标签：#techtalks

FinOps X 2026 , terminó hace apenas una semana y concluyó con JR Storment, el Director Ejecutivo de la FinOps Foundation compartiendo uno de los anuncios más esperados, la presentación de Tokenomics Foundation . ¿Qué es? Es una iniciativa de la Linux Foundation, que busca establecer estándares abiertos, lineamientos referentes, y buenas prácticas de forma específica para el costo en Inteligencia Artificial y el uso de tokens, así como otros elementos relacionados con esta tecnología con el objetivo de guiar a las empresas y organizaciones a optimizar su consumo de IA y generar mejores resultados en el valor tecnológico. Algunas acciones: Visualización de los costos Atribución del valor Estandarización de procesos, entre ellos FOCUS La creación de esta iniciativa surge en un momento en el que la IA, se ha colocado como una de las tendencias más relevantes, desde LATAM y otras regiones, con diferentes niveles de desarrollo, y un nivel de diversidad complejo. De forma aparente el costo de la IA puede verse reflejado en los tokens, pero la realidad es que sólo es una parte de los que representa el costo de soluciones de IA, partiendo particularmente de la estructura de costos de estas tecnología, en lo global, podemos detectar 3: Costos del modelo : Engloban los costos del desarrollo e implementación del modelo Costos indirectos : Están relacionados con el funcionamiento de un modelo a nivel organizacional Costos asociados : Integran las erogaciones, relacionadas con las puesta en marcha del modelo, pero no directamente en él, por ejemplo, la infraestructura, y servicios relacionados Dentro de cada categoría de costos, los servicios y etapas del desarrollo de IA, son variados Los servicios y etapas de la creación de procesos de IA que están involucrados en cada categoría de costos, muestran la complejidad para la creación de valor en estas iniciativas. Durante FinOps X, tuvimos diferentes charlas relacionadas con IA, el principal reto: cómo monitorear, medir, e incremen

Here is the move most technical service providers miss: Every project you deliver quietly dies inside a private folder. Every project you deliver with receipts becomes a trust asset that sells the next sprint without you lifting a finger. The Insight Almost No One Acts On Delivery is not the end of marketing. Delivery is where the next marketing asset is born. The before/after screenshot. The launch-readiness report excerpt. The workflow map. The metric improvement. The buyer quote. All of that is proof. And proof is the compound interest of service work. Claims decay. Proof compounds. 1️⃣ What Proof Actually Looks Like This is the proof asset menu. Every sprint should produce at least 1 item from this list: Before/after screenshot — the most shareable format Launch-readiness report excerpt — shows rigor and standard Workflow map — visual, specific, credibility-dense Dashboard screenshot — metrics that moved Test checklist — shows what was verified, not just what was built Client quote — even 1 sentence is worth 1,000 words of claims Metric improvement — "response time dropped from 24 hours to 4 minutes" Public teardown — anonymous version of the diagnosis Case study — structured story: context → pain → fix → result One-minute walkthrough video — screen-recorded, narrated, personal You do not need all of them. You need 1 per sprint. 2️⃣ The Case Study Structure That Sells A case study is not a trophy. It is a reusable trust asset. Use this structure every time: 1️⃣ Context — who had the problem? (anonymized if needed) 2️⃣ Pain — what was it costing them? 3️⃣ Hidden cause — what was really broken underneath? 4️⃣ Fix — what did you change, specifically? 5️⃣ Result — what improved? With a number. 6️⃣ Proof — what artifact backs it up? 7️⃣ Lesson — what should similar buyers do next? That is 7 steps. The whole thing can fit in a LinkedIn post or a page section. And here is the thing most people are not talking about: a case study with a specific number outperforms 10 po

As a third year student attending multiple internship drives and interviews, I started doubting my own worth ,is it all really confined to DSA? Does the entire tech industry orbit around it? We live in an age where AI has made coding more accessible than ever, yet the curriculum and selection criteria still always leads back to the same thing. Typing out long code is no longer the real challenge, it's available at a click. What actually matters is the knowledge, the architectures, the ability to innovate. But none of that seems to count. Every round, every interview ,it's DSA. Meanwhile, the actual builders, the people who genuinely enjoy creating things and pushing ideas forward, rarely end up with the opportunities they deserve. It's become a rat race. People grinding thousands of DSA problems ,for what? When the answers are already out there, is this process really refining students or just exposing a deep loophole in how the industry hires? The Moment It Hit Me Every company I attended followed the same pattern. DSA in the first round, more complex DSA in the second, and then an interview that circled back to DSA again. At some point you have to ask, do companies actually want talent or just someone who can do what AI already does a thousand times better? Rejection is never easy. But what makes it harder is knowing you are genuinely passionate, you understand how things are built, you know the fundamentals and none of it counts. Meanwhile the ones who get selected are often those who blindly copy projects from GitHub and grind LeetCode day and night without understanding a single thing they have built. Companies ask for your LeetCode profile link. That is it. Not your domain knowledge, not your mindset, not your passion for the field. Just your ranking. Your CGPA defines your worth. Your LeetCode score defines your entire trajectory. It is exhausting. Showing up to round after round, knowing exactly how it is going to go, and still questioning whether you are en

Bugs, crashes, glitches... Game development is full of them, and even experienced teams run into issues. But while no game is perfect, that doesn't mean we should stop chasing better quality. In this live session, we'll look at why even seasoned game development teams make mistakes and how you can reduce the number of issues in your own projects. What's the talk about? The speaker, Gleb Aslamov, developer advocate and static analyzer developer at PVS-Studio, will walk you through common and less obvious reasons behind code errors, share real-world bug examples from actual game projects, discuss development practices that help prevent bugs before release, and demonstrate tools designed to catch those issues early. Gleb will show some amusing bug examples from projects like osu!, GZDoom, and SanAndreas Unity. The discussion will cover how code reviews, testing, and CI/CD, combined with profilers, dynamic analyzers, and static analyzers, can help detect issues long before players ever encounter them. Also, expect to see static analysis in action, including warnings that reveal performance-sensitive issues and other hidden problems in game code. When? Mark your calendar for June 2, 2026, at 1:00 PM UTC+1 . Join the live talk and learn how to make your game code more reliable—one bug at a time. P.S. And don't forget to check your inbox to confirm the registration!