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The move comes as the Trump administration is trying to weaken the act that’s meant to protect endangered species from going extinct in the first place.

Following the launch of the SL3 in 2024 and last year's SL3-S that was optimized for speed and capturing fast moving subjects, Leica announced the new SL3-P today delivering some of the best features from both of its predecessors. Like Leica's previous "P" variants, the new SL3-P lacks the brand's iconic and recognizable red dot […]

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We have all been there: you build a utility app that relies on precise location or time-based triggers, only to find that it works perfectly on your Pixel but dies silently on a Samsung or Xiaomi device. When I started building Muffle, an app designed to automate sound profiles based on prayer times and GPS, I realized that standard AlarmManager usage wasn't enough to survive aggressive battery optimizations. The Problem with OEM Kill-Switches Modern Android versions enforce strict background execution limits. If your app isn't a high-priority foreground service, OEMs will frequently kill your process to save a few milliwatts of battery. For Muffle, if the process dies, the user misses their silent profile trigger, which defeats the entire purpose of the app. I had to move away from relying on a long-running background service and rethink my architecture entirely. Moving to WorkManager with Expedited Jobs Instead of a persistent service, I transitioned the core logic to WorkManager . By utilizing ExistingPeriodicWorkPolicy.UPDATE , I ensure that the scheduling remains consistent even across reboots. However, WorkManager alone can be delayed by Doze mode. To combat this, I implemented setExpedited(OutOfQuotaPolicy.RUN_AS_NON_EXPEDITED_WORK_REQUEST) for critical profile switches. This tells the system that the work is time-sensitive. kotlin val workRequest = PeriodicWorkRequestBuilder(15, TimeUnit.MINUTES) .setConstraints(Constraints.Builder().build()) .setExpedited(OutOfQuotaPolicy.RUN_AS_NON_EXPEDITED_WORK_REQUEST) .build() Leveraging Foreground Services with Notifications For features requiring immediate precision—like geofencing—I had to accept that a persistent notification is non-negotiable. To keep the app from being perceived as 'spammy,' I designed the notification to be low-priority, showing only when a profile is actively being managed. I also had to handle the onTaskRemoved callback in my Service implementation. By calling startService again with a sticky

Found this YouTube video of these kids demoing the densepose repo. However, I believe there was a huge fuss about this being vibe-coded slop and not actually running. This video also seems super suspect as the wifi is only detecting him and not the cameraman or anything else. Has anyone got this Repo up and running? submitted by /u/Wonderful-Bass-8993 [link] [留言]

I wanted to tell my girlfriend 'I missed you today' in Farsi and have it sound like something a person would actually say, not a phrase pulled from a travel guide. Every tool I tried — Google Translate, DeepL — gave me one answer. No register. No note on whether it was too formal for a text message or too casual for a letter. Just a string of words and the implication that language has one correct answer per sentence. So I built konid: it returns three options for anything you want to say, ordered casual to formal, each with the register explained and the cultural nuance between them described. It also plays audio pronunciation through your speakers directly, using node-edge-tts — no API key, no copy-pasting into a separate tab. The interesting engineering constraint was deployment target. I wanted this to live where I already work, not in a separate browser tab I forget to use. That meant MCP. A single MCP server running at https://konid.fly.dev/mcp now serves four clients without any client-specific code: # Claude Code claude mcp add konid-ai -- npx -y konid-ai # ChatGPT (Developer mode, Actions) # endpoint: https://konid.fly.dev/mcp Cursor, VS Code Copilot, Windsurf, Zed, JetBrains, and Claude Cowork all connect the same way. The server doesn't know or care which client called it. The output structure for a query like 'I missed you today' in Japanese looks roughly like this: Option 1 (casual): 今日会いたかった Register: intimate, fine for close friends or a partner Note: dropping the subject is natural here; adding あなたに would feel stiff Option 2 (neutral): 今日、あなたのことが恋しかったです Register: polite, appropriate for someone you're close to but addressing respectfully Option 3 (formal): 本日はお会いできず、寂しく思っておりました Register: formal written Japanese; would be unusual in a personal context The nuance comparison is the part I couldn't get anywhere else. Knowing that option 3 exists and that you would almost never use it for a personal message is actually load-bearing information if you're l

