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I have released the Sovereign package on Github. It deploys Lemmings into up to 3 cloud regions for your choice in configuration flavor with cost estimations through dry-runs and aggregating the report into one final report as lemmings come and go in the result of the load test. I built it for organizations that plan on using AI to build something, not hire somebody like me who helped write The Library to do it for them. You can hire me in consulting if you need help, but it's available now. But, before you spend $50,000 on a television ad driving people to your new app that you just built after spending $50,000 on tokens, why not run Lemmings and Sovereign first? It's 100% free and does not involve me at all in order for you to read through the extensive README.md files and comments in the code for you to understand what to do to run it and adapt to its results. Enjoy using it! There - that is the post. Now - 🙌🏻 - Ask me anything 🙇🏻 👇🏻

After a dismal two years of weakening demand, falling sales, and damage to its brand by Elon Musk's political activities, Tesla's road to recovery continues apace. On the heels of an impressive delivery report, the company released its earnings for the second quarter of 2026 - giving us the latest glimpse at the EV company […]

SoundCloud has acquired decentralized music platform Nina Protocol, months after the startup announced it would shut down. The deal brings Nina’s artists, editorial archive, and music discovery tools to SoundCloud as the company continues expanding its platform for independent musicians.

Elon Musk and the online right convinced themselves that Christopher Nolan’s “woke” epic would go down in flames. Now they’re crashing out.

As access to Anthropic’s and OpenAI’s frontier models becomes more restricted, Chinese labs are pitching their open-source alternatives as stable, accessible, and increasingly capable.

Workflows is a Rust library crate (not a hosted service; the crate name on crates.io/GitHub is tinyflows) that models an automation as a WorkflowGraph: a directed graph of typed nodes and edges. You build or generate that graph, it gets structurally validated, compiled into an opaque CompiledWorkflow, and lowered — once per run — onto tinyagents, a state-graph execution engine, via engine::run. model::WorkflowGraph -> validate -> compiler::compile -> engine::run (typed graph) (structural) (validated handle) (lowers onto tinyagents, drives to done) Run state is a single JSON value shaped like { "run": { "trigger": … }, "nodes": { "": { "items": [ … ] } } }. A merge reducer folds each node's output under its own id, so independent branches never collide — which is what keeps parallel fan-out deterministic. The node catalog Kind What it does trigger Entry node that starts the workflow (exactly one per graph); firing mode is host-driven agent Runs an LLM agent turn, with optional chat-model / memory / tool / output-parser sub-ports tool_call Invokes one specific integration action deterministically, no LLM involved http_request Outbound HTTP request code Sandboxed user code (JavaScript or Python) output_parser Parses/validates an upstream agent's output into a structured shape sub_workflow Runs another workflow as a nested sub-graph and returns its output condition Two-way IF, emits on true/false switch Multi-way branch keyed by an expression result merge Fan-in barrier — waits for every wired predecessor before running split_out Fan-out — emits one item per element of a list transform Pure, expression-based field mapping over the run state Data flows between nodes as arrays of items shaped { json, binary?, paired_item? } — closer to n8n's item-based model than a plain function-composition DAG — and node config can reference the run scope with =-prefixed expressions like =item.name. The part I actually want to talk about: host-agnosticism Every place this engine would n

Union previously warned automaker that any robot deployment must be negotiated.

"The thing that the space needs is a company making $100 million a year of revenue," Science Corp. CEO Max Hodak said.

Yope, a fast-growing social app focused on private groups of friends and family, has raised $12.3 million in seed funding. Instead of chasing creators and algorithmic feeds, the startup is betting that the future of social networking lies in small, private communities powered by messaging, photo sharing, and AI features designed to strengthen real-world relationships.

Como eu mostro que estou contribuindo? Posso colocar no meu LinkedIn? E no meu currículo, como faço? Foi a partir dessas dúvidas que eu elaborei esse guia pra você que quer contribuir do seu jeito e mostrar às empresas e às pessoas, de forma clara e estratégica, o que você está fazendo. Vamos lá? 👇 Por que eu deveria mostrar no LinkedIn? LinkedIn é a porta de entrada para o mundo corporativo no Brasil e no mundo. É por meio dele que você mostra "trabalho". E tem mais: não é só experiência remunerada que conta como evidência de que você tem conhecimento e prática, mas também tudo o que você constrói de forma voluntária , seja tirando dúvida de alguém, participando de um projeto open-source ou escrevendo sobre o que aprendeu. Recrutador não lê currículo pensando só em carteira assinada. Lê pensando em capacidade . Você contribui com algo para a comunidade e quer colocar isso no seu perfil. Existem 3 formas que você pode usar, e elas podem ser usadas todas juntas ou só uma. Escolha aquela que fizer mais sentido pro seu perfil ou busque por outras pessoas que você admira dentro da comunidade e veja como elas colocaram no próprio perfil. 1. Seção de Experiência Use como experiência sempre que estiver contribuindo de forma profissional pra uma área que você busca. Se você participa de contribuições no GitHub, seja através de código, documentação ou outra forma, use como experiência. Pessoas que também estão ajudando na moderação ou administração (community managers) podem destacar as responsabilidades ou resultados das suas ações por aqui. Exemplo de como preencher no LinkedIn: Cargo: [cargo que você faz] Open Source Empresa: [Nome do projeto/organização] Tipo de emprego: Meio período (ou Voluntário) Local: Remoto, Brasil Descrição: - Contribuí com [X] pull requests na documentação do projeto [Nome], focando em clareza para novos contribuidores. - Revisei issues abertas e sugeri melhorias de acessibilidade em componentes de UI usando [ferramenta/stack]. - Participei de re

