{"venture":"sliver-network","count":5,"signals":[{"tweet_id":"2065427656112505017","author":"heynavtoor","author_name":"Nav Toor","text":"Someone cloned Netflix.\nThen cloned Spotify.\nThen cloned Instagram.\nThen cloned Airbnb.\nThen cloned WhatsApp.\nThen cloned TikTok.\nThen cloned Amazon.\n\nThen put the source code for all of them on GitHub. For free.\n\nNot one app. Not ten. Over 100 open source clones of the biggest apps on Earth. With source code. With demos. With tech stacks listed.\n\nIt is called Clone-Wars. 34,555 stars on GitHub.\n\nBuilt by an Indian-origin developer named Gourav Goyal. He started collecting open source clones of popular apps into one list in December 2020. In March 2021, it went from 0 to 4,000+ stars in 7 days. It was on GitHub Trending for 5 days straight. Someone posted it to Hacker News and it hit #1 on the front page.\n\nHere is what is inside.\n\nNetflix clones. React, TMDB API, full streaming UI.\nSpotify clones. Music player, playlists, search, albums.\nInstagram clones. Feed, stories, likes, comments, DMs.\nWhatsApp clones. Real-time messaging, read receipts, group chats.\nAirbnb clones. Search, booking, maps, payments.\nAmazon clones. Products, cart, checkout, Stripe payments.\nTikTok clones. Short video feed, upload, likes.\nTwitter clones. Feed, follow, tweet, retweet.\nSlack clones. Channels, threads, real-time chat.\nTrello clones. Boards, cards, drag and drop.\nYouTube clones. Video player, search, comments.\nAnd 90+ more.\n\nEvery clone has source code, a live demo, and the tech stack listed. React, Next.js, Node, Firebase, MongoDB, GraphQL, Tailwind. Every modern stack represented.\n\nHere is why this matters.\n\nCoding bootcamps charge $10,000 to $20,000 to teach you how to build apps like these.\n\nUdemy courses charge $50 to $200 each. One app at a time. One framework at a time.\n\nThis repo gives you 100+ fully built apps with source code you can read, fork, and learn from. For $0.\n\nHere is the wildest part.\n\nThe best way to learn to build Netflix is to look at someone who already built Netflix. Not a tutorial that teaches you one feature at a time. A complete, working clone with every feature connected.\n\nYou do not learn architecture from tutorials. You learn architecture from reading real projects.\n\n100+ apps. 100+ demos. 100+ source codes. One repo.\n\nBootcamp: $10,000 to $20,000. Teaches 2 to 3 projects.\nUdemy: $50 to $200 per course. One project each.\n\nClone-Wars: $0. 100+ projects. Every big app cloned.\n\n34,555 stars. AGPL-3.0 licensed.\n\nEvery app you use. Cloned. Open sourced. Free to learn from.\n\n(Link in the comments)","created_at":"Fri Jun 12 13:35:01 +0000 2026","like_count":1904,"retweet_count":341,"reply_count":40,"resolved_url":null,"resolved_type":null,"venture_tags":["miny-network","sliver-network","subwaymusician-xyz"],"editorial_note":"Tool relevant to miny network: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:47.376Z"},{"tweet_id":"2060332868140757368","author":"exploraX_","author_name":"m0h","text":"100 free resource websites that should be illegal.\n\n12 categories. all free. all legal. links in the repo, comment below\n\nmedia & downloads\n\n1. cobalt tools — download any social media video\n2. https://t.co/e5YtgqRx2b — find streaming locations for any content\n3. https://t.co/0xp2iuZmAk — access any old webpage, plus free software\n4. https://t.co/aLPeUmOEJR — permanently save any webpage\n5. tunefind  — find songs from any show\n6. radio garden — listen to any global radio station\n7. musicforprogramming — focus music\n8. https://t.co/D6aGLsNemb — custom focus soundscapes\n9. https://t.co/GwDOKYH1bd — summarize any YouTube video\n10. y2mate-style tools aside, cobalt covers most of it\n\nimage & design\n\n11. photopea  — free photoshop in your browser \n12. https://t.co/tOZQjsUP31 — one-click background removal \n13. cleanup pictures — erase objects from photos \n14. https://t.co/ehV6vmslVU — free video background removal \n15. https://t.co/yOldfAaMoR — free compression for any image \n16. tinypng — image compression that just works \n17. https://t.co/L0okOzoZ8L — reverse image search \n18. unsplash — free high-res stock photos \n19. https://t.co/TLbICGcNST — free stock photos + videos \n20. pixabay. — free stock images, vectors, music 21. https://t.co/M2W8kGqiT5 — free illustrations you can recolor \n22. heroicons. — free SVG icons \n23. https://t.co/QQpLvlrwGQ — clean open-source icon set \n24. https://t.co/5Izcd6rO3G — color palette generator\n\nPDF & document tools\n\n25. tinywow — 100+ free tools in one place \n26. smallpdf — free PDF editing \n27. ilovepdf  — merge and split PDFs \n28. pdfdrive  — free PDF downloads (mixed catalog — see note)\n29. pdf24 — full PDF toolkit, free \n30. sejda — browser-based PDF editor\n\nbooks, papers & learning\n\n31. gutenberg — 70,000 