标签：#Automation

Benchmarking AI Agents, Gemma 4 On-Device Workflows & AI System Security Today's Highlights This week, we dive into critical aspects of applied AI: practical benchmarks for controlling AI agent costs and reliability, Google's new Gemma 4 model enabling advanced on-device agentic workflows, and essential techniques for securing AI systems against vulnerabilities. Benchmarking a Kill Switch for Runaway AI Agents (Dev.to Top) Source: https://dev.to/prashar32/benchmarking-a-kill-switch-for-runaway-ai-agents-and-why-the-real-number-is-a-ceiling-not-a--4832 This article addresses the critical challenge of managing costs and ensuring control over autonomous AI agents in production environments. It introduces a practical benchmark designed to evaluate the effectiveness of 'kill switches' for runaway agents, moving beyond vague claims of cost reduction. The author argues that focusing on a ceiling for agent spend, rather than a percentage reduction, provides a more realistic and actionable control mechanism. The benchmark is presented as a runnable script, allowing developers to independently test and verify the reliability and cost-efficiency of their AI agent orchestration strategies. This approach is vital for anyone deploying AI agents, offering concrete methods to prevent uncontrolled resource consumption and ensure operational stability. By providing a tangible way to measure and enforce cost boundaries, the article offers a crucial tool for robust AI workflow automation and production deployment patterns. Comment: This is a must-read for anyone deploying agents in production. The ability to benchmark a kill switch in one command is incredibly practical for ensuring cost control and preventing unexpected resource usage. Gemma 4 12B Enables On-Device, Multimodal Agentic Workflows with an Encoder-free Architecture (InfoQ) Source: https://www.infoq.com/news/2026/06/google-gemma4-12b-local-coding/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global

If you work with Brazilian companies — as an accountant, credit analyst, or anyone processing PJ clients at scale — here's a practical automation approach using free public data. What you can verify automatically For any CNPJ, public data gives you: Situação cadastral : ATIVA, BAIXADA, INAPTA, SUSPENSA — critical for invoice validation Razão social : legal name for contract matching CNAE : is this company allowed to do what they claim? QSA : who are the actual partners/directors? Data abertura : how old is the company? The data 65M+ CNPJs from Receita Federal, indexed and searchable at Jurídico Online . Free. Also available as a Python package: pip install juridico-online from juridico_online import empresa_url , buscar_url # Get company page URL for a CNPJ url = empresa_url ( " 00.000.000/0001-91 " ) print ( url ) # https://juridicoonline.com.br/empresa/00000000000191 # Search by company or partner name search = buscar_url ( " Magazine Luiza " ) print ( search ) Checks worth automating 1. Situação ATIVA before accepting any invoice INAPTA or BAIXADA companies cannot legally issue NF-e. 2. CNAE vs service being billed A company with CNAE "comércio de alimentos" billing for software development is a red flag. 3. Company age vs contract value A 3-month-old company offering a R$500k contract deserves extra scrutiny. 4. Shared partners across suppliers If two suppliers share directors, that's a conflict of interest. Search partner names at juridicoonline.com.br to see all companies they control. Integration patterns ERP/AP : validate CNPJ status before releasing payment Onboarding : auto-fill razão social when client enters CNPJ Batch audit : cross-check your vendor list quarterly Monitoring : alert if a key supplier's CNPJ changes status The data is public, free, and updated regularly. No excuse to check manually at scale.

A compliance team at a mid-size bank in Abu Dhabi processes new customer applications every week....

