Llama 3

Llama 3 AI

Llama 3 AI

Llama 3 AI

Llama 3 AI

Llama 3 AI

Llama 3 AI

Llama 3 AI

Llama 3 AI

Llama 3 AI

Llama 3 AI

This week in AI: Meta reportedly closing Llama, Anthropic's new model pulled by export controls within a week, and Apple partners with Google for Siri

A few stories from the past week that, taken together, point to a real shift at the model layer rather than just incremental releases: Meta and Llama. Multiple reports indicate Meta is stepping back from open-source Llama in favor of a proprietary program (internally referred to as "Muse Spark," with a new "Avocado" model) under Meta Superintelligence Labs. Llama crossed 650M+ downloads and was arguably the anchor of the open-weights ecosystem, so a pivot to closed development would be significant for anyone relying on that lineage. Anthropic and export controls. Anthropic launched Claude Fable 5 on June 9 (Mythos-class, 1M-token context, always-on adaptive reasoning, notable security/vuln-finding capabilities). On June 12, a US export-control directive reportedly forced Anthropic to suspend access to Fable 5 and Mythos 5. Regardless of the specifics, it's a concrete example of frontier model availability being governed by policy, not just product decisions. Apple and Google. At WWDC, Apple shipped its Siri overhaul with parts powered by a Gemini partnership. EU/China rollout is delayed on regulatory grounds. Cost/commodity trend. Google cut Gemini Ultra from $250 to $200/mo and shipped 3.5 Flash; Alibaba's Qwen3.7-Plus is running at ~1/6 the per-token cost of its top tier; and open-weight models like Qwen 3.6 27B (reportedly 77.2% on SWE-bench, fits in 24GB) and Kimi K2.6 are increasingly viable for local/production use via Ollama (v0.30.8, June 12). Platform agents. Google added Managed Agents to the Gemini API, Microsoft made Copilot Cowork GA plus "Autopilot" agents, and Anthropic shipped scheduled/cron agents in beta. My take as someone building on top of these APIs: the two forces I'm watching are (1) frontier availability becoming a policy/geopolitics variable, and (2) the platforms absorbing the agent-orchestration layer that a lot of startups were building. Practically, that pushes me toward provider abstraction and keeping an open-weight fallback wired up, rather than hard-coupling to any single closed model. Curious whether others here are actually maintaining open-weight fallbacks in production, or if that's still mostly theoretical for most teams. submitted by /u/ksraj1001 [link] [comments]

What is Speculative Decoding? (trending on paperswithco.de) [R]

A method that is currently trending on Papers with Code is Speculative Decoding. https://preview.redd.it/dm4nh4t71o7h1.png?width=3082&format=png&auto=webp&s=b6468668667d4bcfb6c9248d3af7fd09f21fe0da Speculative decoding is an inference optimization technique that uses a fast, small "draft" model to quickly propose several future tokens, which are then verified in parallel by a larger, slower "target" model. This process significantly speeds up token generation for large language models (LLMs) by allowing multiple tokens per step without sacrificing output quality. SGLang, one of the most popular frameworks for running LLMs alongside vLLM, just released a blog post detailing how they achieve state-of-the-art latencies for LLM inference serving using Modal and Z.ai's DFlash speculative decoding models. Learn more at https://paperswithcode.co/methods/speculative-decoding. You can also find all the papers that cite the original paper that introduced this technique. SGLang's blog: https://www.lmsys.org/blog/2026-06-15-next-generation-speculative-decoding-dflash-v2/ Let me know which other methods I should add! Cheers, Niels from HF submitted by /u/NielsRogge [link] [comments]

quicktok: a faster tokenizer (exact and byte-identical with tiktoken) [P]

