Ollama is soliciting feedback from teams of five or more users regarding its upcoming Team plan. Users are encouraged to share details about their team by filling out a provided Typeform link. The purpose is to help Ollama design features suitable for collaborative use environments. This request targets individuals who have expressed interest in receiving updates from Ollama.
The content advertises resources for improving ChatGPT usage, featuring a sign-up for Superhuman AI promising 1,000+ proven prompts to boost productivity. It also highlights Mintlify, noting that HubSpot adopted the platform for documentation due to its efficiency gains. Users can learn about adopting modern documentation solutions from this information. The material combines various marketing placements within the "Sloth Bytes" newsletter format.
OpenAI plans to release GPT-5.6 next week, featuring a 1.5 million token context window, Mini/Pro variants, and better coding capabilities. Midjourney is developing the "Midjourney Scanner," a rapid medical device capable of generating detailed full-body scans in under an hour. Perplexity introduced Brain, a memory system creating persistent context graphs for agents by linking memories to original sources. Another development addresses research agent privacy risks with PA-DR, which reduces data leakage from private documents during web retrieval tasks. Finally, the article mentions reinforcement learning as a method to build broadly and persistently beneficial traits within AI models.
Generative AI is transforming financial research, allowing private equity firms to automate the arduous process of sifting through vast amounts of data for investment analysis. Tools like ChatGPT Enterprise can now conduct deep research, surfacing obscure data points that were previously difficult to find and summarizing customer interviews objectively. This capability significantly reduces research time from hours to minutes, enhancing efficiency in areas like strategic planning. Despite its power, users must remain cautious due to the risk of AI hallucination and data privacy concerns. Firms are mitigating these risks by cross-checking information and customizing AI models with proprietary institutional knowledge for greater accuracy.
AI's impact on the workforce is complex, with varying opinions on its ultimate effect. While AI chatbots can perform tasks like summarizing meetings or passing exams, its capabilities do not equate to true intelligence across an entire job role. The likely future involves people and machines working collaboratively; for example, doctors using AI enhances diagnostic ability. Benefits include offloading tedious work, allowing humans to focus on more fulfilling aspects of their jobs. Despite predictions of widespread automation, the text concludes there is no clear path toward a jobless future due to these advancements.
Michelle Kim is an AI reporter working at MIT Technology Review and is supported by the Tarbell Center for AI Journalism. Her previous reporting experience includes covering Labor x Tech for Rest of World and writing on South Korean politics for Foreign Policy. Before entering journalism, she practiced as a corporate lawyer in California. She maintains an active presence with 24 posts under her profile. Contact information provided for reaching Kim is Signal at michellekim.11.
Ernst & Young integrated the Elasticsearch Platform into its generative AI stack to revolutionize unstructured data analysis for regulatory compliance. This integration enables faster, more accurate extraction of insights from complex financial documents containing many tables. The new approach delivers results three times quicker than traditional methods and improves accuracy by 10%–15%. Specifically, EY used this technology to streamline Environmental, Social, and Governance (ESG) reporting. Ultimately, the solution helps clients efficiently extract data while promoting responsible AI practices and ensuring regulatory adherence.
The current excitement surrounding generative AI mirrors the early dot-com boom, creating a sense of rapid, unpredictable change. Like previous transformative technologies such as the internet, generative AI presents potential benefits alongside significant unknown risks. Because it was trained on existing online data, the technology inherits issues like bias, misinformation, and copyright concerns. The article suggests that both its full potential and misuse will only become clear through widespread use by individual users. Addressing these challenges requires acknowledging past failures while proactively working to mitigate systemic problems before they escalate.
A thought-experiment posits a dramatic geopolitical future in 2031 involving the US and China. In this scenario, the US invested heavily in data centers while Europe lagged behind. Furthermore, China reportedly developed robotics capabilities that Europe failed to adopt. American companies allegedly restructured operations around AI, leading to workforce reductions. Meanwhile, European workers supposedly handed off administrative tasks to an AI model named Claude.
Lloyds Banking Group has initiated a hiring effort to bring in 300 technology experts. These new hires are intended to support the bank's use and development of agentic AI by September. Agentic AI refers to autonomous artificial intelligence models capable of planning and executing tasks with minimal human supervision. This recruitment drive precedes the unveiling of a broader strategic plan for the historic lender by its CEO, Charlie Nunn. While the hiring increases current headcount, the report notes that future AI adoption may lead to potential job reductions.
Granta will cease publishing winning entries from the annual Commonwealth short story prize. This decision follows controversy surrounding one of this year's award winners who faced accusations of using AI. Consequently, the magazine announced it will no longer participate in "external publishing partnerships." These agreements were previously made where Granta lacked full editorial control over the published material.
