标签：#learning

Stop Guessing: How I Pick AI API Architecture at Every Scale I've been on both sides of this. Two years ago I was the lone backend engineer at a Series A startup, duct-taping API calls together at 2 AM because the founders wanted a chatbot demo by morning. Last quarter I sat in a procurement meeting at a Fortune 500 where we spent six weeks evaluating three vendors for a single inference workload. Same job title on LinkedIn, wildly different problems. Most AI API guides I've read treat both scenarios like they're the same conversation. They're not. The startup CTO optimizing for burn rate and the enterprise architect worrying about a 99.9% uptime SLA are solving fundamentally different equations. After enough of these conversations, I've developed a framework I'd like to share — and yes, I'll talk about Global API because it's what I actually use, but I'll also explain the reasoning behind each choice so you can adapt it to your own stack. What I Look at First: The p99 Question Before I look at price, I look at the latency distribution. Specifically, the p99. Mean latency tells you almost nothing useful. If your median response is 200ms but your p99 is 4 seconds, your users will see janky behavior on the long tail and you won't know why until production is on fire. For startups in the MVP phase, you can usually get away with best-effort routing. A p99 of 2-3 seconds is fine if you're building an async summarization feature. But the moment you put AI in the synchronous request path — like a customer-facing chatbot or a real-time code suggestion — p99 starts to bite. I learned this the hard way when our startup's "AI assistant" feature had users complaining about slowness that I couldn't reproduce locally. The culprit? Provider cold starts hitting our 1% of users who happened to get routed to a freshly spun-up instance. For enterprises, p99 isn't a nice-to-have, it's a contractual obligation. Most B2B SLAs I've negotiated pin uptime at 99.9% and require reporting on m

Detecta si tu modelo de materiales hace trampa con la "huella bibliográfica" Un modelo de ML puede predecir la propiedad de un material sin entender la química: basta con que "aprenda" qué autores, revistas o años suelen ir con cada resultado. Esta herramienta aplica el test de falsificación de Clever Materials para descubrirlo. El problema: cuando el modelo lee el membrete, no la ciencia Imagina que entrenas un modelo para predecir si un material es estable. El modelo no mira la química: descubre que los artículos del grupo X (publicados en la revista Y, en torno al año Z) casi siempre reportan "estable". Así que aprende a clasificar por el membrete bibliográfico , no por la estructura. Funciona en el papel y se rompe en la práctica. A esto se le llama confounding bibliográfico (o leakage por metadata). No es un error de código: es una señal espuria que el modelo aprovecha. El paper Clever Materials (Jablonka et al., 2026) mostró que este patrón está generalizado en cinco tareas reales de materials science. Qué hace la herramienta materials-confounding-check es una CLI ( mcc check ) que corre cuatro sub-tests de falsificación sobre tu dataset (descriptores químicos + metadata bibliográfica + propiedad objetivo): Clasificador de metadata — ¿se puede predecir la bibliografía (autor/revista/año) a partir de los descriptores químicos? Si es above-chance , hay una señal bibliográfica presente. Huella bibliográfica — ¿un modelo que usa solo la metadata predicha se acerca al modelo con descriptores? Entonces el dataset no descarta hacer "trampa" por bibliografía. Split por grupo/tiempo — ¿colapsa el rendimiento si separas por autor/año en vez de al azar? Veredicto — un score low / medium / high de riesgo de confounding. El rigor que exige el test (para especialistas) El punto delicado de cualquier "test de significancia" es fijar el umbral a mano. Si ajustas el margen hasta que tu fixture pase, el test no prueba nada: es el anti-patrón Clever-Hans que el propio proyecto d

