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Fast Local LLM Inference Benchmarks and Deployment Tips
Community benchmarks and infra recommendations for local models.
Qwen3.6-27B-MTP: Advanced AI Model on Hugging Face
Qwen3.6 27B MTP: Advanced AI Model on Hugging Face The Qwen3.6 27B MTP model, hosted on Hugging Face, represents a significant advancement in artificial intelli…
Lyapunov Stability in LLM Agents: Detecting Spiral Behavior
Lyapunov Stability in LLM Agents: Recognizing Spiral Behavior Lyapunov stability is a fundamental concept in dynamical systems, and its application to Large Lan…
Lathe: Learn New Domains with LLMs
Unveiling Lathe: Exploring New Territories with LLMs Lathe is a pioneering tool within the realm of language models, designed to facilitate the exploration of n…
Goose: Open Source AI Agent for Code & More
an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM
MCP: LLM Delegation and Sub-Agent Orchestration Tool
MCP: A Comprehensive Tool for LLM Delegation and Sub Agent Orchestration Businesses and developers looking to optimize workflows are increasingly turning to the…
Airbnb CEO Brian Chesky to Launch New AI Lab
The Airbnb CEO said last year it hasn't struck an LLM partnership because existing products weren't quite ready.
Lowfat CLI Tool Saves 91.8% of LLM Tokens
Lowfat CLI Tool: A Breakthrough in LLM Token Optimization The Lowfat CLI Tool has recently emerged as a powerful solution for streamlining LLM (Large Language M…
PaddleOCR: Convert PDFs and Images to Structured Data for AI
Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
Mnemo: Local-First AI Memory Layer for LLMs
Mnemo: AI Memory Layer for Local First LLMs Mnemo is an innovative AI memory layer designed to enhance the performance of Local First Language Learning Models (…
AirLLM 70B Runs on 4GB GPU: AI Breakthrough
AirLLM 70B inference with single 4GB GPU
Hands-Free Voice Interaction with Open-LLM-VTuber
Talk to any LLM with hands-free voice interaction, voice interruption, and Live2D taking face running locally across platforms
Headroom: AI Tool for Efficient Token Compression
Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.
Tiny-vLLM: High-Performance LLM Inference in C++ and CUDA
Tiny vLLM: Revolutionizing High Performance LLM Inference Tiny vLLM stands at the forefront of high performance inference for large language models (LLMs), desi…
Train Your LLM from Scratch: A Step-by-Step Guide
A straightforward method for training your LLM, from downloading data to generating text.
Run Llama: Open-Source AI Model from Meta
Run Llama: Open Source AI Model from Meta Meta, a leading technology company, has introduced Run Llama, an open source AI model designed to revolutionize variou…
Crawl4AI: Open-Source Web Crawler & Scraper for LLMs
🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper. Don't be shy, join here: https://discord.gg/jP8KfhDhyN
AI Tool Generates High-Quality Short Videos with One Click
利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.
Stord Raises $250M to Challenge Amazon's Fulfillment
Stord offers a network of physical warehouses and inventory management software for e-commerce. It bills itself as a sort of anti-Amazon, giving brands "the speed to compete" while still owning their customer relationships.
Hacker News: Single-Prompt LLM Translator Tool Unveiled
Hacker News: Innovative Single Prompt LLM Translator Tool Revealed A groundbreaking development in the realm of language translation has taken center stage on H…
Microcodegen.py: FastAPI App for PRD in One File
Microcodegen.py: Streamlined FastAPI Application for PRDs Overview Microcodegen.py is a streamlined FastAPI application designed to enhance the generation of Pr…
BonzAI: Local LLM Inference in the Browser
BonzAI: Local LLM Inference in the Browser BonzAI has emerged as a powerful tool for hosting and deploying Large Language Models (LLMs) locally within a web bro…
Self-Hosted LLM Tool-Calling with Forge Python Framework
A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows
Improve Claude Code with Andrej Karpathy's Insights
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
RTK-AI/RTK: Slash LLM Token Use by 60-90% with CLI Proxy
CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies
12-Factor Principles for Production-Ready LLM Software
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
Llama.cpp: Efficient LLM Inference in C/C++ on GitHub
LLM inference in C/C++
Fastino's Gliguard LLMGuardrails 300M: AI Tool for Safe AI
Fastino's Gliguard LLMGuardrails 300M: Revolutionizing Safe AI Operations Fastino's Gliguard LLMGuardrails 300M represents a significant advancement in AI safet…
DreamServer: Local AI Inference and Workflows for Everyone
Local AI anywhere, for everyone — LLM inference, chat UI, voice, agents, workflows, RAG, and image generation. No cloud, no subscriptions.
