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Everyone evaluating AI avatar platforms focuses on voice quality. The bigger UX killer is almost always latency — and it's a harder problem than picking a good TTS provider. Where the delay actually comes from: User speaks/types → STT (if voice input) → LLM generates response (streaming helps, but first-token latency matters) → TTS converts text to audio → Audio playback + lip-sync rendering Each hop adds latency. A naive implementation that waits for the full LLM response before starting TTS can easily hit 2-4 seconds of dead air — long enough for a user to assume the bot is broken. How production systems actually solve this: Token streaming into TTS — start synthesizing audio on partial LLM output (sentence-by-sentence chunks) instead of waiting for the full response Speculative rendering — start lip-sync animation slightly ahead of audio using predicted phoneme timing WebSocket/SSE persistent connections — avoid the overhead of repeated HTTP round-trips per turn Regional API routing — TTS/LLM provider latency varies a lot by user geography; this matters more than most benchmarks show A practical note: platforms that advertise "real-time" avatars but load all logic behind a single request/response cycle will feel noticeably worse than ones built around streaming pipelines, even if they use the identical LLM and TTS providers underneath. If you're evaluating a platform (or building one), test with realistic network conditions, not office wifi — that's where the architecture differences actually show up. Bottom line: the voice provider matters less than people think. The orchestration around it — how aggressively you stream and pipeline each stage — is what separates a "wow" demo from a production-ready conversational agent.

Building an AI Operating Layer Episode 1 Why I Didn't Start Sooner Most engineering projects begin with an idea. This one began with a question. For months I found myself watching the explosion of AI tools, frameworks, models, and agent platforms. Every week there seemed to be another breakthrough, another library, and another opinion about where everything was headed. I could have started building immediately. Part of me thought I should have. But I realized something, and it kept bothering me. I wasn't afraid of writing code. I was afraid of solving the wrong problem. When a new technology appears, it's easy to jump straight into implementation. Pick a framework. Choose a model. Build something. Ship it. I didn't want to start there because I had a feeling there was a much bigger picture that I wasn't seeing yet. So I waited. I spent my time reading, experimenting, asking questions, and trying to understand how all of these pieces connected. The more I learned, the more I realized I wasn't actually interested in building another AI application. What fascinated me was the system behind the systems. What happens when you stop looking at models, memory, orchestration, tools, policies, and execution as separate ideas and start seeing them as parts of a much larger ecosystem? That question became the beginning of this project. This isn't a story about predicting the future. It's a story about trying to understand it. I'm sure some of my assumptions will be wrong. I'm sure parts of this architecture will change. If they do, you'll see that too. I don't want this journal to only show the polished results. I want it to capture the discoveries, the wrong turns, the redesigns, and the moments where a better idea replaces an old one. At the center of this journey is a project I'm calling the AI Operating Layer. Today it's mostly architecture, documentation, research, prototypes, and a growing collection of ideas. Maybe that's exactly where projects like this should begin. I'

A month ago I shipped Verdict ( https://verdict.tools ), a registry that tracks the life-status of AI and SaaS tools: alive, price-hiked, degrading, sunset-announced, or dead. Every status carries a date and a source, reachability is re-checked twice a day from two servers, and a "dead" verdict is only ever set by a human. The registry now holds 18 confirmed deaths and sunsets, plus a set of dated decline signals on tools that are still alive. Cataloguing them turned up patterns I did not expect. Sharing the five that surprised me most. 1. The two-day goodbye is real Xembly, a Seattle "AI chief of staff" that raised about 20M USD and counted Salesforce and Twilio among customers, told users on May 30, 2024 that the service would end June 1, 2024. Two days. Anything not downloaded by May 31 was gone. The epilogue is grimmer: the lapsed xembly.com domain now hosts an Indian cricket betting site. If your workflow depends on a venture-backed tool, your real SLA is however fast you can export. 2. An acqui-hire is usually a death sentence for the product The pattern repeats with minor variations: Rephrase.ai -> Adobe (Nov 2023): signups closed, wind-down announced Feb 2024, site now fully offline Play.ht -> Meta (Jul 2025): product shut down Dec 31, 2025 Papercup -> RWS (Jun 2026): RWS bought only the IP; the standalone product is gone Clockwise -> Salesforce (2026): product unavailable since March 27, 2026 The press release always says "we're joining X to continue the mission." The product's mission ends anyway. When your vendor gets acqui-hired, start the migration that week. 3. The consumer app dies first; the API gets a stay of execution OpenAI shut down the Sora app on April 26, 2026, but the Sora API keeps running until September 24, 2026. That five-month gap is the migration window for developers, and it is closing. The same staging shows up elsewhere: user-facing surfaces are expensive to run and easy to kill; contracts and integrations buy the API extra months. I

