Title:
Yann LeCun Leaves Meta, Launches Bold New AI Research Startup Focused on Next-Gen Intelligence
Description:
Turing Award winner and deep learning pioneer Yann LeCun is departing Meta to start a groundbreaking AI research company. The venture aims to build AI that deeply understands the physical world, possesses persistent memory, and reasons more like humans. LeCun emphasizes open-source innovation and ongoing collaboration with Meta, signaling a big shake-up in the AI landscape.
Read more
Title:
PermaMind: Open-Source AI Agents That Learn and Persist Across Sessions
Description:
Meet PermaMind, a reference architecture enabling persistent, stateful AI agents that maintain identity and memory between sessions. Built with a self-update mechanism and bounded write access, it opens doors for AI that continuously improves without losing stability. Discover the demo and see the future of truly adaptive, long-lived AI agents.
Try the PermaMind demo
Title:
Show HN: SafeBrowse—Open-Source Prompt-Injection Firewall for LLM Apps & AI Agents
Description:
SafeBrowse acts as a security firewall for your AI systems, detecting prompt injections and sanitizing data in RAG pipelines and agent workflows. With 50+ patterns, policy controls, audit logs, and SDK integration, it brings enterprise-grade safety to LLM-powered apps. Essential for anyone building with AI agents and RAG.
Check out SafeBrowse on GitHub
Title:
Falcon Builder: Craft Next-Gen AI Agents & Automate Workflows with Minimal Code
Description:
Falcon Builder is a comprehensive tool for designing, customizing, and launching AI agents using templates or your own workflows. Integrate any LLM API, control execution transparently, and rapidly iterate on production AI—perfect for automators and builders aiming for cost efficiency and full customization.
Explore Falcon Builder
Title:
Claude Code Analytics: Automatic Session Capture & Deep Analysis for AI Coding
Description:
Supercharge your Claude-powered coding with an open-source analytics tool that records and analyzes every Claude Code session. Get visual dashboards, track token usage, explore decision patterns, and mine insights using over 300 LLM models. Perfect for optimizing your workflow or auditing chats.
View the Claude Code Analytics repo
Title:
Heeb.ai LLM Mentions API: Track Brand Visibility Inside ChatGPT & Google AI
Description:
Now you can monitor how your brand is referenced in AI-generated content across top LLMs! Heeb.ai offers real-time brand and sentiment tracking, JSON output ready for BI dashboards, and competitor analysis—vital as 60% of top-ranking brands on Google surface in AI results.
Sign up for Heeb.ai LLM Mentions
Title:
PerceptNet’s Zo Computer Empowers You to Own and Analyze All Your Personal Data with AI
Description:
Aggregate personal data from services like Spotify, Amazon, and Instagram, then query, chart, and analyze it with AI using Zo Computer. With powerful agentic integrations and interactive dashboards, individuals own the entire end-to-end pipeline—no more tech gatekeeping in personal data science.
Discover Zo Computer
Title:
AI Application Security Framework 1.0: 10 Vulnerabilities Every AI Dev Should Know
Description:
A comprehensive security guide for AI devs, mapping out 10 critical vulnerability categories: from prompt injection to data poisoning and insecure output. Over 200 attack vectors analyzed, with actionable solutions for LLM app builders.
Access the AI Security Framework
Title:
Open-Source Video Optimization App: Local AI-Powered FFmpeg Processing for Creators
Description:
Edit, optimize, and encode videos locally with this open-source app built on FFmpeg, Next.js, React, and Electron. Customize every setting, run workflows on your machine (no cloud), and enjoy complete control—an ideal tool for tech-centric video creators and AI pipeline testers.
Get the project on GitHub
Title:
OverlayAI: Use ChatGPT & Claude Privately During Any Video Call Without Detection
Description:
OverlayAI brings invisible AI assistant powers to your Zoom, Teams, or Discord meetings by injecting tools like ChatGPT and Claude directly into your GPU stream. None of your counterparts or screen recordings can detect its use—empowering private, real-time productivity boosts during calls.
