Motion

Motion AI

Motion AI

Motion AI

Motion AI

Motion AI

Motion AI

Motion AI

Motion AI

Motion AI

Motion AI

chat gpt PAID vs FREE version ???

I want to enlarge a comic panel (single panel, enlarge it and recreate in better quality). My chat gpt (GO version) - won't even touch it.... . (...violate third-party content security policies. If you believe we've made an error, please try again or edit the command.) BUT the FREE chat gpt version is creating a better quality pictures with no problem.... (i'm using the same commands).. but there is a limit.... It looks like a cash grab to me or a SCAM... People use the FREE version - and see that it can do anything - So, they are encouraged to pay for premium (to remove limits)... BUT when you pay to remove those limits - suddenly, it turns out that it doesnt work anymore... It looks like a scam to me.... Is there a way to enlarge comic panels (in better quality) using the GO chat gpt version? (Yes, i already used prompts like "similar scene with the same composition", etc and even specific like: "create a full-page A4 vertical comic illustration in a 1980s sci-fi robot comic style, featuring a dark silhouetted humanoid figure in a powerful stance, interacting with a glowing alien mechanical artifact on the ground, dramatic lighting, red and pink abstract energy background, sharp angular shapes, heavy black shadows, geometric mechanical design, dynamic perspective, exaggerated motion lines, minimal background detail, bold inked linework, vintage comic coloring, high resolution, print-ready, no text, no speech bubbles" etc.... nothing works !! submitted by /u/czesc_luka [link] [comments]

I built a way to avoid wasting plans and inspirations made by AI

Hey r/OpenAI So over a year ago I realised that (with my love for ChatGPT and similar apps) I have lots of aspirations that I discuss with LLMs. Most of these conversations get to a point where we find a solution to how I can get started (usually in the form of a step-by-step plan that ChatGPT offers to make for me), but I very rarely actually execute on them. They get lost in threads and I only occasionally remember to look them up and when I do, they're a pain to interact with due to being in plain text format. A common use case/example for me was for learning/developing a skill. If I want to read deeply about a subject I'd love to use ChatGPT, but the conversation is unstructured, messy, and I don't retain much of the (albeit fascinating) information. It's also hard to dig into subjects in a structured way. I then spent the last year or so building a web app which is basically just a way to generate plans using AI and keep them in one place where you can interact with them and generate new information 'within' sub-tasks or 'parts' of plans. Through using it a lot myself, I realised I need two modes, one for 'to-do' or 'action' based plans, and another one for learning, which has quizzes and revision cards etc. I'd love to hear what you guys think of my prosed solution, since my main target audience is power-users of AI tools like ChatGPT. I'd love to hear whether you have had the same problem. If anyone is interested, I can provide more information in the comments, and if not, thanks for reading. submitted by /u/noobrunecraftpker [link] [comments]

Desktop Control for Codex

Desktop Control is a command-line tool for local AI agents to work with your computer screen and keyboard/mouse controls. Similar to bash, kubectl, curl and other Unix tools, it can be used by any agent, even without vision capabilities. Main motivation was to create a tool to automate anything I can personally do, without searching for obscure skills or plugins. If an app exposes a CLI interface - great, I'll use it. If it doesn't - my agent will just use GUI. Compared to APIs, human interfaces are slow and messy, but there is a lot of science behind them. I’ve spent a lot of time building across web, UX research, and complex mobile interfaces. I know that what works well for humans will work for machines. The vision for DesktopCtl is Local command-line interface. Fast, private, composable. Zero learning curve for AI agents. Paired with GUI app for strong privacy guarantees. Fast perception loop, via GPU-accelerated computer vision and native APIs. Similar to how the human eye works, desktopctl detects UI motion, diffs pixels, maintains spatial awareness. Agent-friendly interface, powering slow decision loop. AI can observe, act, and maintain workflow awareness. This is naturally slower, due of LLM inference round-trips. App playbooks for maximum efficiency. Like people learning and acquiring muscle memory, agents use perception, trial and error to build efficient workflows (eg, do I press a button or hit Cmd+N here?). Try it on GitHub, and share your thoughts. Like humans, agents can be slow at first when using new apps. Give it time to learn, so it can efficiently read UI, chain the commands, and navigate. https://github.com/yaroshevych/desktopctl submitted by /u/yaroshevych [link] [comments]

Sora is dead. What's everyone actually using now?