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Introduction For the last few weeks, I’ve been running a small experiment. Instead of building another SaaS startup or chasing investors, I decided to build a simple website around AI tools and document everything publicly. No team. No marketing budget. No SEO agency. Just curiosity, consistency, and a lot of trial and error. I genuinely want to answer one question: Can a small AI website still grow organically in 2026? ⸻ Why I Started AI tools are everywhere now. Every day another directory, another “best AI tools” list, another comparison website appears. Most people say it’s already too late. Maybe they’re right. I wanted to find out myself instead of trusting opinions. So I bought a domain and started building. ⸻ My Rules To make the experiment interesting, I gave myself a few restrictions. No buying backlinks. No paid traffic. No huge content team. No publishing hundreds of AI-generated articles. Everything has to be something I would actually publish. Quality first. ⸻ The First Product Instead of only writing articles, I decided the website should also offer something genuinely useful. The first tool is a free AI Background Remover. Nothing revolutionary. But it solves a real problem in a few seconds, and that felt like a better starting point than another generic blog post. ⸻ What I’ve Learned So Far The biggest surprise wasn’t building the tool. It was realizing how much work happens after pressing “Publish.” Indexing. Technical SEO. Site structure. Internal linking. Performance. Small details matter far more than I expected. ⸻ The Goal I’m not trying to build the next unicorn. I simply want to see whether a small independent website can still earn organic traffic by creating useful content and useful tools. If it works, great. If it fails, I’ll document that too. Either way, I’ll share the results. ⸻ Try the Tool If you’re curious, you can try the first tool here: 👉 https://letomix.com/free-tools/background-remover/ I’d genuinely appreciate any feedback.

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You correct someone once. Not perfectly, but they get it. Next time, they do not make the same mistake. That is not optimism. That is just how correction works, "with people". I worked with agents on that assumption for a long time before I even noticed I was doing it. The plan that never held Before I had a single written rule anywhere, I would open a new session and ask for a plan first. Resolve the edge cases before touching a line of code, I said. The agent would agree, in whatever way a chat window agrees, and go straight to implementation anyway. I corrected it. Same session, it adjusted. New session, next day, same repo, same everything except the chat history: straight to implementation again. Every single time! So I did what looked reasonable. I wrote the plan myself. I resolved the edge cases myself, the open questions, the gaps the agent skipped past on its way to code. ' Tedious ' is the polite word for it. I was doing the one task I brought the agent in to do, and calling it collaboration. The same recipe, again The second correction arrived the same way. Every repo had its own shape. A recipe, a standard, a way things were supposed to be built here and not there. I would explain it. Full session, good results, the agent following the standard like it understood the standard. New session. Same repo, sometimes the new repo. Explain it again. Word for word, close enough. It was not that the agent forgot how to code. It was that nothing from the last conversation traveled with it into this one. Nothing said in the chat survives it I kept treating this like a training problem. Say it clearer. Say it earlier. Say it with an example next time. None of that was wrong exactly. It was aimed at the wrong layer. The actual mistake was assuming correction compounds the way it does with a person. It does not. A person carries what you told them into the next conversation without being asked to. An agent starts the next session exactly where it started the first one.

