ALL YOU CAN DO IS PLAY THE GAME
“I don't know” can mean everything from apathy to insight. In a world shaped by unpredictable AI breakthroughs, the only reliable path forward is to build and learn as you go.
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“I don't know” can mean everything from apathy to insight. In a world shaped by unpredictable AI breakthroughs, the only reliable path forward is to build and learn as you go.
Oversimplification ignores reality, but complexity without action does not help anyone. Progress comes from acting, prototyping, and engaging while staying open to what the system reveals.
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Customers want to know what things cost ahead of time - not when invoices land. Increase transparency and build trust with Metronome's new cost preview API [9] - showing estimates before users take action. Also announced: an embeddable billing console for self-service invoice management + proactive alerts at critical contract moments. Read more.
AI agents only work when they sit inside real workflows and are powered by trusted data. Used this way, they can massively improve enrichment, personalization, and conversion.
AI prototyping turns PMs into rapid experimenters who can validate ideas fast and reduce wasted work. Mastering the workflow and the tools is quickly becoming a core competitive skill for 2025.
AI excels at tasks that can be verified through repeatable feedback loops, which explains its rapid progress on structured problems and slower gains on creative or contextual ones.
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Quadratic gives PMs fast, trustworthy insights with direct database access, AI-generated SQL, and live dashboards that stay in sync as your product evolves – all within a familiar spreadsheet interface.
When you are uncertain, take action anyway, because even the wrong move creates the information you need to make the right one next.
In consumer software, rapid shipping matters most because constant releases keep you close to users, help you learn what they actually want, and show you where to double down.
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Low test coverage leaves critical flows untested and slows your release velocity. Even with AI-assisted tools, teams can still struggle to reach high coverage and maintain tests as products evolve.
Apple's board and senior executives have recently intensified the company's succession planning for its CEO role. John Ternus is the most likely to take the position, but no final decisions have been made. Apple is unlikely to name a new CEO before its next earnings report in late January. The transition to a new CEO is reportedly not related to the company's current performance.
OpenAI has launched a limited test of its chat app in Japan, New Zealand, South Korea, and Taiwan. It will allow people to chat with AI and other people within the same environment. Group chats are separate from private conversations - personal ChatGPT memory will not be used in group chats, and ChatGPT will not create memories from group chat conversations. ChatGPT will automatically reduce exposure to sensitive content for everyone in the chat if any of the members is under 18.
Researchers at Stockholm University have successfully sequenced the RNA of a young woolly mammoth discovered in 2010. This allowed them to explore new information, such as which genes were active, or whether it was infected with RNA-based viruses, when it died. The ability to recover RNA gives scientists another tool to reconstruct and validate ancient genomes. It could be used to explore how gene expression differs between extinct and living species or even between individuals of the same species.
NASA's ESCAPADE mission, which launched on Thursday towards Mars, will collect data to provide clues about why the planet became cold, dry, and almost airless. The mission was initially rejected when it was proposed, and then it was only approved because of a federal government shutdown in 2018. The mission was delayed several times, but the lessons learned along the way could help NASA get more bang for its buck for future missions. The trajectory the mission is pioneering allows launches to be made outside of tight launch windows.
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Ready for an actually relaxing holiday? Pass the testing to Meticulous. [12] It observes your app and automatically build a continuously evolving suite of E2E UI tests, while you load up on stuffing. The only flakes you'll see this Thanksgiving will be falling off the crust of that sweet pumpkin pie. Book a call [12] and make it happen.
This document looks at how to create a graphical UI framework that considers developer adoption, performance, display effects, and power consumption. The most popular frameworks solve real problems. It is important to ground design in real projects with real customers, be open to new ideas, and be ready to discard inventions to go where the data leads. The most difficult part of designing a product is balancing a coherent vision with flexibility in the face of new information.
The era of small, low-value libraries is over. AI can output functions from these libraries, so developers no longer have to take on extra dependencies with unknown performance, maintenance, and supply-chain risks just for basic functionalities. There is still plenty of open source to write. The most value can be found in big projects, more inventive projects, or niche areas not covered in large language model training data.
Elon Musk recently attended an interview where he discussed his vision for artificial intelligence, robotics, sustainable development, and the future of humanity. He revealed details about Grok 5, a 6-trillion parameter model which will be released in the first quarter of next year. Musk also talked about building a giant wafer factory within the next couple of years to create tens of billions of AI chips. This article contains excerpts from the interview.
Meta plans to tie employee performance to their 'AI-driven impact' starting next year. The company will assess employees on how they use AI to deliver results and build tools that can move the needle in a major way in regards to productivity. Meta is rolling out an 'AI Performance Assistant' to help with performance reviews and feedback to use in this year's performance review cycle. Employees can also use Meta's internal assistant, Metamate, as well as Google Gemini, for their performance content.
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Cutting costs may look good on the balance sheet but there's often a hidden price to pay. Learn how to do consolidation your tech stack the
We need to redesign systems so AI boosts signal instead of burying it.
