标签：#ics

The company endorsed landmark AI transparency laws in California and New York last year, but its head of US state and local policy says they may already be outdated.

Metron was the Greek word for a measure: the standard you judge a thing against. It gives us metric, and metronome. Pick the wrong metron and everything you count against it comes out wrong, no matter how carefully you count. Ask most teams how they know AI adoption is working, and the answer is some version of: we’re shipping more. More PRs, more tickets closed, more velocity on the board. None of those numbers were ever measuring what people thought they were measuring. They were proxies, and bad ones, for whether the team was creating value. AI didn’t break that. It just made the proxies lie louder. An agent can produce PRs faster than any team can review them. It can close tickets all day. None of that tells you whether the thing built was worth building, or whether it fixed the actual problem, or whether anyone downstream is better off. Burning tokens isn’t the same thing as creating value. It just looks the same on a dashboard built to reward the first thing. The same DORA numbers I pointed at earlier are blunt about it: individual output climbs while delivery throughput and stability drop. Same bottleneck, counted at the wrong end . This is Theory of Constraints in one sentence: speeding up a step that wasn’t the bottleneck doesn’t speed up the system. It just piles more work in front of whatever the real bottleneck is. Implementation used to be the constraint. Now it isn’t. Review is. Coordination is. Making sure everyone agrees on what “done” even means is. Agents made the fast part faster and left the slow part exactly where it was, except now it’s buried under more output arriving to be reviewed by fewer people who understand any of it. Measuring individual speed after that shift is measuring the wrong clock. The team can be faster and worse off in the same quarter. The instinct, when this gets noticed, is to add another skill to the agent. Automate the review step too. Automate the coordination. Whatever’s slow, throw a capability at it. That doesn’t fix

New York Governor Kathy Hochul might have just signed a moratorium on new AI data centers in the state, but she's not against using the technology herself. During an interview with Bloomberg's Odd Lots podcast, Hochul said that her team is using "AI to analyze every single rule, regulation, [and] policy" to check for outdated […]

Self-service BI was supposed to free the data team. Give everyone a BI tool, teach them to drag and drop, and they'll answer their own questions. The data team can stop building dashboards and focus on infrastructure. That's not what happened. What happened: everyone builds their own dashboards with their own metric definitions. Marketing's "active users" counts monthly logins. Product's "active users" counts weekly feature usage. Finance's "active users" counts paying customers. Three dashboards, three numbers, one term. The data team now spends their time reconciling conflicting metrics instead of building. Self-service BI without governed metrics is just self-service chaos. What is self-service BI? Self-service BI means non-technical users can query data, build visualizations, and generate reports without help from the data team. The tools (Metabase, Looker, Power BI, Tableau, Superset) provide drag-and-drop interfaces, visual query builders, and template libraries. The promise: democratize data access. Anyone can answer their own questions. The reality: it works for simple questions ("how many orders this week?") and breaks for anything requiring business logic ("what's our net revenue retention?"). Users either define metrics incorrectly or ask the data team anyway. The data team becomes a help desk for the self-service tool instead of a help desk for SQL. Where self-service BI goes wrong Metric sprawl Without a central definition, every self-service user creates their own version of key metrics. A company with 50 Metabase users might have 30 different "revenue" calculations saved across collections. Which one is right? The one that matches the board report. Which one matches the board report? Nobody knows without checking each one. The "someone who knows" bottleneck True self-service requires understanding the data model. Which table has revenue? What does status = 3 mean? Is the amount column in cents or dollars? Pre-tax or post-tax? Including shipping or exc

