Let AI agents build real animated slideshows Discussion | Link

Here's a thing that catches almost everyone the first week they run a model locally. You paste a 600-line file into your shiny new local assistant, ask it to find the bug, and it confidently rewrites a function that isn't even in the part it read. No error. No warning. It just... silently dropped most of your file on the floor before the model ever saw it. That's not the model being dumb. That's Ollama doing exactly what it was told. By default it gives every model a context window of 2048 tokens and quietly truncates anything past that. It's one of a handful of small surprises that separate "I installed Ollama" from "I actually understand what's running on my machine." Let's go through the ones that matter: how the thing works under the hood, what hardware you really need, the gotchas, and the honest answer to "should I even bother instead of just calling an API?" What Ollama actually is Ollama gets described as "Docker for LLMs," and that's a decent first approximation. You pull a model, you run it, there's a registry. But it hides what's doing the heavy lifting. Underneath, Ollama is a friendly wrapper around llama.cpp , the C/C++ inference engine that made running these models on consumer hardware practical in the first place. When you type ollama run , you're really booting a llama.cpp runtime with a sane default config and a tidy HTTP server bolted on. The models it runs are in a format called GGUF (GPT-Generated Unified Format). A GGUF file isn't just weights. It's a self-contained package that bundles the tensors, the tokenizer config, the architecture details, and hyperparameters like the trained context length, all in one file. That's why ollama pull llama3.1 gives you something that just works: everything the runtime needs to reconstruct the model is in the box. Ollama itself is young. The project shipped its first release in early July 2023 , and it rode the wave of open-weight models (Llama 2 landed that same month) that suddenly made "run a real LLM on

The Trump administration's decision that forced Anthropic to pull its latest cybersecurity models could be reactionary, retaliatory, or both, but the message is clear: The AI industry isn't immune from U.S. government interference.

Up, up and away! (Just try not to crash.)

Meet Perri Comic Generator , a lightweight, single-panel comic creator that merges LLM-driven storytelling with real-time diffusion models. By pairing an Gradio frontend with a high-performance backend, Perri orchestrates a seamless pipeline: it takes a simple story seed, structures it into a panel description, generates the art, and burns the dialogue right onto the final image. The best part? It achieves all of this without massive, resource-heavy infrastructure. Every AI model under Perri's hood is under 32 billion parameters , proving that you don't need giant, compute-heavy models to build something amazing. Here is a look inside the architecture and tech stack that powers Perri. The Technical Architecture Perri is built using a clean separation of concerns, splitting the heavy lifting of generation away from the user interface. 1. The Frontend ( app.py ) Built using Gradio 6.16.0 , the frontend provides a sleek, user-friendly interface for inputting story seeds. To match the creative spirit of comics, the UI utilizes a custom theme, incorporating a vintage aesthetic complete with star-twinkle CSS overlays. The frontend's main jobs are: Capturing the user's initial prompt. Shipping the payload to the backend infrastructure via secure API requests. Decoding the backend's response—a Base64-encoded JPEG—and rendering it within the Gradio image component. 2. The Backend Orchestrator ( orchestrator.py ) The orchestrator acts as the brain of the operation, executing three distinct phases in the lifecycle of a single comic panel: Script Generation: It refines the user's raw prompt into a highly structured visual script and dialogue snippet using meta-llama/Meta-Llama-3-8B-Instruct . Image Generation: It passes the visual description to stabilityai/sdxl-turbo to synthesize the retro comic art. Dialogue Overlay Composition: Instead of relying on separate text captions, the orchestrator dynamically draws the generated dialogue directly onto the JPEG image, ensuring an au