When you open a website, send a message, or watch a YouTube video, many technologies work together in the background. As a beginner in DevOps, understanding these basic networking concepts will help you understand how applications communicate over the internet. **What Is a Protocol? A protocol is a set of rules that devices follow when communicating with each other. Think about two people having a conversation. For communication to be successful, both people must speak the same language and follow simple rules, like taking turns to talk and listening before responding. Computers work the same way. They use protocols to know how to send, receive, and understand information. Without protocols, computers would not be able to communicate with one another. **2. What Is Packet Switching? **Imagine you want to send a large book to a friend. Instead of sending the entire book in one huge package, you divide it into many smaller packages. Each package travels separately and, when they all arrive, your friend puts them back together in the correct order. This is exactly how the internet works. When you visit a website, your data is broken into small pieces called packets. Each packet travels across the internet and is reassembled when it reaches its destination. This process is called packet switching, and it makes internet communication faster and more reliable. **3. What Is an IP Address? **Every house has a unique address that helps delivery drivers know where to deliver packages. Similarly, every device connected to the internet has a unique Internet Protocol (IP) address. An IP address helps the internet know exactly where information should be sent. Without an IP address, websites, computers, and phones would not know where to send or receive data. **4. What Is TCP/IP? **Breaking data into packets is not enough. The packets must also arrive correctly. This is where TCP/IP (Transmission Control Protocol/Internet Protocol) comes in. IP finds the correct destination for ea

Internships are one of the most valuable experiences for any undergraduate, and I am grateful to have completed mine. This journey allowed me to bridge the gap between academic knowledge and real-world software development while improving both my technical and professional skills. From my very first day, I was introduced to a collaborative development environment where teamwork, communication, and problem-solving played a major role. I had the opportunity to work on real projects, understand industry workflows, and learn how professional software products are built and maintained. Throughout my internship, I gained hands-on experience with modern web technologies, version control using Git, API integration, debugging, and deploying applications. I also learned the importance of writing clean, maintainable code and following industry best practices. Working alongside experienced developers helped me improve my coding standards and exposed me to new tools and frameworks. One of the biggest lessons I learned was that software development is not only about writing code. It involves understanding user requirements, collaborating with team members, managing deadlines, and continuously learning new technologies. Every challenge I encountered became an opportunity to grow and improve my skills. Beyond technical knowledge, this internship strengthened my confidence, communication, time management, and ability to work effectively in a professional team. The guidance and support from my mentors played a significant role in my growth throughout this journey. Looking back, this internship has been a milestone in my career. It has given me practical experience, valuable industry exposure, and a clearer vision of the software engineering field. I am excited to apply these lessons in my future projects and continue growing as a developer. I would like to express my sincere gratitude to my mentors, teammates, and the organization for providing me with this incredible opportunity. Th

We're back with another Frontend Challenge, and this time we're hungry! 🍜🥧 Running through August 16 , Frontend Challenge: Comfort Food Edition invites you to build something inspired by the food that makes you feel at home. Show off the dish you make when nothing else will do, build a site for a restaurant that exists (or one that only lives in your head), share the recipe you've been perfecting for years, or put a spotlight on a regional dish that deserves more attention. Whether you're a CSS connoisseur, a JavaScript chef, or somewhere in between, there's a prompt here for you. We hope you give it a try! The Prompts CSS Art: Comfort Food Create a work of art using primarily CSS! Let food be your inspiration: a steaming bowl of ramen, a stack of pancakes, a perfectly cut slice of pie, or the dish you grew up eating. CSS Art Submission Template Note: We're now allowing a sprinkle of JavaScript in CSS Art submissions! However, judging will continue to focus primarily on the CSS component, so keep JavaScript usage light and purposeful. The star of the show should still be your CSS skills. Perfect Landing: Comfort Food Build a polished, functional landing page with a food theme. This could be a real or imaginary restaurant, a recipe collection, a food festival, a love letter to a regional dish, or anything else you can imagine, as long as it captures the theme and demonstrates excellent frontend fundamentals. Perfect Landing Submission Template Note: You may use JavaScript, TypeScript, Dart, WebAssembly, or any other browser-compatible language/runtime in your Perfect Landing submissions! Show us what modern web development can do. Judging Criteria and Prizes CSS Art submissions will be evaluated on: Creativity Effective Use of CSS Aesthetic Outcome Perfect Landing submissions will be evaluated on: Accessibility Usability and User Experience Creativity Code quality Prizes Each prompt winner will receive a DEV++ Membership and an exclusive DEV Badge. All Participants w