The AI landscape is evolving faster than ever. Keeping track of the right tools can feel like trying to drink from a firehose. I recently dug through my extensive bookmarks folders and compiled every single AI tool and Autonomous Agent I've saved. Whether you're looking for an autonomous coding agent, a rapid app builder, an LLM benchmark, or a creative suite, you need the right tool for the job. Bookmark this page, because you're going to want to refer back to it. Superdesign Maskara.ai Google Labs: Google's home for AI experiments - Google Labs Kilo Code - Open source AI agent VS Code extension hunyuan bolt.new Rocket.new | Build Web & Mobile Apps 10x Faster Without Code AI Web Scraping Extension | Chat4Data Sarvam AI Lovable Starc- film ShumerPrompt aipai.app Flowe MiniMax Official Website - Intelligence with everyone new.website | Build Websites with AI Higgsfield HeyBoss.ai Mitte Trickle AI - Turn your ideas into live apps and websites with AI. Dora: Start with AI, ship 3D animated websites without code Kimi AI – Think Bigger. Search Smarter. Write Better. a0.dev - Create Mobile Apps with AI sesame Vogent - Create AI Voice Agents Orchids - Make something beautiful Same PromptBase | Prompt Marketplace: Midjourney, ChatGPT, Sora, FLUX & more. LM Studio Mindstone Chat with Z.ai - Free AI for Presentations, Writing & Coding AI Model & API Providers Analysis | Artificial Analysis T3 Chat - Advanced AI Assistant & ChatGPT Alternative | $8/month Poe Freepik | All-in-One AI Creative Suite Replit – Build apps and sites with AI unwind ai Magic Patterns Soapbox - Build Your Decentralized Platform Shakespeare - AI Website Builder AI recruitment engine to hire top global talent | micro1 Ponder AI | New Way to Work with Knowledge Using AI Ask AI Questions · Question AI Search Engine · iAsk is a Free Answer Engine - Ask AI for Homework Help and Question AI for Research Assistance Firecrawl Kiro: The AI IDE for prototype to production Le Chat CodeArena – Which LLM codes best?

Every time a course batch completes, you have a list of students who need certificates. The manual way: open Canva, duplicate the template, change the name, export, repeat — for every single student. If you have 10 students, that's annoying. If you have 200, that's a full afternoon. The better way A single n8n workflow that: Reads student names from Google Sheets Calls the RenderPix batch API Gets back 200 certificate images Emails each student their certificate Total time: ~2.5 minutes. Total manual work: zero. What you'll need A RenderPix account (free tier works for testing, Starter plan for production) n8n (self-hosted or cloud) n8n-nodes-renderpix community node A Google Sheet with student data Install the n8n node: npm install n8n-nodes-renderpix Or search "RenderPix" in n8n's community node panel. Step 1 — Design your certificate template Write your certificate in plain HTML. Here's a clean starting point: <div style= "width:1200px;height:850px;background:white; display:flex;flex-direction:column;align-items:center; justify-content:center;border:20px solid #0f172a; font-family:Georgia,serif;padding:60px;box-sizing:border-box" > <div style= "font-size:16px;letter-spacing:5px;color:#64748b; text-transform:uppercase;margin-bottom:24px" > Certificate of Completion </div> <div style= "width:80px;height:2px;background:#22d3ee;margin-bottom:32px" ></div> <div style= "font-size:52px;font-weight:700;color:#0f172a;margin-bottom:16px" > {{name}} </div> <div style= "font-size:18px;color:#475569;text-align:center;max-width:600px" > has successfully completed </div> <div style= "font-size:28px;font-weight:600;color:#1e293b;margin:16px 0 40px" > {{course}} </div> <div style= "font-size:14px;color:#94a3b8" > {{date}} </div> </div> Notice the {{name}} , {{course}} , {{date}} placeholders — RenderPix replaces these at render time. Step 2 — Set up Google Sheets Create a sheet with these columns: name course date Jane Smith Advanced n8n Automation June 2026 John Doe Advanced n8n

Microsoft announced Logic Apps Automation at Build 2026, a new SKU at auto.azure.com packaging workflows, AI agents, knowledge services, and model access into a managed SaaS experience. Agents integrate via agent-loop orchestration, Foundry agents, and managed sandbox. Knowledge as a Service provides a fully managed RAG pipeline. By Steef-Jan Wiggers

