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Forget neural networks for a second. The real idea inside this repo is a blueprint for letting AI agents run unattended overnight — and it maps onto problems you already have on your team. If you've been anywhere near tech Twitter or LinkedIn this week, you've probably seen people losing their minds over a small GitHub repo called autoresearch , published by Andrej Karpathy — former Tesla AI director and OpenAI founding member. The framing is dramatic: an AI agent that runs machine learning experiments on its own, overnight, while you sleep. Tweak the code, train for five minutes, check if it got better, keep it or throw it away, repeat. Wake up to a log of a hundred experiments and a model that's quietly improved itself. If you're not an ML researcher, your instinct might be to scroll past. "Cool, but I don't train neural networks. How does this apply to me?" Here's the thing — the neural network part is almost incidental. What Karpathy actually open-sourced is a pattern for structuring AI-agent work: a specific way of dividing responsibility between human and AI that happens to generalize to a huge range of engineering problems. Once you see the pattern, you start noticing places in your own job where it fits. What's Actually in This Repo The repo itself is intentionally tiny — and that's the point. There are really only three files that matter: The evaluator (untouchable). A file containing the fixed constants, data preparation, and the scoring logic. The agent is never allowed to modify this. It's the ruler everything else gets measured against. The implementation (the agent's playground). A single file containing the actual model, training loop, and hyperparameters. This is the only file the agent is allowed to change. Architecture, batch size, optimizer — all fair game. The instructions (the human's only job). A plain Markdown file describing what the agent should try, what the constraints are, how to interpret results, and what to do when something breaks. Ka

Most AI demos work perfectly on a laptop. But production AI systems can become fragile when everything is handled inside one synchronous API call. A user sends a request. The API extracts text. The API chunks the content. The API generates embeddings. The API stores data. The API waits for everything to finish. This may look simple in a demo, but it quickly becomes a problem in real systems. The problem with one giant API call In many AI applications, the API is expected to do too much. For example, in a document processing or RAG pipeline, one request may trigger multiple heavy steps: text extraction chunking embedding generation indexing summarization database updates If all of this happens inside one synchronous request, the API becomes slow and fragile. If one downstream step fails, the complete request may fail. If traffic increases suddenly, the API may become overloaded. This is why event-driven architecture becomes useful for AI workloads. A better approach: API + Kafka + workers Instead of making the API do everything, we can split the workflow into smaller services. The API accepts the request and publishes an event. Background workers consume events and continue the processing asynchronously. A simple flow looks like this: User Request ↓ FastAPI ↓ Kafka / Redpanda Topic ↓ Python Worker ↓ Next Processing Stage In my practical demo, I am using: FastAPI Redpanda Python workers Docker Compose Kafka-compatible messaging Why Redpanda? Redpanda is Kafka-compatible, which makes it useful for local demos and event-driven architecture experiments. It allows us to work with Kafka-style topics, producers, and consumers while keeping the setup simple for development. What this architecture gives us This approach helps with: decoupling services handling bursty workloads moving long-running tasks to background workers improving scalability isolating failures building production-style AI pipelines This pattern is especially useful for AI systems involving: document proce

There are already many posts about what agent skills are and how to create your own, so in this post I want to dive into the various skills I use to assist in development. The Skills Grill Me Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me". I start every larger task with this excellent skill created by Matt Pocock. I either start this with an already prepared PRD / detailed task description or use it for discovery purposes. The agent will then ask many questions to align language and functional requirements, so fewer hallucinations happen in follow up requests. You should be well equipped to answer the agent's question or the grill me session can go on for a long time. I had it ask me way over 50 questions when not answering detailed enough. As a little extra I added an extra request to the skill to prompt me if I want to create the PRD when the alignment phase is over, this leads us to the next skill. To PRD Turn the current conversation context into a PRD. Use when user wants to create a PRD from the current context. This will simply take the current conversation and creates a PRD out of it, we do this to summarize the conversation so we can easily start a new context window with all information present To Issue Break a plan, spec, or PRD into independently-grabbable GitHub issues using tracer-bullet vertical slices. Use when user wants to convert a plan into issues, create implementation tickets, or break down work into issues. Another excellent skill by Matt Pocock. I modified the skill slightly to use the GitHub MCP to create issues based on a PRD or planning session. But I often found that letting an agent implement those tasks it resulted in a large amount of code and that is why I added the to tasks skill To Task Break down a single GitHub issue into a sequential list of small i

In a bid to dismiss a lawsuit over xAI’s polluting gas turbines, the Justice Department claimed the company is integral to military operations—including the Iran War.

Anthropic leaders flew to Washington, DC, to meet with White House officials on Monday. After high-level talks, they're still split on the risk Claude Fable 5 presents.

