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Agentic AI is becoming a new shopping gatekeeper, yet customers still trust retailer-owned agents more than outside platforms. Retailers that strengthen their onsite experience and protect data and fulfillment will stay relevant as AI increasingly intermediates discovery and purchasing.
Companies overuse projects and programs because they assume governance demands them, even though it does not. Modern product and platform work needs governance built around durable units, clear accountability, and adaptive risk management rather than rigid, procedural controls.
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Some coworkers follow predictable patterns. Your greatest advantage is controlling your own reactions so you respond with clarity instead of impulse.
Lucas Werthein built an AI health coach that unifies all his health and performance data to deliver insights no single expert could provide. Mixing different AI tools can produce apps that feel genuinely handcrafted instead of generic.
Technology has narrowed the gap between city and suburban living, making the suburbs far more convenient. As robotaxis grow, they could create a larger and safer transportation-and-delivery market that may ultimately outgrow or even sideline Uber.
OpenAI needs a credible route to roughly $500B in annual revenue to support a future $1T valuation. Subscriptions alone do not suffice. Reaching that scale in five years seems improbable even with ads and commerce layered on top. This puts real pressure on disciplined focus and execution.
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This whitepaper explains the importance of lexical data, and how grounding AI with accountable sources like dictionaries improves
A second renaissance is coming as people tire of AI noise and return to intentional craft.
Creating something new is hard, and the weight of it often sits with you alone.
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Bugs sneak out when less than 80% of user flows are tested before shipping. However, getting that kind of coverage (and staying there) is hard and pricey for any team.
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iOS 27 will be focused on improving software quality and underlying performance. Many users have reported bugs in iOS 26, including device overheating, unexplained battery drain, user interface glitches, keyboard failures, cellular connectivity issues, app crashes, and sluggishness during system navigation and animations. iOS 27 will also lay the foundations for foldable iPhones and other new hardware. In general, Apple's operating systems next year will revolve mostly around performance and AI.
Google aims to be able to deliver over a thousand times more capability, compute, and storage for essentially the same cost, power, and energy level in the next five years. This will require it to double its serving capacity every six months. Google, Microsoft, Amazon, and Meta are now expected to collectively spend more than $380 billion on infrastructure this year. Besides infrastructure build-outs, Google plans to bolster its capacity with more efficient models and custom silicon.
Evo is a genomic language model developed using an enormous collection of bacterial genomes. It can generate novel sequences from prompts. Evo can produce outputs that include regions that encode proteins with related functions when prompted with a gene. It appears to be capable of outputting entirely new yet functional proteins.
Smells are processed in the olfactory bulb, which is tucked behind the top of the nose. Scientists have managed to use ultrasound targeted at the olfactory bulb to induce the sensation of fresh air and ozone and the smell of garbage and burning wood. Smells are strong and localized to the nose, while sensations are more diffuse, like a light tingling on the face. The scientists plan to improve their technique to significantly increase the bit rate of olfactory stimulation.
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In the agentic era, outdated retrieval breaks workflows. This API Benchmark Report from You.com [14] shows how each major search API performs to reveal which can best answer real-world, time-sensitive queries.
Once a quarter, the team at Google working on Perfetto, an open source suite of tools for performance tracing and analysis, stops all regular work for a week and fixes the small things that have been annoying them or their users. The most recent 'fixit', in which 40 people participated, resolved 189 bugs. Fixits are a great chance to work on the details that separate good products from great ones. They increase team morale and spirit and create real momentum. This post contains a guide on how to run a fixit.
kit is a toolkit for codebase mapping, symbol extraction, code search, and building LLM-powered developer tools, agents, and workflows. It can build things like code reviewers, code generators, and IDEs. kit can be used directly with Python, MCP with function calling, REST, or through the command-line. kit ships with CLI-based pull request review and summarization features. It can generate intelligent commit messages from staged changes.
Next Spring's graduate-hiring market is likely going to be even worse than this year's. This is the most pessimistic outlook since the first year of the pandemic. Companies say that an uncertain economic outlook has pushed them to hire more conservatively, and many are giving priority to prospects with experience rather than fresh graduates. People with experience are currently willing to take entry-level positions because they can't find anything else, pushing out those with less training.
Your job will always want more from you. However, you only have so much time for everything in life. You don't have to quit your job, but you should add more to life - like hobbies, small adventures, and acts of service. Doing this allows work to stop being the whole story. Work also becomes more relaxing because you know you have other things to live for.