free classic books \n32. openculture — free courses from top universities \n33. libgen — millions of free textbooks (grey area — see note)\n34. sci-hub — free research papers (grey area — see note)\n35. annasarchive — search every book ever written (grey area — see note)\n36. standardebooks — beautifully formatted public domain books \n37. coursera — audit thousands of university courses free \n38. edx — free courses from MIT, Harvard, more \n39. khanacademy — free K-12 + college subjects \n40. freecodecamp — full dev curriculum, free  \n41. theodinproject — free full-stack dev path \n42. cs50.harvard — Harvard's intro CS course, free\n\nresearch & academic\n\n43. elicit — AI research paper assistant \n44. consensus — search scientific consensus \n45. connectedpapers — visualize and map research \n46. semanticscholar — free academic search \n47. scispace — understand any research paper \n48. researchrabbit — discover related papers \n49. https://t.co/CNr8v6gvLh — academic search engine\n\ndeveloper tools\n\n50. regex101  — instantly test any regular expression \n51. codebeautify — cleanly format any code \n52. explainshell  — understand terminal commands \n53. carbon — turn code into artwork \n54. ray  — stunning code screenshots \n55. phind — developer AI search \n56. https://t.co/ntyEtl5571 — every dev doc in one searchable place \n57. https://t.co/x7DgMESCtl — browser support for any web feature \n58. https://t.co/hMHBLDvFwf — format and validate JSON \n59. transform  — convert between data/code formats \n60. https://t.co/WtuMDsxFTC — explain any cron expression 61. https://t.co/9bSBLxNaSF — generate readme badges\n\nproductivity & whiteboarding\n\n62. https://t.co/jNAWwQOU5I — free hand-drawn charts \n63. https://t.co/gUtvx9EAJ8 — infinite whiteboard in your browser \n64. https://t.co/ZlnClDEDas — collaborative whiteboard (free tier) \n65. https://t.co/v82ZYP2SC9 — free notes/docs/databases \n66. obsidian.md — local-first markdown knowledge base \n67. https://t.co/U6Njeoko1y — encrypted google-docs alternative\n\nprivacy & temp tools\n\n68. https://t.co/vyKx3JfABG — one-click temporary email \n69. 10minutemail — instant temporary email \n70. https://t.co/Oip0wREWUm — send self-destructing messages \n71. https://t.co/0BwUGOVZZG — share auto-deleting files \n72. accountkiller  — delete yourself from any website \n73. https://t.co/N16qAL6uEQ — free email aliases \n74. cryptee  — encrypted notes + photos\n\nsecurity & checks\n\n75. haveibeenpwned — check if you've been hacked \n76. virustot  — scan any file for malware \n77. downdetector — check if any website is down \n78. urlvoid — check if a URL is sketchy \n79. whoer — see what sites see about you\n\nutility & misc\n\n80. wolframalpha — instantly solve any math problem \n81. alternativeto — find free app alternatives \n82. flightradar24 — real-time tracking for any flight \n83. camelcamelcamel — track amazon price history \n84. fast — check internet speed \n85. speedtest— bandwidth + latency check \n86. wetransfer — send files up to 2GB free \n87. fakespot — detect fake amazon reviews \n88. exchange-rates — clean currency conversion \n89. timeanddate — meeting planner across timezones \n90. world.taximeter — estimate cab fare anywhere\n\nwriting & content\n\n91. hemingwayapp  — make your writing clearer \n92. languagetool — free grammar checker \n93. deepl — translation that beats google translate \n94. quillbot. — paraphrase + summarize (free tier) \n95. https://t.co/K0geVwVeb4 — AI search with sources \n96. https://t.co/IUSAMbIgho — yes, this thing \n97. https://t.co/OZfOCljPof — AI search + writing \n98. https://t.co/gfwenVL9Vu — free AI writing (small tier)\n\naudio & video\n\n99. https://t.co/M7Nhrxk8qW — free browser audio editor \n100. https://t.co/Xs4kWcEeJ1 — free in-browser video editor","created_at":"Fri May 29 12:10:09 +0000 2026","like_count":266,"retweet_count":50,"reply_count":10,"resolved_url":"https://justwatch.com/","resolved_type":"external","venture_tags":["goodalgo-network","sliver-network","subwaymusician-xyz","instasoiree-com","dank-nyc","misoley-com"],"editorial_note":"Tool relevant to goodalgo network.","signal_type":"tool","month_tag":"2026-05","ingested_at":"2026-07-01T04:05:03.448Z"},{"tweet_id":"2066829231209033890","author":"HowToPrompt__","author_name":"How To Prompt","text":"NVIDIA just made AI detect objects 10x faster by deleting one step.\n\nIt's called LocateAnything, and it kills the single biggest bottleneck nobody else was fixing in vision-language models.\n\nWhen you ask a model \"find the cars in this image,\" it generates each bounding box one coordinate token at a time. x1 → y1 → x2 → y2. Sequentially. For every object. 100 objects = thousands of sequential tokens before you get an answer.\n\nNVIDIA deleted that step entirely.