https://archive.ph/1eBHM

https://archive.ph/dgme4

Real talk: last month I was running a giveaway campaign for a client. The mechanic was simple — comment to enter, tag a friend for a bonus entry. 3,200 comments later, I was staring at a blank Google Sheet wondering how I was going to verify entries, remove duplicates, and pick a winner without losing my mind. Instagram doesn't give you any export functionality. Zero. You can view comments in the app, you can reply, you can delete — but you cannot export them in any structured way. This is apparently a deliberate product decision, and it's been this way for years. What I tried first: Manually copy-pasting — obviously not scalable past ~50 rows The official Instagram Graph API — requires app review, business account verification, and only returns data from your own posts anyway Third-party "Instagram data export" services — most of these ask for your password or OAuth credentials, which is a non-starter What actually worked: I ended up using a browser extension called [Instagram Comments Scraper](https://chromewebstore.google.com/detail/instagram-comments-scrape/hpfnaodfcakdfbnompnfglhjmkoinbfm) that runs entirely within your browser session. No password required — it just operates within your existing logged-in session, the same way you're already viewing the comments. The data is processed locally and never sent anywhere external. The output columns it gives you: comment ID, comment text, username, profile URL, profile pic URL, and timestamp. That's exactly what you need to do any meaningful analysis — filter by date, spot bot accounts, remove duplicates, identify authentic entries. The rate limiting situation: The part I didn't expect was how Instagram's rate limits work. There's no published threshold — it varies by IP and activity patterns. When the scraper hits a limit, it enters a cooldown mode automatically (the timer shows you how long), then doubles the cooldown if the limit persists. Once the cooldown clears and a request succeeds, it goes back to normal. This meant I could walk away and come back to a finished export rather than babysitting it. End result: 3,200 comments exported to Excel in about 40 minutes of unattended processing. Filtered to valid entries (tagged a user + original commenter had 10+ followers) in another 20 minutes using basic Excel formulas. Caveat I'd add for anyone doing this: Be reasonable about volume and timing. Don't run 10,000-comment scrapes back-to-back on the same IP. The human-like delay system in the tool helps, but bulk scraping in one long session still carries some account risk. Space it out if you're working with large datasets. Anyone else found better approaches to this problem? Especially curious if anyone's had success with the official API for use cases beyond your own posts.

Qi Meng is an AI system that designs entire processor chips end to end from natural language spec to to physical layout. Their QiMeng-CPU-v1 produced a 32-bit RISC-V CPU, matching Intel 486 performance with over four million logic gates, in just five hours. QiMeng-CPU-v2, rivals an Arm Cortex A53 from the 2010s, and the whole thing runs on a domain specific model that learns the graph structures of circuits the way GPT learns text. The appeal of Qi Meng is that this open-source effort has three key interconnected layers melding LLM chip design smarts, a hardware and software design agent, and various chip design apps. The paper shows that the system can do in days what takes human teams weeks to achieve. the paper https://arxiv.org/pdf/2506.05007

A directory created by the Centers for Medicare and Medicaid Services (CMS) has exposed the Social Security numbers of a number of US healthcare providers. The Trump administration introduced a new Medicare portal as part of plans to modernize US healthcare technology. However, a database that was part of the directory was left publicly accessible, and exposed providers’ names and Social Security numbers.

The hardware efficiency gains are honestly the most interesting part of the paper. The main reason DeepSeek-V4 is so cheap to run comes down to how they completely bypassed the quadratic cost of standard attention for massive context windows. They built a hybrid attention architecture that interleaves Compressed Sparse Attention and Heavily Compressed Attention. Standard models keep every single token in the KV cache which absolutely kills memory. CSA fixes this by compressing the KV cache of multiple tokens into a single entry and then uses a sparse routing mechanism to only compute attention over the top-k most relevant compressed blocks. HCA takes it a step further by compressing an even larger number of tokens into one entry but computes dense attention over them. So, a 1.6T parameter Pro model only uses a third of the compute FLOPs and 10% of the KV cache memory compared to DeepSeek-V3.2 at a one million token context. They also aggressively pushed low-precision formats applying FP4 quantization-aware training to the Mixture-of-Experts weights and the attention Query-Key paths. MoE models are notoriously memory bound because you have to constantly shuttle massive expert weights into the GPU cores. Dropping these to FP4 slashes the memory bandwidth bottleneck and lets the model run way faster during inference without ruining accuracy since they handle the quantization dynamically during training. On the infrastructure side they wrote a custom fused kernel using TileLang that overlaps communication and computation. When running expert parallelism across multiple GPUs you usually hit a wall waiting for the network. DeepSeek slices the experts into micro-waves so the GPU is crunching matrix math on the first wave while the network is simultaneously pulling the data for the second wave. They basically hid the network latency behind the compute time which means you do not need super expensive interconnects to get peak hardware utilization out of the cluster.

