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AI Powered Canvas for planning visual projects Discussion | Link

Design with AI agents - live on the same canvas Discussion | Link

Precision-floss between teeth while you brush Discussion | Link

Turn text, pics, video, + 3D into camera-controlled HD video Discussion | Link

Google has expanded Gemini Notebook with Expert Intelligence , an initiative that lets users ground notebook interactions in trusted content, beginning with eligible ebooks they own through Google Play Books. The update makes books usable alongside a user's own materials, allowing Gemini Notebook to generate responses and learning artifacts based on the combined sources. For teams that need to turn authoritative material into usable guidance, the change offers a more source-centered way to work with AI. According to Google's official Expert Intelligence announcement , the initial catalog includes more than 100,000 books from major publishers. Google describes the effort as a cross-Google initiative developed with authors and publishers, with broader availability planned over time across additional sources and platforms, including the Gemini app and AI Mode in Search. How Expert Intelligence works in Gemini Notebook The initial implementation is centered on books purchased through Google Play Books. A user can add a supported ebook to a Gemini Notebook, then use the book's content as a source for notebook interactions. Google says the notebook can combine that material with the user's own documents and other sources. That distinction matters. This is not simply a general prompt asking Gemini to summarize a title from its training. The workflow is designed to use the content of a book the user owns as part of the notebook's source material. Google presents that approach as a way to engage with trusted content while preserving the link between access and ownership. From source material to usable artifacts Google says Expert Intelligence can create several kinds of outputs from a book's content, either on its own or in combination with a user's materials: Answers grounded in the book Infographics Audio overviews Quizzes Other notebook artifacts The company's example involving Steven Pinker's The Sense of Style illustrates the intended use: a writer can bring the book in

AI coding tools can write a function in seconds. The harder question is whether that function...

OpenAI is moving ChatGPT closer to the clinical systems healthcare teams use every day. The company is enabling direct interoperability between ChatGPT and health-system data sources , including electronic health records, in supported deployments. The change is designed to bring AI-assisted work into clinical workflows instead of requiring clinicians to move between a separate AI tool and the patient chart. The development expands the company's ChatGPT for Healthcare direction , which includes HIPAA-compliant workspaces and responses backed by trusted medical sources. As described in OpenAI's announcement on connecting ChatGPT to health records and healthcare sources , the focus is on making relevant clinical information and AI assistance available within supported EHR layouts and care-coordination processes. For healthcare providers, the significance is practical rather than merely technical. If deployed appropriately, a connected assistant could reduce context switching around routine documentation and information-review tasks. However, the initial communications do not provide an exhaustive list of supported EHR vendors, regions, user roles, or pricing. Availability will depend on deployment-specific arrangements and enterprise partnerships. What ChatGPT's health record connections change The central shift is from a standalone conversational interface to a more integrated clinical copilot model. OpenAI describes ChatGPT being connected to health records and healthcare sources, enabling AI-supported work where clinicians already review and document care. That could support workflows such as: Drafting notes from information available in the clinical context. Summarizing patient information for review. Supporting care coordination across connected healthcare data sources. Keeping AI-assisted tasks inside the EHR interface rather than requiring a separate workspace. These are examples of the types of workflows OpenAI's high-level description points toward, not a guar

Claude’s most advanced models for coding and knowledge work Discussion | Link

A Google Business Profile can be a major source of calls, website visits, directions, bookings and customer confidence for a local business. That makes a suspension, reverification request, ownership problem or other loss of profile access more than a support-ticket inconvenience. It can interrupt a meaningful part of the lead pipeline. A Search Engine Land continuity-planning guide for Google Business Profiles , published on August 24, 2026, argues that businesses should prepare for this possibility before it happens. Its central point is practical: recovering a profile matters, but so does maintaining lead flow while recovery is underway. This is not an argument for abandoning Google Business Profile. A complete, accurate profile remains an important local discovery asset. The risk comes from treating it as the only dependable route between prospective customers and a business. If access is disrupted, recovery can take time and may involve lost profile content, reviews or historical performance data. A continuity plan gives the team a defined response instead of forcing it to improvise under revenue pressure. The four-part Google Business Profile continuity framework The framework is built around four connected actions: Preserve, Recover, Replace and Reduce . Together, they cover both immediate response and longer-term resilience. Preserve ownership, evidence and profile records Preparation starts with control. Businesses should ensure that the right people have ownership or access to the profile and that account responsibilities are clear. They should also retain the documents likely to be needed for verification or an appeal, such as business registrations, licences, utility bills and other evidence relevant to the business. It is also sensible to maintain copies of important profile information and keep NAP data consistent. NAP means the business name, address and phone number. Consistency across the website, directories and social profiles makes it easier for

Fix grammar in any Mac app with one shortcut Discussion | Link

Mac-native app for screenshots, recordings and collaboration Discussion | Link

Keep your Mac on the right microphone and speakers Discussion | Link

Control your local Grok sessions from your phone Discussion | Link

A maintainer once watched an AI assistant confidently recommend merging a pull request that deleted a test file. The prompt had included the entire issue thread, the last three commits, and a README from another branch. The model trusted every word because the prompt gave it no reason to filter. The result was a confident but false analysis. The root cause was not a bad model. It was context pollution: unrelated diffs, stale comments, and duplicate code snippets pushed the actual change below the model's attention threshold. For open source reviewers on a free tier, every wasted token also makes the loop slower. The fix is not a bigger context window. It is a smaller, better one. Why Full Context Collapses AI Reviews Long paste sessions fail for reasons that have little to do with model quality. The following failure modes appear regularly in OSS review flows when someone dumps everything into a chat: Issue threads contain outdated suggestions that contradict the current implementation. Full-file dumps include boilerplate that drowns the one-line semantic change. Old test output from another environment appears as evidence even when it no longer applies. Models weigh every token relatively evenly, so irrelevant lines consume attention that the diff deserves. Earlier articles on this account covered the reproduce-patch-test loop, but the missing discipline is context slicing. Slicing means choosing exactly which lines the AI sees, and nothing more. The Three Layers of Slicing The practice breaks into three layers, each with a clear source for truth: Patch layer — the diff and commit message only, not the full conversation history. Code layer — the definitions and tests touched by the diff, not every import in the project. Environment layer — exact commands and expected outputs, not historical logs from an old CI run. Together those layers describe "what changed, what it touches, and how to prove it works." That is enough for a reviewer model to produce a focused anal