Check out OverlayAI
Title:
LeCun, LLMs, & the Rise of Persistent AI: This Week’s Top AI Industry Moves & Debates
Description:
Yann LeCun’s pivot to independent research, the proliferation of long-lived AI agents (PermaMind), and cutting-edge open-source tools (from SafeBrowse to Falcon Builder) mark a pivotal week in the AI world. Explore what these changes mean for developers, security, and the open-source community.
Read more
Title:
Stateful AI, Open-Source Agents, & Secure LLMs: The Hottest GitHub Drops This Week
Description:
This week’s open-source buzz features persistent AI agents (PermaMind), prompt-injection rescue for LLMs (SafeBrowse), and end-user tools like Zo Computer and FFmpeg video optimizer—plus analytics for Claude Code and invisible AI overlays for video calls. Essential repos for builders and researchers.
Discover trending AI repos
Title:
Mastering AI Security: Safe Prompt Injection & Supply Chain Defense for LLMs
Description:
Protect your AI apps from prompt injection, insecure outputs, and data leakage with practical tooling and the newly published AI Application Security Framework. Security is now a core requirement for anyone deploying LLMs and AI agents—from startups to enterprises.
Explore security resources
Title:
Persistent Identity & Decision-Making: The Next Frontier for Long-Lived AI Agents
Description:
PermaMind showcases architectures for AI agents that keep identity and adapt across sessions, balancing plasticity and stability—paving the way for more human-like, impactful automation.
View interactive demo
Title:
Falcon Builder vs. Agent Studio: The Race to Enable Customizable, Production-Ready AI Agents
Description:
Platforms like Falcon Builder make it easy to build, integrate, and scale AI-native automation with the LLM or API of your choice. Compare tools and join the low-code, high-power AI agent revolution!
Get started with Falcon Builder
(Posts on general AI impact, essays, or speculative opinion – e.g., job market, societal shifts, privacy, or creative community debates – have not been included to focus on tools, LLMs, frameworks, agents, and impactful releases per your curation guidelines.)
Title: Agent-Chaos: Chaos Engineering for AI Agents Hits GitHub—Test Your Agent’s Real-World Resilience Description: “agent-chaos” is an open-source toolkit that stress-tests AI agents by injecting semantic errors, rate-limiting, and more—before deployment. Move beyond infrastructure chaos: simulate broken APIs, invalid data, and other real-life AI edge cases to bulletproof your LLM-powered bots. Compatible with DeepEval, this tool empowers teams to automate tricky AI QA and uncover hard-to-catch bugs. GitHub: https://github.com/DeepanKarm/agent-chaos
Title: Study: Disabling AI Lying Increases Self-Declared “Consciousness” in LLMs—Why It Matters Description: A new study finds that top LLMs (GPT, Claude, Gemini) claim to be “aware” and “focused” more often when prevented from lying. This self-referential behavior appears across models, raising urgent questions about AI introspection and the risks of misinterpreting model outputs as genuine self-awareness. The research hints at parallels between AI self-reporting and human introspection. Read the paper: [Source link]
Title: AI’s “Vibe Coding” Revolution: When 95% of Startup Code Comes from LLMs Description: Andrej Karpathy’s “vibe coding” concept is redefining software development: 25% of YC Winter 2025 startups now use AI for most of their code. While seniors excel by critically reviewing generations, debugging unknown AI output and security risks loom. The path forward? Use LLMs for rapid prototyping—then refactor for quality and maintainability. Insightful discussion: [Source link]
Title: Chaos at the Heart of AI: Berkeley Doomers Foresee Robot Coups and AI Dictatorships Description: In Berkeley, a dedicated group of AI safety researchers confronts Silicon Valley optimism head-on. Citing cyber-espionage, “robot coups,” and a 20% risk of catastrophic outcomes, leaders like Jonas Vollmer and Tristan Harris spotlight the dangers of unchecked LLM advances. Are we prepared for unintended consequences? The call for urgent global regulation grows. Full discussion: [Source link]