You have seen the posts. AI is coming for the junior engineer first. Why hire someone to write code a model can write for free? The career ladder's bottom rung is gone, so start saving your pity for anyone about to graduate into this market. I think the premise is wrong, and it is wrong in a specific, fixable way. Look closely at what these predictions actually describe. Not a junior engineer. A person whose entire job is turning a finished spec into working code. That role is real, and it is shrinking fast, but it was never the same thing as "junior engineer." We just let the two collapse into one job title for forty years because, until recently, spec-to-code translation was the canonical, critical thing a junior had the skill to do. The task and the title are not the same thing. AI is eating the task. It does not follow that it eats the title too, unless we insist on keeping them welded together. So the real question is not "does the junior engineer survive." It is "what do we train a junior engineer to do now that the translation work is cheap." And the honest answer is: not much of what we have been doing. I think we landed on "junior engineers are doomed" for a reason that has nothing to do with whether it is true. It is the easy conclusion. It requires nothing from us. Training a junior into a senior was never straightforward, even in the old world, and figuring out how to do it without the years of tickets we used to lean on is genuinely hard. "They're doomed" lets everyone off the hook. "How do we train juniors into seniors now" does not, but it is the question with a future in it. The first one just has a shrug. The apprenticeship we built no longer exists For as long as I have been in this field, the plan was the same. Hire someone who can code. Hand them small, well-specified tickets. Let them grind through years of execution: bugs, edge cases, code review, the slow accumulation of pattern recognition that eventually turns into judgment. Somewhere around

Image-to-video generation is often described as a simple interaction: upload image -> describe motion -> get video That description hides the real problem. A single still contains only one view of a subject. When we ask a model for a fast camera orbit, a full-body walk, or expressive gestures, we are asking it to invent information that was never present in the source. That is where identity drift, unstable lighting, texture flicker, and waxy faces come from. The useful way to approach Seedance 2.0 image-to-video is not as a prompt-writing contest. It is a constraint-management workflow. Give the model a strong identity anchor, request motion that the source image can support, and evaluate one variable at a time. This post explains that workflow in a way that is useful whether you are animating a product render, a character portrait, an approved client still, or a visual asset for a prototype. Note: Model capabilities, pricing, model availability, and input limits change quickly. Check the current documentation and the terms of the platform you use before committing a production workflow. Why image-to-video is different from text-to-video Text-to-video is excellent when invention is the point. You describe a scene and let the model make creative decisions about characters, lighting, composition, and motion. Image-to-video is the better tool when those decisions have already been made and must remain stable. Situation Better starting mode Why Product hero shot Image-to-video Label, shape, material, and color must remain recognizable Character-led sequence Image-to-video One strong reference can anchor a character across clips Approved campaign still Image-to-video The source already represents the accepted art direction Atmospheric B-roll Text-to-video Exact subject identity matters less than visual exploration Abstract concept film Text-to-video Inventing a scene is more valuable than preserving one Existing brand-photo library Image-to-video Stills become reusable

Check this out: migrating Off OpenAI: A Backend Engineer's Notes From Production I still remember the morning I opened our team's monthly invoice and nearly spilled cold brew on my mechanical keyboard. We were burning through OpenAI credits like it was nobody's business — specifically, north of $500/month for what amounted to a chat-completion endpoint and some embedding lookups. As the backend engineer who had inherited the LLM integration six months prior, I felt personally responsible. So I did what any self-respecting engineer does at 2 AM with too much caffeine: I benchmarked alternatives. What I found annoyed me. DeepSeek V4 Flash was sitting there at $0.25/M output tokens while GPT-4o charges $10.00/M. That's a 40× price difference for output that, in my blind tests, 80% of users couldn't distinguish. The $500/month bill could plausibly become $12.50. My CFO would weep tears of joy. This post is the migration journal I wish I'd had before I started. fwiw, I've already done the swap across three production services. Here's what worked, what didn't, and exactly how much coffee I drank. The Math That Made Me Pick Up a Keyboard Before I show you code, let's talk numbers — because if you're going to convince your team or your boss, you'll need a slide that fits on one screen. I pulled together the pricing for the models I actually considered routing traffic through. All figures are per million tokens, USD: Model Provider Input $/M Output $/M Relative to GPT-4o GPT-4o OpenAI $2.50 $10.00 1× (baseline) GPT-4o-mini OpenAI $0.15 $0.60 16.7× cheaper DeepSeek V4 Flash Global API $0.18 $0.25 40× cheaper Qwen3-32B Global API $0.18 $0.28 35.7× cheaper DeepSeek V4 Pro Global API $0.57 $0.78 12.8× cheaper GLM-5 Global API $0.73 $1.92 5.2× cheaper Kimi K2.5 Global API $0.59 $3.00 3.3× cheaper Let me be clear about something: those numbers come straight from the provider's pricing pages at the time I ran the analysis. I have not invented, rounded up, or "adjusted" anything her

