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Filtr's New Feature Blocks In-App Ads on iPhone and Mac
This popular ad blocker app for iPhones, iPads, and Macs can now block ads from loading inside apps, including web browsers, thanks to a new feature in the latest Apple software.
Founders Fund Hosts Game Show with Tech Elites
The debut episode, moderated by Founders Fund chief marketing officer Mike Solana, included a star-studded cast of current tech luminaries.
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.
Open Source Notebook LM: Flexible AI Tool on GitHub
An Open Source implementation of Notebook LM with more flexibility and features
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.
TechCrunch Extends Startup Battlefield 200 Deadline to June 8
After overwhelming demand from founders around the world, TechCrunch has extended the Startup Battlefield 200 application deadline to June 8. Nominate a standout startup or apply yours today.
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
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…
Cursed Browser: AI Reads HTML, Hallucinates Pages
Title: Unveiling the Curse: Webpage Creation and Analysis via AI Powered program Intro In the realm of artificial intelligence, innovations continually redefine…
Unsiloed AI Tops olmOCR-Bench Rankings
Unsiloed AI Leads the Way in OLMOCR Benchmark Rankings In the ever evolving landscape of machine learning, the recent prominence of Unsiloed AI on the OCR bench…
Google's AI Glasses: Hands-On with Android XR Prototype
Google demoed prototype Android XR glasses that overlay Gemini-powered translation, navigation, and other information directly into your field of view.
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…
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.
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?
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…
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…
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…
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…
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
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!"
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.*
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.