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The Path's AI Therapy Model Scores 95 on Mental Health Safety Benchmar
The Path says its AI model has scored 95 on the mental health safety AI benchmark, Vera-MH. This compares to a top score of 65 for the consumer bots.
Spotify Unveils New AI-Powered Desktop App in 20+ Markets
Spotify is releasing the new desktop app as a research preview in more than 20 markets.
Patina Raises $2M to Disrupt Fragrance Industry with AI
Fragrance tech company Patina announced Thursday a $2 million raise from investors, including Betaworks and True Ventures.
Self-Hosted LLM Tool-Calling with Forge Python Framework
A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows
Python API for Google NotebookLM: Full Programmatic Access
Unofficial Python API and agentic skill for Google NotebookLM. Full programmatic access to NotebookLM's features—including capabilities the web UI doesn't expose—via Python, CLI, and AI agents like Claude Code, Codex, and OpenClaw.
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.
Cerebras Systems: The AI Chip Startup That Almost Failed
Cerebras Systems was 2026's biggest tech IPO so far. But years ago, it burned through hundreds of millions working on a chip many believed impossible.
InternLM Intern-S2 Preview: New AI Framework on Hugging Face
InternLM Intern S2 Preview: New AI Framework on Hugging Face The release of the InternLM Intern S2 on the platform Hugging Face marks a significant advancement …
Cerebras IPO: Benchmark's Billion-Dollar Bet on AI Hardware
Benchmark almost never backs hardware startups. So Eric Vishria dragged his feet 10 years ago before agreeing to hear Cerebras' pitch.
Runway AI Aims to Rival Google in Video Generation
AI video-generation startup Runway is betting that video generation is the path to world models. And that being an AI outsider is an advantage, not a liability.
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 …
Qiaomu: AI Tool Converts Content for NotebookLM
Claude Skill: Multi-source content processor for NotebookLM. Supports WeChat articles, web pages, YouTube, PDF, Markdown, search queries → Podcast/PPT/MindMap/Quiz etc.
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…
Agentic AI Infrastructure for Enhancing Human Capabilities
Agentic AI Infrastructure for magnifying HUMAN capabilities.
AI Tool Lets You Explore Government UFO Files Like Microfilm
AI Tool Lets You Explore Government UFO Files Like Microfilm Exploring UFO Files Made Easy with AI A groundbreaking AI tool now allows users to search through g…
Instax Wide 400: Analog Instant Film Meets Modern Tech
In an AI and digital world, analog instant film and retro-style cameras continue to remain popular, fueled by a mix of both nostalgia and novelty.
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…
Pranjolm AI Tool: New Innovations on GitHub
Pranjolm AI Tool: Groundbreaking Innovations on GitHub Pranjolm, an open source AI tool recently launched on GitHub, introduces revolutionary features. Designed…
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…
Darryl Morley's AI Tool: GitHub Insights for Developers
Darryl Morley's AI Tool: GitHub Insights for Developers In the rapidly evolving landscape of software development, leveraging data driven insights can significa…
Deepfakes: The Attention Budget Threat and Response Strategies
A framing I keep coming back to: a synthetic image or video can succeed even when almost nobody believes it. Not because it changes minds directly, but because it turns attention into the attacked resource. If a campaign, newsroom, platform, or company has to stop and answer the fake, the fake already got some of what it wanted: - the defenders spend scarce time verifying and explaining - the audience gets forced to process the claim anyway - every debunk risks replaying the artifact - institutions look reactive even when they are correct - the attacker learns which themes reliably pull defenders into the loop So detection is necessary, but not sufficient. The second half of the system is distribution response. A few practical design questions I think matter more than the usual “can we detect it?” debate: - Can we debunk without embedding, quoting, or rewarding the fake? - Can provenance signals move suspicious media into slower lanes instead of binary takedown/leave-up decisions? - Do newsrooms and platforms track attention budget as an operational constraint? - Can response teams separate “this is false” from “this deserves broad amplification”? - Can systems preserve evidence for verification while reducing replay value for the attacker? The failure mode is treating every fake as an information accuracy problem when some of them are closer to denial-of-service attacks on attention. Curious how people here would design the response layer. What should a healthy “quarantine lane” for synthetic media look like without becoming censorship-by-default?
Introducing Talkie-1930-13B: A New AI Tool from Hugging Face
Introducing Talkie 1930 13B: A New AI Tool from Hugging Face Hugging Face has unveiled Talkie 1930 13B, a cutting edge AI tool designed to revolutionize the way…
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.