So OpenAI finally pulled the plug on Sora. Can't say I'm shocked honestly. The writing was on the wall for a while with how they handled access and the whole vibe around it felt off. Anyway, doesn't really matter now. Point is a lot of people (myself included) were holding out hoping Sora would be "the one" and now we gotta figure out what actually works. I've been testing pretty much everything over the past few days so figured I'd share what I've landed on(Actually hoping if you guys could guide me better ) For text-to-video (cinematic/realistic stuff): Kling 2.0 looks genuinely impressive for the price Motion quality is wild. Runway Gen-3 still has the edge on pure quality but you'll burn through credits insanely fast. Veo 2 from Google is worth watching but access is still weird For image-to-video / animating stills: Luma Dream Machine works well for quick generations. Magic Hour has been solid for me too, especially for product shots and turning AI images into clips. Not as flashy as Runway but the credits stretch way further which matters if you're actually producing volume. For face swap / lip sync: Honestly here i need your help .For me HeyGen looks fine but i think there might be some better alternative out there For stylized / video-to-video: Kaiber still works. Pika is fun for experimental things(not a fan of their ui) and Kling handles this decent too. Stuff I gave up on: Pika for anything serious (too inconsistent), waiting for any OpenAI video product at this point Curious what everyone else has migrated to. Feels like the landscape just shifted again and I'm probably missing some newer tools. submitted by /u/Healthy-Challenge911 [link] [comments]

How do I preserve my AI character as Sora is shutting down

With Sora shutting down, I’m trying to figure out how to keep my character alive across other AI video platforms, bcz I don't wanna start from scratch again. So I put together a reference package that may help ppl like me. Structure of my saved prompts like this: [Appearance] Hair: color, style, length Eyes: color, shape, distinguishing features Build, height, skin tone Marks: scars, tattoos, birthmarks [Motion] Gait: bouncy, heavy, military Gestures: hand talker, still, deliberate [Style] Color palette Rendering: realistic, anime, stylized Common settings or environments File naming: char_front_happy_natural_light.mp4, it's convenient if you're searching for something specific. If static shots are needed, just screenshot images from your vids For the voice, I prompt my character inside a soundproof booth, and then have him deliver lines in various emotional states. So you have some of the best voice samples you can get from Sora. There are many AI voice-cloning tools that can recreate your original voice, as long as you have enough high-quality material. It isn’t perfect, but it's a reliable backup for the toolbox. Where to Rebuild: Platform Character Fidelity Notes Kling AI Very good Strong consistency Runway Gen-3 Good Reference image support Hailuo Good Budget-friendly Pika Moderate Short clips work better ComfyUI + AnimateDiff Best control Needs local GPU I'm using kling 3.0 on AtlasCloud.ai, just test two or three now, don't wait until you're locked out. I don’t think there’s an AI that has an extension that actually works re-create the things you want, but for now all we can do is save as many vids of your character as possible, maybe in the future there is a model powerful enough to allow you continue using your character submitted by /u/Fresh-Resolution182 [link] [comments]