Class, Record, and Struct serve as blueprints for creating new objects. Classes Classes are the foundational building blocks of Object-Oriented Programming (OOP). Blueprint and instance // blueprint public class Person { public int ID { get ; set ; } public string Name { get ; set ; } } // instance var p = new Person { ID = 1 , Name = "Mirza" } Inspection Printing the class instance in the console will just display it's name: var p1 = new Person { ID = 1 , Name = "Mirza" }; Console . WriteLine ( p1 ); // Person To print the actual, we'd need print each property explicitly: Console . WriteLine ( p1 . ID ); // 1 Console . WriteLine ( p1 . Name ); // "Mirza" Mutation C# classes are mutable, meaning the originally set values can be altered: var p1 = new Person { ID = 1 , Name = "Mirza" }; Console . WriteLine ( p1 . Name ); // "Mirza" p1 . Name = "Armin" ; Console . WriteLine ( p1 . Name ); // "Armin" That said, this can be tweaked by changing the accessor the class property. public class Person { public int ID { get ; set ; } public string Name { get ; init ; } // <-- } This time around if we try to change the value of the Name property after initialization, the C# compiler will start to complain. var p1 = new Person { ID = 1 , Name = "Mirza" }; p1 . Name = "Edis" ; // ❌ The init accessor allows you to create properties that can only be assigned a value during object initialization. Memory location Classes are reference types and are allocated on the heap. If we create two distinct class objects and assign one to the other, both objects will point to the exact same reference in memory: var p1 = new Person { ID = 1 , Name = "Mirza" }; var p2 = new Person { ID = 2 , Name = "Mirza" }; p1 = p2 ; p1 . Name = "Sead" ; Console . WriteLine ( p1 . Name ); // Sead Console . WriteLine ( p2 . Name ); // Sead If we change a value in one of the objects, it will be reflected in both. Equality Two class instances (objects) aren't equal even if they share the same values: var p1 = new P

At Inithouse — a studio running parallel product experiments — we built Be Recommended , a tool that checks how visible your brand is across ChatGPT, Perplexity, Claude, and Gemini. The idea sounded simple: query multiple AI models, score the results, show a report. It was not simple. Here are four technical mistakes we made shipping v1 — and the fixes that actually survived production. Mistake 1: Rate Limiting Was an Afterthought We treated rate limits as edge cases. They were not. Every AI provider has different rate-limit headers, different backoff expectations, and different definitions of "too many requests." Our first architecture just retried on 429. That turned a rate limit into a cascade — one provider throttling triggered a retry storm that cascaded to the others. The fix: Per-provider circuit breakers with exponential backoff. Each provider gets its own state machine. When a circuit opens, we serve cached results for that provider and mark the score as "partial" in the UI. Users see real data, not a spinner that never resolves. At Audit Vibe Coding — another tool in our portfolio focused on code quality audits — we observed the same pattern in a different domain: external API dependencies need isolation. The lesson transferred directly. Mistake 2: The Caching Strategy Was Too Naive Our first cache key was query + model . That breaks immediately — AI model responses drift over time, and a cached result from two weeks ago is misleading. We also had no invalidation strategy beyond TTL. The fix: Cache by query + model + week_number . Weekly invalidation with stale-while-revalidate: serve the cached score instantly, trigger a background refresh, update the display when new data arrives. Users get instant feedback and fresh data within the same session. We measured the impact across our portfolio: stale-while-revalidate cut perceived load time from 8+ seconds to under 1 second for returning visitors. The background refresh means scores stay current without the

OpenAI introduces Deployment Simulation, a method to predict AI model behavior before deployment using real conversation data to improve safety and evaluation accuracy.

AI is once again at the heart of a college graduation protest — this time for the technology's use in Google's defense contracts.

Um relato honesto de alguém que trabalha com design, vive com TDAH e está cansada de dicas genéricas Tem um tipo de artigo sobre organização que eu já sei de cor. É sempre alguma variação de: “faça uma lista, use Pomodoro, durma 8 horas e beba água”. Só que tem um cenário que quase nunca aparece nessas listas: O momento em que você não é neurotípica, está em burnout, tem duas tarefas importantes com o mesmo prazo e nenhuma técnica milagrosa resolve. É sobre isso que eu quero falar aqui. Sumário: O cenário caótico (e bem real) Por que o Pomodoro não funciona pra todo mundo Burnout em quem tem TDAH O dia em que duas tarefas importantes têm o mesmo prazo Estratégia 1: uma prioridade verdadeira por dia Estratégia 2: subtarefas em vez de cronômetro Estratégia 3: time blocking gentil (agenda que não te esmaga) Estratégia 4: reduzir fricção em vez de exigir mais disciplina Estratégia 5: contratos curtos consigo mesma E quando nada disso parece suficiente? Referências O cenário caótico (e bem real) Imagina o seguinte: Projeto A : entrega do pitch da pós, com prazo na sexta. Projeto B: preparar apresentação do roadmap, também para sexta. Você já está cansada, a cabeça rodando, o corpo em modo economia de energia. Aí você joga no Google “como se organizar” e recebe de volta: “Use a técnica Pomodoro, 25 minutos de foco, 5 de pausa.” E você pensa: “Amiga, eu mal estou levantando da cama. Você quer que eu vire um cronômetro humano?” A real é que muita técnica de produtividade tradicional foi pensada para cérebros neurotípicos. Quando a gente vive com TDAH, burnout ou os dois juntos, essa lógica simplesmente não encaixa tão bem. Por que o Pomodoro não funciona pra todo mundo Pomodoro é ótimo… para algumas pessoas. Mas tem motivos bem específicos para ser um caos para muitos de nós. Por exemplo: A pausa obrigatória, interrompe justo quando o foco finalmente chegou. A sensação do timer contando, aumenta a ansiedade em vez de ajudar. Cada “reinício de ciclo” vira mais uma micro deci