It's 2am. A pipe just let go behind a kitchen wall and water is coming through the ceiling into the room below. The homeowner is standing in the dark in a panic, phone in hand, Googling "emergency plumber near me." They tap the first number. It rings four times and drops to voicemail. They don't leave a message. They tap the second number. You were the first number. You were asleep. And you just lost the most profitable job you'd have booked all week to whoever picked up on the second ring. This is the part of running a plumbing business that nobody puts on a P&L. The call that matters most arrives at the exact moment no human is there to answer it. And in plumbing, unlike any other trade, that's not the exception. It's the majority of the work. Plumbing's problem is different from every other trade An HVAC shop bleeds during summer rush. A roofer loses jobs to slow follow-up over a three-week sales cycle. Plumbing is its own animal, because plumbing is emergency-driven, and emergencies don't keep business hours. Industry call-tracking data tells the story. Depending on the source, anywhere from 40% to over 70% of home-service calls land outside the standard nine-to-five window, and for emergency-driven trades like plumbing it skews to the high end of that range. Either way the takeaway is the same. A huge share of your inbound isn't coming in while someone's at the desk. It's coming in at night, on a Saturday, on Thanksgiving morning when a basement is filling with sewage. If you're staffed to answer the phone nine to five, you are structurally set up to miss a large slice of your own demand. Not because you're doing anything wrong. Because the work shows up when the lights are off. And here's what makes it brutal. The after-hours emergency call isn't just any job. It's your best one. Why the missed emergency call costs more than the tech A routine daytime service call, a dripping faucet, a running toilet, runs a couple hundred dollars and the customer is happy to

LLM-powered Learning, Handwritten Digit Recognition, and AI Career Guidance Today's Highlights This week's top stories showcase practical AI applications: an LLM-powered tool for domain learning, a cloud-enhanced handwritten digit recognition system, and an AI-driven career guide. These projects demonstrate how AI frameworks are being applied to real-world workflows, from knowledge acquisition to personalized advice. Show HN: Lathe – Use LLMs to learn a new domain, not skip past it (Hacker News) Source: https://github.com/devenjarvis/lathe This project, Lathe, presents a novel approach to leveraging Large Language Models (LLMs) not just for quick answers, but for deep, structured learning within a new domain. Unlike traditional LLM interactions that might encourage skipping detailed research, Lathe aims to facilitate a more profound understanding by guiding users through a systematic learning process. It likely employs advanced retrieval augmentation generation (RAG) techniques, potentially combined with iterative prompting strategies and graph-based knowledge representation, to help users build a comprehensive knowledge base on a chosen topic. The framework focuses on transforming raw information into actionable insights and structured learning paths. This makes LLMs a powerful study aid, enabling domain experts or newcomers to grasp complex subjects more efficiently by providing tools for semantic search, concept mapping, and progressive knowledge acquisition, moving beyond simple question-answering into true assisted learning workflows. Comment: This is precisely what's needed for complex enterprise knowledge management – turning LLMs into an active learning partner, not just a summarizer. I'd explore how it structures knowledge graphs or progressive learning paths. Handwritten Digit Recognition System with Cloud and AI Enhancements (Dev.to Top) Source: https://dev.to/yohannesah/handwritten-digit-recognition-system-with-cloud-and-ai-enhancements-i4e This project

I'm starting a new project. It's the most ambitious thing I've attempted from a phone. The goal: an AI agent that controls a smartphone. It opens apps, navigates screens, taps buttons, types text, and completes multi-step tasks. All offline. All local. No cloud. This is Day 1 of a public build log. No fluff. Just what I'm building, how it works, and what breaks along the way. What I'm Building An autonomous AI agent that runs entirely on an Android phone. You give it a command in plain English: · "Open WhatsApp and message Mom I'll call later." · "Search for Kotlin jobs on Wellfound." · "Open my notes and summarize what I wrote yesterday." The agent parses the command, plans the steps, and executes them—opening apps, finding the right buttons, typing text, hitting send. No cloud. No API keys. Just a phone that acts on your behalf. The Stack Component Tool AI Brain Gemma 4 E4B (local, via Ollama) Runtime Termux (Linux on Android) Phone Control ADB + UI Automator Orchestration Python Why This Matters Most AI agents live in the cloud. They need internet, APIs, and someone else's server. A local agent that runs on a phone means: · Privacy: your data never leaves your device. · Offline: works even without internet. · Accessible: built for the device billions of people already own. The Hard Parts I Already See · The agent needs to "see" the screen to know where to tap. Text detection is doable. Image-based buttons are harder. · Multi-step tasks need verification. If one tap misses, the whole chain fails. · Android permissions. ADB requires developer mode. A user-facing version would need a workaround. What's Next · Day 2: Create the repo. Set up the project structure. Push the first working script. · Day 3: Get screen text detection working with OCR. · Day 4: Test a full 3-step task. This is Day 1. The repo goes live tomorrow. Follow along if you want to see something rare get built from scratch.