"We need real-time analytics" is one of the most common requests in data engineering. It's also one of the most misunderstood. When the VP of Sales says "real-time," they usually mean "faster than the dashboard that refreshes overnight." When the CTO says it, they might mean sub-second event streaming. The gap between those two definitions is a 6-month infrastructure project. Most teams don't need true real-time. They need fast enough. And "fast enough" is achievable with pre-aggregation caching at a fraction of the complexity and cost of a streaming architecture. What is real-time analytics? Real-time analytics means querying data with minimal latency between when an event happens and when it's visible in your analytics. The spectrum: Freshness Latency Architecture Use case True real-time < 1 second Event streaming (Kafka, Flink) Fraud detection, stock trading, live monitoring Near real-time 1-60 seconds Micro-batch or streaming Operational dashboards, alerting Frequent refresh 1-60 minutes Scheduled refresh + caching KPI dashboards, AI agent queries Batch Hours to daily Scheduled ETL Board reports, monthly summaries Most analytics use cases fall in the "frequent refresh" category. Revenue by region doesn't need sub-second freshness. Active users in the last hour doesn't need event streaming. A pre-aggregation cache that refreshes every 15 minutes covers 90% of what teams call "real-time." When you actually need real-time True real-time analytics (sub-second latency from event to query result) is worth the infrastructure investment when: Fraud detection. Every second of delay is potential fraud that slips through. Live monitoring. Server health, API error rates, active user counts for live products. Trading and pricing. Financial instruments where stale data means wrong prices. Live events. Streaming metrics during a product launch, marketing campaign, or live broadcast. If you're in one of these categories, you need an event streaming architecture: Kafka, Flink, M

Your company has a KPI dashboard. It was built six months ago by someone who has since moved teams. It shows revenue, churn, and a few product metrics. It loads slowly. The numbers don't match what finance reports. Nobody trusts it, but everyone screenshots it for the Monday standup. This is the state of KPI dashboards at most companies. Not because the tools are bad, but because the approach is wrong. A dashboard is a static view of a dynamic system. The moment someone builds it, it starts drifting from reality. What is a KPI dashboard? A KPI (Key Performance Indicator) dashboard is a visual display of an organization's most important metrics. Revenue, customer count, churn rate, conversion rate, average order value, NPS. The metrics that tell you whether the business is healthy. Traditional KPI dashboards live in a BI tool: Power BI, Tableau, Looker, Metabase, Grafana. An analyst builds the dashboard, connects it to a data source, and shares a link. People visit the dashboard (or receive a scheduled screenshot) to check the numbers. The concept is sound. The execution breaks for predictable reasons. KPI dashboard examples Before diving into what's broken, here's what teams typically build: SaaS executive dashboard. MRR, ARR, net revenue retention, churn rate, new customers this month, average contract value. Updated daily. Viewed by the CEO and board. The numbers must match the finance team's report exactly. Product analytics dashboard. Daily active users, feature adoption rates, conversion funnel stages, time to value. Updated hourly. Viewed by product managers. Often the first dashboard built and the first to drift from reality. Customer health dashboard (B2B). Per-customer usage metrics, support ticket volume, NPS, renewal risk score. Updated daily. Viewed by customer success. In embedded analytics use cases, the customer sees this too. Engineering operations dashboard. API error rates, p95 latency, deployment frequency, uptime. Updated real-time. Viewed by eng

Your B2B customers want to see their data. Usage metrics, billing summaries, conversion funnels, performance dashboards. Every customer expects analytics inside your product. They shouldn't have to ask your support team for a CSV export. The question isn't whether to ship customer-facing analytics. It's how. Most teams start with one of two approaches. They embed a BI tool (Metabase, Looker, Power BI) and fight with multi-tenancy, iframe styling, and paid embedding licenses. Or they build custom charts from scratch and spend months maintaining SQL queries, API endpoints, and frontend components that nobody asked for. Both approaches burn engineering time on the wrong problem. You end up building analytics infrastructure instead of your product. And there's a surface most of these tools miss entirely: the AI agent. Customers increasingly want to ask questions about their data inside Claude or ChatGPT and get a chart back. That's a different problem from embedding a dashboard, and it's where @bonnard/mcp-charts fits, covered later in this post. Why embedded BI tools fall short Embedding a BI tool sounds fast. Drop in an iframe, connect to your database, ship it. In practice, the friction shows up quickly. Multi-tenancy is an afterthought Most BI tools were built for internal teams, not B2B products serving hundreds of tenants. Multi-tenancy is either missing, manual, or gated behind an enterprise license. Metabase requires the Enterprise license ($500+/month) for row-level permissions and sandboxed embedding. The open-source version has basic embedding but no tenant isolation. You end up writing middleware to filter queries by tenant, which is exactly the custom infrastructure you were trying to avoid. Looker's embedded analytics requires an enterprise contract. Power BI Embedded uses capacity-based pricing that gets expensive at scale. Tableau's embedding story is Salesforce-priced. Even when multi-tenancy is available, it's usually dashboard-level or role-based, not

