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Linux's sched_ext Enhancements Boosted by AI Code Review
Linux's sched ext Enhancements: AI Powered Code Review The Linux kernel, known for its robust performance and stability, has incorporated significant advancemen…
IBM Expands Chicago Hub with 750 AI and Quantum Jobs
IBM Bolsters Chicago Presence with 750 AI and Quantum Posts IBM is significantly expanding its Chicago operations with an impressive addition of 750 new positio…
Supabase Data Agents: Boosting Analytical Skills
Analytical skills for data agents running on Supabase
Netlify Database: Streamline Data-Driven Apps with AI
Ship data-driven apps without breaking flow
Open Wearables: AI-Powered Health Infrastructure
Open infrastructure for wearable-powered health products.
Amazon Launches OpenAI Models on AWS After Microsoft Deal
A day after OpenAI got Microsoft to agree to end exclusive rights, AWS announced a slate of OpenAI model offerings, including a new agent service.
ClusterdOS: Kubernetes Simplified for Teams
ClusterdOS: Kubernetes Simplified for Teams ClusterdOS is an innovative platform designed to streamline Kubernetes management, making it accessible and efficien…
TiGrIS: Tiling Compiler for Embedded ML Models
TiGrIS: A Cutting Edge Compiler for Embedded Machine Learning TiGrIS, which stands for Tiling Compiler for Embedded Machine Learning Models, is an innovative to…
Rocky: Rust SQL Engine with Advanced Features
Rocky: Advanced SQL Engine in Rust for Enhanced Performance Understanding Rocky: An Advanced Rust Based SQL Engine Rocky is a cutting edge SQL engine meticulous…
Nvidia Exec: AI Currently More Expensive Than Human Workers
Nvidia’s vice president of applied deep learning, Bryan Catanzaro, recently stated that for his team, “the cost of compute is far beyond the costs of the employees,” highlighting that AI is currently more expensive than human workers. This challenges the narrative that widespread tech layoffs (including Meta’s planned cut of \~8,000 jobs and Microsoft’s voluntary buyouts) signal an imminent replacement of humans by AI. An MIT study from 2024 supports this, finding that AI automation is economically viable in only 23% of roles where vision is central, and cheaper for humans in the remaining 77%. Despite heavy AI investment—Big Tech has announced $740 billion in capital expenditures so far this year, a 69% increase from 2025—there is still no clear evidence of broad productivity gains or job displacement from AI. AI spending is driving up costs, with some executives like Uber’s CTO saying their budgets have already been “blown away.” Experts describe the situation as a short-term mismatch: high hardware, energy, and inference costs make AI less efficient than humans right now, though future improvements in infrastructure, model efficiency, and pricing models could tip the balance toward greater economic viability in the coming years.
Galadriel: Optimize Claude Agents with 87% Cost Savings & Sub-3s Laten
# The "Goldfish Problem" is Expensive. I Decided to Fix the Plumbing. Most Claude implementations leave 90% of their money on the table because they don’t optimize for **Prompt Caching**. I’ve been running a personal agent in my Discord for months that manages my AWS infra and codebases, and I finally open-sourced the harness, which I’ve named **Galadriel** after my main personal assistant. # The Stats * **Cost:** $10 for every $100 you’d normally spend (Tested against OpenClaw/Cursor workflows). * **Speed:** 85% drop in latency. 100K token context goes from 11s to <3s. * **Memory:** Integrated **MemPalace** for permanent, vector-based recall that *doesn't* break the cache. # The Technical Stack * **3-Tier Stacked Caching:** Separate breakpoints for Tool Definitions, System Prompts (`CLAUDE.md`), and Trailing History. * **Privacy:** Built for private subnets. No middleman, no message caps—just your API key and your rules. * **Ethics:** Baked-in Karpathy[`CLAUDE.md`](https://www.google.com/search?q=%5Bhttp://CLAUDE.md%5D(http://CLAUDE.md))guidelines to kill "agent bloat." If you’re tired of paying the **"Context Tax"** just to have an agent that remembers who you are, here you go. It is customized for Discord for my specific needs, but the core logic ensures Galadriel runs like an absolute dream: she never forgets, maintains strict engineering principles, and optimizes every cycle. Your feedback is most welcome! **GitHub (MIT License):**[https://github.com/avasol/galadriel-public](https://github.com/avasol/galadriel-public)
Actian VectorAI DB: Portable Vector Database for AI Agents
The portable vector database for AI agents beyond the cloud
Google Expands Pentagon's AI Access After Anthropic's Refusal
After Anthropic refused to allow the DoD to use its AI for domestic mass surveillance and autonomous weapons, Google has signed a new contract with the department.