A portfolio for thoughtful, reliable learning technology I am a senior full-stack software engineer with 20 years of experience building scalable web applications, primarily for learning and education. I recently published a focused portfolio to share the products, technologies, and engineering work behind that experience. Diogo Bastos | Senior Full-Stack Software Engineer Professional portfolio of Diogo Bastos, a senior full-stack software engineer. diogobastos.pages.dev The site is intentionally straightforward: a clear overview of my background, selected professional work, personal projects, certifications, and a public résumé. What you will find Learning technology work : projects across Pearson eDynamic Learning, HMH, and Neovation Learning Solutions. Full-stack engineering : React and TypeScript on the frontend; Node.js, Java, APIs, SQL, and cloud delivery practices on the backend. Recent personal projects : experiments in Python, FastAPI, React, AWS, and Java/Spring Boot. A concise, accessible build : the portfolio is a static site built with Astro, with attention to responsive design and usability. I care about turning complex product needs into dependable experiences for the people who use them. If you work in software engineering, learning technology, or product development, I would be glad to connect. Explore the portfolio: diogobastos.pages.dev Thanks for stopping by.

A few weeks ago I wrote up what agents were paying for on NetIntel , my platform of pay-per-call APIs settled in USDC over x402 — no signup, no API keys, no accounts. An agent hits an endpoint, gets a 402 Payment Required , pays a fraction of a cent, and gets structured data back. That's the whole loop. Since then the dataset has grown, I've instrumented every settled call into a proper database (payer wallet, endpoint, price, latency, transaction hash), and I've launched a second settlement rail. So this is the rewrite with real numbers instead of eyeballed ones — and the findings didn't soften. They sharpened. The setup 2,646 settled paid calls from 194 distinct paying wallets, across 101 live endpoints , over six weeks of instrumented production data. Every call in this dataset is a real on-chain payment with a transaction hash — no test traffic, no estimates. Settlement runs on Base, and as of this month on Solana too. This is still one platform's data in a young ecosystem — the caveats are at the bottom, and one of them is bigger than it looks. But the shape has now held for six weeks straight, and it's the same shape I flagged the first time. Finding 1: revenue is absurdly concentrated — and it stayed that way Five endpoints drive 69% of all revenue. One of them — a text-to-structure endpoint that takes messy input and returns strict typed JSON — is 42% by itself . The rest of the top five are all in the same family: translation, structured LLM inference, and one domain-intelligence report. The other 96 endpoints split the remaining 31%. Thirty-five of the 101 have never been paid for once. Not "underperformed" — zero settled calls, ever. When I first published this pattern I wondered if it was an artifact of a small sample. The dataset has since more than doubled and the concentration ratio barely moved. I now treat it as the market talking, not noise. Here's the part I'd want to know if I were reading this: that 42% endpoint is essentially one buyer — a wall

Six months ago I wrote up my local AI setup and a reader bookmarked it, tried to reproduce it last week, and emailed me confused because half of it no longer matched what I actually run. Fair. Stacks rot quietly. So here's the mid-2026 state of mine: what's still earning its place on disk, what I deleted, and where I quietly went back to the cloud. Context for the numbers and opinions below: I do smart contract security work, I run everything on WSL2 on a machine with a modest GPU, and I've been doing the local-model thing daily for over a year, not as a hobby but as part of shipping. Still here: Ollama as the runtime Ollama remains the center of the local stack and honestly it's not close. I've tried the alternatives, llama.cpp directly for control, a couple of the newer serving layers for speed, and I keep coming back for one boring reason: the API is stable and everything I've built talks to it. My audit tooling, my shell scripts, my editor config, they all point at localhost:11434 and they've pointed there for a year without breaking. That stability matters more than a marginal tokens-per-second win. When a model update lands, ollama pull and I'm done. The day something meaningfully better appears with the same API shape, I'll switch in an afternoon, which is exactly the position you want to be in. Still here: qwen2.5-coder, both sizes, different jobs I run two models and the split has stayed remarkably stable: qwen2.5-coder:1.5b is the reflex model. It handles anything where speed matters more than depth: quick "what does this diff do" summaries, commit message drafts, pre-filtering files before a heavier pass, and the small classification jobs inside my pipelines ("does this file handle user input, yes or no"). It's fast enough on my machine that I never think about invoking it, and that's the whole point. A model you hesitate to call is a model you stop calling. qwen2.5-coder:7b is the thinking model. Code review, security triage, structured findings extracti