series: Prediksi Survival Klub Debutan Kenapa Project Ini? Setiap musim, klub yang promosi ke liga top (Premier League, La Liga, dst.) menghadapi risiko besar: sekitar 2 dari 3 klub yang naik biasanya kembali terdegradasi di musim pertama mereka. Saya penasaran — bisakah performa di beberapa laga awal musim memberi sinyal dini soal peluang klub tersebut bertahan? Ini jadi project portofolio pertama saya sebagai data scientist yang baru mulai (0-1 tahun pengalaman). Saya sengaja pilih topik yang saya suka (sepak bola) supaya prosesnya tetap enjoyable, bukan cuma "tutorial project" generik. Rencana Project Pertanyaan utama: Berdasarkan performa 8 laga pertama musim debut, seberapa besar peluang klub promosi bertahan hingga musim berikutnya (tidak degradasi)? Data yang dipakai: football-data.co.uk — data hasil pertandingan tiap musim sejak 1993/1994 Wikipedia (halaman musim liga) — daftar klub promosi & klasemen akhir musim Tech stack: pandas , requests untuk data collection scikit-learn untuk modeling (mulai dari Logistic Regression sebagai baseline) imbalanced-learn untuk handle class imbalance Streamlit + Plotly untuk dashboard interaktif Deploy ke Streamlit Community Cloud Timeline (Build in Public) Saya bikin timeline ini publik supaya ada tekanan yang sehat untuk benar-benar menyelesaikannya, bukan cuma jadi ide yang menguap: Checkpoint Target Tanggal Yang Harus Selesai Part 1 (post ini) 11 Juli 2026 Kickoff, rencana, environment siap Part 2 15 Juli 2026 Dataset jadi, push ke GitHub Part 3 17 Juli 2026 EDA selesai, insight awal Part 4 24 Juli 2026 Model final dipilih + evaluasi Part 5 31 Juli 2026 Dashboard live di Streamlit Cloud Part 6 (final) 8 Agustus 2026 Project selesai, recap lengkap Tantangan yang Sudah Saya Antisipasi Data leakage — fitur harus dihitung dari laga awal musim saja, bukan seluruh musim, biar model beneran memprediksi bukan "menyontek" hasil akhir Dataset kecil — kemungkinan hanya ~60-100 sampel klub, jadi saya mulai dari model sederhana (Lo

The RabbitMQ upgrade looked like a straightforward infrastructure task: move from RabbitMQ 3.X to 4.X, provision the new broker, review the client setup, confirm queues still declare correctly, restart consumers, watch the logs, and move on. But infrastructure upgrades rarely test only infrastructure. They also test the assumptions your application has been making for years. In this case, the upgrade forced a more important question: is our messaging system reliable by design, or has it simply been relying on stable conditions? That distinction matters because a message queue can appear healthy when the broker is running, the network is stable, consumers are alive, and messages are acknowledged quickly. But production systems are not judged only by how they behave when everything is fine. They are judged by how they behave during restarts, closed channels, slow handlers, bad configuration, deployment windows, and partial failure. The RabbitMQ upgrade exposed those edges. It revealed assumptions around connection lifecycle, acknowledgements, dead-letter routing, retry behavior, observability, and operational simplicity. The real lesson was not just how to upgrade RabbitMQ. The real lesson was how to build a messaging layer that is easier to operate, easier to reason about, and safer to fail. Simplicity Is an Operational Feature One of the first things the upgrade exposed was complexity. Over time, messaging code can quietly become a small internal framework. A connection helper becomes a connection manager. A consumer wrapper becomes a consumer framework. Retry helpers appear, dead-letter helpers appear, failure handlers appear, and monitoring logic gets layered on top. Each addition may have been reasonable when introduced, but during an incident, complexity has a cost. Every abstraction becomes another place to inspect. Every helper becomes another assumption to validate. Every unused file becomes a possible source of false confidence. RabbitMQ integration code doe