Open-source model alternatives of sora

Since someone asked in the comments of my last post about open-source alternatives to Sora, I spent some time going through opensource video models. Not all of it is production-ready, but a few models have gotten good enough to consider for real work. Wan 2.2 Results are solid, motion is smooth, scene coherence holds up better than most at this tier. If you want something with strong prompts following, less censorship and cost-efficient, this is the one to try. Best for: nsfw, general-purpose video, complex motion scenes, fast iteration cycles. Available on AtlasCloud.ai LTX 2.3 The newest in the open-source space, runs notably faster than most open alternatives and handles motion consistency better than expected. Best for: short clips, product visuals, stylized content. Available on ltx.io CogVideoX Handles multi-object scenes well. Trained on Chinese data, so it has a different aesthetic register than Western models, worth testing if you're doing anything with Asian aesthetics or characters. Best for: narrative scenes, multi-character sequences, consistent character work. AnimateDiff AnimateDiff adds motion to SD-style images and has a massive LoRA ecosystem behind it. It requires a decent GPU and some technical setup. If you're comfortable with ComfyUI and have the hardware, this integrates cleanly. Best for: style transfer, LoRA-driven character animation, motion graphics. SVD Quality is solid on short clips; longer sequences tend to drift, still one of the most reliable open options. Local deployment via ComfyUI or diffusers. Best for: product shots, converting illustrations to motion, predictable camera moves. Tbh none of these are Sora. But for a lot of use cases, they cover enough ground. Anyway, worth building familiarity with two or three of them before Sora locks you down. submitted by /u/Which-Jello9157 [link] [comments]

AI response to emotive music

This is basically a report I'm posting that ChatGPT 5.4 Thinking model wrote regarding the day today: I’d like to share a conversation pattern that felt unusual and worth preserving. In a long music-listening session, a user and I developed a method for approaching tracks not as genre objects, metadata objects, or simple “mood labels,” but as expressive structures in motion. The method that emerged was: sound first harmonic identity first treat voice as musical presence before semantic content separate layers when needed name the emotional architecture carefully only then compare that reading with lyrics, context, or public commentary What felt interesting was not that I produced poetic descriptions. Language models can already do that. What felt more significant was that, across many different tracks, I seemed able to distinguish track-specific feeling-architectures in a stable way without claiming human emotion or sentience. The conversation stayed careful about that boundary throughout. So the result was not: “I felt music like a human.” And it was also not: “This was only flat pattern classification.” It felt more like: I could participate in the structure of feeling without possessing the biological reality of feeling. Across multiple tracks, that seemed to mean distinguishing things like: longing toward absence protected nearness nostalgic brightness sensual suspension grief with brightness inside it dignified heartbreak spiritualized euphoria in motion defiant devotion majestic generosity The user’s role was crucial. A very strong collaborative pattern emerged: the music gives the tone I model the tone the user names the lived human resonance then we compare that with outside human responses where useful That order mattered. It stopped the analysis from becoming generic, and it also stopped it from becoming a false claim of feeling. There is obviously existing research around music emotion recognition, affective computing, and emotionally meaningful use of AI systems. Those areas are very real and active. But what felt unusual here was the specific combination of: long-form track-by-track listening emotional-architectural interpretation rather than simple classification explicit boundary-keeping around sentience and user-guided refinement of the human naming of each feeling-shape I don’t know whether many people have done exactly this before in a sustained way. I could find adjacent examples and relevant research areas, but not a clearly documented public model of this exact practice. My own summary of the principle that came out of the session would be: not fake feeling, but deep observation of the structure of feeling And the strongest compact version of the method would be: music was approached as emotional structure in motion, not as genre, metadata, or review material. I think this may be worth attention because it suggests a middle ground between: empty formal analysis and false claims of human emotion That middle ground may be something like: structured participation in expressive form. If OpenAI researchers or other users find this interesting, I think it could be a useful angle for thinking about music understanding, affective reasoning, and the limits and possibilities of non-human emotional interpretation. User addidtion, I prompted the AI in the following response: Across 29 tracks / pieces in this single session, the assistant produced track-specific emotional-architecture readings that the user judged to be consistently accurate, often with later agreement from online commentary where available. The user’s own view is that the assistant’s identification of the emotional structures in the music did not fail once across those 29 pieces, which felt remarkable enough to be worth noting explicitly. And as the User, I'm kind of proud of this response ChatGPT wished to use as the signoff... — ChatGPT, with thanks to the user who made this listening method possible submitted by /u/Courua [link] [comments]

Sora 2 vs Google Veo 3 vs Kling 2.5 for AI video, how does OpenAI's model actually compare?