RegreSQL implements regression testing facilities for SQL queries in PostgreSQL database systems.
Nvidia plans to sell fully assembled Level-10 VR200 compute trays with all compute hardware, cooling systems, and interfaces pre-installed starting next year.
In the age of AI, a programming language's popularity is a feedback loop that makes it more and more popular.
Generative AI is not capable of creating figurative 'gold' because gold is rare, and that rarity is exactly what makes it valuable.
A list of numbers in computing and networking that developers should know.
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Trusted by OpenAI, NVIDIA, and Anthropic, Metronome is the infrastructure for modern monetization. As part of a week's worth of launches, they're announcing a host of new capabilities - including three new ways to support flexible and hybrid pricing models [4]:
Google plans to launch Gemini 3 and Nano Banana Pro next week. The company has consistently updated its Gemini models across its suite simultaneously, and the same broad rollout is expected again this time. The Pro branding suggests that Google plans to provide accessible and production-grade generative tools across its platform ecosystem.
GPT-5.1 is now rolling out to users. It features 8 unique chat styles, making it easier for users to set the tone and style that feels right for them. This Reddit Ask Me Anything features members from OpenAI's team.
FutureHouse (which is transitioning to commercial spinout Edison Scientific) released an AI scientist that reads 1,500 papers and runs 42,000 lines of analysis code per run. Beta users estimate that a single Kosmos run accomplishes what would take them six months, with 79.4% of conclusions accurate, though the system sometimes pursues statistically significant but scientifically irrelevant findings. It's already made seven discoveries—three reproduced unpublished findings and four contributed novel insights, including one related to Alzheimer's that was validated in human tissue. Kosmos is launching at $200 per run with a generous free tier for academics.
Most modern language models require massive computational resources and months of training time. This post discusses how to achieve the optimal recipe for creating efficient pre-training data sets to achieve high performance while using dramatically less data. It uncovers critical insights about dataset mixing strategy, failure modes in curriculum testing, and the 'goldilocks zone' for synthetic content. The resulting mixture consistently outperforms complex curriculum strategies and avoids catastrophic failures while maintaining excellent generalization.
A race is a competition with clear boundaries and a clearly defined finish line. This isn't the case with the AI 'race' between the US and China. Nobody currently knows where AI is headed. The field is still being explored. The US and China are taking different strategies that play to their unique strengths. They are locked in a structural conflict, and harmony appears impossible.
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Most teams ask "Which tool is best?" The better question: "Which approach gets us from idea to collaboration fastest?" Miro's AI prototyping landscape guide [10] provides a requirement question set, decision framework, and evaluation scorecard so you can pick the tools
Structured outputs are now available in public beta for Claude Sonnet 4.5 and Opus 4.1. Support for Haiku 4.5 is coming soon. Without structured outputs, Claude may generate malformed JSON responses or invalidate tool inputs, breaking applications, even with careful prompting. Structured outputs constrain Claude's responses so they follow a specific schema. This ensures valid, parsable output for downstream processing.
The context window is the entire input that a large language model receives when generating output. It contains messages, the model's replies, tool calls, and the thinking blocks the model outputs to 'reason'. Everything that happens between the user and the model goes into the context window. This article walks users through some of the ways Amp gives users to manage the contents in a context window.
Depth Anything 3 (DA3) estimates spatially consistent depth from single or multi-view images, regardless of camera pose availability. It relies on a plain transformer backbone and a single depth-ray representation to simplify architecture and training.
GAD introduces a new approach for distilling knowledge from black-box LLMs using only generated text, without access to output probabilities. It trains a student model through adversarial learning against a discriminator, enabling on-policy imitation.
AI allows us to write programs by specifying objectives. If a task is verifiable, then a neural net can be trained to do it extremely well. The more a task is verifiable, the more it is amenable to automation using AI.
Microsoft co-designed its new AI superfactory with OpenAI and Nvidia, integrating three generations of frontier training systems and adding AMD for GPT inference to broaden the hardware stack. The company aims to scale this infrastructure so enterprises can train and run their own models without ceding value or control to major AI vendors. The biggest impact will come when industries like pharma, manufacturing, and education use AI to compress timelines and expand output, creating positive-sum economic gains.
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Cisco's AI Networking webinar [17] reveals how to build lossless, scalable Ethernet fabrics for optimal GPU utilization, lower latency, and simpler operations —Register now [17] and power up your AI data center!
Yann LeCun may soon be leaving Meta to pursue a startup focused on world models because he believes that large language models are a dead end in the pursuit of superintelligence.
Sakana AI raised $132 million from investors including MUFG, Santander, and US venture firms, pushing its valuation to approximately $2.6 billion, which is the record for an unlisted Japanese startup.
Google Maps introduced AI tools, including a builder agent and an MCP server, enabling developers to create interactive projects using Maps data.