American readers express apprehension regarding the influence of SpaceX's IPO and the broader AI boom on their retirement savings. Elon Musk reached trillionaire status following SpaceX's market debut with a $1.77 trillion valuation. Consequently, many Americans face potential indirect investment exposure to SpaceX and other rapidly growing AI-focused enterprises. These developments signal a significant shift in US market investments toward artificial intelligence sectors. This trend has raised concerns among the public about their retirement portfolio security.
Banning open-source artificial intelligence would be a significant error in judgment. Such a prohibition risks stifling innovation across various technological sectors. Open source models foster transparency and allow diverse groups to build upon shared research. Restricting this access could slow the pace of AI development globally. Therefore, maintaining an open environment for AI is crucial for future technological progress.
Large language models are transforming engineering workflows by serving as core architectural elements for tasks like vulnerability identification. To use them reliably, technical professionals must understand the underlying transformer architecture rather than treating them merely as conversational tools. Key applications include using APIs to connect LLMs to internal databases, employing Retrieval-Augmented Generation (RAG) to reduce hallucinations, and prioritizing secure private model instances. IEEE has launched a comprehensive online course, "Large Language Models Demystified," to teach the engineering principles behind generative AI, including transformer architectures and PyTorch implementation. Completing this program equips professionals with advanced knowledge essential for mastering LLMs in modern infrastructure development.
Covers core AI concepts including LLMs, agents, RAG, transformers, and RL fundamentals. Details Joint Embedding Predictive Architecture (JEPA) for world modeling through I-JEPA, V-JEPA, and MC-JEPA. Discusses advanced agent capabilities like recursive self-improvement and comparing local agents such as Hermes and OpenClaw. Provides guidance on skill engineering methods like SkillOpt for improving AI agents' abilities. Explains technical aspects of LLM inference end-to-end, from tokenization to decoding, and details the attention mechanism using QKV and KV cache concepts.
Miami-based AI startup Subquadratic claims to have solved a long-standing mathematical bottleneck affecting large language models (LLMs). The company introduced its new LLM, SubQ, which purports to be faster, cheaper, and more energy-efficient than existing models. Subquadratic asserts that SubQ can process up to 12 times the text of other models while matching the performance of top competitors like Google DeepMind. Initial skepticism has been somewhat alleviated by third-party evaluations from Appen, which validated aspects of the company's claims. While SubQ may not entirely replace current leaders, it promises significant improvements in speed and cost for specific data-heavy tasks.
Buildkite offers four pricing tiers: Personal (free forever), Pro ($30/user/mo), Enterprise, and an All Access Trial. Key limitations increase with the lower tiers; for example, concurrent jobs are limited in the free plan but unlimited in Pro and Enterprise. The Professional tier adds crucial features like SSO and increased agent capacity compared to the Personal plan. Advanced governance tools such as SCIM/SAML integration and audit logs are reserved for the Enterprise level. Functionality ranges from basic builds in the free tier to advanced test suite insights and historical exports on paid plans.
OpenAI is scheduled to release GPT-5.6 next week, which may include Mini and Pro versions. A major feature of the new model is a substantial 1.5 million token context window. Furthermore, it promises enhancements in long-horizon coding capabilities and faster Codex response times. OpenAI also intends for competitive pricing to undercut offerings from Anthropic. Additionally, Midjourney is developing a novel ultrasonic scanner capable of creating detailed full-body 3D maps rapidly.
A new newsletter provides daily updates on global technology trends. Startup Subquadratic recently emerged from stealth, claiming to have resolved a major mathematical bottleneck impacting Large Language Models (LLMs). The content also reports that Brain-Computer Interface (BCI) trials are actively progressing. These developments highlight significant advancements in AI infrastructure and human-machine interaction technologies.
Current AI agents frequently fail in production because they lose accuracy or require constant human oversight when processing large amounts of business knowledge. Existing methods like fine-tuning suffer from catastrophic forgetting and retraining lag, while Retrieval-Augmented Generation (RAG) suffers from context rot and silent retrieval misses. A promising third path involves using hypernetworks to generate task-specific models on demand at inference time, circumventing both retraining costs and prompt limitations. This generative approach creates specialized, smaller models that can be dynamically adapted without requiring storing or managing a sprawling library of adapters. Ultimately, achieving high autonomy requires not only this architecture but also transparent grounding of outputs and clear ownership over the resulting feedback loop data.