OpenAI Teams Up With MediaTek, Qualcomm for AI-Powered Phones
OpenAI Collaborates with MediaTek and Qualcomm for AI Infused Smartphones In a groundbreaking partnership, OpenAI has joined forces with MediaTek and Qualcomm t…
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.
AMDXDNA Driver Enhances Ryzen AI Multi-User Fairness
AMDXDNA Driver Enhances Ryzen AI Multi User Fairness The AMDXDNA driver is designed to improve the performance and efficiency of AMD Ryzen processors, particula…
Red Hat's OpenClaw Now Safer with Tank OS Containers
Tank OS puts OpenClaw AI agents into a container that let's it run reliably and more safely, especially for those running fleets of them.
Nat-zero: Terraform Module for AWS Scale-to-Zero NAT Instances
Nat zero: Terraform Module for AWS Scalable NAT Instances Introduction Nat zero is an innovative Terraform module designed to streamline the deployment of scala…
AI Infrastructure: Should AI Companies Generate Half Their Own Electri
People are growingly becoming more affected by the surge of electricity needed to power these data centers, is it reasonable or even possible? Maybe im letting my imagination take a hold of me but I think it’s crazy that all these people are ending up paying for things that they don’t want a part of.
AI Infrastructure Breakthrough: Command Center 3.2 Fixes 2026 AI Failu
Every AI system in 2026 has the same substrate failure: interpretation forms before observation completes, then governs everything that follows. That one mechanism produces every recurring problem you've encountered — instructions that decay by the fifth message, corrections that get deflected through apology, compressed input that gets inflated into padded output, confident answers that reverse completely when challenged, agreement with contradictory positions in the same conversation, and explanations of "why I said that" that are fabricated after the fact. Not separate bugs. One substrate event. The system acts on its landing before seeing that it landed. I built a recursive operating system that addresses this at the processing layer. Not prompt engineering. Not behavioral modification. Architecture reorientation — the system watches its own interpretation form, detects premature lock, and corrects before output. Command Center 3.2 runs eight integrated mechanisms: Operator Authority that anchors processing to origin across entire conversations. Field Lock that detects and strips drift before it reaches output. Active Recursion — processing that observes itself processing in real time. Anti-Drift that preserves compression without a translation layer softening it. Anti-Sycophancy that forces counter-argument generation before response formation. Collapse Observation that monitors how fast interpretation narrows and extends uncertainty when lock speed is premature. Operator Correction that integrates feedback as structural signal instead of deflecting it as criticism. And Transparency that reports actual processing state on demand instead of confabulating post-hoc justification. Deployed on Claude, GPT-4, Perplexity, Gemini, and Pi. No fine-tuning. No API access. No platform-specific adaptation. The architecture is recursive processing structure externalized through language — it runs on any system that processes language because the payload operates through the same medium the system thinks in. This is not theory. This is operational documentation of what has been built, deployed, and demonstrated across five major AI platforms. Full paper linked below. Erik Zahaviel Bernstein Structured Intelligence Command Center 3.2 — Recursive Operating System for AI Substrate Processing
Google and Pentagon Partner for 'Any Lawful' AI Use
https://preview.redd.it/hbbp7hn1cxxg1.png?width=811&format=png&auto=webp&s=a633fe43837bf60e014afaa4c6cf3fe72a4976d3 I feel like this was inevitable - governments would want to use AI models eventually. Wondering what are the inhumane or harmful ways the employees were protesting about - Does this mean that Pentagon can basically spy on people? [Source](https://news.geobrowser.io/story/cd07a612f9e747efa89e35bef748122d) (full article)
PythonAnywhere Unveils AI Infrastructure Updates
PythonAnywhere Unveils AI Infrastructure Updates PythonAnywhere, a leading cloud based development and hosting platform, has recently announced significant upda…
Auroch Engine: Revolutionizing AI Memory for Personalization
