Knowledge catalogue
arXiv:2604.20370v1 Announce Type: new Abstract: Forecasting the life-cycle trajectory of a newly launched product is important for launch planning, resource allocation, and early risk assessment. This
arXiv:2604.12162v1 Announce Type: new Abstract: The rapid deployment of AI agents in commercial settings has outpaced the development of evaluation methodologies that reflect production realities. Exi
You ship an agent. It mostly works. Then the feedback loop becomes a support ticket: a user got the wrong answer, a tool call looped four times, a refund went out that shouldn’t have. Standard observa
arXiv:2610.10957v1 Announce Type: cross Abstract: Production AI systems deployed in high-stakes domains accumulate a governance liability that existing monitoring frameworks fail to detect: the progre
I spent the past year independently building Slow Vale, an LLM-driven life simulation. The Chinese server now has 800+ AI residents sharing one continuously running city. This is an engineering write-
i kinda wish dots was a standalone app and not part of chatgpt. as it stands, it feels like a product feature (e.g. GPTs) and not a big, exciting new release. i might understand why they did it though
Intelligent data infrastructure is becoming critical as enterprises move artificial intelligence from curated pilots into production. NetApp Inc. is positioning its Novus architecture for that transit
arXiv:2609.31641v1 Announce Type: new Abstract: Scalar rounding decisions interact through matrix multiplication. We study deterministic product-aware rounding after scales, clipping bounds, and grids
Help: Project l'm building a shelf audit tool. A photo goes through YOLO, which crops each product, and then I embed the crop and search a small gallery of reference photos to get the SKU. New product
arXiv:2609.21117v1 Announce Type: cross Abstract: AI productivity is often measured by task completion time, economic value, or improvements in outcome quality. However, these measures usually treat c
arXiv:2609.21267v1 Announce Type: new Abstract: Production LLM agents are evaluated repeatedly as they evolve, but full agent benchmarks are costly to rerun. We study efficient recurring evaluation fo
Harness engineering is a top skill right now This new Meta paper is a good production example. Auto-RecSys runs autonomous research on Meta's industry-scale recommendation models, where one training r
arXiv:2508.04125v2 Announce Type: replace-cross Abstract: The application of Large Language Models (LLMs) is growing in the productive completion of Software Engineering tasks. Yet, studies investigat
arXiv:2609.01660v1 Announce Type: cross Abstract: Production deployments of large language model (LLM) agents remain unreliable on long, multi-step workflows even as benchmark success rates climb stea
This is very true, and something we still try to do for all products (and even features) we launch. Find a small group of customers who we think will push us the most on product and then build for the
arXiv:2510.09347v2 Announce Type: replace Abstract: Unlike Business-to-Consumer e-commerce platforms (e.g., Amazon), inexperienced individual sellers on Consumer-to-Consumer platforms (e.g., eBay) oft
Mike Wheatley / SiliconANGLE: Nvidia says its inference accelerator Groq 3 LPX has entered full production and Nebius has signed on as the first customer, and SpaceX will deploy Vera CPUs — Chipmaker
arXiv:2608.16626v1 Announce Type: new Abstract: Radio frequency identification (RFID) technology has been widely implemented for real-time data collection in manufacturing shop floors, which, in turn,
LangSmith’s new Tuned Evaluators look pretty interesting. Perceived Error can flag agent mistakes in production by picking up on user corrections and unresolved conversations. What’s interesting is it
the real unlock is not cheaper evaluation it is making specialized evaluators cheap enough to run continuously on production traces so evaluation stops being a launch checklist and becomes a permanent
@hwchase17 @LangChain I built an open-source CLI that scaffolds, runs, and deploys production LangChain agents — frontend and backend in one command. Every agent project starts the same way: wire up a
arXiv:2608.10010v1 Announce Type: new Abstract: Low-precision datatypes reduce language-model cost, but most formats optimize scalar fidelity while leaving the arithmetic induced by their products unc
arXiv:2608.08395v1 Announce Type: cross Abstract: Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate product discovery to AI agents. We compare
arXiv:2607.24532v1 Announce Type: new Abstract: Recent years have seen a rapid expansion in the production of large-scale geospatial maps derived from Earth observation (EO) data, driven largely by ad
Elon Musk shared observations from visiting Tesla's Optimus humanoid robot production line at the Fremont factory, likely documenting the manufacturing process, production capabilities, or recent deve
Elon Musk emphasizes that a company's product itself should embody and reflect its core mission, rather than the mission being separate from the product offering. This concept suggests that business s
arXiv:2605.08112v1 Announce Type: cross Abstract: AI coding agents powered by large language models can read codebases and produce functional code, but they routinely violate team-specific product dec
arXiv:2604.01527v3 Announce Type: replace-cross Abstract: Production deployment of AI coding agents requires fast, reproducible evaluation signals. Existing industrial practices trade off speed and fi
The agricultural and crop protection supply chain is one of the most intricate networks in the world. It takes up to two years to turn active ingredients into the final products farmers need, and a si
Building AI agents that work well in a demo is one thing, but running them in production requires serious infrastructure. At Google Cloud Next '26, we introduced Gemini Enterprise Agent Platform to he