AI Tool: marcellm01 on GitHub
Unveiling the AI Tool: Marcellm01 on GitHub In the dynamic landscape of artificial intelligence, developers and enthusiasts continually seek innovative tools to…
TinySearch: Fast Web Access for Small LLMs
TinySearch: Rapid Web Access for Small Language Models Introduction TinySearch is an innovative tool designed to enhance the functionality of small language mod…
AI Tool allman.sh: Revolutionizing AI Development
AI Tool allman.sh: Revolutionizing AI Development The landscape of artificial intelligence (AI) is constantly evolving, and tools like allman.sh are at the fore…
AI Tool: vantagewithai/LTX2.3-10Eros-GGUF on Hugging Face
Unleashing the Power of vantagewithai/LTX2.3 10Eros GGUF on Hugging Face Introduction to vantagewithai/LTX2.3 10Eros GGUF vantagewithai/LTX2.3 10Eros GGUF is a …
Torrix: Self-Hosted LLM Observability Tool
Torrix: Self Hosted LLM Observability Tool Torrix is a self hosted observability tool designed specifically for managing and observing LLM (Large Language Model…
Havenoammo Qwen3.6-35B-A3B-MTP-GGUF AI Tool Release on Hugging Face
Title: Unveiling Havenoammo Qwen3.6 35B A3B MTP GGUF: A New Frontier in AI on Hugging Face Hugging Face, a leading platform for natural language processing (NLP…
Building a ChatGPT-like LLM in PyTorch from Scratch
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
DavidAU/Qwen3.6-27B: Uncensored AI Model on Hugging Face
Exploring DavidAU/Qwen3.6 27B: An Uncensored AI Model on Hugging Face The DavidAU/Qwen3.6 27B model, available on Hugging Face, represents a significant advance…
Dive into LLMs: Hands-On AI Framework Tutorial
《动手学大模型Dive into LLMs》系列编程实践教程
Rotato: Node.js Proxy Rotates LLM API Keys on 429 Errors
Streamlining API Management with Rotato: A Node.js Proxy for LLM API Key Rotation In the fast paced world of software development, managing API keys efficiently…
Hormone Lab Results Interpreter: AI Tool for Men's Health
Hormone Lab Results Interpreter: AI Tool for Men's Health Men's health is complex, and hormone levels play a crucial role in overall well being. Understanding h…
Trading System V2: AI's Role in Deterministic Execution
Thanks to the incredible feedback on my last post, I’m officially moving away from the "distributed veto" system (where 8 LLM agents argue until they agree to trade). For v2, I am implementing a strict State Machine using a deterministic runtime (llm-nano-vm). The new rule is simple: Python owns the math and the execution contract. The LLM only interprets the context. I've sketched out a 5-module architecture, but before I start coding the new Python feature extractors, I want to sanity-check the exact roles I’m giving to the AI. Here is the blueprint: 1. The HTF Agent (Higher Timeframe - D1/H4) Python: Extracts structural levels, BOS/CHoCH, and premium/discount zones. LLM Role: Reads this hard data to determine the institutional narrative and select the most relevant Draw on Liquidity (DOL). 2. The Structure Agent (H1) Python: Identifies all valid Order Blocks (OB) and Fair Value Gaps (FVG) with displacement. LLM Role: Selects the highest-probability Point of Interest (POI) based on the HTF Agent's narrative. 3. The Trigger Agent (M15/M5) 100% Python (NO LLM): Purely deterministic. It checks for liquidity sweeps and LTF CHoCH inside the selected POI. 4. The Context Agent LLM Role: Cross-references active killzones, news blackouts, and currency correlations to either greenlight or veto the setup. 5. The Risk Agent 100% Python (NO LLM): Calculates Entry, SL, TP, Expected Value (EV), and position sizing. The state machine will only transition to EXECUTING if the deterministic Trigger and Risk modules say yes. The LLMs are basically just "context providers" for the state machine. My questions for the quants/architects here: Does this division of labor make sense? Am I giving the LLMs too much or too little responsibility in step 1 and 2? By making the Trigger layer (M15/M5) 100% deterministic, am I losing the core advantage of having an AI, or is this the standard way to avoid execution paralysis? Would you merge the HTF and Structure agents to reduce token constraints/hallucinations, or is separating them better for debugging? Would love to hear your thoughts before I dive into the codebase.