\n\nThey built Parallel Box Decoding (PBD): the model predicts the whole bounding box in a single forward pass. As one atomic unit. No more token-by-token coordinate streaming.\n\nThe numbers:\n\n→ 12.7 boxes/sec on a single H100\n→ 10x faster than Qwen3-VL (1.1 BPS)\n→ 2.5x faster than Rex-Omni\n→ +3.8% F1 on LVIS, accuracy went up, not down\n→ 3B params, runs on one consumer GPU\n→ Trained on 138M samples, 785M bounding boxes\n\nPBD doesn't just speed things up. Predicting the box as one atomic unit preserves its geometric coherence, the coordinates stay tied to each other instead of being generated independently. \n\nThat's why accuracy improved instead of dropping.\n\nOne model handles object detection, GUI grounding, OCR, document understanding, and point localization. Drop-in for computer-use agents, robotics, and document pipelines.\n\n100% open source. Weights, code, demo, paper.. all live.","created_at":"Tue Jun 16 10:24:23 +0000 2026","like_count":263,"retweet_count":44,"reply_count":16,"resolved_url":null,"resolved_type":null,"venture_tags":["chipmonk-tech","sliver-network","velab-org","aiblueprints-tech"],"editorial_note":"Tool relevant to chipmonk tech: could inform product or stack decisions.","signal_type":"tool","month_tag":"2026-06","ingested_at":"2026-07-01T01:51:46.885Z"},{"tweet_id":"2010101330514223361","author":"TheAhmadOsman","author_name":"Ahmad","text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- \"tokenizer\" = the algorithm that splits text into tokens\n- common types: BPE (byte pair encoding), SentencePiece\n- token examples:\n- \"hello\" = 1 token or maybe 2 or 3 tokens\n- \"internationalization\" = 5–8 tokens\n- context window = max tokens model can \"see\" at once (2K, 8K, 32K+)\n- longer context = more VRAM for KV cache, slower decode\n\n- during inference, the model predicts next token\n- by running lots of math on its \"weights\"\n- model weights = billions of learned parameters (the knowledge and patterns from training)\n\n- model parameters: usually billions of numbers (called weights) that the model learns during training\n- these weights encode all the model's \"knowledge\" (patterns, language, facts, reasoning)\n- think of them as the knobs and dials inside the model, specifically computed to recognize what could come next\n- when you run inference, the model uses these parameters to compute its predictions, one token at a time\n\n- every prediction is just: model weights + current sequence → probabilities for what comes next\n- pick a token, append it, repeat, each new token becomes part of the sequence for the next prediction\n\n- models are more than weight files\n- neural network architecture: transformer skeleton (layers, heads, RoPE, MQA/GQA, more below)\n- weights: billions of learned numbers (parameters, not \"tokens\", but calculated from tokens)\n- tokenizer: how text gets chunked into tokens (BPE/SentencePiece)\n- config: metadata, shapes, special tokens, license, intended use, etc\n- sometimes: chat template are required for chat/instruct models, or else you get gibberish\n- you give a model a prompt (your text, converted into tokens)\n\n- models differ in parameter size:\n- 7B means ~7 billion learned numbers\n- common sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","created_at":"Sat Jan 10 21:27:57 +0000 2026","like_count":240,"retweet_count":35,"reply_count":7,"resolved_url":null,"resolved_type":null,"venture_tags":["chipmonk-tech","freeintelligence-ai","sliver-network","a3r-network","dochakki-com","chefaid-nyc","dank-nyc","renascence-network"],"editorial_note":"Tool relevant to chipmonk tech.","signal_type":"tool","month_tag":"2026-01","ingested_at":"2026-07-01T04:05:06.033Z"},{"tweet_id":"2070898032762323262","author":"lauriewired","author_name":"","text":"you’ll get mad at me for saying this…but cloud gaming is so obviously more economically efficient than physical hardware I think it’s going to be the default soon.\n\nyour home console / pc is idle 90%+ of the day. meanwhile, data centers targets what, 5%, maybe at worst 10% idle.\n \nevery second a cloud gamer isn’t gaming, that hardware is being used for someone else, training, etc.\n \nI think there should be a new measurement, something like cost-per effective FLOP hour that takes into account the TCO + effective utilization. \n\nIf a gamer spends $500 on a GPU, uses it for 3 years, but it’s only fully active ~5% of that period…the cost-per relative FLOP hour is crazy high! Meanwhile, a $50,000 datacenter GPU might have a *LOWER* cost-per FLOP hour just because the effective utilization is 90+%.","created_at":"","like_count":0,"retweet_count":0,"reply_count":0,"resolved_url":null,"resolved_type":null,"venture_tags":["sliver-network"],"editorial_note":"General intelligence signal for the VE Lab portfolio.","signal_type":"general","month_tag":"2026-06","ingested_at":"2026-07-02T01:42:19.247Z"}]}