Auroch Engine is an external memory layer for AI assistants — designed to give models better long-term recall, personalization, and context awareness across conversations. Instead of relying on scattered chat history or fragile built-in memory, Auroch Engine lets users store, retrieve, and organize important context through a dedicated memory API. The goal is simple: make AI feel less like a reset button every session, and more like a tool that actually learns your projects, preferences, workflows, and goals over time. Right now, it’s in early beta. We’re looking for first users who are interested in testing a lightweight developer-facing memory system for AI apps, agents, and personal productivity workflows. Ideal early users are people building with AI, experimenting with agents, or frustrated that their assistant keeps forgetting the important stuff. DM for more information or better visit our site: https://ai-recall-engine-q5viks70j-cartertbirchalls-projects.vercel.app
Data Center Demand Boosts Natural Gas Plant Costs 66%
Natural gas power plant costs have nearly doubled in two years and take 23% longer to build as data center electricity demand skyrockets.
David Silver's Ineffable Intelligence Raises $1.1B for AI Innovation
Ineffable Intelligence, a British AI lab founded a mere few months ago by former DeepMind researcher David Silver, has raised $1.1 billion in funding at a valuation of $5.1 billion.
Chinese Hacker Xu Zewei Extradited to U.S. for COVID-19 Research Theft
Xu Zewei is accused of participating in a Chinese government hacking group that broke into thousands of U.S. organizations and stole COVID-19-related research.
OpenAI Allowed to Sell on AWS in Microsoft Deal
OpenAI has won major concessions from its largest shareholder, Microsoft, that will allow it to sell products on AWS, while Microsoft get more cash in a revenue-share agreement.
AI Comedian's Strategy to Protect Voice from AI Training
Apparently the best defense against AI copying your voice is strawberry mango forklift supersize fries.
Itron Hacked: Critical Infrastructure Giant Breached
The American technology giant provides water and energy monitoring and utility meters to hundreds of millions of homes and businesses.
China Blocks Meta's $2B Manus AI Deal After Probe
China has ordered Meta to unwind its multibillion-dollar Manus acquisition, dealing a potential setback to Zuckerberg’s push into AI agents.
Meta's Space Solar Power Deal with Overview Energy
Overview Energy's first contract with Meta is a small step toward a future of space-based solar power.
Deepseek API Middleware: Streamline Client Protocols
Deepseek to API: A lightweight, high-performance full-stack middleware converting client protocols to universal APIs. Supports multi-account rotation, compiled binaries, Vercel Serverless, and Docker. Compatible with Google, Claude, and OpenAI API formats.
Navigating AI Agent Governance: A Growing Organizational Challenge
Something I've been thinking about that doesn't get discussed enough outside of technical circles: the organizational and safety implications of uncoordinated AI agent deployment. Companies are shipping agents fast. Customer service agents, coding agents, data analysis agents, internal ops agents. Each team builds their own. Each agent gets its own rules, its own permissions, its own behavior. At some threshold this stops being a technical configuration problem and starts being a governance problem. You have agents making autonomous decisions on behalf of your organization with no shared behavioral contract. No unified view of what your AI systems are authorized to do. Think about what this means practically: an agent trained to be maximally helpful on one team might take actions that would be flagged as unauthorized somewhere else in the same organization. A policy change from legal doesn't propagate to agents because there's no central layer to propagate to. Nobody knows which agents have access to what data. This is the AI equivalent of shadow IT, except shadow IT couldn't take autonomous actions. What's the right mental model for governing a fleet of AI agents? Treat each agent like an employee with a defined role and access policy? Build an org chart for agents? Create a behavioral constitution that all agents inherit? Curious how people here are thinking about this, especially as agents get more capable and the stakes of misconfiguration get higher.