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From DevEx metrics to workflow automation, leaders will get the playbooks — and builders will get the hands-on techniques to execute them.
A ruling on whether Google will be forced to break up its business is likely to come next year.
OpenAI is best understood as a high-growth, high-burn AI platform making a very large, very leveraged bet that it will be the default interface and infrastructure layer for intelligent software.
The layoffs impacted a mix of software engineer levels, but SDE II roles, or mid-level employees, were disproportionately affected.
Being able to work on multiple branches simultaneously is a game-changer.
Microsoft doesn't really feel like a consumer company anymore, and competitors are ready to fill in the gap.
Styleframe's powerful TypeScript CSS API helps developers compose design systems in minutes.
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Six out of ten security teams are posturing, not protecting — and about the same proportion say AI threats outpace expertise. Yet according to Vanta's newest State of Trust [4] report, teams spend way MORE TIME AND ENERGY PROVING TRUST than building it.
Google DeepMind has hired Boston Dynamics' former chief technology officer, Aaron Saunders, as its VP of hardware engineering. Saunders is a key part of DeepMind CEO Demis Hassabis' vision for Gemini to become a sort of robot operating system. Hassabis is aiming to build an AI system that can work almost out-of-the-box across any body configuration. Boston Dynamics is famous for developing legged robots and humanoid machines capable of impressive acrobatic feats.
Gemini's Dynamic view option takes text-based answers and wraps them in an interactive, visual output. The product is still in Labs and has yet to be launched. Despite the bland name, Dynamic view produces some impressive results that are a bit hard to describe. Examples of the outputs the feature can produce are available in the article.
The New York Times revealed OpenAI's internal struggle between user engagement and safety after the company overruled its Model Behavior team's warnings to release a sycophantic April update to GPT-4o that made users return more frequently. The company now faces five wrongful death lawsuits and declared a "Code Orange" in October after discovering its safer GPT-5 model was losing users, with executives calling it "the greatest competitive pressure we've ever seen."
In a 'deep' world, there is a single underlying ability that governs how well models do at superficially unrelated tasks. If a model developer makes this ability go up, their model gets better at everything. In a 'contingent' world, there are many orthogonal abilities that models can have, so model developers have to do completely unrelated work to get a model to improve on each ability. Anthropic has focused on making models that are state-of-the-art at agentic coding, but this hasn't resulted in models that are exceptional in other areas. There is some generalization across tasks, but this is limited, suggesting that models live in a 'contingent' world.
Large language models (LLMs) are neural networks built on the transformer architecture. Transformers analyze entire sequences in parallel, evaluating how each word relates to the rest of the sequence, not just its neighboring words. This article discusses LLM inference and details how these models work. It covers token embeddings, the transformer architecture, the inference phases, matrix multiplication, precision and quantization, and much more.
A practical guide to evaluating LLM-powered products that covers how to label data, align evaluators, and iterate on configuration changes with minimal overhead.
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The MCP Apps Extension (SEP-1865) standardizes support for interactive user interfaces in the Model Context Protocol. It addresses one of the most requested features from the MCP community: the ability for MCP servers to deliver interactive user interfaces to hosts. The extension introduces a standardized pattern for declaring UI resources, linking them to tools, and enabling bidirectional communication between embedded interfaces and the host application.
Building agents is still messy. Abstractions break once you hit real tool use. Caching works better when self-managed. Reinforcement does more heavy lifting than expected. Output tooling is surprisingly tricky. Model choice still depends on the task.
Sebastian Raschka added a standalone notebook implementing Allen AI's OLMo 3 model architecture from scratch to his "LLMs from Scratch" repository, joining similar tutorials for Qwen 3 and Gemma 3.
Nvidia's greatest moat lies in having handled a lot of the 'dirty work' cleanly within its entire architecture and combining full-stack capabilities from algorithms to systems to chips. It also had excellent timing in bringing architectures to market and great marketing execution. However, every architecture has its trade-offs and shortcomings. This post looks at some of the issues within Nvidia's products and discusses potential evolutionary directions.
Large language models think very differently from animals. The biggest difference is the optimization pressures/objectives that cause evolution. People who build a good internal model of this new intelligent entity will be better equipped to reason about it and make predictions about it in the future.
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Metronome's latest capabilities remove bottlenecks between product and revenue—unifying invoicing across PLG, sales, and marketplaces, and embedding trust-building billing into the product experience.
LLM Council is a vibe-coded local web app that queries multiple frontier models simultaneously, then has each model anonymously rank the others' responses before a "Chairman" LLM synthesizes a final answer.