rows.com
rows.com on Beyond Market Intelligence: a running collection of 827 stories we have gathered and hand-picked because they are worth your time. Every post here touches on rows.com in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around rows.com, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
TabFM Studio: point-and-click predictions on spreadsheets with tabular foundation models, fully local [P]
Unlock predictive power directly within your spreadsheets with TabFM Studio, a user-friendly web app enabling point-and-click predictions using tabular foundation models—and it runs fully local. Simply upload a CSV or Excel file, designate the column to predict, and generate forecasts within the grid itself. This accessible tool, built around Google’s TabFM, empowers non-programmers to leverage advanced AI. As explored in our recent article, "The qlora 2e-4 default is wrong under 10k samples," understanding data size is key to effective AI deployment.
Automatically Assign a Category to Uncategorized Rows in Power Query and DAX
Categorized data is foundational for effective reporting and analysis; uncategorized rows hinder grouping and aggregation. When faced with data lacking assigned categories, establishing rules for assignment becomes essential. This post explores a practical solution for automatically assigning categories to uncategorized rows, demonstrated through a facility management project using Power Query and DAX. Discover how this approach unlocks deeper insights from your data. For further exploration of related techniques, see "TabFM Studio" and its application to spreadsheet predictions.
The qlora 2e-4 default is wrong under 10k samples and nobody talks about it [D]
Fine-tuning QLoRA models on smaller datasets—less than 10,000 samples—often leads to unexpected results. The pervasive default learning rate of 2e-4, widely promoted across tutorials and documentation, can actually trigger overfitting. Extensive experimentation reveals that a starting learning rate of 1e-4 or lower, combined with increased epochs, consistently yields significantly improved evaluation metrics. This adjustment, easily implemented, can save practitioners considerable time and frustration, as detailed in a recent discussion about ECCV expenses.
Amazon AGI director says AI agent reliability, not capability, is blocking enterprise deployment at VB Transform 2026
Amazon AGI director Bryan Silverthorn identifies a critical obstacle to enterprise AI agent deployment: reliability, not simply capability. Addressing VentureBeat's Transform 2026 audience, Silverthorn highlighted a concerning trend—85% of enterprises pilot AI agents, yet only 5% reach production. He proposes a framework of consistency, robustness, predictability, and safety to measure agent performance, noting that many agents excel in internal evaluations but falter in real-world use. Ultimately, successful deployment hinges on strong management practices, not just advanced models.
Amazon AGI director says AI agent reliability, not capability, is blocking enterprise deployment at VB Transform 2026
Amazon’s Bryan Silverthorn, Director of AGI Autonomy, recently pinpointed a critical obstacle hindering enterprise AI agent deployment: reliability, not inherent capability. Addressing attendees at VB Transform 2026, Silverthorn highlighted a concerning trend – 85% of enterprises pilot AI agents, yet only 5% reach production. His framework, emphasizing consistency, robustness, predictability, and safety, underscores the need for rigorous measurement, echoing findings that many agents fail after initial evaluations.
OpenAI introduces ChatGPT Work, a cloud-based AI agent that manages tasks across email, Slack and calendars
OpenAI introduces ChatGPT Work, a cloud-based AI agent poised to transform how professionals leverage AI. Embedded within the flagship chatbot, this new platform moves beyond simple Q&A, autonomously managing tasks across email, Slack, and calendars using the advanced GPT-5.6 model. ChatGPT Work streamlines workflows by generating documents, spreadsheets, and even websites, demonstrating OpenAI's commitment to democratizing agentic AI capabilities – a strategy highlighted by their recent confidential SEC filing.
Google's TabFM skips per-dataset training and still predicts on tables it's never seen
Google Research’s TabFM offers a transformative approach to tabular data prediction, bypassing the traditional need for per-dataset training. This innovative foundation model treats tabular prediction as an in-context learning problem, enabling instant predictions on unseen tables with a single API call – a significant acceleration for enterprise developers. By synthesizing strengths from prior architectures, TabFM preserves data structure and unlocks scalable zero-shot prediction, potentially redefining data workflows.
