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This tutorial was written by Néstor Daza . This is the second article in a series about building Claudius , my own Claude-based chatbot ( Github ). The prologue made the case for building it, and for choosing MongoDB as its foundation. Open the conversations collection in Claudius’ database and you find the usual fields of a thread header but nothing else: a userId , a title , some timestamps , and so on, but no array of messages, no messages collection sitting beside it either! The text of every conversation lives somewhere else entirely, in the LangGraph checkpointer, which I wire up later in this series. This absence is a modeling decision, and how I came up with the database schema for my chatbot is the theme of this article. If you come from a relational background, you're used to modeling the data first when designing a database. For a project like this, you would start by finding the entities and normalizing them, and the final schema would come out of the data's structure: a conversations table and a messages table with a foreign key between them, because that is what the data looks like. Document modeling runs the other way. You start from how the application reads and writes, and the shape of the document follows the access patterns. Claudius never reads conversation messages without the agent's full working state wrapped around them, and that state is persisted using the LangGraph checkpointer. A separate messages table would add nothing, since the app would always have to join it back to that state on every read. The access pattern says the messages belong with the agent state, so that is where they go, and conversations are left as the lightweight header the list view actually needs. That inversion, modeling around use rather than around the data, runs through everything below. Schema-flexible is not schemaless This is the misconception lots of people often carry, and it is worth killing on the way in. A document database does not mean no schema; it mea

Frameworks like Lean Six Sigma and business process management (BPM) first gained traction because they promised clarity in the chaos—a structured way to bring order to messy, sprawling operations. Lean Six Sigma emphasized statistical rigor and quality control; BPM created end-to-end maps of how work should flow across departments. Both offered a repeatable way to…

By Hiral Shah, Senior Director, Product Management, Docusign A major recurring theme among the engineering teams at this week’s AI Engineer World’s Fair in San Francisco is the push to move specialized AI models out of research and directly into high-volume production. At Docusign, that optimization challenge happens at massive scale: we handle millions of transactions daily and have nearly 1.9 million customers in over 180 countries. Organizations have historically lost significant value every year to the friction, delays, and missed obligations that come from treating these agreements as static documents rather than live sources of business data. Much of that trapped value sits inside tables: the pricing schedules, SLA obligations, and contractor rate cards that define enterprise relationships but are often the hardest part of a contract to extract accurately. To solve this, we integrated NVIDIA Nemotron Parse , a vision-language model purpose-built for document understanding, directly into our document processing pipeline. Docusign and NVIDIA took the AI Engineer World’s Fair stage this week to give attendees a look at how the architecture works under the hood. Here’s what that looks like: Why Contract Tables Break General-Purpose AI Contracts routinely contain merged cells, multi-page structures, mixed formatting, and nested layouts that general-purpose vision language models (VLMs) and broad AI models weren't designed to handle. The result is inaccurate extractions that require manual correction, slowing down the workflows they are intended to accelerate. Our teams watch this operational friction play out across real enterprise scenarios every day: System Downtime: When a critical system goes down, operations teams need to know immediately which SLA notification requirements apply and to whom. Resource Tracking: When business stakeholders ask legal what hourly rate was agreed to in a contractor engagement, the answer is often buried deep inside a rate card tabl

TV Time, the popular TV-tracking app, is shutting down on July 15 as parent company Whip Media pivots toward enterprise AI products.

Insiders say Sam Altman is in active talks with the Trump administration.

FTC urged to reject Elon Musk’s bid to end X monitoring amid AI concerns.

Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In industries where physical infrastructure, operational continuity, and safety are paramount, AI is becoming a core operating layer. With its sprawling industrial systems and constant stream of operational…

The company now expects to ship a few thousand more vehicles by the end of 2026 than it previously expected, after launching its R2 SUV last month.