This is a submission for Weekend Challenge: Passion Edition What I Built I told an agent Never write directly to the database . A long session later, context window full, it wrote directly to the database. The rule loading mark was still sitting in the prompt. The model had just stopped weighting and attending to it. It's an invisible failure. No error is being thrown. The task comes back subtly wrong, and the rule reads perfectly fine when you go back and check it. I wanted to make it visible, so I built an interactive field you can drag around. Every rule you write for an agent is a hill. Its height is how well the rule is written: a directive-led, backtick -anchored rule stands tall, a hedged and vague one sits low. Then you raise the water. The water is context load. As it rises the low rules go under first, in order of how well they were written. The weak ones drown while you watch. Three of the hills are high-stakes prohibitions, the Never... rules. They drown too. That is the whole point of the piece. A rule you cannot afford to lose does not belong in prose at all; it belongs on a runtime hook that runs as code, not attention. The field flags those in red the moment they go under. Underneath the field is a second tool: a client-side lint that reads an instruction and names the surface tells (hedges, shouting, politeness, a ban placed before its directive). It is deliberately not a score. It catches what a little regex can honestly catch, and points at the real analysis for the rest. Demo Play it on its own page. Drag to orbit, drag the load slider to raise the water: ▶ Open the live demo Each of the nine instruction patterns in the demo links to its rule page on reporails.com/rules . Code Code is available on Codepen: https://codepen.io/editor/G-bor-M-sz-ros-the-reactor/pen/019f4cad-e344-78bf-b7bc-919972f42a4e The whole thing is one self-contained HTML file: no build step, no dependencies, no backend. The CodePen above is the full source, so you can read eve

When learning a new technology, most of us follow a familiar path. We start with the official documentation. Then we search GitHub repositories. We read blog posts. We watch YouTube tutorials. Eventually, we ask an AI assistant when we get stuck. Each resource solves a different problem, and the best developers know when to use each one. Documentation Is the Foundation Official documentation should almost always be your first stop. It tells you how a framework or library is intended to work. The information is usually accurate, maintained, and version-specific. If you're learning React, Next.js, or Node.js, the official docs provide the most reliable starting point. But documentation has limits. It explains what something does, not always why developers use it in real projects. Community Content Fills the Gaps That's where blog posts, conference talks, and open-source repositories become valuable. Experienced developers share: Real-world architecture decisions Common mistakes Performance considerations Debugging strategies Project structure Deployment workflows These practical insights often don't belong in official documentation, but they're essential for becoming a better engineer. AI Has Changed the Workflow AI assistants have become another tool in the developer toolbox. Instead of searching through multiple pages, developers can ask targeted questions like: Why is this hook re-rendering? What's the difference between these two approaches? How can I improve this query? Can you explain this error message? AI doesn't replace documentation. It helps you understand it faster. The most effective workflow is using documentation as the source of truth while letting AI explain concepts, compare approaches, or clarify confusing examples. Build Your Own Reference Library One habit that's improved my productivity is creating a personal knowledge base. Whenever I solve a difficult problem, I write down: The issue Why it happened The solution What I learned Links to relevant

**_Hi everyone! We're three 16-year-old friends learning to code. Instead of building "just another app," we want to solve a real problem that developers actually face. So we have one question: Think about a moment when you caught yourself saying, "Why hasn't anyone built a good solution for this yet?" What was the problem? It can be anything: something that wastes your time, something frustrating, a repetitive task, a confusing workflow, or anything that made you wish a better tool existed. We're not trying to sell anything. We're simply listening and looking for real problems worth solving. Every answer means a lot to us. Thank you!_**