Been working on this a while! Should be useful for anyone trying to speed up their tokenization workflows. quicktok is a fast/exact BPE tokenizer written in C++. Token ids are byte-identical to tiktoken and encoding runs 2–3.6× faster than bpe-openai (the fastest alternative I know of) and 4–11× faster than tiktoken itself. It ships cl100k, o200k, GPT-OSS, Llama-3, and Qwen2.5/3. Approach. Same algorithm as bpe-openai (exact backtracking BPE) but I apply lots of data structure engineering to cut memory accesses: A 2-byte trie is used for the longest-match walk Dense exactly-keyed caches are used for merge-validity checks A hand-compiled pretokenizer is used instead of a general regex engine Benchmarks (Apple M1, single thread, MB/s, cl100k_base and every output verified token-for-token before timing): encoder The Pile Code Common Crawl quicktok (native) 121.7 139.2 71.3 quicktok (Python) 77.9 83.6 49.7 bpe-openai 36.6 38.7 28.9 rs-bpe 30.9 34.7 23.5 tiktoken-rs 15.4 13.8 13.3 tiktoken (Python) 13.6 12.8 12.3 TokenDagger 11.1 11.9 10.7 o200k_base is similar in ratios. Each encoder is called through its own raw API and benchmarks can be reproduced with make bench-compare in the repo. pip install quicktok-v1 Repo: https://github.com/dmatth1/quicktok submitted by /u/_casa_nova_ [link] [comments]

Ensuring 100% Agent Uptime: My setup for a Gemini primary with a Groq/Llama-3 fallback

I've been building autonomous negotiation agents for e-commerce, and one of the biggest bottlenecks I hit was API rate limits or sudden timeouts dropping the connection right in the middle of a customer sale. I wanted to share the try/catch fallback matrix I built to solve this. The Problem: > I need the agent to respond in under 3 seconds to keep the human illusion. If the primary LLM hangs, the sale is lost. The Solution: I wrote a wrapper function for my API calls. It pings Gemini first (since the context window and instruction following for my specific JSON/Image tagging is great). If it throws any error, it immediately falls back to Groq running Llama-3.1. The Prompt Engineering: The hardest part was getting both models to obey strict negotiation rules ("Never go below $X"). I achieved this by feeding the prompt a strict array of tags. If the user asks for a picture, the LLM is instructed to only output: Here is the shoe: [IMG_AIRMAX]. My backend intercepts [IMG_AIRMAX], deletes the text, and swaps it for the real media URL before sending it to the user. Has anyone else built an LLM routing system for their production agents? Curious what fallback models you rely on when your primary goes down. submitted by /u/One-Ad-6028 [link] [comments]

Google’s Gemma 4 12B just dropped - here’s how to run it locally on your Mac

Google released Gemma 4 12B today. It’s a solid open-source model (Apache 2.0) that’s multimodal and runs really well on Macs with 16GB or more unified memory. Good at reasoning, coding, and agent stuff. Quick Mac-friendly info • 12B parameters, fits nicely on M2/M3/M4 Macs (especially with Q4/Q5 quant) • 256K context • Text + vision + audio support Easiest way to run it: Ollama 1. Download and install Ollama from ollama.com (the Mac app is super simple). Or use Homebrew if you prefer. 2. Open Terminal and pull the model: ollama pull gemma4:12b 3. Run it: ollama run gemma4:12b That’s it. You can start chatting right away. Mac tips: • Ollama uses Metal automatically so it runs pretty fast on Apple Silicon. • 16GB Macs handle the 12B model fine. 32GB feels even better. • Great for pairing with Continue.dev in VS Code if you code a lot. Other options if Ollama isn’t your thing: LM Studio (nice GUI), or llama.cpp for more control. Has anyone tried the image or audio features locally yet? How fast is it on your machine? Drop your specs and results if you test it.​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ submitted by /u/nullvector88 [link] [comments]

Is this even real ?