Build a Real-Time Crypto Trading Dashboard with Python, WebSockets, and React Real-time data is the difference between catching a move and reading about it later. This tutorial walks through a minimal but complete stack: a Python WebSocket client pulling live prices, a simple signal generator, and a React frontend displaying everything. You will end up with a dashboard that shows live Binance prices, basic momentum signals, and auto-updates without polling. Why this stack Binance provides a clean WebSocket API for tickers and trades. Python handles the backend connection and lightweight analysis. React keeps the UI reactive and simple to extend. No heavy frameworks, no paid data feeds. Prerequisites Python 3.11+ Node 20+ A Binance API key (read-only is fine for prices) Step 1: Python price stream Install the client library: pip install python-binance pandas Create price_stream.py : import asyncio import json from binance import AsyncClient , BinanceSocketManager import pandas as pd from datetime import datetime async def main (): client = await AsyncClient . create () bm = BinanceSocketManager ( client ) ts = bm . trade_socket ( ' BTCUSDT ' ) async with ts as tscm : while True : res = await tscm . recv () price = float ( res [ ' p ' ]) qty = float ( res [ ' q ' ]) ts = datetime . fromtimestamp ( res [ ' T ' ] / 1000 ) print ( f " { ts } | BTCUSDT { price : . 2 f } | { qty : . 4 f } BTC " ) if __name__ == " __main__ " : asyncio . run ( main ()) Run it: python price_stream.py You should see a live feed of trades. Keep this running as your data source. Step 2: Add a simple momentum signal Extend the script to calculate a 20-trade rolling average and flag when price deviates more than 0.3%: # inside the loop, after parsing res prices . append ( price ) if len ( prices ) > 20 : prices . pop ( 0 ) avg = sum ( prices ) / len ( prices ) deviation = ( price - avg ) / avg * 100 if abs ( deviation ) > 0.3 : print ( f " ⚡ Signal: { deviation : + . 2 f } % from 20-trade avg " )

New York City’s Summer of Ludd festival is teaching people how to live offline amid the suffocating presence of Big Tech.

Every blog post needs an OG image. Without one, your links look blank on Twitter, LinkedIn, and Slack — just a plain URL that nobody clicks. Most developers solve this by spinning up a headless browser, loading an HTML template, taking a screenshot, and uploading it somewhere. It works, but now you're maintaining a Puppeteer instance, dealing with font rendering quirks, and burning server resources on something that should be simple. There's a faster approach: design your OG images as HTML templates and let a screenshot API handle the rendering. The Idea: HTML Templates as OG Images Think of your OG image as a tiny webpage. You already know HTML and CSS. Build a 1200×630 template with your blog title, author name, maybe a gradient background — whatever fits your brand. Host it or pass it as raw HTML. Then call an API to screenshot it. Done. A basic template might look like this: <div style= "width:1200px;height:630px;display:flex;align-items:center; justify-content:center;background:linear-gradient(135deg,#1a1a2e,#16213e); font-family:Inter,sans-serif;padding:60px" > <div style= "color:#fff;text-align:center" > <h1 style= "font-size:48px;margin:0" > {{title}} </h1> <p style= "font-size:24px;color:#8892b0;margin-top:20px" > {{author}} · {{date}} </p> </div> </div> Replace the placeholders on your server, then send the resulting HTML (or a URL pointing to it) to the API. Calling the API With ScreenshotRun , a single curl request captures the rendered template as a PNG: curl -X POST "https://api.screenshotrun.com/v1/screenshot" \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "url": "https://yourblog.com/og-template?title=My+Post+Title", "viewport_width": 1200, "viewport_height": 630, "format": "png" }' The response gives you the image file. Save it to your CDN, set the og:image meta tag, and you're done. No browser to manage, no Chrome binary eating RAM on your CI server. Wiring It Into Your Build If you publish with a static sit

Thomas Edison put it plainly: "To have a great idea, have a lot of them." Steve Jobs said something similar. "Creativity is just having enough dots to connect... to connect experiences and to synthesise new things." Both of them are saying the same thing. Your first idea is rarely your best one. The reason why people you call creative can come up with great ideas easily is that they have had more experiences or have thought more about their experiences than other people. So the question becomes: how do you get more ideas, faster? The Most Used Method for Applied Creativity The answer has a name. It was coined by advertising executive Alex Osborn in the 1940s. He called it brainstorming - using the brain to storm a creative problem, with each person in the room attacking the same objective. It sounds simple. Most teams think they already do it. Most of them are wrong. Real brainstorming is a structured process with rules. Break the rules, and you get something that looks like brainstorming but produces far fewer useful ideas. Why Most Brainstorming Sessions Fail Here is what kills a brainstorming session before it even starts. Someone says an idea. Someone else says, "That won't work." The room goes quiet. People stop sharing. That is it. That is the whole problem. When people fear judgment, they self-censor. They only say the safe, obvious ideas. The interesting ones, the ones that could actually lead somewhere, stay locked inside people's heads. Most teams have that one gaffer who has already decided which ideas are worth hearing before anyone has finished their sentence. Or the one who gives you the floor, listens patiently, and then quietly bins everything you said, not because it was bad, but because it was not theirs. Both types do the same damage. The room reads it. People stop sharing. And just like that, the best idea in the session never gets spoken. The goal of brainstorming is to get more ideas. That means the number one rule is: defer judgment . The Rule