YaFF is an open-source, zero-copy wire format developed by Yandex for the Protocol Buffers ecosystem. It maintains the existing `.proto` file as the single source of truth while optimizing how data resides in memory. The format features four distinct layouts: Fixed, Flat, Sparse, and Dynamic. Benchmarks show that the Flat Layout achieves read speeds within 1.2 times of a raw C++ struct. Implementing YaFF can yield tangible production benefits, such as a reported 10–20% CPU saving in an advertising recommendation system.
An end-to-end forecasting workflow was built using TimeCopilot, tested on real airline passenger data and synthetic seasonal data with injected anomalies. The evaluation compared statistical, foundation, and GPU-based models using rolling cross-validation across various error metrics. The process generates probabilistic forecasts complete with prediction intervals and visualizes future trends while flagging unusual observations. Furthermore, the system incorporates an optional LLM agent capable of selecting a model and providing explanations for its predictions.
SpatialClaw is a novel, training-free agent designed for 3D spatial reasoning. It functions by writing Python code within a persistent kernel environment. This capability allows the agent to dynamically compose perception tools necessary for its reasoning tasks. Essentially, it treats writing and executing code as its primary interface for interacting with and understanding spatial data. The tool demonstrates advanced capabilities without requiring explicit training on specific datasets for novel scenarios.
VibeThinker-3B is a 3 billion parameter dense reasoning model derived from Qwen2.5-Coder-3B. It utilizes a specialized post-training technique called the Spectrum-to-Signal pipeline. The model's performance has been demonstrated to match other advanced models like DeepSeek V3.2 and Kimi K2.5 on verifiable benchmarks. This information was initially published on MarkTechPost.
Python 3.14 introduces a new Just-In-Time (JIT) compiler. A detailed technical overview accompanies this feature. The content also includes various performance benchmarks to illustrate the changes. These details were originally published on Towards Data Science. The core focus is explaining and testing the capabilities of the updated JIT technology.
Developing custom inference within NVIDIA DeepStream is beneficial for specific use cases. The method of achieving this involves building a custom GStreamer plugin. This technique allows developers to integrate unique processing steps directly into the DeepStream pipeline. Such customization extends the model's capabilities beyond standard components. Utilizing a custom plugin offers granular control over the inference workflow.
Comparing two document intelligence engines using the same 1974 scanned PDF reveals structural limitations in basic OCR tools. While EasyOCR successfully recovers raw text from the scan, it yields only a flat string of words. In contrast, Docling provides richer output by recovering not just text, but also identifiable sections and figures. This difference in output structure is significant for downstream applications requiring organized information. The comparison highlights that readily available free OCR methods offer word-level extraction rather than document understanding.
PCIe transfer latency is identified as a key bottleneck in agentic inference systems. Implementing a custom, device-resident vector search kernel mitigates this by keeping computations entirely on the GPU. This technique bypasses slower CPU involvement during the retrieval step of Retrieval-Augmented Generation (RAG). By doing so, it achieves deterministic microsecond tail latencies for the crucial retrieval phase. The work details building and applying such a CUDA kernel solution.
Researchers at MIT have developed an improved method for modeling the behavior of metal alloys. This novel approach excels by capturing subtle atomic-level patterns within the materials. By incorporating this detailed understanding, scientists can make more accurate predictions about various material properties. Such advancements are significant for materials science and engineering disciplines. These predictive models offer a better tool for alloy design than previous methods.
A guide was created to bridge the knowledge gap between supervised learning and reinforcement learning (RL). The proposed path structures learning sequentially, starting with intuition building. It then progresses through foundational concepts using resources like Sutton & Barto selectively. Following foundations are modern RL algorithms, each presented with a rationale for why it follows the preceding material. The author requests community feedback to improve the ordering or suggest missing topics in this resource.
A user has submitted a link detailing the process of building an artificial intelligence programmed to play the rhythm game osu!lazer. The content suggests that the project involves training machine learning models on gameplay data. Successfully developing such AI requires significant computational resources and expertise in reinforcement learning. The submission is presented on Reddit, indicating it is community-shared technical work. Further details regarding the methodology or results are available via the linked post.
The author questions whether all reward functions should necessarily result in a sigmoid-like outcome. When designing a reward function, the choice of optimization goal is significant for model performance. While aiming for the peak reward might maximize immediate return, it can lead to an unstable and non-robust model. Conversely, operating where the reward plateaus may yield a more reliable system overall. The discussion suggests balancing peak potential against steady reliability in reinforcement learning design.
Offers developers an update service allowing them to improve skills and stay current in approximately five minutes. Features content from sources like "The Coding Sloth," providing articles on various tech topics. Recent headlines include Cursor being reportedly bought for $60 billion, and guides on system design resources. Other listed articles cover dangerous AI models, project ideas for 2026, and the concept of tokenmaxxing. The newsletter is supported by a community exceeding 50,000 developers.