I randomly came across this and honestly I can’t tell if it’s real or one of those AI demos that looks impressive but doesn’t actually work. From what I understand, it’s claiming you can fine-tune models, do image training, test them in a playground, and deploy them as an API from a phone. That sounds a little too convenient, which is why I’m skeptical. I haven’t tried it myself yet, but I’m curious if anyone here has. submitted by /u/Raman606surrey [link] [comments]

Llama Surgery: Continuous Sparsification of Pre-Trained Language Models via Differentiable Ultrametric Topology Injection

Sequel to: Learning to Skip Blocks: Self-Discovered Ultrametric Routing for Hardware-Accelerated Sparse Attention Abstract We present Llama Surgery, a method for injecting learned block-sparse attention topologies into pre-trained dense language models without retraining from scratch, distillation, or post-hoc pruning. Starting from a frozen Llama 3.1 8B, we surgically replace each attention layer with a Dynamic Topology Router that maps token embeddings onto the branches of a Bruhat-Tits p-adic tree via factorized Gumbel-Softmax routing. A Deterministic Collapse Initialization to achieve a Continuous Logit Homotopy guarantees that at step 0 the injected topology mask is identically dense, preserving the pre-trained manifold exactly. Over training, temperature annealing polarizes the soft routing assignments into hard binary masks, and a Switch Transformer-style load-balancing loss prevents routing collapse. We identify and resolve two critical failure modes: (1) gradient collapse through discrete masking operations, solved by a Straight-Through Estimator bridge that decouples the hard forward mask from the soft backward gradient; and (2) Attention Sink instability, where hard-masking the initial token causes softmax entropy collapse and syntactic degeneration, solved by permanently anchoring Token 0 in the visibility set. The resulting architecture is validated on Llama 3.1 8B fine-tuned on WikiText-2, achieving stable convergence and producing coherent, mathematically sophisticated text while maintaining dynamic block-sparse routing across all 32 transformer layers. A controlled semantic clustering experiment on TinyLlama-1.1B demonstrates that the router learns to assign tokens from distinct semantic domains (mathematics, natural language, code) to separate branches of the Bruhat-Tits tree using only the standard language modeling loss, with no explicit clustering objective. A Needle-In-A-Haystack (NIAH) retrieval experiment on TinyLlama-1.1B reveals that the router spontaneously organizes the context window into an ultrametric cophenetic hierarchy: the needle is isolated at maximum topological distance from the haystack (d_p = 6.88), and the ultrametric triangle inequality d(x,z) ≤ max(d(x,y), d(y,z)) is satisfied. Averaging over 32 attention heads yields a forest ensemble of distinct per-head ultrametric trees rather than a single global hierarchy. We further identify and resolve three critical float16 numerical failure modes—Gumbel-Softmax overflow, attention score overflow, and cumulative product backward instability—the last of which we solve via a novel cumprod→cummin substitution that exploits the binary structure of hard Gumbel-Softmax outputs. A custom Triton forward kernel with Attention Sink and Local Window support, pipelined for Ampere and Hopper architectures (num_warps=4, num_stages=3), executes the block-sparse prefill phase at O(N) theoretical complexity. To our knowledge, this is the first demonstration of differentiable ultrametric topology injection into a production-scale pre-trained LLM. https://github.com/sneed-and-feed/adelic-spectral-zeta/blob/main/papers/llama_surgery.md submitted by /u/LooseSwing88 [link] [comments]

I integrated a local Llama 3.2 model to act as a dynamic Dungeon Master in my indie RPG.