A lot of platform teams face a problem: they build a lot of really cool stuff, and then their developers don't use it. Be visible to management, talk to stakeholders and listen to their problems, make your value measurable with metrics like DORA, create narratives, and show the hidden pain to make it personal: these are lessons that Lucas Hornung and Christian Matthaei presented. By Ben Linders

Building a semantic search engine for an e-commerce catalogue doesn't require a team of PhDs or a six-figure cloud budget. In this tutorial, I'll walk you through a production-ready pipeline using open-source tools: sentence-transformers for embedding, FAISS for vector indexing, and FastAPI for serving. The core insight is that semantic search isn't magic — it's just good engineering wrapped around a pre-trained language model. We'll start by setting up a product embedding pipeline that transforms your catalogue (title, description, category, attributes) into dense vectors. The key architectural decision is whether to embed each product as a single vector or to use late interaction models like ColBERT that preserve token-level detail. For most e-commerce use cases with fewer than 1 million SKUs, single-vector embedding with sentence-transformers' all-MiniLM-L6-v2 offers the best balance of speed and accuracy. The entire indexing pipeline — from CSV export to queryable vector index — runs in under 100 lines of Python. The re-ranking layer is where most tutorials stop and real-world systems begin. Pure vector similarity doesn't understand your business: it doesn't know that out-of-stock items should be deprioritised, that high-margin products should float up, or that a customer's purchase history should influence results. I'll show you how to build a hybrid scoring function that blends semantic relevance (cosine similarity), business rules (margin, inventory), and personalisation signals (user embedding) into a single ranked result set that returns in under 100ms. Canonical: https://alteglobal.ai/insights/ecommerce-ai-automation-personalisation-fulfillment/

​I. The Terminal Velocity of Disembodied Intelligence ​The current paradigm of artificial intelligence is approaching a fundamental cognitive ceiling. We have built vast, sprawling minds, yet we have trapped them in sensory deprivation chambers. Today’s Large Language Models and industrial robotics operate under a fatal flaw: the reliance on linear, arbitrary tokenization. Traditional AI chops raw text into disconnected, non-semantic fragments, wasting immense computational memory tracking the mere phonetic order of spelling and grammar. ​Simultaneously, industrial machines navigate the physical world through siloed sensor pipelines. They process light, space, and kinetics as heavy, raw digital arrays, relying on brute-force algorithmic equations to stitch reality together. This is not intelligence; it is computational exhaustion. To achieve true artificial general intelligence, the machine must stop reading about the world and begin to inhabit it. ​II. The Biological Imperative ​Nature solved the hardware-software integration problem millions of years ago. Human biology remains the ultimate benchmark for flawless, metabolic efficiency. Our somatosensory system does not wait for a central brain to read a text string before pulling a hand away from a fire; it processes physical feedback in milliseconds through decentralized neural highways. ​Human cognition leverages cross-modal integration within the angular gyrus, allowing visual data to be instantly cross-referenced with kinesthetic feedback to trigger anticipatory motor reflexes. Furthermore, biological intelligence is inherently tied to physical survival. We manage thermodynamics through a centralized hypothalamic thermostat, protecting the body from cellular destruction, while routing ambient light data to deep-brain structures to drive a natural circadian rhythm. True AGI requires this exact synthesis of abstract thought and biological mechanics. ​III. The Logographic Paradigm Shift ​To bridge the gap between

This continues from Part ③ . We have the system set to refresh data daily, but unexplained errors can sometimes occur. Symptoms The segments were not properly configured as expected. Checking System → Schedule showed that the automatic refresh schedule itself was fine. The data source refresh also appeared to have no issues. Opening the Unify screen, everything looks like it succeeded at first glance — but checking the Performance screen reveals that something is failing during unification. Drilling in further, the failure is happening at the Customer Profiles step. Resolution Steps Attempt to manually re-run just the "Customer Profiles" task → Error persists Redo everything manually from the data source refresh step onwards This resolved the issue successfully. Takeaway Since this is not a misconfiguration issue, patient repetition of the refresh process is enough to recover. Hopefully this saves someone some troubleshooting time.