Casey Harrell, who has amyotrophic lateral sclerosis (ALS) and is paralyzed, utilizes a brain implant containing embedded electrodes. The research team developed a system that decodes electrical activity from his speech motor cortex into discernible speech. After implantation, which involved four arrays of 64 electrodes, Harrell gained the ability to speak using this technology. He has since become a highly independent user, performing tasks like browsing the web and working with minimal assistance. Initial reports indicate Harrell used the device for over 3,800 hours at home without researchers present within two years of implantation.
Physicists are expanding their search methods in the ongoing quest to understand dark matter composition. Developments noted include biotech trials featuring brain-computer interfaces and a new invasive chip approved by China. In AI, startups claim breakthroughs for large language models, while Anthropic’s Code with Claude signals a shift toward automated coding practices. Experts also address the practical challenges facing ambitious concepts like geoengineering alongside market analyses of AI's labor impact.
An autocomplete-style model like GPT-3 unexpectedly demonstrated correct answers on abstract problems typically found in IQ tests. Research published in *Nature* showed GPT-3 excelled at analogical reasoning tasks, scoring higher than some undergraduates. Later developments showed models like GPT-4 passing academic assessments, including parts of the U.S. Medical Licensing Examination. These abilities suggest proficiency across human cognitive skills like chain-of-thought reasoning and theory of mind. However, experts caution that current evaluation methods may create an illusion of intelligence, necessitating a major overhaul in testing methodologies.
An instructor at Duke University utilized an AI coding assistant named Copilot to teach his students a complex language called Rust, despite having only recently learned it himself. Copilot, built on OpenAI's GPT models, functions as turbocharged autocomplete by predicting code snippets based on keystrokes. The technology is revolutionizing programming education and development across the industry. Beyond GitHub's product, other generative tools like Meta's Code Llama and ChatGPT are emerging for coding assistance. These AI assistants are fundamentally changing how professionals perform traditional software tasks.
AI assistance significantly improves breast cancer screening accuracy when used with radiologists compared to standalone performance. In a study published in The Lancet Digital Health, an AI system was tested alongside human experts analyzing mammograms. When working together, the doctor and AI were 2.6% better at detecting cancer and generated fewer false alarms than a radiologist alone. This combined process successfully streamlined workflows by automatically setting aside about 63% of scans classified as "confident normal." These findings suggest AI tools could improve life-saving detection rates while easing specialist workload burdens.
Three widely used AI agent frameworks—LangGraph, Langflow, and LangChain-core—contain vulnerabilities like SQL injection, path traversal, and unsafe deserialization that can lead to Remote Code Execution (RCE). Specific flaws include an unsanitized SQLite checkpointer in LangGraph and path traversal in Langflow's file upload endpoint. These issues highlight a systemic risk where insecure defaults within imported frameworks expose critical secrets and credentials. The core problem is that established security tools fail to view the framework itself as a necessary boundary needing protection, treating it like standard code plumbing. Ultimately, the danger stems from shipping these complex integrations with unmitigated, convenient default settings.
Americans increasingly describe current customer service experiences using negative terms like "depressing" and "enraging." Hundreds of reader submissions detailed personal struggles with major corporations. These accounts highlighted the significant financial, time, and emotional costs imposed by businesses prioritizing profit over customers. A key finding revealed that the general public strongly dislikes artificial intelligence-driven customer support systems. Overall, consumers feel exploited by companies' pursuit of profits.
Autonomous AI agents using LLMs pose complex governance challenges requiring more than simple access controls due to their ability to manipulate data and coordinate actions. Existing policy engines, such as XACML and Rego, fail to address obligations, conflict resolution, or conditional dispensations. The authors propose AgenticRei, a system implementing key governance needs using a deontic policy language based on OWL. This framework is evaluated at runtime by an external logic engine, governing both agent tool use and inter-agent communication. Deontic policies thus capture comprehensive security and privacy constraints beyond the capability of current production engines.
Developed a human-in-the-loop pipeline to longitudinally measure how well an accredited Computer Science program covers external knowledge guidelines (CS2013 and CS2023). This system uses semantic retrieval for initial matches, confirmed by human judgment according to a defined coverage standard. The program showed near-constant coverage of 50% across both curricular versions over a decade. While competency articulation was strong at ~88% for covered units, the delivered cognitive depth lagged behind new guideline expectations (76% for CS2023 vs. 95% for CS2013). The framework successfully identifies persistent structural gaps separate from natural evolution in curriculum standards.