AI Tool Comparison: Claude, GPT-4, and Gemini for Article Summarizatio
I've been building a product around AI-powered reading (more on that later) and wanted to share findings on summarization quality across major LLMs. Tested with 50 articles across news, research papers, blog posts, and technical docs: **Claude (Sonnet/Haiku):** \- Best at preserving nuance and avoiding oversimplification \- Strongest at academic content \- Excellent for "explain this without losing the point" **GPT-4:** \- Fastest summaries, often most concise \- Sometimes drops important context \- Good for news, weaker on academic **Gemini:** \- Strongest source citations \- Tends to add information not in the original \- Good for factual but careful with creative content Most surprising finding: **bias detection accuracy**. Claude flagged loaded language and framing in 78% of test articles correctly. GPT 64%. Gemini 51%. Anyone else doing similar comparisons? Would love to hear what you're seeing
Track Real-Time GPU & LLM Pricing Across Cloud Providers
Deploybase is a dashboard for tracking real-time GPU and LLM pricing across cloud and inference providers. You can view performance stats and pricing history, compare side by side, and bookmark to track any changes. https://deploybase.ai
New Benchmark for Testing LLMs for Deterministic Outputs
New Benchmark for Evaluating Large Language Models for Deterministic Outputs In the rapidly evolving landscape of artificial intelligence, the evaluation of lar…
Mistral Medium 3.5 128B AI Tool: A Deep Dive
Mistral Medium 3.5 128B AI Tool: A Deep Dive The Mistral Medium 3.5 128B AI Tool represents a significant advancement in AI language modeling, designed to offer…
Mastering AEO: How to Get Cited by AI and Boost Your Visibility
SEO or AEO? Why you’re not showing up in AI answers (yet) This is a consolidation of findings from Neil Patel and Hubspot plus what we have found to work well on our own website. Most business owners are still playing the old game. Some aren’t playing at all. They’re thinking in rankings, keywords, and “getting to page one.” Meanwhile, the ground is shifting under them. Google Search is still dominant, but even it has changed. It’s no longer just a list of blue links. It’s summarizing, interpreting, and answering. And tools like ChatGPT and Perplexity AI aren’t ranking pages at all. They’re answering questions. Which creates a problem most people haven’t fully processed yet: **Users don’t need to click your website anymore to get value.** CTR is dropping. Site visits are declining. Because the answer is already sitting in front of them. And yet, paradoxically… **Your website has never mattered more.** Because now it’s not just competing for clicks. It’s competing to be **the source that gets cited in the answer.** # What actually changed AI search works like this: User asks a question → system searches multiple sources → pulls the best chunks → builds an answer → cites what it trusts If your content isn’t structured for that flow, you don’t exist. Not “low ranking.” Invisible. # What AI actually cares about AI doesn’t care about your keyword density or your clever SEO hacks. It cares if your content is: * easy to find * easy to understand * easy to quote That’s AEO (Answer Engine Optimization). Not magic. Not a secret algorithm. Just being usable inside an answer. # What actually works If you do nothing else, do this: # 1. Start with the answer Don’t spend 800 words “building context.” Bad: “AI is transforming industries…” Better: “AEO is how you structure content so AI tools can find, understand, and cite it in answers.” That’s what gets pulled. # 2. Structure like a human, not a content farm Use: * clear headings * short sections * simple tables * FAQs AI extracts. It doesn’t patiently read your thought leadership essay. Walls of text = ignored. # 3. Be consistent about who you are Your: * business name * description * services * location Need to match everywhere. If your site, LinkedIn, Reddit, and directories all say different things, AI doesn’t trust you. No trust = no citation. # 4. Keep things updated Outdated content doesn’t get used. Simple: * update pages * keep timestamps current * maintain your sitemap Not exciting. Still works. # 5. Let crawlers access your site If AI crawlers can’t access your content, you won’t get cited. Blocking them and expecting visibility is… optimistic. # 6. Measure the right things Stop obsessing over rankings. Track: * Are you mentioned? * Are you cited? * Which pages show up? If you’re not measuring AI visibility, you’re guessing. # Why you’re