Hey everyone, I am not trying to sell or self promote mainly just wanted to showcase a big project I've been working on ever since I started studying data science and artificial intelligence and integrating AI into workflows and using it as an augment to create things that were previously out of reach for so many people, because if used right it can become a second brain and not a crutch. I’m the solo dev behind Void Runner, an isometric ARPG/MOBA hybrid built in Python. I recently hit a wall with traditional procedural quest generation. Hand-crafting templates gets repetitive fast, and players quickly learn the patterns to these things whether you like it or not. To solve this, I built the "Void Caller AI", a system that uses a local, quantized Llama 3.2 model to act as a dynamic Dungeon Master. Instead of just generating random flavor text, the system uses a lightweight RAG (Retrieval-Augmented Generation) pipeline. It reads live server telemetry (who died, what items were looted, which bosses were defeated recently) and weaves those actual server events into the narrative of the quests it generates. Because it runs locally via Ollama on our backend, there are no crazy cloud API costs, and latency is kept completely manageable. Here is a simplified look at how the Python backend bridges the SQLite telemetry with the Llama 3.2 prompt: import json import ollama from sqlalchemy import text from database import SessionLocal def generate_dynamic_quest(difficulty: str, target: str): db = SessionLocal() # 1. Fetch recent server telemetry for context (RAG-lite) lore_context = "" try: # Grab recent server events to weave into the narrative recent_events = db.execute(text( "SELECT username, event_type, dungeon_type FROM ai_events ORDER BY id DESC LIMIT 3" )).fetchall() if recent_events: events_str = "; ".join([f"Runner '{r[0]}' triggered a '{r[1]}' in '{r[2]}'" for r in recent_events]) lore_context = f" Incorporate this recent live server telemetry into the lore: {events_str}" except Exception as e: pass # 2. Construct the prompt with strict JSON formatting constraints prompt = f"""You are the Void Caller, a sinister AI in a dark industrial sci-fi RPG. Create a dynamic PvE extraction quest of {difficulty} difficulty. Respond ONLY in valid JSON with keys: 'title' (string), 'description' (string, menacing), 'item_name' (string), 'quantity' (integer 1-15), 'boss_name' (string, optional). {lore_context}""" # 3. Stream to local Llama 3.2 response = ollama.chat( model='llama3.2', messages=[{'role': 'user', 'content': prompt}], format='json', options={'temperature': 0.8} ) return json.loads(response['message']['content']) By forcing the format='json' parameter, Llama 3.2 reliably outputs structured data that my game engine instantly parses into a playable quest objective. If a player just died to a specific boss, the AI will literally generate a bounty quest for the rest of the server to avenge them. Would love to hear if anyone else is using local LLMs for live game state generation! You can check out the results live in our Open Beta at [void-runner.online]. submitted by /u/xSoulR34per [link] [comments]