Adapted from an appendix of my MS thesis. Markov Chain Monte Carlo Almost as soon as computers were invented, they were used for simulation. Markov chain Monte Carlo (MCMC) was invested as Los Alamos, Metropolis et al (1953) simulated a liquid in equilibrium with its gas phase. Their tour de force was the realization that they did not need to simulate the exact dynamics, they only needed to simulate some Markov chain with the same equilibrium distribution. The Metropolis algorithm was widely used by chemists and physicists, but was not widely known among statisticians until after 1990. Hastings (1970) generalized the Metropolis algorithm, and simulations following his scheme are said to use the Metropolis-Hastings (MH) algorithm [1]. A special case of the MH algorithm was introduced by Geman et al (1984) discussing optimization to find the posterior mode rather than simulation. Algorithms following their scheme are said to use the Gibbs sampler. It took some time for the spatial statistics community to understand that the Gibbs sampler simulated the posterior distribution, thus enabling full Bayesian inference of all kinds. Gelfand et al (1990) made the wider Bayesian community aware of the Gibbs sampler, and then it was rapidly realized that most Bayesian inference could be done using MCMC, whereas very little could be done without MCMC. Green (1995) generalized the MH algorithm as much as it could be generalized [1]. Theoretical Foundations A sequence X 1 ​ , X 2 ​ , … of random elements of some set is a Markov chain if the conditional distribution of X n + 1 ​ given X 1 ​ , … , X n ​ depends on X n ​ only. The set in which the X i ​ take values is called the state space of the Markov chain. A Markov chain has stationary transition probabilities if the conditional distribution of X n + 1 ​ given X n ​ does not depend on n . This is the main kind of Markov chain of interest in MCMC. The joint distribution of a Markov chain is determined by the following [1]. The ma

Modern applications, especially AI agents and distributed systems, need more than logs to understand what is happening. That's why I explored SigNoz, an open-source observability platform built on OpenTelemetry. Setting up SigNoz with Docker was simple. After connecting a sample application, I could view logs, metrics, and traces from a single dashboard within minutes. My favorite feature is distributed tracing. Instead of guessing where requests slow down or fail, SigNoz clearly shows the complete request journey across services, making debugging much easier. The built-in dashboards provide valuable insights into CPU usage, memory, request latency, throughput, and error rates. Having centralized logs alongside metrics and traces saves time by eliminating the need to switch between multiple tools. I also liked the alerting feature, which helps detect issues before they affect users. For AI applications, observability is essential. AI agents make multiple API calls, use tools, and perform complex workflows. SigNoz makes it easier to understand each step, identify failures, measure latency, and optimize performance. Overall, my experience with SigNoz was excellent. It combines logs, metrics, traces, dashboards, and alerts into one intuitive platform. Among all its features, distributed tracing impressed me the most because it provides deep visibility into application behavior and simplifies troubleshooting. I'm excited to use SigNoz in future AI and cloud-native projects.

My GPT got dumber after the update" gets blamed on the model regressing, or on you prompting worse. Both are unfalsifiable, and both send you to fix the wrong layer. The layer that actually moved is the one you can pin. "The model" is two layers. The weights — the trained network, slow to change, and when they do change it's announced under a new name. And the wrapper — the router that picks which model answers, the system prompt, the default reasoning effort, verbosity caps. The wrapper changes silently, on its own schedule, per product. It's almost always what moved under you. So stop re-tuning prompts to chase it. Pin the wrapper: Force the route. Don't leave it on Auto — set Thinking (or say "think hard") so the router can't quietly demote your prompt to a faster, weaker model. OpenAI's own GPT-5 launch post describes exactly this router (it scores prompts "simple" vs hard); after the backlash they put the picker back (Auto/Fast/Thinking — TechCrunch, Aug 2025). Pin the version. If you build on a model, call its exact versioned ID via the API. A model ID's weights don't change — new versions ship under new IDs — so router and system-prompt churn can't reach you. Own the harness. Running agents? Set the system prompt, reasoning effort, and verbosity yourself instead of inheriting a default. Anthropic's own April 23 post-mortem is the proof: six weeks of "Claude Code got worse" traced to three wrapper changes (a reasoning-effort downgrade, a reasoning-history bug, a verbosity cap their ablations put at ~3% quality) — API weights never touched. A real weights change — a new model — will still move behavior. But that's announced, and you choose when to adopt it. The silent stuff is all wrapper, and the wrapper is the part you can pin. Sources: OpenAI GPT-5 launch (router + "think hard"); TechCrunch, Aug 2025 (model picker reinstated); Anthropic April 23 post-mortem (anthropic.com/engineering/april-23-postmortem); InfoQ and VentureBeat (corroboration); Claude platfor