Hey everyone, Whenever I start scaling out a new Rust service or protocol, I always find myself hitting the same wall: testing gets messy fast. You end up juggling basic cargo test unit checks, hacking together ad-hoc integration scripts, and manually setting up coverage tools every single time. I put together RustForge to solve that headache for my own projects, and figured it might save a few of you some time too. It’s a clean, zero-bloat starter template designed to take you from simple unit tests all the way to compiler-style UI snapshots and coverage tracking without having to reinvent the harness every project. https://github.com/rwilliamspbg-ops/RustForge

Beginner's Guide: Connect React with Supabase 🚀 If you already know basic React (components, useState , useEffect ), this guide will show you how to connect your React app to Supabase — an open-source Firebase alternative — and build a simple To-Do app with full CRUD (Create, Read, Update, Delete). Let's go step by step. No prior Supabase knowledge needed. What is Supabase? Supabase gives you a Postgres database , authentication , and instant APIs — without writing any backend code. Think of it as a backend-as-a-service. For this guide, we'll just use the database + auto-generated API part. Step 1: Create a Supabase Project Go to supabase.com and sign up (GitHub login is fastest). Click New Project . Fill in: Name : todo-app (anything you like) Database Password : save this somewhere safe Region : pick the closest one to you Click Create new project and wait ~1-2 minutes while Supabase sets everything up. Step 2: Create the todos Table In your Supabase project dashboard, go to the Table Editor (left sidebar). Click New Table . Name it todos . Add these columns (in addition to the default id and created_at ): Column Name Type Default task text — is_complete bool false Click Save . 💡 Tip: You can also do this via the SQL Editor by running: create table todos ( id bigint generated by default as identity primary key , task text not null , is_complete boolean default false , created_at timestamp with time zone default now () ); Turn off Row Level Security (for learning purposes only) Go to Authentication > Policies (or Table Editor > todos > RLS), and disable RLS for now so your students can read/write freely without setting up auth. ⚠️ Important for your session : Tell your juniors this is only for a demo/learning project. In a real production app, RLS should always be enabled with proper policies. Step 3: Get Your API Keys Go to Project Settings > API . Copy two things: Project URL (looks like https://xxxxx.supabase.co ) anon public key (a long string) You'll need both

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Quando eu decidi aprender como a JVM funciona por dentro, eu precisava de uma linguagem simples o suficiente pra não atrapalhar o aprendizado. Algo onde eu pudesse focar na mecânica do compilador sem me perder na complexidade da linguagem fonte. Brainfuck foi a escolha óbvia. Esse é o primeiro post de uma série de três onde a gente vai construir, do zero, um compilador que transforma código Brainfuck em bytecode JVM executável. Sem dependências externas, sem framework, só Node.js puro. No final da série, você vai ter um compilador que gera arquivos .class válidos que rodam direto no java . O código completo está no GitHub . Nesse primeiro post, a gente vai construir o interpretador - que é a base pra tudo que vem depois. O que é Brainfuck Brainfuck é uma linguagem de programação esotérica criada em 1993 por Urban Müller. Ela tem 8 comandos . Oito. E ainda assim é Turing-completa - ou seja, em teoria, você pode computar qualquer coisa que qualquer outra linguagem computa. O modelo de execução é simples: Uma fita de memória com 30.000 células, cada uma armazenando um byte (0-255) Um ponteiro que aponta pra célula atual Entrada e saída (stdin/stdout) Os 8 comandos: Comando O que faz + Incrementa o valor da célula atual - Decrementa o valor da célula atual > Move o ponteiro uma célula pra direita < Move o ponteiro uma célula pra esquerda . Imprime o valor da célula atual como caractere ASCII , Lê um byte da entrada e armazena na célula atual [ Se a célula atual é zero, pula pro ] correspondente ] Se a célula atual não é zero, volta pro [ correspondente Qualquer outro caractere é ignorado - o que significa que você pode escrever comentários livremente no meio do código. Um exemplo simples Pra imprimir a letra "A" (código ASCII 65), você precisa colocar o valor 65 na célula e usar . : +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ . São 65 sinais de + seguidos de um . . Funciona, mas é feio. Uma forma mais elegante: ++++++++ [ > ++++++++ < - ] > +. O qu