Diffusion Language Models (DLMs) offer an alternative to autoregressive LLM generation by using iterative denoising for text synthesis, allowing parallel refinement of sequences. Existing research presents challenges due to varied evaluation protocols and hyperparameters across different diffusion-based architectures. This work provides a systematic analysis, evaluating eight state-of-the-art DLMs on eight benchmarks spanning diverse tasks like coding and reasoning. The study explicitly considers both generation quality and computational efficiency by analyzing factors such as denoising steps and context length. Findings reveal that DLM performance is highly sensitive to generation design choices, necessitating trade-offs between capability and computational cost.
Multi-agent LLM deliberation improves reasoning by simulating human decision-making processes. The authors propose modeling this as a closed-loop dynamical system incorporating each agent's hidden internal belief, or "anchor." This anchor acts independently of neighborhood influences, which classical models overlook. By recovering the anchor solely from deliberation data, they explain behaviors forbidden by standard consensus rules, such as confidence exceeding the initial belief space. Testing the recovered anchor on unseen runs provides a generalizable metric for assessing if this inherent drive is truly responsible for the model's output.
DeFi presents supervisors with complex, fast-moving credit risks that general LLM agents struggle to manage accurately. Introducing DeXposure-Claw, a novel agentic supervision system designed for this environment. It integrates three stages: DeXposure-FM forecasts future exposure networks using graph time-series modeling. Deterministic monitors and stress scenarios translate these forecasts into structured alerts and evidence. Finally, data-health and confidence gates ensure auditable supervisory tickets are generated with clear rationales before escalation. The system is validated by DeXposure-Bench, an evaluation harness that scores decisions against regulator-aligned loss ground truth.
Researchers investigated whether Large Language Models (LLMs) can detect their own knowledge gaps when analyzing structured clinical data using cross-model attribution divergence. Findings show that the confidence scores generated by LLMs are often uninformative, remaining near constant regardless of prediction accuracy. Furthermore, the models displayed an inverse difficulty effect, performing worse when compared to highly accurate benchmarks like XGBoost. Interventions such as few-shot examples and SHAP feature evidence significantly reduced model uncertainty without requiring retraining. Finally, a novel cross-model calibrator utilizing attribution divergence successfully replaced misleading verbalized confidence with reliable patient-specific certainty estimates.
Retinal imaging offers a noninvasive method for predicting Alzheimer's disease (AD) risk by aligning visual and language data. Previous models use discrete phenotypic groupings during contrastive learning, imposing rigid constraints on similarity. This work introduces a continuous formulation, treating inter-subject similarity as a differentiable weighting function derived from embedding similarities. These soft weights define graded, multi-positive relationships through continuous aggregation, enabling nuanced supervision. Evaluating on UK Biobank data, the proposed framework outperforms discrete methods by modeling phenotypic similarity as a learnable signal for robust risk prediction.
Researchers developed an "conscience step" that allows Large Language Models (LLMs) to review and self-correct their reasoning and outputs. They extended the training loss using Direct Preference Optimization (DPO) with an alignment component to guide models away from unethical content. This online technique is applicable across various scenarios, including fine-tuning, adversarial prompting, and zero-shot learning. Uniquely, it operates without needing a separate judge, using instead a frozen copy of the model itself for evaluation. By implementing this method, researchers demonstrated how an emergent ethical alignment can be achieved by directing training with a single high-level introspective question during code hacking scenarios.
Introduces ITNet, an Integral Transform Network unifying diverse architectures like convolutional networks, recurrent models, and transformers. This framework postulates that these distinct methods stem from incomplete views of a singular underlying mathematical object: a learnable integral transform. ITNet utilizes a jointly position-and-feature dependent kernel, modeled by an MLP, to capture pairwise interactions. The model demonstrates that convolution, self-attention, and various recurrence types emerge as special cases under specific parameter settings. Through efficient computational techniques, a single ITNet architecture matches or surpasses specialized state-of-the-art models across multiple benchmarks (ImageNet-1K, GLUE, etc.).
Traditional uncertainty frameworks are insufficient for interactive LLM agents needing proactive clarification seeking. Given deployment constraints like black-box APIs, the approach proposes a simple prompt-based decomposition separating action confidence from request uncertainty ($\text{u}$). This enables agents to autonomously seek clarification when task specifications are ambiguous. Evaluating this method on two new clarification benchmarks (WebShop-Clarification and ALFWorld-Clarification) showed significant gains. Specifically, the proposed decomposition improved clarification F1 on ALFWorld-Clarification by 73% over ReAct+UE across five different LLM backbones.