AI Dev Weekly is a Thursday series where I cover the week's most important AI developer news, with my take as someone who actually uses these tools daily. OCR had a week. Mistral dropped OCR 4 with bounding boxes. Baidu open-sourced a model that beats DeepSeek-OCR. Claude got a permanent home inside Slack. And the Fable 5 ban fallout keeps getting uglier: Alibaba was apparently stealing Claude's capabilities, and even the NSA lost access to Mythos. Meanwhile, GPT-5.6 is delayed to mid-July. Let's go. 1. Mistral OCR 4: document AI gets serious Mistral launched OCR 4 this week. It's not just another OCR model. It's a full document understanding system with paragraph-level bounding boxes, confidence scores, and support for 170 languages. The specs: $4 per 1,000 pages (standard), $2 per 1,000 pages (batch) Paragraph-level bounding boxes with coordinates 72% win rate in blind tests against competitors Available on la Plateforme, Microsoft Foundry, and self-hosted for enterprise Top score on OlmOCRBench Why this matters for developers: Bounding boxes change everything. Previous OCR models gave you text. Mistral gives you text + where it is on the page. That unlocks document search, compliance systems, and any workflow where page structure matters. My take: At $4/1000 pages, this is competitive with Google Document AI ($5) and significantly cheaper than building your own pipeline. For enterprise document processing, this is probably the best option right now. For budget-conscious developers, Baidu's free alternative (see below) is worth considering. Full comparison in our Mistral vs DeepSeek vs Baidu breakdown. 2. Baidu open-sources Unlimited-OCR While Mistral went commercial, Baidu went open. Unlimited-OCR is a 3B-parameter MIT-licensed model that processes multi-page PDFs in a single inference pass. Key features: Built on DeepSeek-OCR architecture (SAM+CLIP + DeepSeek-V2 MoE decoder) Reference Sliding Window Attention for memory efficiency on long documents Tables to HTM

Hello everyone! I'm posting our research work as you might be interested in how we used ML to map part of the brain cells of the human hippocampus :) We used various human brain slices at high resolution (1 micrometer per pixel) and developed a custom segmentation pipeline that uses SoTA whole slice cell segmentation networks, like CellPoseSAM with good zero shot performances. We then refined semi-automatically those annotations and ensembled more finetuned models within the pipeline, adding a merging algorithm and a cell classification for 3 classes (excitatory and inhibitory neurons, and glial cells). But the high-res slices covered only a few parts of the hippocampus with respect to other slices scanned at 20x less the resolution where the cell nuclei are only 1 pixel wide. So we tried to map the high-res annotations we obtained to the low-res corresponding slices, and used a small UNet to supervise a density estimation task for 3 classes. We obtained a network that outputs a density map that can be sampled to obtain a probabilistic map of the cellular positions. Finally, to reconstruct the volume, we stacked together all the low-resolution density maps from all the slices that covered the hippocampus and obtained a point cloud, which you can see in the GIF along the corresponding anatomical CA (Cornus Ammonis) areas. The performances are still limited by the quantity of data and low-resolution slices, but we showed that the results were biologically plausible given previous estimates by other researchers. The paper was accepted at MICCAI 2026 a few weeks ago! Feedback is very welcome, especially on the density-estimation formulation and possible uses of the generated point cloud. submitted by /u/V_ector [link] [留言]