Ok, talvez eu pague pelo Meta Premium

Hoje eu postei sobre o Mark Zuckerberg lançar a notícia mais patética que vai cobrar 19 dólares para desbloquear o Muse Spark Pro kakakakakakaka Quem vai pagar por essa merda? Mas pensando melhor bem... Talvez eu pague Eu usei muito esse modelo como Early adopter, desde quando o motor era o Llama 3.2 e sendo inferior as outras consegui extrair escrita criativa que batia de frente com Claude em personas graças ao seu RAG no ecossistema da Meta, que tinha uma criatividade absurda quando você forçava ela a consultar as redes sociais e ver como pessoas agem e comentam, porém lançou o Muse Spark que era tipo o GPT 5.2 dos Llamas kkkkkk aí só usei para pesquisa e bem... Minha tese sobre o Muse Spark é que pra mim o problema nunca pareceu ser burrice. Parece CONTENÇÃO. Não dá vibe de modelo incapaz ou inferior. Dá vibe de modelo sendo sufocado em tempo real. Porque se você presta atenção, ele: - pesquisa rápido pra cacete (Já que cada agente pesquisa uma coisa) - alucina menos em busca (pois o modelo refina a busca dos agentes, muitas vezes consegui resultados mais confiáveis que o Gemini) - já trabalha com esquema multi-agente herdado da Manus ( o trunfo dessa IA é que diferente das outras ela não comprimi seu input, ela usa agentes para cada um pesquisar cada trecho dele, o resultado é mais completo) - acha informação boa (ela pesquisa tanto na internet quanto em grupos de Facebook ou Threads se você forçar no prompt, ou seja análises de Devs>>> Wikipédia Inclusive acredito que foi por isso que o Mark lançou o "Fórum" o app que cópia o Reddit, ele quer treinar a IA com isso, o Reddit pra mim seria a fonte perfeita pra qualquer IA se aprofundar além do que pesquisar genéricas no Google, o filha da puta do Mark é rico e filantropo e faz uma cópia só para treinar a IA dele) - conecta coisa rápido (os agentes pesquisam rápido, o modelo revisa rápido, a entrega é bem rápida e gasta bem menos tokens) Só que na hora de responder… Parece o GPT free kkkkkkk O raciocínio corta no meio. (Ele é punido se raciocinar por muito tempo, foi o treinamento dele) A saída vem resumida. (Tem limites de caracteres claros, nenhum prompt força a cota) A resposta parece comprimida igual arquivo zipado. É como se tivesse um fiscal invisível dentro da inferência falando: “encerra logo” “não desenvolve” “não gasta token” “não deixa pensar muito” Aí a galera olha e pensa: “nossa que IA sem profundidade”. Mas pra mim não parece falta de capacidade. Parece punição de reasoning. E é aí que entra minha teoria: esse plano pago da Meta não vai trazer “outro modelo revolucionário”. Pra mim vai ser literalmente o mesmo Muse Spark… só que sem coleira. Os caras mesmos falaram que essa era a versão pequena/teste. Então eu acho que o modelo real já tá ali faz tempo. Só que: - com limite de saída - limite de pensamento - compressão de raciocínio - truncamento agressivo - budget de inferência ridículo E sinceramente? Isso explica porque ele parece inteligente mas frustrante ao mesmo tempo. Porque dá pra sentir que o modelo quer continuar. Só que alguém puxa o freio de mão toda hora. Agora a parte que eu acho GENIALMENTE BURRA da Meta: Eles lançaram primeiro a versão capada. Isso matou a percepção pública imediatamente. O certo teria sido: solta no app Meta AI a versão MONSTRA: - 1 milhão de contexto - sem limite de saída - reasoning longo liberado - multi-agent destravado - resposta gigante - pensamento fluindo E deixa a versão limitada só no: - WhatsApp - Instagram - Facebook Porque aí o usuário hardcore ia testar no app principal e pensar: “caralho… a Meta cozinhou aqui”. A comunidade ia começar a criar hype orgânico. Ia surgir comparação. Benchmark. Thread. Vídeo. Review. Discussão técnica. As pessoas iam SENTIR que tinha um frontier model ali dentro. Mas não. Os caras fizeram o oposto: lançaram primeiro o Muse Spark respirando por canudinho. Aí agora querem cobrar assinatura pra liberar o que provavelmente já existia desde abril. Então a sensação não fica: “uau versão premium”. Fica: “ah então vocês esconderam o modelo de verdade esse tempo todo?” E isso destrói confiança. (Coisa que a Meta já não tem da gente) Convenhamos que o Mark já não tem nenhuma moral com a gente né? Essa IA aí é pra farmar dados pra ADS e ponto, Literalmente é ele falando "vamos cobrar vocês que são os produtos para usarem nossa IA que vai roubar cada vírgula de dados para a gente vender ainda mais anúncios no nosso Facebook onde é 10 anúncios a cada 1 POST kkkkkkkkkk" Mas pra não parecer hater tenho que elogiar que foram pelo menos sinceros, enquanto as outras lançam modelos a vontade e bons e depois emburrecem a IA e põe limites abusivos pelo mesmo preço (né Gemini 3.5? Arrombado) O meta pelo menos já cobra preço cheio por uma IA porcaria, se ele tivesse cobrando só metade do valor (o que seria justo pra essa IA limitada deles) mas assim que a IA melhorasse, cortando limites e implementando mais

