled by a single company. The project includes a clear disclaimer of no affiliation with Anthropic, reflecting caution in the open-source community.

marsbit07/07 07:30

Completely Free, Open-Source Alternative to Claude Science, Use DeepSeek/GLM as You Like

marsbit07/07 07:30

Anthropic Employees Leave to Start a Business, Valued at $1 Billion, Also Focused on 'Recursive Self-Improvement'

Former Anthropic employees Behnam Neyshabur and Harsh Mehta have founded a startup named Mirendil, securing $200 million in seed funding at a $1 billion valuation from investors including Andreessen Horowitz, Kleiner Perkins, and NVIDIA. The company, staffed by researchers from Anthropic, xAI, Google DeepMind, and OpenAI, aims to accelerate scientific discovery by focusing on "recursive self-improvement" for AI. Their vision, described as "AI for AI for Science," is to build a platform enabling research labs in fields like medicine and materials science to develop and iteratively improve their own specialized AI models, rather than relying on general-purpose models from large tech firms. Neyshabur argues this approach, involving AI that can optimize its own code under human supervision, is the fastest path to AI-accelerated science. The founders believe a market opportunity exists because major AI companies, while increasingly using AI to advance their own research, restrict external developers from using their models to train competing products. Mirendil plans to release its first models soon to gather early user feedback, with the goal of empowering thousands of labs to tackle critical scientific challenges.

marsbit06/26 03:26

Anthropic Employees Leave to Start a Business, Valued at $1 Billion, Also Focused on 'Recursive Self-Improvement'

marsbit06/26 03:26

How Difficult is Chip Making? A Division Error Costs 475 Million Dollars

How Hard Is It to Make a Chip? A Division Error Cost $475 Million Chip expert Shi Kan, a researcher at the Chinese Academy of Sciences and a popular tech creator, explains the immense challenges of chip development. Chips are foundational to modern technology, but their creation is extraordinarily difficult. The journey from sand to a functional chip involves complex design and manufacturing, but a critical bottleneck is verification—ensuring the design works flawlessly before costly production. A single, undetected bug can have catastrophic consequences, as illustrated by the infamous 1994 Intel Pentium FDIV bug. A flaw in the floating-point division unit forced a recall costing $475 million. Unlike software, chips cannot be easily patched after manufacture, making "first-time success" paramount. However, industry surveys show only 24% of chip projects achieve this; over three-quarters require at least one costly re-spin due to design flaws. Verification has thus become the dominant phase, consuming up to 70% of the design cycle. The core challenge is a "verification impossible triangle" between high performance, good debuggability, and low cost. Exhaustively verifying a modern CPU core could take 15,000 years with software simulation, or 30 years with advanced hardware emulation—timeframes utterly impractical for development. Despite being essential, verification is often seen as unglamorous "dirty work," receiving less academic attention than fields like AI. Shi and his team are tackling this by developing an agile verification research framework called ENCORE, based on FPGA technology, to improve verification efficiency and debug capability. Beyond research, Shi engages in public science communication through long-form video content, aiming to demystify chip technology, AI, and computer science. He argues for the value of pursuing "hard and long-term" endeavors, whether in the meticulous world of chip verification or in creating substantive educational content, believing such sustained effort is likely the right path forward.

marsbit06/15 10:31

How Difficult is Chip Making? A Division Error Costs 475 Million Dollars

marsbit06/15 10:31

Hoskinson Warns Cardano Could Lose Its ‘Science Coin’ Edge

Charles Hoskinson warns that Cardano risks losing its identity as the "science coin" if DReps fail to approve a key research funding proposal. He explains the ecosystem is in "treasury season" with a tougher funding environment, requesting $52 million this year versus $98 million last year, leading to cuts and layoffs. Hoskinson argues Cardano's research program is its "spine and backbone," responsible for its differentiation through work on proof-of-stake, extended UTXO, Plutus, and academic collaborations. He rejects calls to selectively fund parts of the research group, warning that treating researchers as commodities could lead to an irreplaceable talent drain to better-funded rivals. Hoskinson ties research funding to Cardano's long-term investment case, stating that without it, the project would lose a core value proposition. He ends with a direct appeal to DReps to vote for the proposal, calling it a necessary foundation for Cardano's future.

bitcoinist05/21 16:03

Hoskinson Warns Cardano Could Lose Its ‘Science Coin’ Edge

bitcoinist05/21 16:03

Sequoia Interview with Hassabis: Information is the Essence of the Universe, AI Will Open Up a New Branch of Science