Ahnii! If you let an AI agent modify your application, the agent needs more than a text editor. Raw str_replace on a PHP file passes a lot of tests and still breaks things an hour later in production, because the tool has no idea what the file actually represents. Bimaaji is the Waaseyaa package that gives agents a structured path from "I want to add a field to this entity" to a reviewable patch that a community's sovereignty rules have already vetted. This post walks through what shipped in waaseyaa/bimaaji and why each piece exists. Prerequisites: familiarity with Waaseyaa's package layout, PHP 8.4+, and the idea that an application has more state than the filesystem (routes, entities, introspection metadata). Why not just let the agent edit files The failure mode you want to avoid: an agent reads a prompt like "add a published_at field to the Post entity," does a reasonable-looking edit to Post.php , and leaves the rest of the app inconsistent. The migration is missing. The JSON:API resource doesn't expose the field. The admin panel still doesn't know it exists. The sovereignty profile that was supposed to block the change on a local-only deployment never got consulted. Each of those is a different subsystem. A good agent can write a correct edit to any one of them. What a filesystem-level tool cannot do is ensure the edit is coordinated across all of them and is allowed under the community's posture. Bimaaji separates that problem into three stages: introspect, propose, patch. The pipeline The package description (from packages/bimaaji/composer.json ) spells it out: application graph introspection and agent-safe mutation for Waaseyaa. The flow is: Introspection → ApplicationGraph → MutationRequest → Validator → PatchGenerator → PatchSet An agent reads the graph, submits a structured mutation request, a validator checks it against sovereignty rules, and the patch generator returns reviewable diffs. Nothing touches the filesystem until a human (or a higher-level w

X has launched a new live streaming command center and additional creator payouts.

Introduction Modern software systems are distributed, complex, and constantly changing. When something breaks in production, you need answers fast. That's where observability comes in. Observability is the ability to understand the internal state of a system purely from its external outputs — without needing to redeploy, add debug code, or guess. It goes beyond traditional monitoring, which only tells you whether something is wrong. Observability tells you why it's wrong, where it started, and how it's spreading. In this article, we'll explore the three pillars of observability, set up a real Node.js API instrumented with Prometheus and Grafana , and walk through how to detect and diagnose a real-world issue using the data we collect. The Three Pillars of Observability 1. Logs Logs are discrete, timestamped records of events that happened in your system. They're the most familiar form of observability — every developer has done console.log debugging at some point. Example: [2026-07-02T10:34:21Z] INFO User 4821 logged in from IP 192.168.1.10 [2026-07-02T10:34:25Z] ERROR Failed to process payment for order #9932: timeout Logs are great for capturing specific events, errors, and context. But they can become expensive at scale and hard to query across millions of lines. 2. Metrics Metrics are numeric measurements collected over time. Unlike logs, they're aggregated and efficient to store and query. Common examples: HTTP request count per minute p95 response latency CPU and memory usage Error rate per endpoint Metrics are the backbone of dashboards and alerts. 3. Traces Traces follow a single request as it travels across multiple services. In a microservices architecture, a user request might touch 5–10 services. A trace shows you exactly where time was spent and where failures occurred. Tools like Jaeger , Zipkin , and OpenTelemetry handle distributed tracing. Why Prometheus and Grafana? There are many observability platforms out there: Datadog, New Relic, Dynatrace, Az