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

Agentic development environment to run agents at scale Discussion | Link

PyPI Supply Chain, OWASP LLM Top 10, & eBPF Cloud-Native Security Today's Highlights Today's security highlights include a critical new malicious PyPI package targeting developers, a comprehensive guide to the OWASP Top 10 vulnerabilities for LLM applications, and practical insights into leveraging eBPF for advanced cloud-native security monitoring. New Malicious PyPI Package 'ColorLib' Targets Developers with Info-Stealing Malware (The Hacker News) Source: https://thehackernews.com/2026/06/new-malicious-pypi-package-colorlib.html This story details the discovery of a malicious package named 'ColorLib' uploaded to the Python Package Index (PyPI). The package is designed to act as info-stealing malware, specifically targeting developers who might inadvertently incorporate it into their projects. Upon execution, the malware attempts to exfiltrate sensitive data, such as environment variables, cryptocurrency wallet details, and various credentials, from the compromised system. This incident underscores the ongoing threat of software supply chain attacks, where attackers inject malicious code into commonly used open-source repositories. Developers relying on public package managers like PyPI must exercise extreme caution and implement robust security practices, including vetting packages, using dependency scanners, and maintaining a principle of least privilege. The rapid proliferation of such attacks necessitates constant vigilance and proactive security measures to prevent widespread compromise. Comment: Developers should immediately check their requirements.txt and pip freeze output for 'colorlib' and ensure all dependencies are from trusted sources, as these attacks are increasingly common. Exploring the OWASP Top 10 for LLM Applications (The Hacker News) Source: https://thehackernews.com/2026/06/exploring-owasp-top-10-for-llm.html The Open Worldwide Application Security Project (OWASP) has released its highly anticipated Top 10 list specifically tailored for Large

Claude LLM Execution Harnesses, RAG Rerank, & Browser-based Edge AI Today's Highlights This week's top stories delve into advanced LLM orchestration with Anthropic's execution harnesses, highlight rerankers as a critical RAG pipeline upgrade, and explore practical browser-based AI for sign language recognition without cloud dependencies. Anthropic Explains How Claude Builds Its Own Execution Harnesses (InfoQ) Source: https://www.infoq.com/news/2026/06/claude-code-harnesses/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global This InfoQ article provides a deep dive into Anthropic's sophisticated orchestration system designed for managing multi-step processes with large language models (LLMs) like Claude. It details how the AI company constructs "execution harnesses" that enable Claude to chain together various operations, handle complex tasks, and recover from errors, going beyond simple prompt-response interactions. The system effectively functions as an internal agentic framework, showcasing advanced patterns for LLM workflow automation and robust production deployment. Understanding these internal mechanisms offers valuable insights for developers and architects aiming to build more resilient and capable AI agents that can tackle intricate, real-world workflows, from dynamic task planning to adaptive execution. It highlights the importance of modularity, self-correction, and tool integration in scaling LLM applications for enterprise use, providing a blueprint for building sophisticated AI agent orchestration layers. Comment: This is a fantastic look behind the curtain at how a leading LLM provider tackles agent orchestration at scale. It underscores that robust LLM applications require sophisticated workflow management, not just better models. RAG Rerank: the Highest-Leverage Upgrade to Your Retrieval Pipeline (Dev.to Top) Source: https://dev.to/dev48v/rag-rerank-the-highest-leverage-upgrade-to-your-retrieval-pipeline-7o5 This Dev.to artic

PostgreSQL HA Risks, Replication Internals, & Rapid Branching Today's Highlights Today's highlights include critical insights into Patroni's replication slot management, an architectural deep dive into PostgreSQL's synchronous commit behavior, and a look at achieving sub-second database branching for enhanced developer workflows. When Patroni Silently Deletes Your Replication Slots (Planet PostgreSQL) Source: https://postgr.es/p/9lM This article uncovers a critical operational pitfall when using Patroni, a popular high-availability solution for PostgreSQL, with logical replication. It details how Patroni, under specific failure scenarios or configuration changes, can silently remove replication slots without warning. Replication slots are vital for ensuring that standbys or logical replication consumers do not miss any changes, making their deletion a potentially severe data integrity issue. The author explains the underlying reasons for this behavior, often related to how Patroni manages pg_basebackup or restores, and how it might not re-create logical replication slots automatically. The post provides concrete scenarios where this can occur, such as when a new primary is elected and old slots aren't re-established, or during certain recovery operations. It emphasizes the importance of diligent monitoring of replication slot status and proposes strategies to mitigate the risk of silent deletion, including careful Patroni configuration and robust alerting mechanisms. This insight is crucial for database administrators and developers relying on Patroni for resilient PostgreSQL deployments, highlighting a subtle but dangerous interaction between these two powerful components. Comment: This is a must-read for anyone running Patroni with PostgreSQL, especially if using logical replication. Understanding this specific behavior of Patroni deleting replication slots silently is essential to prevent unexpected data loss or integrity issues in production. Why Postgres Doesn'