AlloyDB Ships Proxy Models That Replace LLM Calls with Local Inference Inside the Database
Google's AlloyDB AI functions are now generally available, introducing a transformative approach to AI-powered data management. This innovation utilizes proxy models—lightweight, locally trained versions of large language models—to execute queries directly within the database, eliminating external calls and dramatically accelerating performance. Early internal testing demonstrates a remarkable 2,400x throughput improvement, achieving speeds up to 100,000 rows per second. For further insights into real-time systems, explore our related article, "Beat-Aligned Mobile Audio Streaming with Virtual Chunks and Native Playback."
Meet Kirki: WordPress’s First Visual Builder With An Infinite Canvas
For years, WordPress website design has been constrained by rigid templates. Now, meet Kirki, the first freeform visual builder offering an infinite canvas. This innovative tool redefines the WordPress experience with cleaner performance and unparalleled design freedom—all without plugin dependency. Discover a more intuitive workflow and unlock your creative potential. Explore Kirki and elevate your website building. For deeper insights into scaling complex systems, see our article on "How HubSpot Scaled Semantic Search to 20 Billion Vectors."
CPU TTS benchmark with UTMOS MOS scoring: Kokoro, Supertonic, Inflect-Nano, and Kyutai's new Pocket TTS [P]
Evaluating small text-to-speech (TTS) models requires careful benchmarking, and we’ve compiled a CPU-based assessment of Kokoro, Supertonic, Inflect-Nano, and Kyutai’s Pocket TTS. Utilizing UTMOS MOS scoring across 180 runs on an Intel Xeon platform, our findings reveal interesting performance nuances, particularly regarding Pocket TTS's consistent RTF scaling and the limitations of UTMOS in assessing smaller vocoders.
Changing cells that previously required exact match, but now it is enough if a cell just contains the text
To accommodate users belonging to multiple regions, your existing formula requires modification. Given your preference for a single "Region" column with delimited values (e.g., "Hawaii and Okinawa"), adjust the formula in column V to: `=IF(AND(ISNUMBER(SEARCH(V$36,C39)),NOT(V$36="")),"Yes","")`. This utilizes the `SEARCH` function, which returns a number if the text in V$36 is found within C39, enabling partial matches. For the "Region 2" column approach, a similar formula can be applied to the new column, referencing the appropriate region field. See
how to flag cell with conditional formatting, if one criteria is false out of a batch of true/false?
To efficiently identify "ending" rows in your spreadsheet where preceding tasks remain incomplete, leverage conditional formatting. Specifically, flag the "ending" row if *any* corresponding month’s tasks have a FALSE value in column I. This can be achieved with a formula examining column I for TRUE values within the relevant date range. Consider a helper column (column J) for clarity, though it's possible to maintain your existing formulas in columns H-I.
Project update tracking with dedicated notes below each project row.
Managing a growing project list—particularly when physical printouts are essential—demands a thoughtful approach. For users familiar with traditional spreadsheets, transitioning to a system that balances data organization with readability is key. Our solution allows you to track project details (budget, status, action items) in a standard row, while maintaining dedicated notes below each project. Critically, this design preserves sortability by project information while optimizing print clarity.
Need suggestions for reconciliation of capital gain statement with uneven data set.
Reconciling capital gains statements with mismatched data sets can be challenging, especially when dealing with data from disparate sources. As a junior accountant, you're right to seek efficient solutions beyond manual processes. For smaller datasets (100-200 rows), leveraging Excel’s lookup functions—even with data misalignment—is a viable approach. Focus on establishing a clear mapping between the Capital Gain Statement and the Annual Information Statement columns, then utilize lookup formulas to align the data.
Item Sales with additional category
Navigating complex food and beverage sales data can be challenging, especially when aiming for streamlined ordering and prep. You’ve already made significant progress by automating data import from Square using Power Query and Pivot Tables—a key step toward a future-focused, efficient workflow. Now, the goal is to distill this data into a clear “sales per 1k guests” metric on your dashboard. As you explore this, consider the strategies outlined in our article, "Excel Spreadsheet to organize my Pills/Supplements," for managing and tracking data effectively.
Best AI Projects to Build in 2026 (Sequenced for Hiring)
Navigating the landscape of AI projects for 2026 requires a focused approach. The most compelling projects aren't about sheer complexity; they're about demonstrating a clear understanding of system limitations and articulating those failures confidently to potential employers. Forget wading through 50 ideas – this post delivers the top 10 AI projects poised to impress. Discover how to build demonstrable skills and showcase your expertise. For deeper insights into user interface design within AI, explore "Matching AI Modality To User Intent."