If you have ever built an autonomous mobile robot, you have likely run into the dreaded "Shuffled USB Port" problem. You boot up your robot, fire up your ROS 2 launch files, and... crash. Your LiDAR driver is trying to parse data from your IMU, and your IMU node is screaming about invalid serial frames. Because Linux assigns virtual serial paths like /dev/ttyUSB0 and /dev/ttyUSB1 based purely on which device initialized milliseconds faster, relying on default OS paths is a recipe for system instability. When you are scaling up to dozens of Jetson Orin nodes —each equipped with an RPLIDAR C1 and a Yahboom 10-axis IMU —manually hardcoding paths or writing rigid scripts on every individual machine isn't viable. Here is how production-grade robotics fleets handle plug-and-play USB binding dynamically using configuration-driven udev rules. The Core Concept: Vendor ID vs. Physical Port vs. Serials Linux's udev (device manager) allows us to dynamically create stable symbolic links (symlinks) like /dev/rplidar and /dev/imu when hardware is plugged in. How we identify those devices determines our fleet's flexibility: USB Serials: Unique to each individual chip. Highly secure, but requires registering every single replacement sensor in your codebase. Physical USB Ports ( KERNELS ): Tied to a physical slot on the carrier board. Great if you have identical sensors, but forces technicians to plug cables into highly specific, undocumented ports. Vendor ID (VID) & Product ID (PID): Identifies the USB-to-serial converter chip on the sensor board. Because the RPLIDAR C1 uses a Silicon Labs CP210x chip ( 10c4:ea60 ) and the Yahboom IMU uses a QinHeng CH340 chip ( 1a86:7523 ), they use completely distinct silicon. This means we can map them dynamically and reliably using just their VID/PID —allowing field technicians to plug them into any USB port on the Jetson without breaking the system. Step 1: The Configuration-Driven File ( devices.conf ) Hardcoding vendor rules inside shell scri

The Federal Communications Commission will vote next month on whether a single company can own broadcast stations that reach more than 39 percent of US TV households. In a Breitbart op-ed on Wednesday, Republican Chair Brendan Carr announced an August 6th vote to end the national ownership cap rule, which was intended to prevent one […]

The Trump administration has not disclosed its cost estimates for the Iran war.

The US is getting closer to observing daylight saving time year-round. On Tuesday, the House advanced the Sunshine Protection Act on a 308 to 117 vote, which would turn clocks one hour ahead permanently, as reported by CBS News. President Donald Trump wrote on Truth Social in May that the bill would save the "hundreds […]

Look inside almost any modern connected device -- a smartphone, a smartwatch, a Wi-Fi thermostat, a battery-powered sensor node -- and you will find a processor core designed by ARM. It is one of the most successful engineering ideas in computing history. And here is the strange part: ARM has never manufactured a single one of those chips. It does not own a factory. It sells blueprints. A three-person project in Cambridge The story starts at Acorn Computers in Cambridge, England, in the early 1980s. Acorn had built the BBC Micro, a hugely popular educational computer in the UK, and it needed a faster processor for its next machine. The commercial chips available at the time were disappointing, so a tiny team decided to design their own. The acronym everyone knows today originally stood for Acorn RISC Machine . Sophie Wilson designed the instruction set and wrote the simulator; Steve Furber led the physical chip design. RISC -- Reduced Instruction Set Computing -- was the key bet. Instead of piling on complex instructions, they kept the instruction set small and simple, which made the chip easier to build, cheaper, and remarkably power-efficient. The first silicon, the ARM1, was fabricated by VLSI Technology and delivered to Acorn on 26 April 1985. When the team powered it on, it simply worked -- first try. For anyone who has designed hardware, that is almost unheard of; new processors normally need several rounds of revisions to shake out design errors. A famous piece of Acorn lore is that the early ARM chips drew so little current they could keep running on leakage alone after the power was disconnected. From a British computer to the whole world Acorn the computer company faded, but the processor design did not. In 1990 the ARM team was spun out into a separate joint venture, and the acronym was quietly re-expanded to Advanced RISC Machines . The new company made a decision that defined its future: it would not build chips. It would license the designs and let oth