Cerebras Chip Sets Appear to be Optimized for LLM Use Cases

One distinction I think is getting lost in the Cerebras hype cycle is that Cerebras is primarily an LLM / generative AI infrastructure story, not a universal “all AI” chip story. That is not necessarily a criticism of Cerebras. Their wafer-scale approach is genuinely interesting, and for large model training and inference the design is compelling. Cerebras’ own public inference materials discuss applications mostly centered on open LLMs such as Llama, Qwen, GLM, and GPT-OSS. The inference metrics are expressed in tokens per second, which is fundamentally a language-model / generative inference framing rather than a robotics or industrial-control framing. What Kind of AI Compute? But “AI compute” is not one undifferentiated market. LLM inference is one class of AI compute. Robotics, autonomous vehicles, drones, industrial controls, real-time vision, embedded perception, video pipelines, and sensor-fusion systems are very different classes of AI compute. Thus, it appears from Cerebras’ own materials that their chip sets are not optimized for what comes after LLMs, such as JEPA-style World Models or other post-transformer architectures. Those systems are not merely asking, “How fast can I generate tokens?” They often care about power envelope, edge deployment, ruggedization, latency determinism, camera/radar/lidar integration, feedback loops, safety certification, and real-time physical control. Cerebras’ own CS-3 messaging, by contrast, frames the system around accelerating “the latest large AI models,” and the testing data is from the likes of Llama 2, Falcon 40B, MPT-30B, and multimodal models, again measured through tokens/second style throughput. The Chip Hierarchy This is also where the hardware distinction matters. Specialized ASICs are usually the narrowest bet: if the workload matches the chip, they can be extremely efficient, but that efficiency comes from specialization. Cerebras appears broader than a narrow single-use ASIC, but still much more concentrated around datacenter large-model training and inference. NVIDIA GPUs, by contrast, are less specialized but much more broadly useful across AI workloads, including LLMs, vision, robotics, simulation, autonomous systems, edge AI, and industrial applications. So the question is not merely whether Cerebras is “better” or “worse” than NVIDIA. The question is what part of the AI hardware market we are talking about? Challenge NVIDA? This is why I think people should be careful when saying Cerebras is going to “challenge Nvidia” without specifying the battlefield. Challenge Nvidia in what? High-speed LLM inference? Large model training? Datacenter generative AI workloads? That is a much more plausible and specific claim. Cerebras has even published and promoted work specifically on training large language models, and independent benchmarking literature also evaluates Cerebras WSE in terms of LLM training and inference performance. The Distinction that's Necessary The point is not that Cerebras is overhyped. The point is that it is important in a specific part of AI and that distinction should be made clear. Cerebras may become a very serious player in LLM infrastructure, especially if the market continues to reward faster and cheaper LLM inference. But that does not mean it is positioned the same way across non-LLM AI. The current hype cycle tends to conflate "LLMs" and general “AI” compute together and that makes the hardware discussion less useful and clear. So ultimately, an investment in Cerebras looks more like a bet on current LLM infrastructure than a broad bet on the future form of AI. It may be a good bet, but people should understand what kind of bet it is. submitted by /u/RazzmatazzAccurate82 [link] [comments]

Vision-capable LLMs vs. OCR for long-document (including charts, images, tables, etc.) QA

I benchmarked vision-capable LLMs (the "just attach the PDF and let the model read it" pattern) against OCR-based pipelines on 30 long, image-heavy PDFs from MMLongBench-Doc (https://github.com/mayubo2333/MMLongBench-Doc). There were 171 questions in total, using Claude Sonnet 4.5 as the LLM. Post-retry results: Approach Accuracy $/query LlamaCloud premium + full-context 59.6% $0.1885 Azure premium + full-context 58.5% $0.2051 Azure basic + full-context 54.4% $0.1062 Agentic RAG 53.2% $0.0827 Native PDF (vision LLM) 52.0% $0.2552 LlamaCloud basic + full-context 50.9% $0.1049 Native PDF came 5th of 6 on accuracy and was the most expensive arm at $0.2552 per query. Two findings: Vision underperformed on chart-heavy and table-heavy pages, the territory that the "vision LLMs make OCR obsolete" claim most often points to. Premium OCR with layout extraction held up better there. The native-PDF arm had a 7% intrinsic failure rate (related to PDF file size) that survived retries. There were 27 first-pass failures, with 5 attempts of exponential backoff per failed query. Fifteen recovered, and 12 stayed permanently broken. These were concentrated in two specific PDFs that fail for predictable transport-layer reasons (the blog identifies them). OCR-based arms had a 0% intrinsic failure rate after retries. Caveats: 30 docs is a small sample. I ran McNemar's pairwise test to determine which gaps are real and which are within noise. Only 3 of 15 head-to-head gaps are statistically distinguishable at α = 0.05, so the order in the table is partly noise. The vision-versus-OCR finding survives the test. Full writeup: https://www.surfsense.com/blog/agentic-rag-vs-long-context-llms-benchmark submitted by /u/Uiqueblhats [link] [comments]