AI harnesses are the new vendor lock-in. To swap across harnesses easily without rewriting your app, LiteLLM launched the Lite-Harness SDK . Run your prompt across different harnesses: from lite_harness import query , AgentOptions prompt = " Fix the failing test " # Claude Code harness async for message in query ( prompt = prompt , options = AgentOptions ( harness = " claude-code " , model = " claude-opus-4-8 " ), ): print ( message ) # Codex harness async for message in query ( prompt = prompt , options = AgentOptions ( harness = " codex " , model = " gpt-5.5 " ), ): print ( message ) To enable cost controls, fallbacks, and logging, point it to your LiteLLM AI Gateway: export LITELLM_API_BASE = https://litellm.your-company.com/v1 export LITELLM_API_KEY = sk-litellm-... Engineer's Takeaway: This SDK unifies how you invoke the agents, not how they run internally. Each harness keeps its native loop and tool-calling semantics. It is perfect for A/B testing agent performance and centralizing costs, but remember it is in public beta, so custom tool injection might require extra work! The Problem I Had My team was building an internal bot to fix failing CI/CD tests. We had three engineers advocating for three different harnesses: one wanted Claude Code, another Codex, and another Pi AI. Without an abstraction layer, we would have had to maintain three forks of the same bot , with three different SDKs, three logging systems, and three ways to track costs. It would have been an impossible maintenance burden. How Lite-Harness Helped The SDK solved that exact pain point in three concrete dimensions : 1. Unified Invocation (Time Savings) Instead of maintaining three separate implementations, I had a single query() that routed to whichever harness I wanted. Switching from Claude Code to Codex was literally just changing a string in the options. This allowed us to do real A/B testing in production for two weeks without rewriting any core logic. 2. Cost Observability (The Killer

Here's a thing that surprises people the first time: an email sent through the API does not carry the signature the user set up in Gmail or Outlook. Provider signatures live in the provider's compose UI, and a programmatic send bypasses that entirely, so a message your app sends goes out with no signature at all unless you add one. The Nylas Signatures API is how you add it: store an HTML signature once, then attach it to a send by ID, and the signature gets appended to the message for you. This post covers signatures from two angles: the HTTP API your backend calls, and the nylas CLI for creating and testing one from the terminal. I work on the CLI, so the terminal commands below are the ones I reach for when I'm setting a signature up. Nylas signatures are separate from provider signatures The first thing to get straight is that these are not the user's existing signature. Nylas doesn't sync the signature configured in Gmail, Outlook, or any other provider, and that provider signature is never applied to mail sent through the API. If a message your app sends needs a sign-off, you create that signature with this API and attach it explicitly; there's no inheriting it from the connected account. That separation is deliberate, because a programmatic send is a different context from a person typing in their webmail. It does mean the responsibility is yours: a user who connects their mailbox expecting their familiar signature to appear on app-sent mail won't get it automatically. Stored signatures are grant-scoped, living at /v3/grants/{grant_id}/signatures , so each connected account has its own set, and they're HTML, so a branded sign-off with a logo and links works the same as a plain one. Create a signature Creating a signature is a POST /v3/grants/{grant_id}/signatures with a name and an HTML body . The name is for you, a label to find it by later; the body is the markup that gets appended to outbound mail. The response returns the signature with its ID, which is w

Software teams often face a false choice: move fast and accumulate technical debt, or build the perfect system and miss the deadline. But there may be a third path, the Best Simple System for Now (BSSN). This session introduces a pragmatic yet principled approach to software design: systems that are as simple as possible but no simpler. You’ll learn how to design code that is robust, change-friendly, and fit for purpose, while avoiding both gold-plating and corner-cutting. Through practical examples, we explore how to manage complexity, embrace uncertainty, and make sound decisions using concepts like Cost of Delay and RAROC. submitted by /u/goto-con [link] [留言]

Link:- https://easy-assign.vercel.app Specially need some suggestions for ui Want some suggestions what to do next It is a freelance platform for students and freshers so they can easily get some gigs or post task for help they need In last 3 days I got around 500 users and some paid tasks What could be improved here ? The ui ,the way we make them apply ,profiles streaks or what? I want to scale it more and bring bigger projects here so what to do to make it more trustworthy ( I know first suggestion will be payment integration that will be done after 2000 users) submitted by /u/detective8421 [link] [留言]

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Introduction I'm Ahmer. I'm 19, I'm doing a 4-year Software Engineering degree at a fairly...

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