Last week a colleague pasted a production config.yml into an AI chat, asked why the connection kept dropping, and got a working fix in three minutes. The file also contained a client secret. Now that secret sits in a model provider's logs. Maybe training data, too. You don't know. That's the problem. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The opinions are mine. I've written here about repo quarantine and dependency triage. This post is narrower: where do you draw the line between your code and a free model? Free model access and a free server are real options, but they shift trust boundaries. MonkeyCode, the open-source platform, offers both. I'm not going to quote quotas or hardware specs — they change faster than blog posts. The question is what you should send in the first place. The Trust Boundary Nobody Draws Think of your AI-assisted workflow as four zones: Zone 0: your terminal / IDE Zone 1: the agent or CLI process Zone 2: the platform API and its logs Zone 3: the model provider's infrastructure Every hop expands the attack surface. Zone 0 is yours. Zone 1 is mostly yours — unless the tool phones home. Zone 2 is someone else's server. "Free server" means Zone 2 is external by default. "Free model access" means your prompt leaves your network and lands in Zone 3. The trust boundary isn't the API call. It's the paste. Three Things That Should Never Cross the Boundary Secrets and credentials. API keys, passwords, tokens, private keys. Obvious, still happens daily. File paths and internal IPs. A stack trace like /srv/customer-42/checkout.py:314 reveals product structure, hostnames, and environment info. Unreleased code structure. AI models may memorize and regurgitate patterns. If your code is patent-sensitive or under NDA, don't feed it the source. Describe the logic instead. A Reproducible Gate: boundary_check.sh Stop relying on discipline. Add a mechanical gate. #!/usr/bin/env bash # boundary_check.sh - blocks high-ris

The first embroidery digitizing agent Discussion | Link

The Dynamic Island for Windows Discussion | Link

Introduction When you're scaling infrastructure or managing network security, proxies become essential tools—but they solve fundamentally different problems. A reverse proxy sits between your users and your backend servers, while a forward proxy sits between your users and the internet. This distinction might sound academic, but it shapes your entire architecture: from load balancing and security posture to compliance requirements and cost structures. Choosing the wrong proxy type can lead to bottlenecks, security vulnerabilities, or unnecessary infrastructure complexity. This article walks you through real-world scenarios, pricing considerations, and decision frameworks to help you deploy the right solution. Forward Proxies: Controlling Outbound Traffic What Forward Proxies Do A forward proxy intercepts requests from your internal network and forwards them to external servers on the internet. From the external server's perspective, the proxy is the client—the real origin of the request is masked or modified. Common use cases include: Employee internet access control : A company deploys a forward proxy so IT can block malicious domains, filter content, and enforce acceptable use policies Data residency compliance : A financial services firm routes all outbound API calls through a forward proxy in a specific geographic region to meet regulatory requirements Web scraping at scale : When extracting data from multiple websites, forward proxies rotate request sources to avoid IP-based blocking DDoS mitigation for outbound traffic : Distributed request aggregation through a forward proxy can reduce fingerprinting risks Pricing and Infrastructure Costs Forward proxies typically charge per: Concurrent connections : Enterprise solutions like Zscaler or Palo Alto Networks start around $5–15 per user/month Data transferred : Cloud-based forward proxies charge $0.05–$0.30 per GB, depending on geography and provider IP rotation : Proxy services offering residential IPs (for non-

Once, just to change an interface field, I spent nearly half an hour switching back and forth between several tools. The code was modified in VSCode. Because the project includes not only Swift but also Flutter modules and some script files. After making changes, I switched back to Xcode to compile, then the test package was handed over to an automation script, and finally I had to open another tool to upload. That day I suddenly realized something: many developers are actually no longer completely dependent on Xcode. To be more precise, it's not that they 'don't use Xcode,' but that the development workflow is being broken apart. Editors, compilers, build tools, and upload tools are each taking on different responsibilities. What many people really want to replace is not Xcode itself When discussing Xcode alternatives, we actually need to know what developers really want to replace. Xcode actually contains many parts: code editing, project management, compilation and building, simulator, on-device debugging, Archive, signing and distribution. Some people want to replace the editing experience, some want to reduce dependence on a full IDE, and others simply want to put different technology stacks into a unified workflow. Therefore, many current 'alternatives' are not complete replacements, but rather split some of these aspects. VSCode: The most common alternative Now more and more iOS developers use VSCode to write code. The reason is that many projects are no longer just native Swift; Flutter, Node services, Shell scripts, JSON configuration, and Web frontends may all be in the same repository. If everything is handled in one editor, the development context becomes more continuous. Swift plugins, Git plugins, and AI-assisted tools have also made VSCode increasingly used in iOS projects. However, it mostly replaces the 'editor' layer. When it comes to compilation and runtime, many projects still return to the Xcode toolchain. AppCode: Another route with a JetBrains

Free Docsend alternative Discussion | Link