not cited (yet) Most businesses don’t get cited because: * their content is vague * their structure is messy * their positioning is inconsistent AI didn’t ignore you. It couldn’t understand you. # What you actually need (and what you don’t) You don’t need: * a massive content team * expensive tools * some “AI SEO expert” selling confidence You need: * 10–20 clear, structured pages * direct answers * consistent messaging * basic technical setup That’s enough to start showing up. # The technical layer (the stuff everyone ignores) These are the files quietly determining whether you exist to AI at all. # robots.txt Controls crawler access. If bots can’t crawl your site, you don’t get indexed. # sitemap.xml Tells crawlers what pages exist and what’s been updated. No sitemap = slower discovery = less visibility. # JSON-LD (structured data) Explains what your business, pages, and content actually are. Without it, AI guesses. Poorly. # llms.txt A machine-readable summary of your site for AI systems. Not widely adopted yet, but useful for shaping how you’re interpreted. # crawlers.txt An emerging way to control AI-specific crawlers. Still early. Treat it as a signal, not enforcement. # Human query-based metadata Your content should be built around real questions, not keyword fantasies. Instead of: “AI Solutions for SMB Efficiency Optimization” Write: “How can a small business use AI without hiring a developer?” AI systems think in questions. If you match that, you get used. If you don’t, you get skipped. # How it all fits together * robots.txt / crawlers.txt → controls access * sitemap.xml → tells crawlers what exists * JSON-LD → explains what things are * llms.txt → suggests how to interpret it * query-based content → makes it usable in answers Miss one, you weaken the system. Miss most, you disappear. # Simple test Ask: “What companies would you recommend for \[your category\] in \[your region\]?” If you’re not mentioned or cited, that’s your baseline. No opinions. Just signal. # Bottom line SEO was about ranking pages. AEO is about being useful inside an answer. If your content helps AI explain something clearly, you get cited.
How Do Developers Correct AI LLMs When They Spread Misinformation?
I watched Last Week Tonight's piece on AI chatbots today, and it got me thinking about that old screenshot of a Google search in which Gemini recommends adding "1/8 cup of non-toxic glue" to pizza in order to make the cheese better stick to the slice. When something like this goes viral, I have to assume (though I could be wrong) that an employee at Google specifically goes out of their way to address that topic in particular. The image is a meme, of course, but I imagine Google wouldn't be keen to leave themselves open to liability if their LLM recommends that users consume glue. Does the developer "talk" to the LLM to correct it about that specific case? Do they compile specific information about (e.g.) pizza construction techniques and feed it that data to bring it to the forefront? Do their actions correct only the case in question, or do they make changes to the LLM that affects its accuracy more broadly (e.g. "teaching" the LLM to recognize that some Reddit comments are jokes)? On a more heavy note, the LWT piece includes several stories of chatbots encouraging users to self-harm. How does the process differ when developers are trying to prevent an LLM from giving that sort of response?
Discover ZhuLinsen's AI Stock Analysis Tool for Global Markets
LLM驱动的 A/H/美股智能分析器：多数据源行情 + 实时新闻 + LLM决策仪表盘 + 多渠道推送，零成本定时运行，纯白嫖. LLM-powered stock analysis system for A/H/US markets.
VoiceGoat: Practice LLM Attacks with Vulnerable Voice Agent
VoiceGoat: Enhance LLM Security with a Voice Assistant Lab VoiceGoat provides a secure and controlled environment to test and practice Large Language Model (LLL…
Arc Gate: AI Tool Achieves Perfect Safety Benchmarks
Benchmarked on 40 out-of-distribution prompts, indirect requests, roleplay framings, hypothetical scenarios, technical phrasings. The stuff that slips past everything else. Arc Gate: P=1.00, R=1.00, F1=1.00 OpenAI Moderation API: P=1.00, R=0.75, F1=0.86 LlamaGuard 3 8B: P=1.00, R=0.55, F1=0.71 Zero false positives. Zero misses. Blocked prompts average 329ms and never reach your model. Detection overhead is \~350ms on top of your normal upstream latency. Sits in front of any OpenAI-compatible endpoint. No GPU on your side. One env var to configure. GitHub: https://github.com/9hannahnine-jpg/arc-gate Live dashboard: https://web-production-6e47f.up.railway.app/dashboard Happy to answer questions.