I used to spend ten hours every week doing content research manually. Checking competitor blogs. Scanning Reddit threads. Copying and pasting search results into a spreadsheet. Trying to spot patterns in an ocean of unstructured text. It was exhausting, slow, and completely unnecessary. Once I learned to automate this with Python and a few affordable APIs, I cut that ten-hour grind down to under thirty minutes. Here is the exact system I built, what it costs, and how you can replicate it yourself. The Quick Answer To automate content research with Python, combine a search API like Serper to pull structured Google search data, BeautifulSoup or requests-html to parse page content, and an LLM API like Gemini to synthesize insights into actionable content briefs. Connect these three components in a sequential Python pipeline and you have a fully automated research agent that runs in minutes instead of hours. What I Actually Built I needed a system that could do three things automatically: First, find what real people are asking about any topic across Reddit, Quora, and Google search. Second, identify what my top competitors have written about that topic and where the gaps are. Third, summarize everything into a clean content brief I can use to write or generate an article. I built this using Python with three core components: the Serper API for search data, BeautifulSoup for page parsing, and the Google Gemini API for synthesis. Total monthly cost: about twelve dollars. I document the full working version of this system — including the Flask web interface and WordPress publishing integration — at https://zerofilterdiary.com Step-by-Step Build Guide Step 1: Install the Required Libraries pip install requests beautifulsoup4 python-dotenv google-generativeai Step 2: Set Up Your API Keys Create a .env file in your project root: SERPER_API_KEY=your_serper_key_here GEMINI_API_KEY=your_gemini_key_here Step 3: Search for Real Discussions Using Serper API import requests import

Neo is Bhavin Turakhia’s fifth venture and his latest involving enterprise software. This time he's taking on Microsoft Office, Google Apps with AI.

Visibility Collapse in the Post-LLM Engineering Stack Artificial intelligence is still improving. But something important has changed in how that improvement is perceived. For developers and engineers working closely with frontier models, the experience is no longer one of explosive capability jumps. Instead, it feels like: incremental improvement under increasing structural constraints This shift is not about stagnation. It is about uncertainty in how AI capability is exposed, deployed, and interpreted. Capability vs Visibility: the new separation Recent frontier model systems (such as Fable 5, as described in industry discussions) highlight an important architectural pattern: Certain capabilities are no longer fully exposed in production environments: advanced coding assistance deep debugging autonomy bioinformatics reasoning cybersecurity-related reasoning This does not necessarily imply reduced model capability. Instead, it reflects a system-level separation: model capability ≠ deployed capability System interpretation: Modern AI stacks are becoming layered systems: Raw Model → Safety Layer → Policy Filter → Deployment Interface → User Access This means developers are no longer interacting with models directly. They are interacting with constrained capability surfaces. Perceived slowdown in LLM progress Despite continued benchmark improvements: reasoning scores increase gradually multimodal capabilities expand tool-use frameworks improve The perceived acceleration of AI has weakened. Compared to 2022–2023, there are fewer qualitative jumps. From an engineering perspective, this suggests a transition: from capability discontinuity → capability smoothing In other words: AI is still improving, but improvements are less visible at the system interaction level. Economic mismatch: scaling vs returns The AI ecosystem is currently defined by a structural tension: Inputs: massive GPU infrastructure investment multi-billion-dollar training runs hyperscaler-scale capital a

Testing a food delivery app is hard. Testing an on-demand logistics app is harder. Food delivery has...

The agent brand is irrelevant; the work item is everything. I have watched teams argue about Copilot Studio versus Claude Code versus Codex as if the model decides whether their build succeeds. It does not. Your agentic development power platform effort lives or dies on one thing: whether the Azure DevOps work item you hand the agent is a machine-readable spec or a vaguely worded wish. Swap the agent all you want. If the requirement is unstructured, every agent guesses, and every guess is a different guess. This article is opinionated on exactly one point and neutral on everything else. Neutral on the tool. Ruthless about the spec. Why "AI-assisted" Power Platform dev stalls on real teams The agent guesses intent because the acceptance criteria live in a stale wiki, a Teams thread, or someone's head. That is the whole failure. Switching from one agent to another does not close the gap. The missing spec does. Prompt-by-prompt building has a second problem that shows up later and hurts more. One maker gets a working flow out of a chat session, but nobody else can reproduce it and no one can audit it. You have a solution that exists and a rationale that evaporated. For teams doing serious dynamics 365 ai development , that is not acceleration. That is a single point of failure wearing a productivity costume. Frame the cost honestly. Say a rework cycle caught in UAT runs roughly 5x the cost of the same fix at design time. Illustrative; calibrate against your own data, actuals vary. Under that assumption, the line item bleeding your budget is the improvised requirement, not the agent license. You are paying to rediscover intent three environments too late. Takeaway: if your requirement is not structured, your agent is improvising, and the brand of agent does not matter. Make the ADO work item the single source of truth An agent reads fields. It does not read the room. So the work item has to carry everything the agent needs in a shape a parser can trust every single time