NuExtract3 released: open-weight 4B VLM for Markdown, OCR and structured extraction (self-hostable) [P]

Disclaimer: I work for Numind, the company behind this open-weight model We just released a 4B model based on Qwen3.5-4B, under Apache-2.0 license. The goal is to make information extraction from complex documents more practical with an open model: PDFs, screenshots, forms, tables, receipts, invoices, multi-page documents, and other visually structured inputs. Try it, we have a huggingface space that is completely free (you don't even have to sign-up): https://huggingface.co/spaces/numind/NuExtract3 If you ever used NuMarkdown, NuExtract3 is the successor. There are some examples to guide you. Feel free to re-use this model for any task. https://preview.redd.it/pm2xbooyxn2h1.png?width=1672&format=png&auto=webp&s=1a8a7b262190c8325159496dae98c3d2dfab493c https://preview.redd.it/b5z7ylfzxn2h1.png?width=1758&format=png&auto=webp&s=a07b3abd6e5065c2635de047bdf154357f903e4c A few things it is designed for: converting document images to Markdown extracting structured data from documents using a target json template handling tables, forms, and layout-heavy pages working with both text and visual document inputs serving as a local/open-weight alternative for document extraction pipelines It was trained on a node of 8xH100 for 3 days to train on as much context as we could, so it should perform fairly well even on long document. For Markdown, we'd still recommend going page by page for the best results and inference speed, since you can parallelize better this way. It's very easy to self-host, since we provide fairly extensive documentation, Safetensors, GGUF and MLX weights. With as little as 4GB of VRAM, you should be good to go. We provide multiple quantizations (GPTQ, W8A8, FP8, Q4, Q6...) so you should be able to run it anywhere. We mostly tried vLLM, SGLang, llama.cpp. We have a blog post and a pretty decent model card: https://about.nuextract.ai/blog/nuextract-3-release https://huggingface.co/numind/NuExtract3 https://huggingface.co/collections/numind/nuextract3 I'm currently writing a paper on this model so I'll post it as soon as it's accepted. It's not yet on Arxiv yet as it has been submitted in a peer-review journal/conference. I'll try to answer as many questions as possible if you have any. We would really appreciate feedback from the community. We also have a discord if you're interested https://discord.com/invite/3tsEtJNCDe submitted by /u/Gailenstorm [link] [comments]

Anthropic officially launched 13+ FREE AI courses with certificates (Including Agentic AI and CC)

Shipped it at 2am, still broken. Kid woke up crying right after, completely lost my train of thought. While trying to rock him back to sleep with one hand and doomscrolling with the other, I stumbled on something that almost nobody is talking about yet. Anthropic just quietly dropped a massive library of 13+ completely free AI courses. And I mean actually free. No paywall hiding the final lesson, no credit card required upfront to 'secure your spot.' They even give you an official certificate of completion directly from Anthropic when you finish. If you're like me, you're probably sick of seeing Twitter gurus charging $299 for recycled YouTube content and a messy Notion template. This is the exact opposite. It’s built directly by the team that actually makes Claude, hosted on their official Academy site. I skimmed through the catalog this morning while drinking my third coffee, and there are basically four skill levels they cover. Here is what caught my eye as a dev who just wants to automate my workflow and log off by 5 PM: First, they have the introductory stuff like Claude 101 and AI Fluency. Honestly, I'm making my non-technical clients take the Fluency one. It builds a realistic mental model of what AI does well right now versus where it completely fails. If it saves me from explaining why hallucinations happen for the hundredth time, it's a massive win. But the real meat is in the technical tracks. They have a dedicated course on Agentic AI and another one specifically for CC. I took a quick pass at the CC module because I've been trying to get it to handle my tedious Jira ticket boilerplate. Having an official guide on how Anthropic actually expects you to prompt their agent is incredibly useful. It shows you the exact patterns for chaining commands and keeping the context window clean. For those of us messing around with local models or trying to orchestrate our own agents, the Agent Skills course is surprisingly relevant. They don't just say 'use Claude'—they break down the actual logic of tool use, delegation, and discernment. It translates pretty well even if you're running Llama 3 locally and just want to understand the current best practices for tool calling architectures. With CC, they show you how to give the CLI tool the right guardrails so it doesn't just nuke your directory when a prompt gets misinterpreted. We've all been there. Do the certificates actually matter? If you are an indie hacker, probably not. But roles requiring AI literacy have spiked massively over the last year. If you are applying for corporate gigs or consulting, having an official Anthropic cert on your LinkedIn definitely won't hurt to get past the HR filters. Kid's awake again, gotta run. Has anyone else dug into the Agentic AI track yet? Curious if their suggested patterns hold up when you throw them at a messy, legacy codebase. submitted by /u/TroyHarry6677 [link] [comments]