## Flipbook (sketchapedia.com)  *Image: [Flipbook - Flipbook](https://sketchapedia.com/)* [Flipbook](https://flipbook.page/) (hosted at sketchapedia.com) is an AI-powered visual browser that generates illustrated, interactive infographics on demand in real time. You type any topic, and it renders a clickable, sometimes animated image explaining it — similar to prompting ChatGPT or Claude, but the output is visual rather than text. According to [LinkedIn](https://www.linkedin.com/posts/dan-zinkin_flipbook-the-infinite-visual-browser-flipbook-activity-7453062289869533184-1ESi), the tool was built by Zain Shah and team. It describes itself as "an infinite visual browser generated entirely on demand in real time." Japanese bookmarking site [Hatena](https://b.hatena.ne.jp/entry/s/flipbook.page/) categorises it under AI, LLM, and web tools, with users tagging it as worth reading later. Sources: [LinkedIn](https://www.linkedin.com/posts/dan-zinkin_flipbook-the-infinite-visual-browser-flipbook-activity-7453062289869533184-1ESi), [Hatena](https://b.hatena.ne.jp/entry/s/flipbook.page/) 

Cross posted from https://lemmy.ml/post/46710548

cross-posted from: https://lemmy.world/post/1894070 > ## **Welcome to the Llama-2 FOSAI & LLM Roundup Series!** > > **(Summer 2023 Edition)** > > Hello everyone! > > The wave of innovation I mentioned in our [Llama-2 announcement](https://lemmy.world/post/1750098) is already on its way. The first tsunami of base models and configurations are being released as you read this post. > > That being said, I'd like to take a moment to shoutout [TheBloke](https://huggingface.co/TheBloke), who is rapidly converting many of these models for the greater good of FOSS & FOSAI. > > You can support [TheBloke](https://huggingface.co/TheBloke) here. > - https://ko-fi.com/TheBlokeAI > > Below you will find all of the latest Llama-2 models that are FOSAI friendly. This means they are commercially available, ready to use, and open for development. I will be continuing this series exclusively for Llama models. I have a feeling it will continue being a popular choice for quite some time. I will consider giving other foundational models a similar series if they garner enough support and consideration. For now, enjoy this new herd of Llamas! > > All that you need to get started is capable hardware and a few moments setting up your inference platform (selected from any of your preferred software choices in the [Lemmy Crash Course for Free Open-Source AI](https://lemmy.world/post/76020) > or [FOSAI Nexus](https://lemmy.world/post/814816) resource, which is also shared at the bottom of this post). > > Keep reading to learn more about the exciting new models coming out of Llama-2! > > ### **8-bit System Requirements** > > | Model | VRAM Used | Minimum Total VRAM | Card Examples | RAM/Swap to Load* | > |-----------|-----------|--------------------|-------------------|-------------------| > | LLaMA-7B | 9.2GB | 10GB | 3060 12GB, 3080 10GB | 24 GB | > | LLaMA-13B | 16.3GB | 20GB | 3090, 3090 Ti, 4090 | 32 GB | > | LLaMA-30B | 36GB | 40GB | A6000 48GB, A100 40GB | 64 GB | > | LLaMA-65B | 74GB | 80GB | A100 80GB | 128 GB | > > ### **4-bit System Requirements** > > | Model | Minimum Total VRAM | Card Examples | RAM/Swap to Load* | > |-----------|--------------------|--------------------------------|-------------------| > | LLaMA-7B | 6GB | GTX 1660, 2060, AMD 5700 XT, RTX 3050, 3060 | 6 GB | > | LLaMA-13B | 10GB | AMD 6900 XT, RTX 2060 12GB, 3060 12GB, 3080, A2000 | 12 GB | > | LLaMA-30B | 20GB | RTX 3080 20GB, A4500, A5000, 3090, 4090, 6000, Tesla V100 | 32 GB | > | LLaMA-65B | 40GB | A100 40GB, 2x3090, 2x4090, A40, RTX A6000, 8000 | 64 GB | > > *System RAM (not VRAM), is utilized to initially load a model. You can use swap space if you do not have enough RAM to support your LLM. > > --- > > ### **The Bloke** > One of the most popular and consistent developers releasing consumer-friendly versions of LLMs. These active conversions of trending models allow for many of us to run these GPTQ or GGML variants at home on our own PCs and hardware. > > **70B** > > - [TheBloke/Llama-2-70B-chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-70B-chat-GPTQ) > > - [TheBloke/Llama-2-70B-Chat-fp16](https://huggingface.co/TheBloke/Llama-2-70B-Chat-fp16) > > - [TheBloke/Llama-2-70B-GPTQ](https://huggingface.co/TheBloke/Llama-2-70B-GPTQ) > > - [TheBloke/Llama-2-70B-fp16](https://huggingface.co/TheBloke/Llama-2-70B-fp16) > > **13B** > > - [TheBloke/Llama-2-13B-chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ) > > - [TheBloke/Llama-2-13B-chat-GGML](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML) > > - [TheBloke/Llama-2-13B-GPTQ](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ) > > - [TheBloke/Llama-2-13B-GGML](https://huggingface.co/TheBloke/Llama-2-13B-GGML) > > - [TheBloke/Llama-2-13B-fp16](https://huggingface.co/TheBloke/Llama-2-13B-fp16) > > **7B** > > - [TheBloke/Llama-2-7B-GPTQ](https://huggingface.co/TheBloke/Llama-2-7B-GPTQ) > > - [TheBloke/Llama-2-7B-GGML)](https://huggingface.co/TheBloke/Llama-2-7B-GGML) > > - [TheBloke/Llama-2-7B-fp16](https://huggingface.co/TheBloke/Llama-2-7B-fp16) > > - [TheBloke/Llama-2-7B-fp16](https://huggingface.co/TheBloke/Llama-2-7B-fp16) > > - [TheBloke/Llama-2-7b-Chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ) > > ### **LLongMA** > LLongMA-2, a