There's a moment in every Solana project where the work stops being about whether the program works and starts being about whether it's ready . You've tested it, the logic holds, the constraints are tight. Then you point it at mainnet, and a different set of questions shows up: questions about money, permanence, and strangers. This post is about that transition. Not the commands, which are short and well documented, but the shift in what you're responsible for once real users can touch your code. If you've been building on devnet and you're starting to think about a live launch, this is the mental model to carry in. Devnet was a sandbox. Mainnet is not. Devnet is a practice field. The SOL is free, you airdrop more whenever you run low, and if you deploy something broken, the only casualty is your afternoon. That safety is the whole point of devnet: it lets you fail cheaply and often, which is exactly how you should be learning. Mainnet removes the safety net, and three things change the moment you cross over. The SOL is real. Deploying a program allocates an on-chain account sized to your compiled binary, and you pay rent for that space in actual SOL. Larger programs cost more. This isn't a huge sum for a typical program, but it's real money leaving a real wallet, and that alone tends to sharpen how carefully you check things before you hit deploy. The audience is real. On devnet the only person calling your program is you. On mainnet, anyone can find your program and send it any transaction they like, the moment it's live. Everything from the security arc stops being theoretical: the accounts strangers pass in, the inputs you didn't expect, the edge cases you hoped no one would hit. Mainnet is where "every account is attacker-controlled until proven otherwise" becomes a live condition rather than a lesson. The mistakes are visible. A bad devnet deploy disappears into the noise. A bad mainnet deploy is a public event, on a permanent ledger, in front of the users you

If you were to look up into the night sky, what would you see? Countless points of light, scattered in every direction. Most of what you're looking at are stars. But some of those points are whole galaxies—vast collections of stars, spread across incomprehensible distances, compressed by that distance into a single pinprick of light. And what you can see with the naked eye is only a small fraction of what's actually out there. I want to use this as a way to offer you a way of thinking about how large language models work. Just an analogy, not literally what's happening inside the mathematics—that's not my forte. My hope is that it captures something true about the mechanics, and more importantly, it gives you a mental model you can actually use when you're working with these systems. About two years ago, I was wrestling with finding a way of explaining what an LLM does. My first analogy was that of a dictionary. The naive view was that a dictionary uses words to define other words, and an LLM holds a matrix of words with weights that describe their relationships to each other. So the parallel seemed natural: both systems work through relational structure. However, a dictionary gives you denotation—the surface-level meaning. It's a lookup tool for individual words, not a model of language itself. And critically, you have to already understand language before a dictionary is useful to you at all. The analogy didn't capture what was actually happening in the weight relationships—the distributional semantics, the contextual patterns that let an LLM generate coherent text. Ok, so back to galaxies, when you look up at the night sky, you're not seeing distance—you're seeing direction. That galaxy over there, the one that looks like a point of light, could be millions of light-years away, but what matters for our analogy isn't how far it is. It's which way you're looking. And when you point yourself in that direction and venture toward it, you discover it's not a point at a