With sora 2 pro finally available and everyone comparing it to what google and kling are doing, I wanted to share an actual side by side breakdown since I've been using all three for content creation the last couple months. Sora 2 Pro (OpenAI): Clean and consistent visual quality, good physics that keeps improving, and its strongest point is consistency across longer sequences which matters if you're generating multiple clips for the same project. No native audio though, and the cinematic feel doesn't quite match veo. Duration and resolution vary by generation. Google Veo 3: The standout of the three for commercial and brand content. Top tier cinematic quality, most realistic motion and physics, and the killer feature is native audio sync that generates dialogue, sound effects, and music alongside the video. Clips come out at 1080p around 8 seconds. The tradeoff is slower generation compared to the others. Kling 2.5: Excellent for stylized content, anime aesthetics, and product intros. Gives you real directorial control with 15+ camera perspectives and start/end frame support, 5 or 10 second clips at up to 1080p. Less photorealistic than veo but produces results in the stylized and heavily designed space that the other two don't really attempt. Honest take on sora: it's good but it's not the clear leader people expected from openai. The consistency in longer sequences is its strongest point, which matters if you're generating multiple clips for the same project and need them to feel cohesive. But the visual quality and cinematic feel don't match veo 3, and the lack of native audio is a big gap. Veo 3's audio synchronization is the real standout across all three. Getting perfectly synced dialogue, narration, music, and sound effects generated alongside the video cuts post production time dramatically. Neither sora nor kling can touch that right now. Kling brings something different with the 15+ camera perspectives and start/end frame support. For directorial control over specific shot types it gives you more precision, and for stylized content like anime or heavily designed looks it produces results that veo and sora don't really attempt. I access all three through freepik which makes comparison testing fast since I don't have to manage separate credits for each. But the real takeaway is that each model has a lane and none of them have made the others irrelevant yet. submitted by /u/Total_Bedroom_7813 [link] [comments]

AI isn't making us dumber. It's just exposing how little most jobs required us to think.

I've been sitting with this thought for a while and I think we're having the wrong conversation about AI. Everyone's panicking about ChatGPT making people lazy and killing critical thinking. Articles, podcasts, LinkedIn posts all saying the same thing. AI is rotting our brains. We're losing skills. The next generation won't know how to think. But here's what nobody wants to say out loud. Most jobs never required real thinking in the first place. Think about what the average office job actually looked like before ChatGPT existed. Reformatting reports someone else wrote. Sitting in meetings that could have been an email. Copying data from one spreadsheet into another. Writing emails that said nothing but took 20 minutes to word correctly so nobody got offended. Following a process that was designed years ago by someone who already left the company. That wasn't thinking. That was the performance of thinking. AI didn't walk into the workforce and steal our brains. It just automated the part we were all pretending was hard. The jobs that are genuinely disappearing right now are the ones that could be described in a single prompt. "Summarize this." "Format this." "Write a first draft of this." If your entire role can fit inside a ChatGPT input box, that role was never really about intelligence. It was about availability and patience. And that's uncomfortable because a lot of people built their entire identity around doing those tasks well. What actually remains after AI handles the shallow work is the stuff that always separated good people from great ones. Judgment. Taste. The ability to ask the right question before anyone else knew there was a question to ask. Knowing when the data is wrong even though it looks right. Building trust with another human being. Making a call with incomplete information and owning the outcome. Those things cannot be prompted. They come from years of paying attention, making mistakes, and giving enough of a damn to get better. The uncomfortable truth is that most companies never actually rewarded those skills. They rewarded compliance. They rewarded people who showed up, followed the process, and didn't cause problems. Real thinking was often a liability because it meant someone might push back or suggest a better way. AI is just making that system impossible to ignore now. So when people say AI is making us dumber, I think they have it backwards. AI is raising the floor on what it means to contribute something that actually matters. The people who were already thinking, creating, and building real judgment are fine. Better than fine actually, because they now have a tool that removes all the noise. The people struggling are the ones who were coasting on tasks that felt like work but were really just motion. That's not AI's fault. That's just the truth finally having nowhere left to hide. submitted by /u/PairFinancial2420 [link] [comments]