Best way to give each row its own edit history, selectable from a dropdown?
Maintaining a detailed audit trail for frequently updated spreadsheet rows is crucial for data integrity. To empower users like /u/bigdeekenergy, who manage document trackers with numerous revisions, consider implementing a system where each row’s history is readily accessible. Our approach prioritizes clarity: a dropdown menu, specific to each row, would display only its previous versions, allowing for easy review and, ideally, selective rollback of individual cells.
Matching AI Modality To User Intent: Designing The Right Interface
The rush to integrate AI often defaults to chat interfaces, overlooking a fundamental principle of user experience: matching modality to intent. Simply because Large Language Models thrive on dialogue doesn’t mean every AI capability should be presented conversationally. Great UX prioritizes the user, adapting the interface to their context and cognitive load. Explore how shifting beyond conversational tunnel vision unlocks more intuitive and effective data interactions—as discussed further in "Users Don’t Need More Tools: They Need Seamless Integrations."
Built a Global AQ (PM2.5) Forecaster ML Model [P]
This project delivers a globally accurate Air Quality (PM2.5) forecasting model across the US, UK, India, and Australia, addressing a critical challenge: variance. Initial models struggled in regions with high volatility, performing worse than naive carryover predictions. The solution? A horizon-aligned architecture decoupling forecast horizons and incorporating a volatility matrix to prevent data leakage. Achieving a consistent MASE below 1.0, even at a 30-day horizon, demonstrates a 57% predictive accuracy improvement. See "Time Series Modeling Needs a Dynamical Systems Perspective" for related insights. [https://global-aq-intelligence.vercel.app/](https://global-
7,000 Langflow servers are under attack. LangGraph and LangChain have the same holes
Three widely deployed AI agent frameworks – LangGraph, Langflow, and LangChain – share a critical vulnerability, exposing sensitive data like OpenAI keys, database credentials, and CRM tokens. Recent attacks exploiting a SQL injection in LangGraph and a path traversal in Langflow demonstrate that these frameworks, adopted rapidly, have outpaced security measures. Now, researchers have identified a similar flaw in LangChain-core. Addressing this requires immediate action: patching to the latest versions and reviewing framework configurations to minimize exposure.
Copilot searched your mailbox. LiteLLM handed out admin keys. Run this 5-check audit before your stack is next
Enterprise AI is rapidly expanding, but a concerning pattern is emerging: external input is being accepted without robust trust boundaries. Recent disclosures like SearchLeak (affecting Microsoft Copilot) and vulnerabilities in LiteLLM highlight this risk. Four independent teams have now uncovered similar flaws across diverse tools, demonstrating a systemic operating failure. This five-check trust-boundary audit maps these gaps to concrete actions, allowing you to proactively address vulnerabilities and communicate risks clearly to your board—starting before lunch.
Databricks says it solved the decades-old data pipeline problem that's been slowing AI agents
For decades, data professionals have grappled with unifying operational and analytical databases—a challenge now acutely amplified by the demands of AI agents. Databricks is addressing this structural problem with Lakehouse//RT and LTAP, aiming to eliminate the latency and complexity of traditional data pipelines. Lakehouse//RT delivers millisecond query speeds directly on governed data, while LTAP stores transactional data in Delta and Iceberg format.
Larger Context Windows Don’t Fix RAG — So I Built a System That Does
Traditional approaches to Retrieval-Augmented Generation (RAG), like simply expanding context windows, often fail to improve accuracy—and can even mask errors—when dealing with aggregation tasks. Our recent analysis, benchmarking RAG pipelines against a full-scan engine across a substantial dataset, reveals a critical limitation: computation queries should be rerouted entirely. Explore how we've built a system to address this, offering a more reliable solution. For deeper insights into related challenges facing AI developers, see our article on Anthropic's recent restrictions.
How can I find the percentage of how often two distinct drop down options are selected? (Google Sheets)
Calculating win rates across multiple dropdown options in Google Sheets can be a challenge, but it’s readily achievable with the right approach. For your card game tracker, leverage `COUNTIFS` to analyze the relationship between two columns – for example, 'Win/Loss' and 'Opponent's Faction'. This function allows you to count rows meeting multiple criteria, providing a clear percentage breakdown for each faction. Explore similar data synchronization strategies as outlined in "How do you keep Excel data in sync with the rest of a project?