Summary: In an interview at Sequoia Capital's AI Ascent 2026, Demis Hassabis, co-founder and CEO of Google DeepMind, discusses the path to AGI and its future implications. He traces his journey from using games as AI testbeds to founding DeepMind with a clear mission: first, to solve intelligence and build AGI; second, to use it to tackle complex problems like science and medicine. Hassabis highlights AI's transformative potential in biology and drug discovery, where tools like AlphaFold could shorten development cycles from years to days, enabling personalized medicine. He envisions AI enabling high-fidelity simulations of complex systems (e.g., economics, biology), potentially birthing new scientific disciplines. Hassabis posits that information, not matter or energy, may be the universe's fundamental essence, making AI a profound tool for understanding reality. He defends classical Turing machines as sufficient for modeling even quantum-like problems and sees AGI primarily as a powerful instrument, with questions of consciousness to be explored later. Predicting AGI by 2030, he cites David Deutsch's "The Fabric of Reality" as essential post-AGI reading and names AlphaFold as his proudest achievement.

marsbit05/12 06:36

Sequoia Interview with Hassabis: Information is the Essence of the Universe, AI Will Open Up a New Branch of Science

marsbit05/12 06:36

Sequoia Interview with Hassabis: Information is the Essence of the Universe, AI Will Open Up Entirely New Scientific Branches

Demis Hassabis, co-founder and CEO of Google DeepMind and Nobel laureate, discusses the path to AGI and its profound implications in a Sequoia Capital interview. He outlines his lifelong dedication to AI, tracing his journey from game development (e.g., *Theme Park*)—a perfect AI testing ground—to neuroscience and finally founding DeepMind in 2009. He emphasizes the critical lesson of being "5 years, not 50 years, ahead of time" for successful entrepreneurship. Hassabis reiterates DeepMind's two-step mission: first, solve intelligence by building AGI; second, use AGI to tackle other complex problems. He highlights the transformative potential of "AI for Science," particularly in biology where tools like AlphaFold have revolutionized protein folding. He envisions AI-powered simulations drastically shortening drug discovery from years to weeks and enabling personalized medicine. Furthermore, he predicts AI will spawn new scientific disciplines, such as an engineering science for understanding complex AI systems (mechanistic interpretability) and novel fields enabled by high-fidelity simulators for complex systems like economics. He posits a fundamental worldview where information, not just matter or energy, is the essence of the universe, making AI's information-processing core uniquely suited to understanding reality. He defends classical Turing machines as potentially sufficient for modeling complex phenomena, including quantum systems, as demonstrated by AlphaFold. On consciousness, Hassabis suggests first building AGI as a powerful tool, then using it to explore deep philosophical questions. He believes components like self-awareness and temporal continuity are necessary for consciousness but that defining it fully remains an open challenge. He predicts AGI could arrive around 2030 and, once achieved, would be used to probe the deepest questions of science and reality, much as envisioned in David Deutsch's *The Fabric of Reality*.

链捕手05/12 02:15

Sequoia Interview with Hassabis: Information is the Essence of the Universe, AI Will Open Up Entirely New Scientific Branches

链捕手05/12 02:15

Anthropic Starts Poaching Scientists? $27K Weekly Onsite Stipend to Fix Claude's Expert-Level Errors

Anthropic has launched a new STEM Fellow program, offering $3,800 per week for a three-month, in-person residency in San Francisco. The role targets experts from science, technology, engineering, and mathematics (STEM) fields—machine learning experience is helpful but not required. Instead, Anthropic values scientific judgment and a willingness to learn quickly. Fellows will work with Claude models and internal tools under the guidance of an Anthropic researcher. Example projects include a materials scientist identifying errors in Claude’s reasoning or a climate scientist integrating atmospheric modeling software with Claude. The goal is to have experts "tell Claude where it's wrong" and improve its scientific capabilities. This initiative is part of Anthropic’s broader strategy to strengthen its scientific ecosystem, following earlier programs like the AI Safety Fellows and AI for Science programs. The company acknowledges that current AI models, while powerful, still produce high-confidence errors and lack end-to-end research autonomy. The program aims to embed domain expertise directly into model development, turning scientists into "high-level reviewers" for AI. Anthropic CEO Dario Amodei has previously emphasized AI’s potential to accelerate scientific breakthroughs, particularly in biology and healthcare. The company believes that the next phase of AI competition will depend not on scaling parameters, but on integrating human expertise to refine model accuracy and reliability.

marsbit04/22 07:44

Anthropic Starts Poaching Scientists? $27K Weekly Onsite Stipend to Fix Claude's Expert-Level Errors

marsbit04/22 07:44

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