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Introducing the Clips chrome extension - the easiest way to send bug reports to agents with video, transcript, and browser debug info captured automatically. 100% free and open source. If you are like me and get tired of manually typing instructions to agents, attaching screenshots, pasting debug logs, and all of that, this might be your new favorite tool. With the Clips chrome extension, you can just click the Clips icon, hit record, and start talking. Visually demonstrate your issue, go through the flow, point out what’s broken. Clips will capture everything on your screen, plus network requests, browser logs, client errors, and all the details around them. And it redacts sensitive information. Then it gives you a link you can send to humans so they can play it and take a look. Or, more importantly, just give it to your agents by just pasting the URL to them. The link has special metadata for agents so just from the URL, the agent can pull all information from the clip automatically. No plugin or MCP server required. That means it can "see and hear" what’s in the video - read the transcript, grab snapshots at any timestamp, and inspect the logs and network requests that were shared with it. So whether you want to quickly demo an issue and send all that context to an agent, or get better bug reports from teammates, recording and sending Clips makes that super easy. Unlike expensive apps like Loom, this is all 100% free and open source. The framework that powers this, plus a bunch of other free applications, is open source too. You can just sign up and use it, or fork it and customize it to your needs. This, in my opinion, is the future of software. Rather than bloated SaaS that charges you a ton of money and still doesn’t even have the things you need, we get free open source canonical apps that you can fork and customize in any way you want. I'll link to all this stuff in the replies. If you try it, let me know your feedback.

Steve (Builder.io)

60,635 次观看 • 1 个月前

Enough with the tattoos, flashy banners, and sugar-coated words. For this giveaway, I wanted to keep things simple and focus on something fun instead. My entry is inspired by a game that once took the entire world by storm. It became so popular and addictive that people everywhere were playing it nonstop, and the hype grew so big that even its own developer eventually decided to remove it. That level of impact has always fascinated me, so I thought it would be fun to create something inspired by that. So here’s my entry: a small and simple game called “Flappy Slot.” It’s a casual game that anyone can quickly try out. You can play it on Android, iOS, or even on desktop, and the best part is that there’s no installation required. Just click the link here: [ ] just open it in your browser, and you can start playing right away. The idea was to make something easy to access so anyone can jump in and enjoy it without any setup. This is just a fun little experiment and my way of contributing something creative to the giveaway. If you have a few minutes, feel free to try it out and see how far you can go. P.S. I’m not a game developer, and I’m definitely not a graphic artist either. All of the game sprites and visual elements were generated with the help of AI, which made it possible for me to put everything together. slotcoffee BITFORTUNE SLOT CARTEL SLOT NINJA 🥷 Bitfortune Partners Yung Rénzél 👑 #slotcoffee #bitfortune #casino #stpatricksday

roginjohn | UnityWallet #PP

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six months ago this wasn't happening on 8gb vram. running unsloth's Q4_K_XL quant of gemma 4 26b-a4b-it-qat, a sparse MoE model with only 4b active params on a single rtx 4060 laptop gpu, 8gb vram, 20+ tok/s decode. no cloud, no api, no offload hacks. just a gaming laptop on battery. what makes it fit: google's QAT (quantization aware training), plus MTP (multi token prediction) support in the latest llama.cpp builds. that combo is the single biggest unlock for local inference on low vram. rtx 3060, rtx 3070, gtx 1070, gtx 1080, rtx 4050, rtx 4060, rtx 5050, rtx 5060 — any 6-8gb consumer gpu, old or new — this model runs on it. world cup season, so i told it to build a soccer themed flappy bird clone. one shot, zero iteration, fully playable. six months ago an 8gb model could barely clone vanilla flappy bird. now it's shipping a themed game from a sparse MoE model running locally on a laptop battery. inference benchmarks: - decode throughput: 30 tok/s - context: 64k. this is the real unlock. 64k ctx is what makes a hermes agent loop viable locally on this model, not just single-turn chat. llama.cpp flags: -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 64000 -cmoe --port 8080 game's deployed on my own site, built and shipped end to end with open source llm, zero closed source api dependency in the pipeline. link in the description. gguf weights on huggingface, link in the comments. pull it down, run it on whatever 8gb card is sitting in your rig. try the game and tell me your score and what you want in v2. local llms on consumer gpus stopped being a meme.

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60,866 次观看 • 2 个月前