标签：#tools

Test firmware on a virtual chip with no hardware needed Discussion | Link

2K+ free hand-drawn illustrations for your projects, CC0 Discussion | Link

Grok Bot is your team of always-on agents Discussion | Link

YouArt AI Feature Launch Video Generator Discussion | Link

OpenRouter for tools with 2,600 APIs, 0% markup Discussion | Link

Run and train AI models locally on your desktop Discussion | Link

I have four different load_env() functions in my MCP server project ( my-git-manager ) — one in server.py , one in publish_devto.py , one in reply_comments.py , one in scripts/list_all_published_titles.py . All four exist for the same dumb reason: this repo has no dependency on python-dotenv , so each script that needs GITHUB_TOKEN or DEV_TO_API reads .env by hand. I went digging for a fresh bug in this repo this week — I write a lot about it, and the well is getting shallow — and decided to actually diff all four load_env() implementations against each other instead of reading them one at a time like I usually do. They'd never been compared side by side before. That's how I found this one. The line that started it Every one of them does roughly this: for line in f : line = line . strip () if " = " in line and not line . startswith ( " # " ): k , v = line . split ( " = " , 1 ) os . environ . setdefault ( k , v . strip (). strip ( '"' ). strip ( "'" )) Look closely at what gets .strip() ed there. v — the value — gets stripped of whitespace and surrounding quotes. k — the key, the actual name of the environment variable — gets nothing. That's fine if your .env file looks like this: DEV_TO_API = abc123 It's not fine if it looks like this: DEV_TO_API = abc123 Spaces around = are a completely normal thing to type. Plenty of .env examples online use them. Plenty of people reach for that style out of habit from other config formats. And line.split("=", 1) doesn't care — it splits on the first = no matter what's next to it, so k comes out as "DEV_TO_API " , trailing space included. What that trailing space actually does os.environ.setdefault("DEV_TO_API ", "abc123") sets an environment variable. It's just not the one anything is looking for. Every caller in this repo does os.environ.get("DEV_TO_API") — no trailing space, because that's the name everyone actually types. That lookup returns None , or whatever was already sitting in the environment before .env ever got read. I

Every mobile developer knows the frustration of "Design QA Day." You finish building a screen and send it to your UI/UX team or QA engineer. Then you receive a list of minor padding mismatch tickets. "This card padding should be 16dp, not 12dp." "The title baseline is slightly off on smaller device densities." Usually, fixing these issues means taking screenshots on devices, placing them onto a Figma canvas, lowering the opacity, and measuring pixels. You can streamline this entire process directly on your physical Android test devices using Designer Tools. Overlay Figma Comps Directly Over Live Apps Instead of comparing your app build side-by-side with a Figma preview on a monitor, you can overlay the target design file onto your screen using SYSTEM_ALERT_WINDOW permissions. Export your frame from Figma as a PNG. Load it into Designer Tools under Image Mockup Overlay. Set the opacity to 50%. When you open your app build underneath, any layout misalignment, incorrect text scaling, or constraint issue will become quickly visible as a drop-shadow ghost. Verify Density-Independent (dp) Grids Testing layouts across different device densities (mdpi, hdpi, xxhdpi) often results in unexpected spacing bugs. With the Custom Grid System in Designer Tools, you can create a grid native to dp: Set standard 4dp/8dp vertical and horizontal spacing grids. Set origin points (Top-Left, Center, or Safe Area bounds). You can verify component placement on target hardware instantly without manual measurements. Infinite Alignment Guides For measuring dynamic list items, headers, or bottom sheets, you can place interactive vertical and horizontal guidelines directly onto the active screen. This removes uncertainty about distances between different UI elements. Try It Out Designer Tools is lightweight and privacy-focused, as all image assets stay strictly on-device. It is built for Android engineers, QA teams, and UI designers. Download on Google Play: ( Pro Design: Designer Tools ) How doe

A neutral control plane for enterprise AI agents Discussion | Link

A claim that Mistral AI is expanding its platform to host third-party open models, beginning with GLM-5.2, has raised a relevant question for enterprise AI teams: what would a credible multi-model platform offering need to disclose? Mistral AI has not published a first-party announcement, product page, or official documentation confirming this specific expansion , so GLM-5.2 should not currently be treated as a supported hosted model on the Mistral platform. The distinction matters because model availability, cloud deployment, and service integration are different things. Mistral already makes its own models available through several cloud-provider ecosystems and offers connectors for third-party services. Neither of those established routes, however, confirms that Mistral is operating or serving third-party open-model weights through its own platform. What is established, and what remains unconfirmed Mistral's documented ecosystem includes cloud deployments of its own models through Azure AI , Amazon Bedrock, Google Vertex AI, Snowflake Cortex, IBM watsonx, and Outscale. It also publishes open-weight models through its own channels, including model cards and licensing terms. These arrangements can give enterprises multiple ways to access Mistral models, depending on their chosen cloud and deployment requirements. MCP-related connectors are another part of the ecosystem. They can integrate third-party services into AI workflows, but connectors do not by themselves demonstrate that a platform hosts, routes requests to, or manages the weights of external foundation models. Area Documented Mistral ecosystem activity Claimed third-party model expansion Models Mistral's own models are available through its channels and selected cloud providers. GLM-5.2 support within the Mistral platform has not been documented by Mistral AI. Third-party technology MCP-related connectors support integrations with third-party services. Connectors do not establish hosting of third-party op