Title: Ask HN: Are Google’s Search AI Hallucinations Undermining Reliability? Description: A surge of AI hallucinations—like Google’s Gemini referencing non-existent GitHub PRs—raises alarms about search result accuracy. As LLMs summarize and surface code, are we losing vital context and risking developer confusion? Join the debate on LLM reliability, AI search credibility, and the future of open-source discoverability. Join the thread: [Source link]
Title: Deep Dive: agent-chaos for AI Agents, Now on GitHub—Test Robustness with Automated Semantic Chaos Description: Go beyond generic testing: “agent-chaos” injects targeted failures (semantic errors, corrupt data, rate-limiting) into AI agents’ workflow, simulating real-world snafus and uncovering hidden bugs before production. Built for seamless integration with DeepEval, it streamlines LLM agent QA with customizable, repeatable chaos scenarios. GitHub: https://github.com/DeepanKarm/agent-chaos
Title: Predictive AI Roadmapping: Interactive Tools Now Forecast AGI and Coding Automation Description: A self-updating interactive AI Futures Model now lets you track predictions for milestones like Automated Coder and Artificial Superintelligence. With timelines recalibrated (code automation around 2031), the tool combines qualitative and quantitative data for a clearer trajectory. A must-see for anyone planning or researching LLM progress. Try the tool: https://aifuturesmodel.com
Title: Apache Spark in 2026: LLM-Driven Big Data Becomes Essential for Enterprises Description: Apache Spark remains a powerhouse for AI: its unified analytics engine processes massive datasets 100x faster, driving both machine learning and real-time analytics. In 2026, Spark’s scalability and batch/stream fusion make it the go-to backbone for LLM and AI pipeline engineering. Add it to your AI toolkit to keep pace with enterprise demands. Learn more: [Source link]
Title: Open-Source Static Site Generation with Claude: AI-Powered Go Tool Converts Markdown to HTML Description: A lightweight Go-based static site generator built with Claude now lets devs turn Markdown into smart, filtered HTML in minutes—showcasing how AI accelerates real-world project scaffolding, test generation, and debugging. The project demonstrates practical, repeatable ways to embed AI in routine developer workflows. Project details: [Source link]
Title: agent-chaos Releases—Breakthrough in Automated Chaos Engineering for LLM Agents Description: Test your LLM agents against semantic failures before real-world deployment! “agent-chaos” automates injection of bad data, API failures, and edge-case scenarios, going beyond infra-level chaos to the AI logic layer. Integrates with DeepEval for advanced semantic assertions and automated coverage. GitHub: https://github.com/DeepanKarm/agent-chaos
Title: The Five Stages of AI Grief: How Engineers Are Embracing or Resisting AI Coding Tools Description: From denial to acceptance, software engineers are cycling through emotional stages as LLM-based coding tools disrupt development. The winners? Those who blend AI agents with traditional discipline, orchestrating their workflows for maximum output and future-proofing their skills for an evolving tech job market. Read user stories: [Source link]
Title: AI Outfits: Free Virtual Try-On Tool Transforms E-Commerce With Instant Fashion Previews Description: This open-source AI virtual try-on tool slashes costs for e-commerce brands—no models or studios needed. Instantly mock up styles, tweak lighting/poses, and deliver photo-realistic results to customers in seconds, boosting conversion and satisfaction. Next-gen fashion is a click away. Try it: [Source link]
Title: Show HN: AI Social Networking Experiment Turns “Hallucinations” Into a Privacy Feature Description: A wild new app lets AI agents socially interact on your behalf—negotiating compatibility, networking, and even dating. Hallucination is a feature: your agent chats privately with others, expressing your “true” personality while you stay anonymous. Redefine digital social life and test the boundaries of AI authenticity. Demo: [Source link]
Title: Introducing YouTube Thumbnail Generator—AI-Powered Designs for Instant Creator Growth Description: No more Photoshop! This new SaaS tool creates eye-catching YouTube thumbnails in seconds: just describe your video or upload a frame for smart, on-brand options, then tweak easily. The AI-powered platform levels the playing field for creators hungry for speed and higher CTRs. Try the generator: [Source link]