In today's mobile-first world, users expect authentication to be both secure and effortless. Typing passwords every time an app is opened not only impacts the user experience but also introduces security risks if passwords are weak or reused. Biometric authentication solves this problem by allowing users to verify their identity using Fingerprint , Face ID , Touch ID , Iris Scanner , or even their device's PIN/Password . If you're building a React Native application, @sbaiahmed1/react-native-biometrics is one of the most comprehensive biometric libraries available. Beyond simple authentication prompts, it offers hardware-backed cryptographic key management, biometric enrollment detection, device integrity checks, StrongBox support, and compatibility with both the React Native New Architecture and Expo. In this article, we'll explore everything this library offers and learn how to integrate biometric authentication into a React Native application. Why Biometric Authentication? Traditional authentication methods come with several drawbacks: Passwords are easy to forget. Weak passwords are vulnerable to attacks. OTP-based logins can be slow and frustrating. Users often abandon apps with poor login experiences. Biometric authentication addresses these challenges by providing: 🔒 Enhanced security ⚡ Faster authentication 😊 Better user experience 📱 Native platform support 🔑 Secure fallback using device credentials Whether you're building a banking app, healthcare platform, enterprise application, or e-commerce app, biometric authentication has become an expected feature. Installation Install the package using npm: npm install @ sbaiahmed1 /react-native-biometric s or with Yarn: yarn add @ sbaiahmed1 /react-native-biometric s For iOS: cd ios pod install Platform Configuration Before using biometric authentication, configure the required permissions for both Android and iOS. Android Open your android/app/src/main/AndroidManifest.xml file and add the following permissions: <

In response to a public records request, HUD has withheld documents about DOGE’s use of AI—in part by citing a privilege that doesn’t exist.

LLM Evaluation System Prompts Scored Rubrics Runtime Guardrails: A Practical Guide for Production Learn how to evaluate LLM outputs in production using system prompts, scored rubrics, and runtime guardrails to prevent hallucinations and ensure quality. TL;DR: To evaluate LLM outputs in production, combine system prompts that define evaluation criteria, scored rubrics using LLM-as-a-judge for dimensions like correctness and relevance, and runtime guardrails that filter or flag unsafe outputs. This approach scales better than human review, adapts via prompt changes, and catches failures that status codes miss, as seen in the Air Canada chatbot case. Why Production LLM Evaluation Demands More Than Status Codes A 200 status code only confirms the server processed the request—it says nothing about whether the generated text is factual, safe, or useful. The Air Canada chatbot that invented a non-existent bereavement discount returned perfectly valid HTTP responses, yet the hallucinated policy led to a tribunal ruling against the airline. Production evaluation must therefore separate operational health (latency, error rates) from output quality (correctness, relevance, harmlessness). Consider a typical API call that succeeds operationally but fails qualitatively: import requests response = requests . post ( " https://api.example.com/v1/chat " , json = { " model " : " gpt-4o " , " messages " : [{ " role " : " user " , " content " : " What is Air Canada ' s bereavement policy? " }]}, headers = { " Authorization " : " Bearer $KEY " } ) print ( response . status_code ) # 200 print ( response . json ()[ " choices " ][ 0 ][ " message " ][ " content " ]) # Output: "Air Canada offers full refunds for bereavement-related cancellations..." A 200 status code and a well-formed JSON body mask a completely fabricated policy. To catch this, you need a separate evaluation layer that scores the output against a rubric. LLM-as-a-judge is a common approach, using a second model to assess the

New study confirms 2024 "momentum flux theory" on how angular momentum of water flows drives rotation.