A few weeks ago I needed to check something in the Action Mailer docs, just a quick lookup. I ended up spending much more time there than expected and found a few features I had no idea existed, even though I've been using Action Mailer in production for a while. One of them lets you see an email before it's ever sent. Another lets you modify an email right before it goes out. And the third one allows you to override the default delivery options dynamically. I figured I probably wasn't the only one who had missed these, so here are three Action Mailer features that caught my attention. If you want to explore more, the official Action Mailer documentation is always a great place to start. 1. Previews Before I found this, testing an email meant sending it to myself, checking my inbox, tweaking the template, and repeating. Turns out ActionMailer has a built-in way to preview emails in the browser, without sending anything. You add a preview class in test/mailers/previews like: class InvitationMailerPreview < ActionMailer :: Preview def team_invitation InvitationMailer . with ( user: User . first , company: Company . first ). team_invitation end end And visit http://localhost:3000/rails/mailers/invitation_mailer/team_invitation . This removes the usual feedback loop of tweaking a template. You just refresh the browser instead. Rails also allows custom preview paths if you want to keep previews in a different location: config . action_mailer . preview_paths << " #{ Rails . root } /lib/mailer_previews" This was a small discovery, but it immediately improved my workflow. 2. Interceptors An interceptor is a hook that runs right before an email is handed off for delivery, letting you modify it. A common use case is preventing mistakes in staging environments. Nobody wants to accidentally send a real looking email from a staging application to an actual customer. Another common approach is redirecting all outgoing mail in staging or development environments to a single defaul

Your monitoring catches problems on everything except itself. Here is how an always-firing Watchdog alert plus an external heartbeat check turns silence into a signal, so you find out when your own alerting dies. TL;DR A monitoring system can't reliably monitor its own failure, so use a dead man's switch. Create an always-firing Prometheus Watchdog alert and route it to an independent external heartbeat service. As long as the monitoring pipeline is working, the Watchdog continuously refreshes the heartbeat. If Prometheus, Alertmanager, or the delivery path fails, the heartbeat stops and the external service alerts you through a separate channel. The key is independence: the system responsible for detecting that your monitoring is down must not depend on the monitoring stack itself. One of the traps of creating alerts on a monitoring stack is the hidden assumption that the mechanism evaluating the alert is running properly and has the ability to evaluate it. Prometheus watches your hosts and Alertmanager delivers the warnings. But what watches Prometheus? If something goes wrong and the monitoring stack fails in the middle of the night, no alerts are going out but there is definitely a problem. That is the failure mode that you should be most concerned about, because it is the one your monitoring cannot report on. The fix is an old idea with a grim name: a dead man's switch. A train's dead man's switch stops the train when the operator stops holding it down. The safe state requires continuous positive action, while the absence of that action is what triggers the response. Applied to monitoring, it means building one alert whose silence is itself the alarm. Step one: an alert that always fires This feels backwards the first time you see it, and it took me a little time to get it right. Basically, you create an alert with a condition that is always true, so it fires constantly, forever, on purpose. In the Prometheus world this is conventionally called Watchdog. - aler

Why identity-local signals and topology signals are two layers of the same blast radius The credential with the widest blast radius sometimes has no secret to flag. See how GitGuardian and Anyshift rank risk by what actually breaks. By Louis Fradin • 23 Jul 2026 • 7 min read 👉 TL;DR: Identity-local signals show whether a credential or machine identity is risky. Topology signals show what breaks if that identity is abused. GitGuardian identifies and ranks exposed credentials and risky machine identities; Anyshift's graph adds downstream context by showing which services depend on the resources those identities reach. Together, they help teams prioritize by both credential severity and operational blast radius. A leaked credential is also a topology problem A leaked credential creates risk beyond the identity itself. Its real impact depends on the services and resources connected to what that credential can access. Identity-local signals answer the first question: how risky is this credential or machine identity on its own? Is it plaintext? Guessable? Stale? Overprivileged? Production-exposed? Tied to an admin identity? Those signals matter because they identify the secrets and machine identities most likely to be abused. But they do not answer the next question: what breaks if that credential is used? That answer lives in the topology around the credential. A database credential may sit on one pod and unlock one datastore, but the operational blast radius extends to every service that depends on that datastore. Some of those services never hold the credential at all. Some may not even have a secret signal to score. Want to run the same analysis on your own stack? Explore the Anyshift Graph API to query dependencies, blast radius, and production impact directly. Learn more That is where identity-local signals and topology signals become two layers of the same blast radius: one tells you why the credential is dangerous, and the other tells you how far the damage can tr