Your startup just got its first SOC 2 audit. The auditor asks: "Where are your database passwords, API keys, and service tokens stored?" Your senior engineer goes quiet. Turns out half of them are in .env files committed to git 18 months ago. Three are hardcoded in Lambda environment variables. One is in a Slack message from 2023. You have 6 services in production, 4 environments, and zero rotation policy. Here's the setup: • NestJS API → Postgres (password in env var) • NestJS API → Stripe (API key in env var) • Background workers → SQS, S3 (AWS credentials in env var) • 3rd-party webhooks → HMAC secrets in env var • Zero rotation. Zero audit trail. Zero centralized access control. You need to fix this. And you can't take downtime. A) Move everything to AWS Secrets Manager — SDK calls at runtime, IAM controls access, auto-rotation built in. B) Use HashiCorp Vault — dynamic secrets, fine-grained policies, works across any cloud or on-prem. C) Use environment variables injected at deploy time via CI/CD — secrets stored in GitHub Actions / GitLab CI secrets vault, never touch disk. D) Encrypt secrets with KMS and store ciphertext in your own database — decrypt at runtime, full control. All four are used in production at real companies. Pick one — A, B, C, or D — and tell me why. I'll drop the full breakdown in the comments. If your team is having this argument right now, share this post. Someone needs to see it. Drop your answer 👇 30DaysOfSystemDesign #SystemDesign #BackendEngineering #CloudArchitecture

How to Auto-Sync Your Hashnode Blog to Dev.to Using GitHub Actions (2026 Guide) FOLASAYO SAMUEL OLAYEMI FOLASAYO SAMUEL OLAYEMI FOLASAYO SAMUEL OLAYEMI Follow Jun 7 How to Auto-Sync Your Hashnode Blog to Dev.to Using GitHub Actions (2026 Guide) # discuss # automation # tutorial # devops 5 reactions Comments Add Comment 5 min read

Hi everyone, As a Full-Stack and Cloud engineer, I’m used to automating everything I can. Whether I'm managing my 28+ container Kubernetes homelab on Proxmox or writing deployment scripts, I absolutely hate doing the same manual task twice. But a few months ago, when I was hunting for a new role, I found myself doing exactly that: manually tweaking my resume for every single job, copy-pasting into black-box ATS portals, and tracking it all in a chaotic spreadsheet. It was completely draining. So, I took a break from the applications and built a tool to solve my own problem. It’s called OneApply. It started as a small browser extension to check ATS keywords, but it quickly snowballed into a complete workspace. Here is what the stack handles now: Resume Tailoring: Automatically adjusts your resume to fit specific job descriptions. ATS Keyword Scoring: Checks your overlap with the job description so you know you'll actually pass the automated filters. Cover Letter Generation: Drafts contextual cover letters based on the role and your specific engineering experience. Pipeline Tracking: Manages all your applications natively so you can finally ditch the spreadsheets. Building this and using it to automate the worst parts of the daily grind actually helped me land my current SRE role. Since it worked for me, I decided to polish it up and release it for other devs who are currently stuck in the application trenches. We all know the tech market is tough right now, and any edge helps. I would love for this community to try it out and roast the UX, the workflow, or the core features. Check it out here: https://www.oneapply.app I am more than happy to hand out some premium access codes to anyone here who is actively applying and wants to test drive the full feature set. Just drop a comment below!