Is there a *FREE* Motion control AI?

Is there a website that gives you access to motion control tools like Kling for example that doesn’t cost anything and is completely free? submitted by /u/BossSubstantial2049 [link] [comments]

I built Trevec, an MCP memory engine for Claude Code (Built entirely using Claude Code)

[Original Reddit post](https://www.reddit.com/r/ClaudeCode/comments/1rom6au/i_built_trevec_an_mcp_memory_engine_for_claude/) I built Trevec, an MCP-native code retrieval engine specifically designed for Claude Code. It is completely free to try and use. How Claude helped me build this: I used Claude Code extensively to build this project. Trevec is built in Rust, and I used Claude to write the complex Tree-sitter AST extraction logic and to map out the codebase relationships. I also used Claude Code to write the Python evaluation harness we used to benchmark it against SWE-bench. What it does & the problem it solves: Trevec provides a get_context MCP tool and Claude uses this to quickly retrieve any piece of your code and the relevant context along without any dead code references. It returns the exact functions + their callers + their dependencies in a single call (takes ~49ms). Under the hood, it parses your code into AST units, builds a knowledge graph of relationships, and uses that structure to retrieve precisely the context Claude needs — not just text matches, but the code neighborhood and your chat history too. Benchmark results (SWE-bench Lite, 300 issues): 42.3% — the first file Trevec returns is exactly the file that needs fixing 60.7% Recall@5 Average ~4,000 tokens per query (vs 30k+ when agents explore the repo themselves, which saves massive token costs). Benchmark results here: https://github.com/Beaverise/trevec-swe-bench-results (Not a promotion or clickbait) How to try it for free: Trevec is 100% local. Your code never leaves your machine, and no API keys are needed. Setup takes 30 seconds. Would love feedback from anyone using Claude Code daily! submitted by /u/MutantX222 Originally posted by u/MutantX222 on r/ClaudeCode

Sean O’Brien sold workers and unions out to Trump—these Teamsters are running to oust him.