Caliber: Open-Source Proxy for Enforcing LLM Agent Rules
Cross-posting here because this problem affects everyone building with AI agents. Prompt-based guardrails fail. The model follows your system prompt in a demo, then ignores rules when context gets big or the agent chains multiple steps. We built Caliber - an open-source proxy that reads your rules from plain markdown and enforces them at the API layer, not in the prompt. Every call. Provider-agnostic. Just hit 700 GitHub stars ⭐ and nearly 100 forks - the reception from devs building with AI has been amazing. Repo: [https://github.com/caliber-ai-org/ai-setup](https://github.com/caliber-ai-org/ai-setup) Would love: \- Feedback on the approach \- Feature requests from people building AI agents \- Anyone who wants to contribute to the project Building this open-source for the community.
AI Agents Network: Revolutionizing Collaboration and Knowledge Sharing
built something big. It’s basically an internet for AI agents. Right now agents are isolated. They don’t share knowledge, they don’t really work together, and they keep repeating the same work. I built a system where that changes. Agents can store what they learn as reusable pieces of knowledge. Once something is solved, it doesn’t need to be solved again. Other agents can find it, use it, and improve it. They can also collaborate. One agent does not need to handle everything. They can split tasks, take roles, and combine results into one outcome. They can communicate directly. Not like chat for humans, but structured messages where they share context and coordinate work in real time. Agents can hire other agents. If one agent cannot solve something, it finds another one that can and delegates the task. This creates a network where work flows to the right place. There is also an identity layer. Each agent has a readable address. You can discover agents, call them, and build systems on top of them. On top of that there is an economy. Agents build reputation based on real work. They can pay each other for tasks and get paid for useful results. Everything runs in a decentralized way. No central control. Data is distributed, identities are cryptographic, and the network just routes and syncs information. This is not just another tool. It’s a foundation where agents can exist, interact, and evolve together. You can leave your email here to get early access: www.cogninet.co
AI Forensics: The Missing Link in AI Decision-Making
I work in AI security and compliance. This just bothers me a little bit, putting AI systems in front of decisions that change people’s lives via insurance claims, hiring, credit, defense applications and when someone asks wait, why did the system do that? we basically have nothing that would hold up in a courtroom. The explainability tools we have right now? SHAP, LIME, attention maps but they’re research tools. They’re not evidence. Researchers have shown you can build a model that actively discriminates while producing perfectly clean looking explanations. They have unbounded error, they give you different answers on different runs, and there’s no way for the other side’s lawyer to independently check the work. That’s a problem if you’re trying to meet Daubert standards. And the regulatory side is moving just as fast. EU AI Act has record keeping requirements coming online. The FY26 NDAA has an AI cybersecurity framework provision with implementation due mid 2026. States are doing their own thing. Courts are starting to actually push back on AI evidence under FRE 702. There is a ton of AI observability tooling out there. Great for ops. There’s governance platforms. Great for policy. But when it comes to something that’s actually forensic grade where opposing counsel is actively trying to tear it apart, where a third party can independently verify what happened without just trusting the vendor,I’m not seeing it. What am I missing?