Most web scraping projects are not unique snowflakes. Track competitor prices. Enrich a list of leads. Audit a site for SEO. Pull training data for a model. It is the same handful of recipes, over and over. A web scraping template is one of those recipes, pre-wired: a ready-to-use JSON config that chains the right tools in the right order, so you copy it, point it at your targets, and run. CrawlForge ships 24 of them in the templates gallery . This guide is about using them well — not just copy-paste, but read, adapt, and cost them out before you scale. TL;DR: A CrawlForge template is a copy-paste JSON config that chains multiple MCP tools into one workflow (price monitoring, lead enrichment, SEO audits, market research, AI training data). There are 24 across 9 categories, each costing 3–19 credits per run. Run them from Claude/Cursor, the crawlforge CLI, or the REST API. Free tier = 1,000 credits, no credit card. Table of Contents What Is a Web Scraping Template? Templates Gallery vs the scrape_template Tool How to Use a Template the Right Way 8 Templates Worth Copying First The Other 16 Templates Customizing or Building Your Own FAQ What Is a Web Scraping Template? A template is a saved configuration that orchestrates two or three CrawlForge tools into one workflow with a business outcome attached. Instead of wiring search_web then scrape_structured then analyze_content yourself — and guessing every parameter — you copy a config that already does it. Each template in the gallery carries: A category — E-commerce, Research, Data Collection, Monitoring, AI & LLM, Sales, SEO, Content, or Advanced Scraping (nine in total). A difficulty — beginner, intermediate, or advanced. The tool chain it runs and a fixed credit cost per run (3–19 credits). A copy-paste JSON config with sensible default parameters. You run that config from any MCP client (Claude, Cursor, Windsurf), the crawlforge CLI, or the REST API. Same config, same shape of result. Templates Gallery vs the scrap

Table of Contents The Integration Problem That Broke Industry 4.0 MCP: The Vertical Connection Layer How MCP Connects to Servers, Tools, and Databases MCP in Real World Industrial Automation ACP: The Horizontal Communication Layer How ACP Works Under the Hood ACP in Real World Industrial Coordination The Six Precise Differences How They Work Together: The Complete Stack Decision Framework for Industrial AI Architects 1. The Integration Problem That Broke Industry 4.0 Industry 4.0 promised connected factories, intelligent automation, and seamless data flow between machines, systems, and humans. The technology arrived. The connectivity did not. The reason is a number called N times M. An enterprise manufacturing facility might have 12 AI agents across quality, maintenance, and planning — and 28 data sources including ERP, MES, SCADA, IoT sensors, databases, CAD repositories, and supplier APIs. Without a standard protocol: 12 agents multiplied by 28 data sources equals 336 custom integrations. Each integration is bespoke code. Each breaks when either side updates. Each requires maintenance. Each represents a point of failure and a security surface that must be independently managed. IBM VP Armand Ruiz stated this precisely: "Without a common standard, every integration is costly duct tape." MCP and ACP together replace 336 pieces of duct tape with two standard protocols — one governing how agents connect to systems, one governing how agents connect to each other. The smart manufacturing market is projected to reach 374 billion dollars by 2025 at 11.8 percent CAGR. Over 50 percent of companies in industrial automation are expected to adopt MCP-based connectivity. The integration problem is not theoretical. The solution is being deployed at scale right now. 2. MCP: The Vertical Connection Layer MCP connects agents to tools and data — the vertical integration layer. It handles the connection between an AI agent and everything it needs to interact with in the external worl

Imagine your smartwatch detects an irregular heart rhythm at 3 AM. Instead of just waking you up with a frantic "beep," an AI agent immediately analyzes your historical health data, searches for the best cardiologist nearby, and prepares a calendar invite for a consultation. This isn't science fiction—it's the power of Healthcare Automation driven by AI Agents . In this tutorial, we are diving deep into LangGraph , the cutting-edge framework for building stateful, multi-agent applications. We’ll explore how to use State Machines to orchestrate a complex medical workflow, moving from an "Abnormal Heart Rate Alert" to a "Specialist Appointment" using the Tavily API for research and Twilio for urgent notifications. By the end of this guide, you’ll understand how to manage non-linear LLM workflows that require reliability and precision. The Architecture: Why LangGraph? Traditional LLM chains are linear. But medical emergencies are not. They require loops, conditional branching (e.g., "Is this an emergency or a routine check-up?"), and state persistence. LangGraph allows us to define a graph where each node is a function and edges define the transition logic. Data Flow Overview The following diagram illustrates how our agent processes a heart rate alert: graph TD A[Start: Heart Rate Alert] --> B{Severity Triage} B -- Emergency --> C[Twilio: Alert Emergency Services] B -- High Risk --> D[Tavily API: Find Best Specialist] B -- Normal/Review --> E[Log to Health Records] D --> F[Google Calendar: Draft Appointment] F --> G[Twilio: SMS Patient Confirmation] C --> H[End] G --> H E --> H Prerequisites 🛠️ To follow along with this advanced tutorial, you'll need: Python 3.10+ LangGraph & LangChain : The orchestration engine. Tavily API Key : For searching local medical specialists. Twilio Account : For SMS/Voice alerting. An OpenAI API Key (GPT-4o is recommended for medical reasoning). Step 1: Defining the Agent State In LangGraph, the State is a shared schema that evolves as it m