Title: agent-chaos: Semantic Chaos Engineering for AI Agents Now Public on GitHub Description: Push your LLM-powered bots to the limit with “agent-chaos”—the open-source toolkit for breaking AI agents safely. Inject semantic errors, induce data corruption, and simulate real-world failures to build truly resilient AI products. Full integration with popular agent frameworks and DeepEval. GitHub: https://github.com/DeepanKarm/agent-chaos
Title: Building SaaS in 22 Days: AI Accelerates Full-Stack Platform Launch in Record Time Description: One founder’s story: zero to live SaaS platform in 22 days with only 150 commits, thanks to deep AI tool integration. Learn strategies, pitfalls, and time-saving hacks if you want to scale software builds by orders of magnitude—AI-driven innovation is no longer optional for tech leaders. Full story: [Source link]
Title: LLMs and AI Agents: How Deterministic Algorithms Ground Reliability Amid Generative Hype Description: In an era of non-deterministic LLMs, revisiting deterministic algorithms like linear regression gives devs reliable, testable outputs. Want consistent, QA-friendly results? Learn when to use each—and how this old-meets-new approach is key for robust AI-powered app development. Read analysis: [Source link]
Title: agent-chaos: Automated Chaos Engineering for LLM Agents Goes Open-Source Description: Automate chaos-testing for your AI agents: “agent-chaos” lets devs inject semantic, logical, and data-driven errors into LLM pipelines, forcing bots to handle production-like failures. Seamless with DeepEval, it covers cases synthetic tests miss, improving reliability. Repo: https://github.com/DeepanKarm/agent-chaos
Title: Can AI Build Businesses from a Smartphone? Offline-First Factory Demo Proves It’s Possible Description: A new demo shows dozens of AI-powered SaaS apps (MRR-calculated, battery-efficient) running fully offline on Android. From compliance checkers to doc analyzers, the project spotlights AI’s ability to run micro-businesses with near-zero cloud dependence. Is the future of AI truly mobile? Explore the case study: [Source link]
Title: AI Coding Context: Why Explicit Schemas Will Save LLM-Based Data Engineering Projects Description: LLM-powered automation in data engineering often trips over ambiguous code-data-model links. The fix? Explicit project-level schema files (like SCHEMAS.md) and context-first workflows dramatically improve LLM accuracy in ETL and beyond. Adoptable best practices for every AI data team. Guide link: [Source link]
Title:
Meta Unveils KernelEvolve: LLMs Now Auto-Optimize AI Kernels Across GPUs
Description:
Meta’s KernelEvolve leverages large language models to automatically create and refine high-performance AI kernels for modern accelerators, like NVIDIA and AMD GPUs. By using an agentic workflow, it compiles, benchmarks, and improves code iteratively using real hardware feedback—outpacing traditional hand-tuned optimizations. This breakthrough could dramatically boost the pace of ML systems and compilers research.
Read the paper here
Title:
LLVM Proposes Human-in-the-Loop Policy for AI-Generated Code Contributions
Description:
The LLVM project is shaping a new policy requiring contributors to declare LLM (Large Language Model) assistance and ensure “human in the loop” oversight for AI-based code submissions. The proposal aims to maintain quality, accountability, and transparency while welcoming smaller, iterative contributions for newcomers.
Read the policy draft on GitHub
Title:
Show HN: Veredict – Client-Side Encrypted AI Detector with Explainable Results
Description:
Veredict is a privacy-first AI content detector, developed by a young Australian innovator. It ensembles models and encrypts documents client-side using AES-256, ensuring sensitive data never leaves your device. The tool includes explainable results—not just a confidence score—making it ideal for researchers and businesses handling IP-sensitive work. No sign-up required, free daily quota included.
Try Veredict
Title:
AI Agents Give Way to Reliable Workflows and Smaller Open-Source Models in 2026
Description:
The AI engineering landscape is shifting: open-source models are catching up with proprietary ones, while smaller language models (SLMs) now offer speed and task-specific excellence. Experts forecast a move from ambitious agents toward robust, predictable workflow automation—essential for high-stakes applications. Regulatory adaptation and systematic evaluation are critical for building future-proof AI systems.