Project: https://quven.tv/ Security model: https://quven.tv/security/ For the last 18 months, I have been building Quven, a self-hosted media server for personal movie, TV, and documentary libraries. I started with a seemingly simple goal: let people keep their media on their own hardware while giving them a polished client experience. Playback quickly became the hardest part. A media server does not simply send a video file to a screen. It has to understand the source, the client, the network, and the user's choices, then select a playback path without making any of that complexity feel visible. These are some of the lessons I learned. 1. "Can this file play?" is the wrong question The real question is whether a particular client can play a particular combination of: container; video codec and profile; audio codec and channel layout; subtitle format; resolution, bitrate, and frame rate; HDR format; network conditions. A client might support the video codec but not the audio track. A browser might decode the video but require a different container. Enabling an image-based subtitle can turn an otherwise direct-playable file into a video transcode. Playback compatibility is therefore not a boolean property of a file. It is a negotiation between the source and the active client. 2. Direct play should be the preferred outcome, not a promise Direct play preserves the original file and avoids unnecessary server work. When the client supports the selected combination, it is usually the best path. But forcing direct play at all costs produces a worse experience. A high-bitrate file may technically be supported while still exceeding the available connection. A selected subtitle might require burning into the video. A television may accept a container while rejecting one of its audio formats. The practical hierarchy I settled on is: Direct play when the complete source is compatible. Remux when the streams are compatible but the container is not. Transcode only what must chan

It turns out some of Benjamin Franklin's opinions from 240 years ago are still relevant today.

Those who have been following me for a while will know that teaching and mentoring are a Big Deal™️ to me. Before I got into tech I was a teacher, and still consider myself a teacher at heart. Being a teacher and mentor was never separate in my eyes from being a good programmer and engineer; on the contrary, teaching was a tool that helped me become better at my craft at every stage of the journey. But in the last few years, and accelerating in the last few months, the landscape for teaching and learning has been changing at a scary pace. The advent of LLMs and "AI" coding assistants has drastically shifted how we acquire engineering skills in ways that we are definitely not prepared for. All of that has prompted many thoughts and conversations, and I hope to distill some of them in this blog post. In true Talmudic fashion, this post doesn't contain too many answers and will hopefully leave you with more questions than you started with. But asking the questions is how we start these conversations, and these conversations need to be happening if we are to do right by the coming generation of programmers and engineers. Don't Spoon-Feed Me As a student, whether in Yeshiva when I used to spend hours each day poring over dense Talmudic legal debates and esoteric Chassidic philosophy or later while learning Rails and React at the Flatiron bootcamp, I quickly realized an uncomfortable truth about skill acquisition. My best, most profound learning never happened when a lesson went smoothly; it happened when I was painfully stuck, banging my head against a cryptic error message or wrestling with a concept that just wouldn't click (usually at 2 AM, fueled by cold coffee and sheer stubbornness). When you strip away that struggle, you get rid of the growth. And when you get rid of the growth, the learning just doesn't happen. Even if you memorize just enough to pass the test, "easy come easy go." Without that cognitive friction, the knowledge evaporates the moment you close you

GitHub热门项目 | A hive mind communication platform | Stars: 3,079 | 2,720 stars this week | 语言: Rust

GitHub热门项目 | RustDesk Server Program | Stars: 10,102 | 10 stars today | 语言: Rust

GitHub热门项目 | CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies | Stars: 72,532 | 274 stars today | 语言: Rust

GitHub热门项目 | Empowering everyone to host fast and efficient Minecraft servers. | Stars: 8,141 | 96 stars today | 语言: Rust

Synthesia launched AI Roleplay Sessions, an interactive enterprise training platform where employees practice workplace conversations with AI avatars that provide feedback, scoring, and analytics to help companies measure training effectiveness.

Substack shipped an AI detector this week. Every post, note, and comment over 100 words can now be...

The company donated more than $3.6 million to conservative groups between 2020 and 2025, including $1 million to the MAGA Inc. super PAC.