Dropbox Nova for AI Coding Agents, OpenAI's Codex Sandbox, & Puppeteer MCP Server Today's Highlights This week, we dive into Dropbox's Nova platform for scaling AI coding agents and OpenAI's secure sandbox architecture for Codex, highlighting advanced production deployments. We also examine practical solutions for safer browser automation for AI agents, detailing a custom Puppeteer MCP server. Dropbox Introduces Nova, an Internal Platform for Running AI Coding Agents at Scale (InfoQ) Source: https://www.infoq.com/news/2026/06/dropbox-nova-ai-coding-agents/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global Dropbox has unveiled Nova, an internal platform meticulously engineered to orchestrate and scale AI coding agents. This platform tackles the complex challenges of managing autonomous AI entities performing tasks like code generation, bug fixing, and refactoring across a large codebase. Nova's architecture focuses on reliability, efficiency, and safety, providing a robust environment for thousands of agents to operate concurrently without overwhelming system resources or introducing instability. The platform acts as a critical layer between AI models and the vast codebase, enabling agents to interpret development tasks, interact with repositories, and propose changes in a controlled manner. The significance of Nova lies in its ability to industrialize the use of AI in software development workflows. By abstracting away the operational complexities of agent deployment and execution, Dropbox empowers its engineering teams to leverage AI as a force multiplier, accelerating development cycles and improving code quality. Nova represents a practical, large-scale implementation of AI agent orchestration, demonstrating how companies are moving beyond experimental AI tools to integrate them deeply into core business processes. This showcases a production-grade pattern for applied AI, particularly relevant for "code generation" and "workflow automati

Most AI short-form video demos skip the boring part. They show a finished TikTok, Reel, or YouTube Short. Maybe they show the prompt. Maybe they show the generated script or the final render. But the hard part is not making one video. The hard part is making the fifteenth video without the whole system turning into a pile of one-off scripts, half-remembered FFmpeg commands, broken captions, inconsistent hooks, and manual upload steps. That is where I think the conversation around AI video automation gets more interesting. Not: Can an AI generate a Short? But: What workflow does an AI agent need to generate Shorts repeatedly? I was looking at a Terminal Skills use case for building an AI short video generator, and the useful part is not the fantasy of "push one button, print infinite content." The useful part is the stack. The real job is a pipeline A short-form video generator sounds like one tool. In practice, it is a pipeline: topic research -> script -> voiceover -> footage or visual generation -> subtitles -> assembly -> platform formatting -> upload -> analytics Each step has different failure modes. Topic research can produce generic ideas. Scripts can be too long. Voice can drift from the brand. Footage can mismatch the narration. Subtitles can land under platform UI. FFmpeg can export a technically valid file that a platform still hates. Uploads can succeed in the API but fail the actual publishing workflow. If you try to solve all of that with one giant prompt, the agent has to keep too much operational knowledge in its head. That is fragile. The better pattern is to split the workflow into skills. What a skill gives the agent A skill is not just a code snippet. For this kind of workflow, a useful skill tells the agent: when to use this capability what inputs are expected what output should exist afterward what validation is required when to stop instead of pretending success That last point matters. For media automation, "the command ran" is not enough. Th