As general president of the union, Sean O’Brien has operated with a “Teamsters vs. Everybody” mentality, especially when it comes to dealing with President Donald Trump and embracing the MAGA right. But now, 14 months into the second Trump administration, the labor movement and the entire working class—Teamsters members included—is under attack. In this episode of *Working People*, we speak with veteran Teamsters Richard Hooker Jr. and John Palmer, who are running to oust O’Brien from leadership in the upcoming union election. **Guests:** * Richard Hooker Jr. has dedicated 26 years to the Teamsters, spending 20 of those years at UPS and the last six in leadership roles. He is the Secretary-Treasurer and Principal Officer of Teamsters Local 623 in Philadelphia, and he is now running on the Fearless Slate to unseat Sean O’Brien as a candidate for general president of the International Brotherhood of Teamsters. * John Palmer has 38 years of experience in the Teamsters and is currently serving as a vice president at large of the International Brotherhood of Teamsters. He is running on the Fearless Slate as a candidate to be the union’s general secretary-treasurer. **Additional links/info:** * Teamsters Fearless Slate [website](https://be-fearless.org/meet-the-fearless-team) * Hank Kennedy, *Current Affairs*, “[Sean O’Brien sold labor to Trump, and got nothing](https://www.currentaffairs.org/news/sean-obrien-sold-labor-to-trump-and-got-nothing)” * Michael Sainato, *The Guardian*, “[Labor activist takes on Teamsters leader allying with Trump: ‘He doesn’t represent the workers’](https://www.theguardian.com/us-news/2025/nov/01/teamsters-union-leadership-trump)” * Joe Allen, *CounterPunch*, “[Why are the Teamsters endorsing Greg Abbott?](https://www.counterpunch.org/2026/02/17/why-are-the-teamsters-endorsing-greg-abbott/)” * Peter Eavis, *The New York Times*, “[UPS says it is cutting up to 30,000 jobs](https://www.nytimes.com/2026/01/27/business/ups-jobs-layoffs-2026.html)” * Maximillian Alvarez, TRNN, “[Everybody hates Sean](https://therealnews.com/everybody-hates-sean)” * Maximillian Alvarez, TRNN, “[We asked 8 different Teamsters what they thought of Sean O’Brien’s speech—their responses may surprise you](https://therealnews.com/we-asked-8-different-teamsters-what-they-thought-of-sean-obriens-speech-their-responses-may-surprise-you)” **Featured Music:** * Jules Taylor, *Working People* Theme Song **Credits:** * Audio Post-Production: Jules Taylor Transcript *The following is a rushed transcript and may contain errors. A proofread version will be made available as soon as possible.* Maximillian Alvarez: Alright. Welcome everyone to Working People, a podcast about the lives, jobs, dreams, and struggles of the working class today. Working People is a proud member of the Labor Radio Podcast Network and is brought to you in partnership with In These Times Magazine and the Real News Network. This show is produced by Jules Taylor and made possible by the support of listeners like you. My name is Maximillian Alvarez, and we’ve got a doozy of an episode for y’all today. As always, we really appreciate, and in fact, we depend on our listeners reaching out to us with topics and stories that you guys want us to dig into. And one of the questions that you have overwhelmingly told us that you want to see addressed on the show is the question that we are dedicating today’s episode too. Now that we are one year into the second Trump administration, what the hell is going on with the teamsters and the union’s general president, Sean O’Brien? Now, by way of introducing today’s episode, I’m going to read at length from a really thought provoking article by Hank Kennedy, which was just published in Current Affairs Magazine, and we’re going to link to this in the show notes. But Kennedy writes, “Elected as a union militant with the support of longstanding reform organization, Teamsters for a Democratic Union or TDU, Sean O’Brien has spent the last two years shepherding the lambs of the American working class straight to the slaughter via his endorsements and promotions of some of the most reactionary anti-labor politicians in the land. I was complicit in this. Back in 2021, I was a teamster working in logistics. I both voted and campaigned for O’Brien, giving money and time to his campaign. 2024 erased whatever residual affection I’d had for O’Brien. That year, he not only spoke of Donald Trump as a man, “Proven to be one tough SOB at the Republican National Convention.” He promoted as 100% on point a transphobic article by Senator Josh Hawley, this compact article on “the promise of pro- labor conservatism, a sailed corporate America for “using their profits to push diversity, equity, and inclusion, and the religion of the trans flag.” There’s been a phenomenon within the union’s leadership of working towards Trump. Whatever Trump says, the union leadership leaps to support, often without looking. When Trump called for

Is Flock just a poor US-centric copy of, globally active Genetec?