Builder Journal · ARC Prize 2026 There is a moment in every role-playing game where you load your character with so much heavy gear that they can barely walk. Strongest sword in the game, can't reach the fight. I did the machine-learning version of that this month. I kept making my agent more capable, and the scoreboard kept punishing me for it, and it took me two tries to understand that the upgrades were the problem. A quick frame, in case this is your first entry in this thread : I'm in the ARC Prize 2026, building an agent that has to learn small games it has never seen, with no instructions. As the benchmark's creator measured it, the hardest part by far is the piece that figures out the rules of a game by experimenting on it. So that piece is where I have been pouring my effort. The obvious upgrade The obvious way to make that piece better is to teach it more kinds of games. If it can model three families of puzzle today, teach it a fourth, and it should win more. So I did exactly that. I built support for a new class of game it could recognize and solve, wrote it carefully, tested it, and confirmed the thing I wanted to confirm: the agent now beat a game it provably could not beat the day before. Real, verified, new capability. Not a story I was telling myself, a genuine new skill on the board. Then I submitted, and the score went down. Twice This is the part I want to be honest about, because one bad result is noise and two is a pattern. My agent's attempts to use this theory-building component had already been underwhelming on the real board, landing around 0.05, 0.07, and 0.09 across earlier tries, all of them under the 0.25 my plain, careful agent scores when it does not reach for the fancy component at all. The fourth skill was supposed to turn that corner. Instead the next submission came in at 0.04, the worst of the lot. I had added ability and the number had dropped, again. So I stopped adding and started counting. I ran a survey across twenty-five of

An interactive guide to the architecture behind modern language models. Instead of predicting the next word, this Transformer predicts the next move in a game of fading Tic-Tac-Toe—making every step of the model easy to visualize and understand. Play the game, inspect every matrix multiplication, and watch tokens flow through the network in real time. What's covered Tokenization and embeddings Learned positional encoding Self-attention (Q, K, V) Multi-head attention Causal masking and softmax Residual connections and layer normalization MLP (feed-forward network) Unembedding and sampling Model ablations (no positional encoding, no causal mask, no MLP, no residual stream) Includes interactive visualizations for every stage of the Transformer pipeline - from input tokens to the final prediction. https://sbondaryev.dev/articles/transformer

Billed as the “world’s first museum of AI arts,” Dataland uses wearables and troves of material from the Amazon to merge nature, biometrics, and art.

Building an explainable AI platform that helps district administrators allocate resources and farmers make better crop decisions using Gemini, Vertex AI, BigQuery, and Google Cloud. Climate disasters are not just weather events. They are decision problems. When forecasts predict a strong El Niño, governments do not simply need more data. They need answers to questions like: Which districts will be affected first? Where should limited water resources be sent? Which crops are likely to fail? What should farmers sow instead? Why is the AI recommending this action? Existing dashboards provide plenty of charts. Very few provide decisions. That became the motivation behind El Niño 2026 Decision Copilot , an AI-powered decision intelligence platform built during the Google Cloud Gen AI Academy APAC Hackathon . The Problem India depends heavily on the monsoon. A severe El Niño can lead to: Rainfall deficits Reservoir depletion Groundwater stress Crop failures Rising food prices Rural employment challenges The information already exists across dozens of government portals, weather services, satellite datasets, and agricultural reports. The challenge is that it is scattered. District collectors do not have time to manually combine: Weather forecasts NDVI satellite imagery Reservoir levels Mandi prices Contingency plans Drought indicators Farmers face an even bigger challenge. Most need a simple answer: Given my district, should I plant the usual crop this season? The Goal Instead of building another dashboard, I wanted to build an AI system that reasons over multiple data sources and produces explainable recommendations. The platform serves two audiences through the same intelligence engine. District Administrators They receive: District risk scores Interactive risk maps Reservoir outlook Crop stress indicators Resource allocation recommendations AI-generated explanations Instead of simply showing that a district has high risk, the system explains why . Farmers Farmers intera

Durante o desenvolvimento do Templo Digital, meu projeto de hackathon (uma vitrine 3D de cursos...