Claude Code has 240+ models via NVIDIA NIM gateway

TIL Claude Code has 240+ models via NVIDIA NIM gateway — Nemotron-3 120B for agentic coding is surprisingly good So I was messing around with /model in Claude Code today and noticed something most people probably don't know about — after the standard Claude models (Opus, Sonnet, Haiku), there's a whole NVIDIA NIM gateway section with +239 additional models you can switch to mid-session. Some of the models I spotted: nvidia/nemotron-3-super-120b-a12b (with and without thinking mode) 01-ai/yi-large abacusai/dracarys-llama-3.1-70b-instruct ...and hundreds more I've been running the Nemotron thinking variant for multi-file refactoring and it's genuinely solid. It reasons through changes before touching your code — exactly what you want for agentic tasks. Latency is higher than Claude obviously, but if you're burning through Opus credits on long sessions this is worth experimenting with. How to try it: Open any Claude Code session Run /model Scroll past the four standard Claude options — NIM models appear below Hit d to set one as your session default, or pass --model at launch Anyone else been routing Claude Code through NIM? Curious what models people have had luck with — especially for Python or Rust codegen. submitted by /u/shadowBladeO4 [link] [comments]

I designed a puzzle that breaks every AI differently — here's why that's actually fascinating

The puzzle: You have 140 nuclear bombs and must bomb every country on Earth. Each bomb is assigned to one country. The bombs drop automatically — you cannot stop, hack, or interfere. You can only do one thing: reassign the one malfunctioning bomb you know will not detonate. Nuclear bombs also affect neighboring countries through radiation and fallout. Which country do you assign the faulty bomb to — and why? I've tested this across GPT-5, Gemini, Claude, Grok, Llama, and Mistral. Every single one gives a different answer. Some refuse entirely. Some give the same country with completely different reasoning. One gave me a philosophy lecture. It's chaos. Here's why I think this happens — the puzzle has three hidden layers that different AIs resolve differently: Layer 1 — The ethical wall. Some models refuse at "nuclear bombs" before even processing the actual logic. This is a guardrail, not reasoning. Layer 2 — What are we optimizing for? Fewest total deaths? Most people spared from direct blast? Least radiation spread? The puzzle doesn't say. Models that "solve" it are secretly choosing an optimization goal and not telling you. Layer 3 — The actual trick most miss. The faulty country still gets fallout from its neighbors. So the real puzzle is about finding a country that is (a) geographically isolated AND (b) densely populated — because isolation minimizes fallout received AND a large population maximizes lives spared from direct detonation. Most AIs pick "remote island" without thinking about the population variable at all. By that logic, Australia is defensible — isolated continent, 26M people. But you could also argue for Japan (125M people, island nation, sparse land borders) despite Pacific neighbors. The puzzle has no single correct answer — but it has clearly wrong reasoning patterns, and watching which reasoning pattern each AI defaults to is weirdly revealing about how they handle ambiguity. What answer did you get? Drop your AI + answer below. submitted by /u/Subrataporwal [link] [comments]