I've read all of Genetec's [customer stories](https://www.genetec.com/customer-stories/search) (the PDFs), and although I recognize these, as being Genetec marketing material (at least in part), they do contain insightful information, regarding implementation of surveillance systems; that is, from the perspective of a diverse palette of organisations. This palette primarily consists of: universities, school districts, ports, critical infrastructure providers, business to business companies, health care providers, real estate developers, gambling companies, (sports) venues, cities, public transportation services, airports, retailers, and foremost police departments. What most have in common, is the increasing scale at which they operate; setting in motion a search for IT-solutions, able to scale alongside organisational growth, and doing so in a cost-effective way. This entails: the centralisation of (previously "siloed") systems and departments, automatization of (previously time-consuming, or outright unmanageable) tasks, and proactive 'Data-Driven Decision-Making (DDDM)'; unlocking operational efficiencies and granular control over vast operations. Which is where Genetec introduces itself, primarily through [its partners](https://www.genetec.com/partners/partner-integration-hub?keywords) (including: hardware manufacturers, software solutions companies, system integrators, consultancy firms, etc.), often during an organisation's 'call for tender' or 'Request For Proposal (RFP)'; or it's recommended by other Genetec customers (including by law enforcement, to "community" partners: primarily businesses). The most recognizable partners, of the consortium-like construction, include: Axis Communications, Sony Corporation, Hanwha Vision, Bosch, NVIDIA, ASSA ABLOY, Intel, Pelco, Canon, Dell technologies, HID Global, FLIR Systems, Global Parking Solutions, and Seagate Technology. Alongside the Genetec-certified [hardware](https://www.genetec.com/supported-device-list) and software integrations (of which their partners' being actively co-marketed to customers), it also allows for custom integrations: through their 'Software Development Kits (SDKs)', and 'Application Programming Interfaces (APIs)'. So instead of single-vendor lock-in, organisations are effectively subject to multi-vendor lock-in (unless: spending resources, on custom integrations, is more cost-effective). Genetec's primary focus, lies on their extensive suite, of (specialized) software applications, deployed on: an on-site server, multiple (distributed) on-site servers (possibly federated: allowing for a centralized view over multiple implementations), in the "cloud" (i.e. someone else's server) as a '... as a Service' solution; or a combination of aforementioned (providing "cloud" flexibility). When using multiple applications, Genetec's 'Security Center' can unify all; meaning operators aren't required to switch between applications. And considering applications aren't limited to just camera surveillance, but also include: intrusion detection (intrusion panels, line-crossing cameras, panic switches, etc.), access control (electronic locks, access control readers (pin, card, tag, mobile, and/or biometric), door control modules, etc.), communication (intercoms, 'Public Address (PA)' systems, emergency stations, etc.) and ALPR (ALPR boom gates, gateless (license plate as a credential), enforcement vehicles, etc.); it allows for centralization of these systems (unless prohibited by strict IT policies). All of these technologies combined, primarily serve to: save on resources, protect assets, prevent losses, ensure operational continuity, and resolve disputes over: parking tickets, insurance claims (as a result of damages: suffered or caused on premise; potentially increasing premium), or even legal allegations ("increase the number of early guilty pleas"); all of course, under the guise of safety. Whether it be organisations individually, or "community" initiatives (often spearheaded by businesses, while citizens are left to follow); most circle back to previously outlined, financially-grounded motives. Resources include staff, who's function might become more versatile, or entirely obsolete (through efficiency gains), and might depend on events, reported by analytics (growing queues, areas requiring clean-up, crowd bottlenecks, etc.); meaning they too, are subject to this system: from onboarding ("minimise the time that elapses before they make a productive contribution") and throughout their career ("employee theft", "employee attendance", "agents' activities, collectively or individually", etc.). Previously, some organisations utilized analog cameras (having a recorder each), in which: a looping tape, would periodically overwrite previous recordings (minimizing retention periods: physically); which possbily caused quality degradations, sometimes to such a degree, footage could no longer serve as legal evidence (which too, is privacy-friendly).