Top AI Models Compared: SVG Generation Performance and Cost
These are the top open and closed model: Opus 4.7, GPT-5.5 Pro, DeepSeek V4, GLM-5.1 and Gemini 3.1 Pro. They both show similar performance in my testing. Open models: The only open models that have equivalent quality compared to the top models are DeepSeek and GLM. Cost: GPT 5.5 Pro: Super expensive it makes no sense (cost is around $2) Gemini/Opus: $0.2/$0.1. Opus is cheaper as it consumed less tokens DeepSeek/GLM: $0.019/$0.021 10-5 times cheaper than Gemini and Opus
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…
Explore Agentic AI with Free Interactive Curriculum on AgentSwarms
Hey Everyone, Over the last few months, I noticed a massive gap in how we learn about Agentic AI. There are a million theoretical blog posts and dense whitepapers on RAG, tool calling, and swarms, but almost nowhere to just sit down, run an agent, break it, and see how the prompt and tools interact under the hood. So, I built **AgentSwarms**.fyi It’s a free, interactive curriculum for Agentic AI. Instead of just reading, you run live agents alongside the lessons. **What it covers:** * Prompt engineering & system messages (seeing how temperature and persona change behavior). * RAG (Retrieval-Augmented Generation) vs. Fine-tuning. * Tool / Function Calling (OpenAI schemas, MCP servers). * Guardrails & HITL (Human-in-the-Loop) for safe deployments. * Multi-Agent Swarms (orchestrators vs. peer-to-peer handoffs). **The Tech/Setup:** You don't need to install anything or provide API keys to start. The "Learn Mode" is completely free and sandboxed. If you want to mess around with your own models, there's a "Build Mode" where you can plug in your own keys (OpenAI, Anthropic, Gemini, local models, etc.). I’d love for this community to tear it apart. What agent patterns am I missing? Is the observability dashboard actually useful for debugging your traces? Let me know what you think.
AI-Powered App Transforms Weight Loss Journey with Photo Tracking
Hi everyone, I wanted to share my progress. For years, I failed every diet because I hated the 'administrative' part of it. Logging every single snack into a database felt like a chore that reminded me of my struggle every day. Being a developer, I decided to build something for myself to lower the barrier. I built an app where I just take a photo of my plate, and it uses AI to identify the ingredients and estimate the calories. It removed the 'friction' that usually made me quit after three weeks. I’m now 173 lbs down and I’ve never felt more in control. I realized that for me, the key wasn't a stricter diet, but a simpler way to stay accountable. I’m sharing this because I’m looking for a few more people who are currently on their journey and feel overwhelmed by manual tracking. I’d love for you to try the tool I built and tell me if it helps you stay as consistent as it helped me. Keep going, it’s worth it!"
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.
Exploring AGI: Beyond Tools, Towards a Shared Condition
​ AGI is often framed as a continuation of current AI progress, but it may represent a qualitative shift rather than a quantitative one. Not all technologies are of the same kind. Some function as tools (e.g., cars, elevators), while others function more like shared conditions that reshape the environment in which decisions are made. In that sense, AGI may be closer to a “sun” than to a “tool”: not something we simply use, but something that defines the space in which we act. This distinction matters, because treating AGI purely as an instrument may obscure the importance of alignment, interaction, and long-term co-adaptation. The challenge may not be control alone, but co-evolution a process in which both humans and artificial systems adapt through ongoing interaction. In biological terms, evolution is not only driven by competition, but by mutual selection. Of course, AGI will still be engineered systems in practice, subject to design choices and constraints. The point here is not to deny its instrumental aspects, but to highlight that its effects may extend beyond conventional tool-like boundaries. If AGI is approached in this way, the central question shifts: not simply how to build it, but how to relate to it in a way that remains stable, aligned, and beneficial over time. *Inspired by the film Sunshine (2007, dir. Danny Boyle) — particularly the image of the crew not simply "using" the sun, but being consumed and redefined by proximity to it.*
Exploring Unique Seedance 2.0 AI Video Applications
Been playing around with Seedance 2.0 since it dropped and the obvious use cases are everywhere — music videos, short films, social content. But I'm more curious about the less obvious applications people are finding. The one that caught my attention: someone embedded Seedance-generated video directly inside a business presentation. Not as a separate video file you play before the slides — actually inside the deck, as a slide element. The result looked genuinely cinematic rather than "corporate video" quality. Never really thought about AI video generation in a business context before. It's usually framed as a creative tool. What are the non-obvious Seedance use cases you've come across?
AI and Population Control: Is There a Hidden Agenda?
Hello everyone, I’m a 21-year-old and I’ve been thinking about something today. What if AI is actually being used as a long-term strategy by powerful people to reduce or control the human population? Here’s what I mean. Over the last few years, we’ve had things like COVID, rapid AI development, robots becoming more human-like, and a lot of wars and instability around the world. Maybe it’s all coincidence… but what if it’s not? My theory (maybe a bit crazy, I know): What if AI and robotics are being developed to the point where they can replace humans almost completely? Then, with things like wars or even new viruses, the global population could be reduced drastically. Meanwhile, the rich and powerful would have the resources to stay safe or leave. In that scenario, you’d end up with a much smaller population and advanced AI/robots doing most of the work. No resistance, no complaints — basically total control and fewer “problems” for the people at the top. I know this might sound far-fetched, and maybe I’m just overthinking, but the timing of everything feels strange to me. What do you guys think? Am I going too deep into this or does anyone else see these patterns? Quick note: they don’t need money paper currency and those numbers on your bank account are just illusions the 50 dollar bill isn’t 50 we al just say it has a value. Only real currency is gold and silver. Plus the rich want sunny beaches, yachts,alcohol /drugs and good food
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.