TL;DR I built AutoMaintainer , a multi-agent AI system that transforms GitHub issues into production-ready pull requests during the Qwen Cloud AI Hackathon. It coordinates specialized agents (Issue Analyst, Developer, QA, Security, Documentation, Reviewer) to solve problems like a real engineering team—all while keeping humans in control. Here's what I learned. The Problem Open-source maintainers face a brutal reality: 📚 Overwhelming issue backlogs 🔄 Repetitive bug fixes and documentation gaps ⏱️ Code review bottlenecks 😴 Burnout from handling everything solo Existing AI tools help write code, but they don't orchestrate the entire workflow: planning, development, testing, security review, documentation, and human approval. What if we could build an AI engineering team that collaborates like real developers? The Solution: AutoMaintainer AutoMaintainer is a multi-agent orchestration system that mirrors a real software company: Issue Analyst – Reads GitHub issues, extracts requirements, assesses severity Architect – Analyzes repo structure, designs the implementation approach Developer – Writes code, updates files, creates new modules QA Tester – Generates tests, validates fixes, checks edge cases Security Agent – Scans for vulnerabilities, prevents dangerous patterns Documentation – Updates changelogs, PR summaries, release notes Reviewer – Scores code quality, recommends improvements Human Approval Gateway – Final human review before merge The result? A pull request that's analyzed, built, tested, secured, documented, and reviewed—all before a human ever sees it. Tech Stack Frontend Next.js – React framework for the dashboard UI Tailwind CSS – Rapid, utility-first styling TypeScript – Type safety for the frontend layer Backend FastAPI (Python) – Lightweight, async-first API Qwen-compatible LLM API – AI model integration for all agents SQLite + Async (aiosqlite) – Persistent pipeline and memory storage Redis-ready architecture – Prepared for distributed queuing Integr

Over the past few months I’ve been building VoiceIntego, an AI voice agent that answers calls and books appointments for service businesses (dental clinics, HVAC, plumbing). Here are some of the technical lessons that surprised me along the way. Latency is the whole game With text chatbots, a 2-second delay is fine. On a phone call, anything over ~800ms feels broken — people start talking over the AI. The hard part isn’t the LLM response; it’s the round trip: speech-to-text → LLM → text-to-speech, all streaming. You have to stream every stage and start TTS before the full response is generated. Interruptions break naive pipelines Real callers interrupt. “Actually, can we do Tuesday instead—” mid-sentence. A simple request/response loop can’t handle this. You need barge-in detection: monitor the incoming audio stream and cancel the current TTS playback the moment the caller starts speaking again. Booking logic needs guardrails, not vibes Letting the LLM “decide” availability is a recipe for double-bookings. The reliable pattern: the LLM extracts intent (date, time, service), then deterministic code checks the actual calendar API and confirms. The model handles language; your code handles truth. Confirmation loops matter more than you’d think Always read the booking back: “So that’s a cleaning on Tuesday the 9th at 2pm — correct?” Phone audio is noisy and names/times get misheard constantly. One extra confirmation turn cuts errors dramatically. Phone numbers and edge cases everywhere Voicemail detection, callers who mumble, background noise, people who say “yeah” to mean no. The happy path is maybe 20% of the work. If you’re building something in this space, happy to compare notes. You can see what I’m working on at VoiceIntego .

LLM Cost Attribution with OTel, Next.js for AI Agents, LLM Security Testing Today's Highlights This week, we delve into practical strategies for managing LLM costs in production using OpenTelemetry and explore Next.js 16.2's new tooling for building AI agent frontends. We also examine an experiment on LLMs' ability to exploit application vulnerabilities, emphasizing security in applied AI. Per-project LLM cost attribution with OTel spans: the wiring (Dev.to Top) Source: https://dev.to/jasmine_park_dev/per-project-llm-cost-attribution-with-otel-spans-the-wiring-3897 This article details a practical approach to attributing Large Language Model (LLM) costs to specific teams or projects within an organization. Facing a common problem of LLM bills appearing as a single line item, the author describes how to implement granular cost tracking using OpenTelemetry (OTel) spans. The core idea involves instrumenting the LLM gateway to tag every request span with relevant metadata like team.id and llm.model_name . This allows for detailed reporting and chargebacks, enabling organizations to understand and manage their LLM expenditure effectively. The implementation focuses on "the wiring" behind this system, leveraging OTel for observability. By attaching custom attributes to spans, teams can aggregate usage data by project, department, or even specific application features. This moves beyond opaque cloud invoices to actionable insights, a crucial step for companies scaling their AI adoption and seeking to optimize resource allocation and financial accountability for generative AI services. The article provides a blueprint for integrating this mechanism into existing LLM infrastructure. Comment: Setting up OTel spans for LLM cost attribution is a game-changer for production environments, finally giving us visibility into who's spending what on which models. This technique is essential for scaling LLM applications sustainably. Next.js 16.2: Deeper Tooling for AI Agents (InfoQ) So