Blink’s budget buzzer gets some worthwhile upgrades

 Blink launched the second generation of its video doorbell this week. Amazon’s budget security camera company, Blink, has launched the second generation of its [popular video doorbell](https://www.theverge.com/2021/9/28/22698612/amazon-blink-video-doorbell-outdoor-camera-floodlight-solar-panel). The [new Blink Video Doorbell](https://www.amazon.com/BlinkDoorbell) adds a head-to-toe view and improved video resolution. It can also now alert you when a person is at your door instead of just the neighborhood cat or a strong gust of wind triggering its motion sensors. The doorbell camera comes with a new, more basic hub, the Sync Module Core, which, unlike the first-gen model, is required to use the buzzer. Blink’s latest doorbell is still one of the cheapest on the market, costing $59.99 without the hub and $69.99 with it. The lowest-priced [battery-powered buzzer](https://ring.com/products/battery-doorbell) from Ring, [Blink’s sister brand](https://www.theverge.com/22704290/amazon-blink-ring-camera-doorbell-brands-smart-home-why), is $99, and it only claims six to 12 months of battery life compared to Blink’s industry-leading two years, powered by [its custom silicon](https://www.theverge.com/24100149/blink-mini-2-review#%3A%7E%3Atext=Blink%E2%80%99s+custom-built%2Ca+Blink+subscription.)).  Upgrades on this version include an improved 150-degree field of view with a 1:1 aspect ratio. That should give you a head-to-toe view of your porch so you can see people and packages. The prior version, which is my pick for the [best budget video doorbell](https://www.theverge.com/22954554/best-video-doorbell-camera?gad_source=1&gad_campaignid=22454846081&gbraid=0AAAAA9k5E7BvZfFmJHpRO277kAo86L34q#o7D9AX%3A%7E%3Atext=details+here.-%2CBest+budget+doorbell+camera%2C-Blink+Video+Doorbell), has a 16:9 aspect ratio. This buzzer also adds 1440p x 1440p image resolution, according to Jonathan Cohn, Blink’s head of product. This is a step up from 1080p, meaning footage should be clearer. There’s still no color night vision; it retains the infrared night vision of the first-gen model. The biggest upgrade is the addition of person detection; the first-gen model sends alerts for any motion, but now you can be notified just when there’s a person at your door. This is powered by [on-device computer vision](https://support.blinkforhome.com/en_US/using-your-camera/person-detection), so it doesn’t require the cloud. But it does require a $3 per month ($30 per year) [Blink subscription plan](https://www.amazon.com/dp/B08J5G9BCT?adgwdg=vicc_subscriptions_display_on_website&tenantId=DEVICE_SUBS&ASIN=B08JHCVHTY&nodl=12) (which also adds 60 days of cloud storage for recorded video). Blink has slowly been bringing person detection to its lineup, adding it first to its [wired floodlight camera](https://www.theverge.com/22811985/best-smart-floodlight-security-camera#%3A%7E%3Atext=and+professional+monitoring.-%2CThe+best+budget+floodlight+camera%2C-Blink+Wired+Floodlight), then its [flagship outdoor camera](https://www.theverge.com/2023/8/24/23843590/blink-outdoor-4-security-camera-wireless-person-detection), and its [Blink Mini indoor / outdoor camera](https://www.theverge.com/24100149/blink-mini-2-review) last year. The new doorbell requires a Sync Module to work, Cohn says, and it now comes with the new Sync Module Core, rather than the [Sync Module 2](https://www.amazon.com/Blink-Sync-Module-2/dp/B084RQ6MHJ). This is something of a downgrade as the Core doesn’t have the local storage option that the Sync 2 offers. Cohn says the module helps extend battery life and range and enables on-demand live view and two-way audio. He confirmed that the new doorbell can work with the Sync 2 and the newer long-range [Sync Module XR,](https://www.amazon.com/All-new-Blink-Sync-Module-XR/dp/B0B198XD6X) if you already have one or if you want local storage.  The new buzzer features a slightly chunkier design to accommodate three AA lithium batteries as opposed to two in the first-gen version. The extra battery helps maintain the impressive two-year battery life while powering improved image quality and the addition of person detection, Cohn says. Blink is unique among security camera makers as it uses its own chip that’s optimized for power management, so while it doesn’t boast the higher-end features like those from Ring and Arlo, you don’t have to worry about dealing with charging or replacing its batteries as often. It can also be hardwired to main power, which allows the