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GETadb.com: AI Tool Turns Every GET Request into a Database
Transforming GET Requests into Databases with GETadb.com Are you eager to harness the power of every GET request you send and convert it into a structured datab…
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…
Coatue's New Venture: AI Data Centers Near Power Sources
Coatue, one of the biggest names in venture capital, has a new venture that is reportedly buying land near large power sources.
Amazon's AWS Surges, Drives Massive Cloud Spending
The e-commerce giant is making more money than expected from AWS but it's also spending a lot, and will continue to do so in the near term, its chief executive said.
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)
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.
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
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
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.
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.
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.