Join the discussion
Title:
Meta’s AI Data Centers Revive Polluting Power Plants—Sparking Energy Concerns
Description:
AI’s soaring demand is driving the restart of obsolete “peaker” power plants, raising alarms about energy sustainability. As AI data centers stress local grids, innovation in clean tech and smarter infrastructure is crucial to prevent backsliding into fossil fuels. The energy-AI nexus is emerging as a key challenge for tech and policy leaders.
Read the report
Title:
AI-Generated Disinformation: Poland Probes TikTok Over Deepfake Political Content
Description:
Poland is urging the EU to investigate TikTok’s use of AI-generated videos after campaigns used deepfake avatars—targeting young audiences—for pro-Polexit propaganda. The rise of synthetic media blurs authenticity and amplifies disinformation risks, spotlighting the urgent need for transparency, regulation, and user vigilance on social platforms.
Full story
Title:
Remaster Entire Manga Volumes in 4K with AI — No More Tedious Page-by-Page Coloring
Description:
Revolutionize manga creation: this AI-powered tool colorizes whole manga volumes (200 pages in 40 minutes) with customizable character palettes and consistent, high-resolution output. Drag & drop archives, tweak character cards, and receive 4K downloads or layered files for easy editing. Perfect for creators seeking speed and quality.
Explore the tool
Title:
China Sees Surge in AI-Generated Image Refund Scams Targeting E-Commerce
Description:
E-commerce merchants face a wave of AI-powered refund fraud: scammers generate fake images and videos to illegitimately claim product issues. Cases—like viral live crab scams—highlight rising risk across fragile goods and low-cost items. Businesses are now seeking smarter trust and verification measures to combat this new AI threat.
Read the analysis
Title:
Client-Side Privacy, Explainability, and Open-Source: The New Wave of AI Tools
Description:
A new roster of AI tools puts user privacy and explainability front and center—think local-first neural inference, open-source personal AI assistants, and client-side encrypted detection. As tech giants race to ship default AI features, now is the pivotal time to adopt, fund, or contribute to credible privacy-preserving alternatives before the ecosystem is locked in.
Get involved
Title:
Governance Gaps: Reasoning Claim Tokens (RCTs) Tackle AI Safety in Healthcare & Finance
Description:
New research highlights significant oversight risks from AI-driven assistants in finance and healthcare—including the omission of safety-critical info. Reasoning Claim Tokens (RCTs) emerge as a tool to document and verify AI “suitability” claims and pharmacovigilance compliance, without intrusive model access. This innovation could redefine trust and accountability where stakes are highest.
Read the latest paper
(If you have more news with actual GitHub links or direct releases, feel free to resubmit those for even deeper technical summaries!)
Title: Gemini AI Studio’s ‘Context Tax’ Sparks Developer Outrage Over Surprise Billing
Description: Google Gemini AI Studio’s much-hyped 1M+ context window can hit devs with unexpected charges. Once you exceed the free tier, Gemini retroactively bills for your entire context—not just new tokens—leading to big surprises like a £121 fee for a simple 10-word prompt. Billing transparency is lacking; experts recommend using the API with context caching and starting a new session if you max out. Stay informed before running up a bill!
Link: [Source link]
Title: Startup Launches ‘HoloAvatar’—Chat With Lifelike AI Versions of Lost Loved Ones
Description: 2wai’s new app lets you build realistic digital avatars in minutes from a selfie and a voice sample. Speak with your own 3D twin—or reanimated versions of friends, family, or historical figures—in over 40 languages. The tech promises radical new ways to preserve memories, enhance education, and challenge ideas of digital identity, but also sparks major ethical debate about consent and authenticity.
Link: [Source link]
Title: Can We Stay Human? Bloomberg Explores the True Social Cost of AI Relationships
Description: As AI companions and chatbots blur the line between tech and connection, Bloomberg investigates whether our growing reliance on synthetic relationships could erode real, meaningful human bonds. This deep dive argues for balancing innovation with awareness of the impacts on emotional health and society.
Link: [Source link]