suite of Llama-2 models, trained at 8k context length using linear positional interpolation scaling. > > **13B** > > - [conceptofmind/LLongMA-2-13b](https://huggingface.co/conceptofmind/LLongMA-2-13b) > > **7B** > > - [conceptofmind/LLongMA-2-7b](https://huggingface.co/conceptofmind/LLongMA-2-7b) > > Also available from The Bloke in GPTQ and GGML formats: > > **7B** > > - [TheBloke/LLongMA-2-7B-GPTQ](https://huggingface.co/TheBloke/LLongMA-2-7B-GPTQ) > > - [TheBloke/LLongMA-2-7B-GGML](https://huggingface.co/TheBloke/LLongMA-2-7B-GGML) > > ### **Puffin** > The first commercially available language model released by Nous Research! Available in 13B parameters. > > **13B** > > - [NousResearch/Redmond-Puffin-13B-GGML](https://huggingface.co/NousResearch/Redmond-Puffin-13B-GGML) > > - [NousResearch/Redmond-Puffin-13B](https://huggingface.co/NousResearch/Redmond-Puffin-13B) > > Also available from The Bloke in GPTQ and GGML formats: > > **13B** > > - [TheBloke/Redmond-Puffin-13B-GPTQ](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GPTQ) > > - [TheBloke/Redmond-Puffin-13B-GGML](https://huggingface.co/TheBloke/Redmond-Puffin-13B-GGML) > > ### **Other Models** > Leaving a section here for 'other' LLMs or fine tunings derivative of Llama-2 models. > > **7B** > > - [georgesung/llama2_7b_chat_uncensored](https://huggingface.co/georgesung/llama2_7b_chat_uncensored) > > --- > > ### **Getting Started w/ FOSAI!** > > Have no idea where to begin with AI/LLMs? [Try starting here with ](https://understandgpt.ai/docs/getting-started/what-is-a-llm) [UnderstandGPT](https://understandgpt.ai/) to learn the basics of LLMs before visiting our [Lemmy Crash Course for Free Open-Source AI](https://lemmy.world/post/76020) > > If you're looking to explore more resources, see our [FOSAI Nexus](https://lemmy.world/post/814816) for a list of all the major FOSS/FOSAI in the space. > > If you're looking to jump right in, visit some of the links below and stick to models that are <13B in parameter (unless you have the power and hardware to spare). > > **FOSAI Resources** > > **Fediverse / FOSAI** > - [The Internet is Healing](https://www.youtube.com/watch?v=TrNE2fSCeFo) > - [FOSAI Welcome Message](https://lemmy.world/post/67758) > - [FOSAI Crash Course](https://lemmy.world/post/76020) > - [FOSAI Nexus Resource Hub](https://lemmy.world/post/814816) > > **LLM Leaderboards** > - [HF Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) > - [LMSYS Chatbot Arena](https://chat.lmsys.org/?leaderboard) > > **LLM Search Tools** > - [LLM Explorer](https://llm.extractum.io/) > - [Open LLMs](https://github.com/eugeneyan/open-llms) > > ### **GL, HF!** > > If you found anything about this post interesting - consider subscribing to [email protected] where I do my best to keep you in the know about the most important updates in free open-source artificial intelligence. > > I will try to continue doing this series season by season, making this a living post for the rest of this summer. If I have missed a noteworthy model, don't hesitate to let me know in the comments so I can keep this resource up-to-date. > > Thank you for reading! I hope you find what you're looking for. Be sure to subscribe and bookmark the [main post](https://lemmy.world/post/1894070) if you want a quick one-stop shop for all of the new Llama-2 models that will be emerging the rest of this summer!

A tragic story has emerged from northern Israel that combines shocking allegations, powerful families, and claims of online censorship. Shoshana Strook, 34, daughter of Israeli National Missions Minister Orit Strook, was found dead in her home after publicly accusing her parents and brother of sexual and ritual abuse dating back to her early childhood. The claims, including alleged trafficking and involvement in so-called paedophile ceremonies, have ignited debates online as reports and social media posts appear to vanish from Google searches. As her story spread online, users reported that news about Shoshana was being scrubbed or buried. Social media platforms and search engines appeared to remove or downrank posts, leading to speculation about deliberate censorship. Comments across forums highlighted comparisons to high-profile abuse cases elsewhere, pointing to elite networks and abuse cover-ups.

https://archive.ph/QH4hl

cross-posted from: https://feditown.com/post/2911581 Edit: Adding a warning here; The post was probably heavily AI written and contains mistakes to that effect, which is unfortunate. The data in general is still interesting though.