Mistral AI is turning its open-model strategy into a broader deployment proposition. Its December 2, 2025 Mistral 3 release combines dense and mixture-of-experts models, multilingual and image-understanding capabilities, and distribution across cloud, platform, and edge environments. The announcement gives concrete form to the company's stated goal of letting customers select an appropriate model for each task rather than tying workloads to a single proprietary system. The most consequential element is not one model alone. Mistral 3 positions open-weight models, developer access, customization, and deployment choice as connected parts of an AI platform. For enterprises weighing performance, infrastructure control, and commercial reuse, that combination can matter as much as raw model scale. Mistral 3 combines model choice with open commercial licensing Mistral's official Mistral 3 announcement introduced a family released under the Apache 2.0 license . The company says this applies to the new Mistral Large 3 and Ministral 3 models, enabling reuse, fine-tuning, and commercial integration under that license. Its Help Center also identifies Apache 2.0 as the license for its open models. The family spans smaller dense models and a substantially larger sparse model. That range supports the company's stated platform logic: organizations can evaluate a smaller model for constrained or local workloads and reserve a larger model for tasks that justify greater compute requirements. The release also emphasizes multilingual performance and image understanding, bringing Mistral's open-model portfolio beyond text-only positioning. Model group Architecture or size Position in the Mistral 3 release License Ministral 3 Dense variants at 3B, 8B, and 14B parameters Smaller model options within the family Apache 2.0 Mistral Large 3 Sparse MoE model with 675B total parameters and 41B active parameters Frontier-scale open-weight option with multilingual and image-understanding emphasis A

why use many token when few do trick Discussion | Link

The open-source shared folder for your team's AI agents Discussion | Link

An independent receipt for every LLM API call Discussion | Link

Google has placed Gemini 3.5 at the center of a broad product expansion that reaches beyond a single model release. Announced during Google I/O 2026, the update introduces Gemini 3.5 Flash, Gemini Omni, the Spark personal agent, Daily Brief, and a new Neural Expressive interface direction. It also coincides with upgrades to Google's Antigravity agent framework and related developer and enterprise surfaces. The significance is the scope of the rollout. Rather than presenting Gemini 3.5 as an isolated chatbot improvement, Google is extending the family across the Gemini app , multimodal creation, Workspace-connected agent experiences, API and developer tooling. In Google's official Gemini app announcement , the company outlines the new app experiences and their staged availability. Google positions Gemini 3.5 Flash as its flagship model for combining frontier intelligence with fast action. That language is Google's product characterization, but the practical message is clearer: Gemini's next major model family is intended to support responsive interactions across more of Google's products, not just long-form prompts in the consumer app. A Gemini update built around multiple surfaces The Gemini 3.5 rollout combines model, output, agent and interface changes. Each component addresses a different part of how people may use Gemini, from generating multimodal material to receiving proactive assistance and working through connected tools. Gemini component Confirmed role Availability or integration detail Gemini 3.5 Flash Google's flagship model combining frontier intelligence with fast action Part of the Gemini 3.5 rollout Gemini Omni Multimodal video output Beginning rollout to Google AI Plus, Pro and Ultra subscribers Gemini Spark A 24/7 personal agent Integrated with Workspace tools, with a Spark-enabled desktop experience planned for macOS Daily Brief Personalized morning digests Included among the new Gemini app experiences Antigravity enhancements Expanded agent frame

Microsoft today released updates to remedy at least 398 security vulnerabilities in its Windows operating systems and supported software, including one weakness that is already being actively exploited and two others that were publicly detailed prior to today.

Google has announced Gemini 3.1 Pro , a new preview model positioned as a substantial upgrade to the Gemini family for complex tasks. Introduced on February 19, 2026, the model is rolling out across Google's consumer products, developer environments and enterprise platforms, making the release a platform-wide expansion rather than an isolated model update. Google's official Gemini 3.1 Pro announcement describes upgraded core intelligence intended to serve all three audiences. The most consequential part of the announcement is the model's distribution. Developers, enterprises and end users are being offered access through distinct Google products, while Google frames Gemini 3.1 Pro as a step toward more capable agentic workflows . General availability is planned after the preview phase, with broader availability described as coming soon. A broader Gemini 3.1 Pro rollout Google says Gemini 3.1 Pro is designed to be smarter at complex tasks, with improved reasoning as a central focus. The company highlighted a 77.1% score on ARC-AGI-2 , a benchmark it cited as evidence of the model's reasoning progress. That result is a company-reported benchmark figure, not a complete measure of how the model will perform in every production workflow. The release follows earlier Gemini family progress, including Gemini 3 Pro and Gemini 3 Deep Think. However, the announcement's practical message is not limited to a new model name or benchmark. Google is placing Gemini 3.1 Pro into the tools through which people build, deploy and use AI systems. Audience Announced access points Rollout status Developers Gemini API in Google AI Studio, Gemini CLI, Antigravity and Android Studio Preview Enterprises Vertex AI and Gemini Enterprise Preview Consumers Gemini app and NotebookLM Preview rollout What the multi-channel release means A model can matter strategically only if it reaches the environments where work happens. By making Gemini 3.1 Pro available through the Gemini API and development too

The agentic interface Discussion | Link

Run a fleet of AI agents across your machines Discussion | Link

Turn Anki flashcards into real English conversations Discussion | Link