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From Termux to a Freestyle VM: My Osintgram and HikerAPI Experiment After experimenting with Osintgram directly in Termux, I wanted to see how the same project behaved inside a Linux environment running through a Freestyle VM. The idea was not simply to reproduce the installation. I wanted to understand whether moving the project into the VM would make the HikerAPI troubleshooting any clearer. Why use a VM? Termux is capable of running many command-line tools directly on Android, but a VM provides a more conventional Linux environment. I connected to the Freestyle VM from Termux and worked with Osintgram from there. The project could start, but the API side still required investigation. The dependency confusion One of the first things I noticed was that there were multiple API-related components involved. I initially looked at the installed "hikerapi" package and its "Client" class. That alone wasn't enough to explain what Osintgram was doing. So I switched from inspecting only the Python environment to inspecting the project's source code. The HikerAPI-related code pointed me toward: src/hikercli.py This was much more informative because it showed where the client was being configured and how the access token entered the application. Checking the installed library I also checked the installed HikerAPI package rather than assuming I had the expected version. For example: python3 -m pip show hikerapi This let me verify the package that was actually installed in the VM. The important point here is that checking a package version and understanding how the application uses that package are two different troubleshooting steps. Separating authentication from Osintgram I found it useful to test the API independently instead of using Osintgram as the only diagnostic tool. For example: import requests headers = { "x-access-key": "YOUR_KEY" } r = requests.get( " https://api.hikerapi.com/v2/user/by/username?username=natgeo ", headers=headers ) print(r.json()) Again, "YOUR_KEY"

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Google has expanded Gemini in Chrome with a desktop workflow that lets users send a selected part of a web page directly to Gemini. Called Select from screen , the feature is designed for moments when a full page is not the relevant context: a user can draw a box around particular text, an image, or a mixed section of page content and ask Gemini to analyze or act on it in Chrome's side panel. The change makes Gemini more closely embedded in everyday browser work. Rather than manually describing what is on a page or switching between tools, users can identify the exact on-screen material they want Gemini to consider. For teams that regularly research products, review creative assets, compare information, or work from web-based documents, that can make AI assistance more immediate. Its usefulness will still depend on whether Gemini in Chrome is enabled for the user and, for managed environments, how administrators configure access. How Select from screen works Google's official instructions for sharing specific parts of a screen with Gemini in Chrome describe a straightforward process. Users open the Gemini side panel in Chrome, choose Select from screen , then draw around the area they want to share. The chosen content is sent to Gemini as the basis for the next interaction. The important distinction is that the feature is not limited to a single content type. Google says the selected region can contain text and/or images . That gives users a more precise way to supply context from a web page without treating the entire page as the prompt. Workflow element General Gemini interaction in Chrome Select from screen Context provided User supplies a request in the Chrome side panel User selects a defined region of a web page for Gemini Content types Depends on the interaction and context available Selected text, images, or a region containing both Selection method No region-selection step Draw a bounding box around the relevant content Why the workflow matters The value is

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Lica co-founders are going to work on Gamma's new research team.

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See what any page is built with and how much of it is AI Discussion | Link

Se você já perdeu tempo com essa sequência: git stash git checkout outra-branch # resolve o problema urgente git checkout branch-original git stash pop ...só pra descobrir depois que esqueceu o que tinha no stash, ou que o venv / node_modules da outra branch estava desatualizado — este artigo é pra você. O problema Um repositório Git tradicional tem uma única pasta de trabalho ligada a uma branch por vez. Trocar de branch significa trocar todo o conteúdo dessa pasta. Isso funciona bem quando você faz uma coisa de cada vez, mas quebra assim que você precisa: Revisar um PR urgente enquanto está no meio de uma feature grande Rodar testes de uma branch enquanto edita outra Manter ambientes de dependências diferentes (versões de libs, .env ) para features distintas sem reinstalar tudo a cada troca A saída mais comum é o stash , mas ele é frágil: some da vista, acumula, e é fácil esquecer o que tinha ali dentro. A solução: git worktree O git worktree permite ter várias pastas de trabalho simultâneas , cada uma vinculada a uma branch diferente, todas compartilhando o mesmo histórico de commits (o .git ). Pense em uma biblioteca central (o histórico do repositório) com várias mesas de leitura (as worktrees), cada uma com um livro diferente aberto. Você não precisa fechar um livro pra abrir outro. O que é compartilhado, o que é separado Compartilhado entre worktrees Separado por worktree Histórico de commits Arquivos da working directory Objetos do Git (blobs, trees) Arquivos não versionados ( .env , venv , node_modules ) Configuração do repositório Saída do git status Um commit feito em uma worktree aparece imediatamente no git log das outras — mas os arquivos físicos de cada pasta continuam independentes. Colocando em prática Criando uma worktree com branch nova git worktree add ../meu-projeto-feature-x -b feature/nome-da-feature Isso cria a pasta ../meu-projeto-feature-x , já com uma branch nova feature/nome-da-feature criada a partir do commit atual. Criando uma worktree

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AI visibility is not a single score that a business can measure once and treat as settled. A cross-engine study of local-service searches found that ChatGPT and Gemini named the same top business in only 4.2% of identical queries . For small businesses trying to be discovered through AI assistants, that gap means a strong result in one engine may say very little about how another assistant presents the market. The research, published by Steady Demand in its AI Citation Ledger , examined 1,487 queries across 50 U.S. metropolitan areas and 10 service verticals. It focused on prompts such as “best plumber near me,” tracking the businesses named and the sources used to ground responses. Its central finding is practical: AI-driven discovery is fragmented by engine, source mix, location, and category . That does not prove that AI responses drive more leads than conventional local search. The study measures citations and top-name outcomes, not conversions or overall ranking quality. But it provides a useful baseline for marketers because it shows why checking a brand in one AI assistant is not enough to understand its broader AI visibility. What the cross-engine data shows The study compared how Gemini and ChatGPT answered the same local-business prompts. Their differences extended beyond the final recommendation. The systems often drew on different source ecosystems, which helps explain why they surface different businesses. Measure Gemini ChatGPT Exact top-business match between engines 4.2% of identical queries produced the same top business Typical citation mix About 60% of citations were business websites More reliance on Reddit and traditional directories Overlap in cited domains About 8% overlap Repeated-query source alignment About 40% alignment, described as grounding drift Top-result repeatability benchmark About 7% top-match stability in AI-generated results Not specified separately in the supplied research The contrast with Google’s local pack is notable. In th

Learn languages from games, videos, and websites Discussion | Link