arXiv:2604.28049v1 Announce Type: new Abstract: Text-to-SQL (T2SQL) evaluation in production environments poses fundamental challenges that existing benchmarks do not address. Current evaluation metho
first up in the 'going to production' series: durable execution! long running agents need to be able to 1. survive crashes 2. resume after an indefinite pause durable execution solves this by checkpoi
LangChain Community Spotlight: Distributed LangGraph Architecture with RemoteGraph 🔗 Production-ready example for distributed LangGraph apps with HTTP-invoked subgraphs. Key insight: RemoteGraph retur
Probably the most useful advice I can give you if you run an ad agency, a marketing company, a production studio, or anything in that space. Hear me out. I’ve spent a decade talking to teams about thi
We are officially moving past the era of one-size-fits-all AI. Enterprises today require highly specialized, role-specific tools to drive real productivity — but they cannot afford to sacrifice securi
This guide addresses the infrastructure and operational requirements for deploying long-running AI agents in production environments. It likely covers deployment strategies, monitoring, scaling, and b
we want to help you navigate all the stuff that comes with shipping an agent to production - so we wrote a guide! this guide packages learnings across our + customer deployments, building open source
arXiv:2604.06451v1 Announce Type: new Abstract: Manufacturing test flows in high-volume electronics production are typically fixed during product development and executed unchanged on every unit, even
ServiceNow Inc. today announced a sweeping overhaul of its entire product lineup, saying that every single one of its services, platforms and products has been “AI-enabled” to enhance agentic automati
the real future of the very best vertical products is Model/Harness Choice + Openness easy to deploy infra is nice (great release from Ant) but it’s not the lever that matters the most at all to build
Keigo Yoshida / Nikkei Asia: Toshiba plans to double HDD production capacity for AI data centers within FY2027 from its 2025 level, targeting a 30% share by storage capacity, up from ~10% — MANILA — J
Anthropic's Claude hillclimb loop designs evals and improves agents one change at a time. Here's how to run that loop against production traces in Arize AX. The post Claude’s hillclimb loop for AI age
arXiv:2609.33289v2 Announce Type: replace Abstract: Autonomous large language model (LLM) agents operating in multi-product markets must make sequential decisions under information asymmetry and resou
arXiv:2609.23769v1 Announce Type: new Abstract: The disambiguation of semantically similar statutory text across jurisdictions is a retrieval problem that existing methods do not solve. This inter-con
arXiv:2609.23427v1 Announce Type: new Abstract: Vision foundation models targeting Earth observation (EO) tasks are commonly evaluated on clean downstream benchmarks, but operational EO products can a
We're excited to release the State of Generative Media Report, Volume 2. Generative media has moved from experimentation into production and that shift looks different across every industry. Built fro
Hi all! i am currently working as a software engineer for a pretty big enterprise fintech company here in the states. In the the last 12 months at my job there has been a huge push for developers to u
arXiv:2609.06410v1 Announce Type: cross Abstract: Existing approaches to visual attribute value extraction (AVE) primarily rely on static product images, failing to capture temporal cues, multi-angle
arXiv:2606.20419v2 Announce Type: replace Abstract: Vision-language models (VLMs) often generate fluent but visually unsupported descriptions, especially by mentioning objects absent from the image. W
arXiv:2608.24132v1 Announce Type: cross Abstract: Product catalogs in fast-moving service businesses are shifting from static, independently priced SKUs toward dynamically bundled, discount-coupled of
This summer, we brought in two first-year Waterloo interns (‘rising sophomores’ in 🇺🇸) and tasked them with shipping complex production features that help push the frontier of our document processing
arXiv:2608.18543v1 Announce Type: new Abstract: Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities for proactive c
arXiv:2608.07398v1 Announce Type: cross Abstract: Production planning in the manufacturing industry often relies on the use of optimization models, but defining an appropriate objective function can b
arXiv:2511.12449v3 Announce Type: replace Abstract: Recent Multimodal Large Language Models (MLLMs) have significantly advanced e-commerce product understanding. However, they still face three challen
A new model launch is not a product update ‼ It's one of three things, and you don't know which until you test it. Sometimes it's nothing: the model improved inside the same distribution, your harness
// Agentic Context Management // Great read for the weekend. (bookmark it) Production agents fail less on reasoning and more on what sits in their context. Conversation history, big prompts, huge tool
The Washington Post processed 1.79B input tokens per month through Together AI, running open models like Llama and Mistral in production with predictable costs and full control over the model stack. T
arXiv:2607.19766v1 Announce Type: new Abstract: Geo-spatial rasters of building footprint area are useful for a variety of tasks, such as monitoring urbanization, improving energy efficiency, and trac
OpenAI announced a 5.6 model release alongside three major product launches: ChatGPT Work (an enterprise offering), a new ChatGPT desktop application, and an unspecified third product feature. The ann
One of our creative technologists @hellorob just ran a full production pipeline without touching a single node - using Comfy MCP + Claude Fable 5. Production pipeline: → Shot pulled from web → scene d