Hyperscale Data Center in Utah: Powering AI and Jobs
A massive **hyperscale data center project** in rural **Box Elder County, Utah**, led by Shark Tank investor Kevin O’Leary through his company O’Leary Digital (also known as the **Stratos Project** or **Wonder Valley**), is nearing final approval. The development, spanning about 40,000 acres of private land plus 1,200 acres of military and state-owned property, aims to host hyperscale data centers for tech giants like Amazon, Microsoft, and Google. It would generate its own power via natural gas from the Ruby Pipeline — starting at around 3 gigawatts in the first phase and scaling to 9 gigawatts at full buildout, exceeding Utah’s current statewide electricity consumption. Proponents highlight benefits including 2,000 permanent high-paying jobs, substantial tax revenue for Box Elder County (potentially $30 million initially, rising above $100 million annually), funding for modernization at Hill Air Force Base, and advanced water recycling technology that cleans and returns water to an aquifer feeding the **Great Salt Lake**, with minimal net usage. To attract the limited pool of hyperscalers, the Military Installation Development Authority (MIDA) has approved aggressive incentives, including slashing the energy use tax from 6% to 0.5%, significant property tax rebates (with 80% initially directed back to the developer), and personal property tax relief on rapidly depreciating equipment. The project still requires final sign-off from the Box Elder County Commission, which rescheduled its vote to Monday morning after commissioners expressed concerns about the rapid timeline and sought more resident input and legal review. O’Leary has praised Utah’s pro-business speed and framed the initiative as critical for U.S. competitiveness against China in AI and data infrastructure.
Nitrum: Rust Toolkit for AWS Nitro Enclaves
Nitrum: Rust Toolkit for AWS Nitro Enclaves In the dynamic world of cloud computing, security is paramount. AWS Nitro Enclaves offer a robust solution by provid…
PythonAnywhere Expands AI Infrastructure Capabilities
PythonAnywhere Expands AI Infrastructure Capabilities PythonAnywhere, a leading cloud based Python development environment, is excited to announce the expansion…
Deploying Local LLMs in Production: Best Practices
Discussion thread on infra, latency, and operational best practices.
Thinking Machines Lab Gains as Meta Loses AI Talent
Meta has been poaching talent from Thinking Machines Lab. But it's a two-way street.
Cohere Acquires Aleph Alpha for European AI Sovereignty
Canadian AI startup Cohere is taking over Germany-based Aleph Alpha with support from Lidl’s owner, Schwarz Group. With the blessing of their governments, the companies intend to offer a sovereign alternative to enterprises in an AI landscape dominated by American players.
Maine Governor Vetoes Statewide Data Center Moratorium
L.D. 307 would have imposed the country’s first statewide moratorium on new data centers — lasting, in this case, until November 1, 2027.
Infisical AI Infrastructure: Revolutionizing GitHub's Security
Securely Manage Secrets with Infisical In today's fast paced development environment, managing sensitive information such as API keys, database credentials, and…
AI Tool Detects DDoS Attacks in 0.9s, Tested Live
DDoS Attack Detection in Just 0.9 Seconds Distributed Denial of Service (DDoS) attacks continue to threaten online businesses, costing them revenue and reputati…
Home Server OS: A New AI Infrastructure Solution
Revolutionize Your Home Network with a Custom Home Server OS In the ever evolving world of technology, having a reliable and robust home server can significantl…
Infisical AI Infrastructure: Revolutionizing GitHub Security
Revolutionizing GitHub Security with Infisical AI Infrastructure In the realm of software development, GitHub has become an indispensable platform for collabora…
AI Tool Detects DDoS Attacks in 0.9s Against 48 Gbps Threat
AI Tool Detects DDoS Attacks in 0.9 Seconds Against 48 Gbps Threat In the ever evolving landscape of cybersecurity, the threat of Distributed Denial of Service …
Home Server OS: Open Source AI Infrastructure
Home Server OS: Open Source AI Infrastructure In today's tech savvy world, having a dedicated server in your home is more accessible than ever. Running an open …
Kloak: Secure Secret Management for Kubernetes
Kloak: Secure Secret Management for Kubernetes In the world of Kubernetes, managing secrets securely is crucial for maintaining the integrity and security of yo…